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Sample records for applications predicting drug-target

  1. Drug-Target Interactions: Prediction Methods and Applications.

    Science.gov (United States)

    Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

    2018-01-01

    Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. Prediction of potential drug targets based on simple sequence properties

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    Lai Luhua

    2007-09-01

    Full Text Available Abstract Background During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets. Results Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research. Conclusion We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

  3. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

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    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  4. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  5. Deep-Learning-Based Drug-Target Interaction Prediction.

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    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  6. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.

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    Luo, Yunan; Zhao, Xinbin; Zhou, Jingtian; Yang, Jinglin; Zhang, Yanqing; Kuang, Wenhua; Peng, Jian; Chen, Ligong; Zeng, Jianyang

    2017-09-18

    The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

  7. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

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    Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

    2015-07-15

    Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Predicting drug-target interactions using restricted Boltzmann machines.

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    Wang, Yuhao; Zeng, Jianyang

    2013-07-01

    In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

  9. Drug-target interaction prediction: A Bayesian ranking approach.

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    Peska, Ladislav; Buza, Krisztian; Koller, Júlia

    2017-12-01

    In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

  10. Gaussian interaction profile kernels for predicting drug-target interaction.

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    van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

    2011-11-01

    The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

  11. Drug-target interaction prediction via class imbalance-aware ensemble learning.

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    Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2016-12-22

    Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

  12. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

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    Zhang, Wen; Chen, Yanlin; Li, Dingfang

    2017-11-25

    Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

  13. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization.

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    Ezzat, Ali; Zhao, Peilin; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2017-01-01

    Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., "orphan" nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the "new drug" and "new target" cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some "new drug" and "new target" cases and found that GRMF predicted the left-out interactions reasonably well.

  14. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: yizeng_liang@263.net [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)

    2012-11-08

    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug-target

  15. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

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    Liu, Yong; Wu, Min; Miao, Chunyan; Zhao, Peilin; Li, Xiao-Li

    2016-02-01

    In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

  16. Drug-target interaction prediction from PSSM based evolutionary information.

    Science.gov (United States)

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Design of a tripartite network for the prediction of drug targets

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    Kunimoto, Ryo; Bajorath, Jürgen

    2018-02-01

    Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.

  18. Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

    Directory of Open Access Journals (Sweden)

    Hall Ross S

    2010-04-01

    Full Text Available Abstract Background New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset. Results Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from Ancylostoma caninum and Haemonchus contortus - two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools. Conclusions The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.

  19. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

    KAUST Repository

    Ba Alawi, Wail

    2016-08-31

    The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction

  20. Some Remarks on Prediction of Drug-Target Interaction with Network Models.

    Science.gov (United States)

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

    System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. Drug-targeting methodologies with applications: A review

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    Kleinstreuer, Clement; Feng, Yu; Childress, Emily

    2014-01-01

    Targeted drug delivery to solid tumors is a very active research area, focusing mainly on improved drug formulation and associated best delivery methods/devices. Drug-targeting has the potential to greatly improve drug-delivery efficacy, reduce side effects, and lower the treatment costs. However, the vast majority of drug-targeting studies assume that the drug-particles are already at the target site or at least in its direct vicinity. In this review, drug-delivery methodologies, drug types and drug-delivery devices are discussed with examples in two major application areas: (1) inhaled drug-aerosol delivery into human lung-airways; and (2) intravascular drug-delivery for solid tumor targeting. The major problem addressed is how to deliver efficiently the drug-particles from the entry/infusion point to the target site. So far, most experimental results are based on animal studies. Concerning pulmonary drug delivery, the focus is on the pros and cons of three inhaler types, i.e., pressurized metered dose inhaler, dry powder inhaler and nebulizer, in addition to drug-aerosol formulations. Computational fluid-particle dynamics techniques and the underlying methodology for a smart inhaler system are discussed as well. Concerning intravascular drug-delivery for solid tumor targeting, passive and active targeting are reviewed as well as direct drug-targeting, using optimal delivery of radioactive microspheres to liver tumors as an example. The review concludes with suggestions for future work, considereing both pulmonary drug targeting and direct drug delivery to solid tumors in the vascular system. PMID:25516850

  2. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

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    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  3. Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

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    Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

    2016-01-01

    The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

  4. Mining predicted essential genes of Brugia malayi for nematode drug targets.

    Directory of Open Access Journals (Sweden)

    Sanjay Kumar

    Full Text Available We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression.

  5. Predicting Drug-Target Interactions Based on Small Positive Samples.

    Science.gov (United States)

    Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

    2018-01-01

    A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

  6. Cell-specific prediction and application of drug-induced gene expression profiles.

    Science.gov (United States)

    Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

    2018-01-01

    Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

  7. Mathematical description of drug-target interactions: application to biologics that bind to targets with two binding sites.

    Science.gov (United States)

    Gibiansky, Leonid; Gibiansky, Ekaterina

    2018-02-01

    The emerging discipline of mathematical pharmacology occupies the space between advanced pharmacometrics and systems biology. A characteristic feature of the approach is application of advance mathematical methods to study the behavior of biological systems as described by mathematical (most often differential) equations. One of the early application of mathematical pharmacology (that was not called this name at the time) was formulation and investigation of the target-mediated drug disposition (TMDD) model and its approximations. The model was shown to be remarkably successful, not only in describing the observed data for drug-target interactions, but also in advancing the qualitative and quantitative understanding of those interactions and their role in pharmacokinetic and pharmacodynamic properties of biologics. The TMDD model in its original formulation describes the interaction of the drug that has one binding site with the target that also has only one binding site. Following the framework developed earlier for drugs with one-to-one binding, this work aims to describe a rigorous approach for working with similar systems and to apply it to drugs that bind to targets with two binding sites. The quasi-steady-state, quasi-equilibrium, irreversible binding, and Michaelis-Menten approximations of the model are also derived. These equations can be used, in particular, to predict concentrations of the partially bound target (RC). This could be clinically important if RC remains active and has slow internalization rate. In this case, introduction of the drug aimed to suppress target activity may lead to the opposite effect due to RC accumulation.

  8. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

    Science.gov (United States)

    Zong, Nansu; Kim, Hyeoneui; Ngo, Victoria; Harismendy, Olivier

    2017-08-01

    A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . nazong@ucsd.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  9. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors.

    Science.gov (United States)

    Mitra, A K; Mukherjee, U K; Harding, T; Jang, J S; Stessman, H; Li, Y; Abyzov, A; Jen, J; Kumar, S; Rajkumar, V; Van Ness, B

    2016-05-01

    Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that have a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Although genome profiling studies have demonstrated heterogeneity in subclonal architecture that may ultimately lead to relapse, a gene expression-based prediction program that can identify, distinguish and quantify drug response in sub-populations within a bulk population of myeloma cells is lacking. In this study, we performed targeted transcriptome analysis on 528 pre-treatment single cells from 11 myeloma cell lines and 418 single cells from 8 drug-naïve MM patients, followed by intensive bioinformatics and statistical analysis for prediction of proteasome inhibitor sensitivity in individual cells. Using our previously reported drug response gene expression profile signature at the single-cell level, we developed an R Statistical analysis package available at https://github.com/bvnlabSCATTome, SCATTome (single-cell analysis of targeted transcriptome), that restructures the data obtained from Fluidigm single-cell quantitative real-time-PCR analysis run, filters missing data, performs scaling of filtered data, builds classification models and predicts drug response of individual cells based on targeted transcriptome using an assortment of machine learning methods. Application of SCATT should contribute to clinically relevant analysis of intratumor heterogeneity, and better inform drug choices based on subclonal cellular responses.

  10. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

    Science.gov (United States)

    Hao, Ming; Bryant, Stephen H; Wang, Yanli

    2018-02-06

    While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

  11. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    International Nuclear Information System (INIS)

    Cao Dongsheng; Liu Shao; Xu Qingsong; Lu Hongmei; Huang Jianhua; Hu Qiannan; Liang Yizeng

    2012-01-01

    Highlights: ► Drug–target interactions are predicted using an extended SAR methodology. ► A drug–target interaction is regarded as an event triggered by many factors. ► Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. ► Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR

  12. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  13. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  14. Identifying Drug-Target Interactions with Decision Templates.

    Science.gov (United States)

    Yan, Xiao-Ying; Zhang, Shao-Wu

    2018-01-01

    During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can

  15. In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

    Science.gov (United States)

    Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

    2017-07-01

    Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

    Science.gov (United States)

    LaBute, Montiago X; Zhang, Xiaohua; Lenderman, Jason; Bennion, Brian J; Wong, Sergio E; Lightstone, Felice C

    2014-01-01

    Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number

  17. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

    Directory of Open Access Journals (Sweden)

    Montiago X LaBute

    Full Text Available Late-stage or post-market identification of adverse drug reactions (ADRs is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409 of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively. Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with

  18. Multifunctional Nanoparticles for Drug Delivery Applications Imaging, Targeting, and Delivery

    CERN Document Server

    Prud'homme, Robert

    2012-01-01

    This book clearly demonstrates the progression of nanoparticle therapeutics from basic research to applications. Unlike other books covering nanoparticles used in medical applications, Multifunctional Nanoparticles for Drug Delivery Applications presents the medical challenges that can be reduced or even overcome by recent advances in nanoscale drug delivery. Each chapter highlights recent progress in the design and engineering of select multifunctional nanoparticles with topics covering targeting, imaging, delivery, diagnostics, and therapy.

  19. Drug-Target Kinetics in Drug Discovery.

    Science.gov (United States)

    Tonge, Peter J

    2018-01-17

    The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.

  20. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    Directory of Open Access Journals (Sweden)

    Huthmacher Carola

    2010-08-01

    Full Text Available Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte. Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

  1. An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

    KAUST Repository

    Naveed, Hammad; Hameed, Umar Farook Shahul; Harrus, Deborah; Bourguet, William; Arold, Stefan T.; Gao, Xin

    2015-01-01

    Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering, and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of eleven drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the nuclear receptor PPARγ and the oncogene Bcl-2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development.

  2. Mathematical Model to Predict Skin Concentration after Topical Application of Drugs

    Directory of Open Access Journals (Sweden)

    Hiroaki Todo

    2013-12-01

    Full Text Available Skin permeation experiments have been broadly done since 1970s to 1980s as an evaluation method for transdermal drug delivery systems. In topically applied drug and cosmetic formulations, skin concentration of chemical compounds is more important than their skin permeations, because primary target site of the chemical compounds is skin surface or skin tissues. Furthermore, the direct pharmacological reaction of a metabolically stable drug that binds with specific receptors of known expression levels in an organ can be determined by Hill’s equation. Nevertheless, little investigation was carried out on the test method of skin concentration after topically application of chemical compounds. Recently we investigated an estimating method of skin concentration of the chemical compounds from their skin permeation profiles. In the study, we took care of “3Rs” issues for animal experiments. We have proposed an equation which was capable to estimate animal skin concentration from permeation profile through the artificial membrane (silicone membrane and animal skin. This new approach may allow the skin concentration of a drug to be predicted using Fick’s second law of diffusion. The silicone membrane was found to be useful as an alternative membrane to animal skin for predicting skin concentration of chemical compounds, because an extremely excellent extrapolation to animal skin concentration was attained by calculation using the silicone membrane permeation data. In this chapter, we aimed to establish an accurate and convenient method for predicting the concentration profiles of drugs in the skin based on the skin permeation parameters of topically active drugs derived from steady-state skin permeation experiments.

  3. Elucidating antimalarial drug targets/mode-of-action by application of system biology technologies

    CSIR Research Space (South Africa)

    Becker, J

    2008-11-01

    Full Text Available targets/mode-of-action by application of systems biology technologies J BECKER, L MTWISHA, B CRAMPTON AND D MANCAMA CSIR Biosciences, PO Box 395, Pretoria, 0001, South Africa Email: JBecker@csir.co.za – www.csir.co.za INTRODUCTION Malaria is one... The objective of this study was to use systems biology tools to unravel the drug target/mode-of-action (MoA) of an antimalarial drug (cyclohexylamine) with a known drug target/MoA, by analysing differential expression profiles of drug treated vs untreated...

  4. Macromolecular target prediction by self-organizing feature maps.

    Science.gov (United States)

    Schneider, Gisbert; Schneider, Petra

    2017-03-01

    Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

  5. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1 chemical interaction between drugs, (2 protein interactions between drugs’ targets, and (3 target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

  6. An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

    KAUST Repository

    Naveed, Hammad

    2015-08-18

    Motivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering, and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of eleven drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the nuclear receptor PPARγ and the oncogene Bcl-2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development.

  7. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.

    Directory of Open Access Journals (Sweden)

    Anna Cichonska

    2017-08-01

    Full Text Available Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001 between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel

  8. Properties of Protein Drug Target Classes

    Science.gov (United States)

    Bull, Simon C.; Doig, Andrew J.

    2015-01-01

    Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme. PMID

  9. Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

    Science.gov (United States)

    Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

    2017-12-04

    Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

  10. Mathematical modeling of antibody drug conjugates with the target and tubulin dynamics to predict AUC.

    Science.gov (United States)

    Byun, Jong Hyuk; Jung, Il Hyo

    2018-04-14

    Antibody drug conjugates (ADCs)are one of the most recently developed chemotherapeutics to treat some types of tumor cells. They consist of monoclonal antibodies (mAbs), linkers, and potent cytotoxic drugs. Unlike common chemotherapies, ADCs combine selectively with a target at the surface of the tumor cell, and a potent cytotoxic drug (payload) effectively prevents microtubule polymerization. In this work, we construct an ADC model that considers both the target of antibodies and the receptor (tubulin) of the cytotoxic payloads. The model is simulated with brentuximab vedotin, one of ADCs, and used to investigate the pharmacokinetic (PK) characteristics of ADCs in vivo. It also predicts area under the curve (AUC) of ADCs and the payloads by identifying the half-life. The results show that dynamical behaviors fairly coincide with the observed data and half-life and capture AUC. Thus, the model can be used for estimating some parameters, fitting experimental observations, predicting AUC, and exploring various dynamical behaviors of the target and the receptor. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Quantitative self-assembly prediction yields targeted nanomedicines

    Science.gov (United States)

    Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

    2018-02-01

    Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

  12. The application of carbon nanotubes in target drug delivery systems for cancer therapies

    Science.gov (United States)

    Zhang, Wuxu; Zhang, Zhenzhong; Zhang, Yingge

    2011-10-01

    Among all cancer treatment options, chemotherapy continues to play a major role in killing free cancer cells and removing undetectable tumor micro-focuses. Although chemotherapies are successful in some cases, systemic toxicity may develop at the same time due to lack of selectivity of the drugs for cancer tissues and cells, which often leads to the failure of chemotherapies. Obviously, the therapeutic effects will be revolutionarily improved if human can deliver the anticancer drugs with high selectivity to cancer cells or cancer tissues. This selective delivery of the drugs has been called target treatment. To realize target treatment, the first step of the strategies is to build up effective target drug delivery systems. Generally speaking, such a system is often made up of the carriers and drugs, of which the carriers play the roles of target delivery. An ideal carrier for target drug delivery systems should have three pre-requisites for their functions: (1) they themselves have target effects; (2) they have sufficiently strong adsorptive effects for anticancer drugs to ensure they can transport the drugs to the effect-relevant sites; and (3) they can release the drugs from them in the effect-relevant sites, and only in this way can the treatment effects develop. The transporting capabilities of carbon nanotubes combined with appropriate surface modifications and their unique physicochemical properties show great promise to meet the three pre-requisites. Here, we review the progress in the study on the application of carbon nanotubes as target carriers in drug delivery systems for cancer therapies.

  13. Drug-induced regulation of target expression

    DEFF Research Database (Denmark)

    Iskar, Murat; Campillos, Monica; Kuhn, Michael

    2010-01-01

    Drug perturbations of human cells lead to complex responses upon target binding. One of the known mechanisms is a (positive or negative) feedback loop that adjusts the expression level of the respective target protein. To quantify this mechanism systems-wide in an unbiased way, drug......-induced differential expression of drug target mRNA was examined in three cell lines using the Connectivity Map. To overcome various biases in this valuable resource, we have developed a computational normalization and scoring procedure that is applicable to gene expression recording upon heterogeneous drug treatments....... In 1290 drug-target relations, corresponding to 466 drugs acting on 167 drug targets studied, 8% of the targets are subject to regulation at the mRNA level. We confirmed systematically that in particular G-protein coupled receptors, when serving as known targets, are regulated upon drug treatment. We...

  14. In Vitro Drug Sensitivity Tests to Predict Molecular Target Drug Responses in Surgically Resected Lung Cancer.

    Directory of Open Access Journals (Sweden)

    Ryohei Miyazaki

    Full Text Available Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs and anaplastic lymphoma kinase (ALK inhibitors have dramatically changed the strategy of medical treatment of lung cancer. Patients should be screened for the presence of the EGFR mutation or echinoderm microtubule-associated protein-like 4 (EML4-ALK fusion gene prior to chemotherapy to predict their clinical response. The succinate dehydrogenase inhibition (SDI test and collagen gel droplet embedded culture drug sensitivity test (CD-DST are established in vitro drug sensitivity tests, which may predict the sensitivity of patients to cytotoxic anticancer drugs. We applied in vitro drug sensitivity tests for cyclopedic prediction of clinical responses to different molecular targeting drugs.The growth inhibitory effects of erlotinib and crizotinib were confirmed for lung cancer cell lines using SDI and CD-DST. The sensitivity of 35 cases of surgically resected lung cancer to erlotinib was examined using SDI or CD-DST, and compared with EGFR mutation status.HCC827 (Exon19: E746-A750 del and H3122 (EML4-ALK cells were inhibited by lower concentrations of erlotinib and crizotinib, respectively than A549, H460, and H1975 (L858R+T790M cells were. The viability of the surgically resected lung cancer was 60.0 ± 9.8 and 86.8 ± 13.9% in EGFR-mutants vs. wild types in the SDI (p = 0.0003. The cell viability was 33.5 ± 21.2 and 79.0 ± 18.6% in EGFR mutants vs. wild-type cases (p = 0.026 in CD-DST.In vitro drug sensitivity evaluated by either SDI or CD-DST correlated with EGFR gene status. Therefore, SDI and CD-DST may be useful predictors of potential clinical responses to the molecular anticancer drugs, cyclopedically.

  15. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

    NARCIS (Netherlands)

    Yamamoto, Yumi; Valitalo, Pyry A.; van den Berg, Dirk-Jan; Hartman, Robin; van den Brink, Willem; Wong, Yin Cheong; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Bakshi, Suruchi; Aranzana-Climent, Vincent; Marchand, Sandrine; Dahyot-Fizelier, Claire; Couet, William; Danhof, Meindert; van Hasselt, Johan G. C.; de lange, Elizabeth C. M.

    Purpose Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human

  17. Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

    Directory of Open Access Journals (Sweden)

    Hansaim Lim

    2016-10-01

    Full Text Available Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing

  18. Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

    Science.gov (United States)

    Lim, Hansaim; Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; He, Di; Zhuang, Luke; Meng, Patrick; Xie, Lei

    2016-10-01

    Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and

  19. Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

    Science.gov (United States)

    Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

    2014-08-01

    Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-03-16

    Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind

  1. Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

    Science.gov (United States)

    Chetty, Manoranjenni; Rose, Rachel H; Abduljalil, Khaled; Patel, Nikunjkumar; Lu, Gaohua; Cain, Theresa; Jamei, Masoud; Rostami-Hodjegan, Amin

    2014-01-01

    This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

  2. Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

    Directory of Open Access Journals (Sweden)

    Manoranjenni eChetty

    2014-11-01

    Full Text Available This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK models with pharmacodynamics (PD models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PK and PD compared favourably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release formulation (CR were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modelling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

  3. Drug targeting to tumors: principles, pitfalls and (pre-) clinical progress.

    Science.gov (United States)

    Lammers, Twan; Kiessling, Fabian; Hennink, Wim E; Storm, Gert

    2012-07-20

    Many different systems and strategies have been evaluated for drug targeting to tumors over the years. Routinely used systems include liposomes, polymers, micelles, nanoparticles and antibodies, and examples of strategies are passive drug targeting, active drug targeting to cancer cells, active drug targeting to endothelial cells and triggered drug delivery. Significant progress has been made in this area of research both at the preclinical and at the clinical level, and a number of (primarily passively tumor-targeted) nanomedicine formulations have been approved for clinical use. Significant progress has also been made with regard to better understanding the (patho-) physiological principles of drug targeting to tumors. This has led to the identification of several important pitfalls in tumor-targeted drug delivery, including I) overinterpretation of the EPR effect; II) poor tumor and tissue penetration of nanomedicines; III) misunderstanding of the potential usefulness of active drug targeting; IV) irrational formulation design, based on materials which are too complex and not broadly applicable; V) insufficient incorporation of nanomedicine formulations in clinically relevant combination regimens; VI) negligence of the notion that the highest medical need relates to metastasis, and not to solid tumor treatment; VII) insufficient integration of non-invasive imaging techniques and theranostics, which could be used to personalize nanomedicine-based therapeutic interventions; and VIII) lack of (efficacy analyses in) proper animal models, which are physiologically more relevant and more predictive for the clinical situation. These insights strongly suggest that besides making ever more nanomedicine formulations, future efforts should also address some of the conceptual drawbacks of drug targeting to tumors, and that strategies should be developed to overcome these shortcomings. Copyright © 2011 Elsevier B.V. All rights reserved.

  4. A computational approach to finding novel targets for existing drugs.

    Directory of Open Access Journals (Sweden)

    Yvonne Y Li

    2011-09-01

    Full Text Available Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM, suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.

  5. Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

    Science.gov (United States)

    Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

    2017-09-01

    Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  6. Applications of Dynamic Clamp to Cardiac Arrhythmia Research: Role in Drug Target Discovery and Safety Pharmacology Testing

    Directory of Open Access Journals (Sweden)

    Francis A. Ortega

    2018-01-01

    Full Text Available Dynamic clamp, a hybrid-computational-experimental technique that has been used to elucidate ionic mechanisms underlying cardiac electrophysiology, is emerging as a promising tool in the discovery of potential anti-arrhythmic targets and in pharmacological safety testing. Through the injection of computationally simulated conductances into isolated cardiomyocytes in a real-time continuous loop, dynamic clamp has greatly expanded the capabilities of patch clamp outside traditional static voltage and current protocols. Recent applications include fine manipulation of injected artificial conductances to identify promising drug targets in the prevention of arrhythmia and the direct testing of model-based hypotheses. Furthermore, dynamic clamp has been used to enhance existing experimental models by addressing their intrinsic limitations, which increased predictive power in identifying pro-arrhythmic pharmacological compounds. Here, we review the recent advances of the dynamic clamp technique in cardiac electrophysiology with a focus on its future role in the development of safety testing and discovery of anti-arrhythmic drugs.

  7. Drug target identification using side-effect similarity

    DEFF Research Database (Denmark)

    Campillos, Monica; Kuhn, Michael; Gavin, Anne-Claude

    2008-01-01

    Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed...... drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro...... binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs....

  8. Drug target identification in protozoan parasites.

    Science.gov (United States)

    Müller, Joachim; Hemphill, Andrew

    2016-08-01

    Despite the fact that diseases caused by protozoan parasites represent serious challenges for public health, animal production and welfare, only a limited panel of drugs has been marketed for clinical applications. Herein, the authors investigate two strategies, namely whole organism screening and target-based drug design. The present pharmacopoeia has resulted from whole organism screening, and the mode of action and targets of selected drugs are discussed. However, the more recent extensive genome sequencing efforts and the development of dry and wet lab genomics and proteomics that allow high-throughput screening of interactions between micromolecules and recombinant proteins has resulted in target-based drug design as the predominant focus in anti-parasitic drug development. Selected examples of target-based drug design studies are presented, and calcium-dependent protein kinases, important drug targets in apicomplexan parasites, are discussed in more detail. Despite the enormous efforts in target-based drug development, this approach has not yet generated market-ready antiprotozoal drugs. However, whole-organism screening approaches, comprising of both in vitro and in vivo investigations, should not be disregarded. The repurposing of already approved and marketed drugs could be a suitable strategy to avoid fastidious approval procedures, especially in the case of neglected or veterinary parasitoses.

  9. The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

    Science.gov (United States)

    Vilar, Santiago; Hripcsak, George

    2017-07-01

    Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  10. Computational prediction of drug-drug interactions based on drugs functional similarities.

    Science.gov (United States)

    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

    Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.

  11. A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.

    Science.gov (United States)

    Guo, Wei-Feng; Zhang, Shao-Wu; Shi, Qian-Qian; Zhang, Cheng-Ming; Zeng, Tao; Chen, Luonan

    2018-01-19

    The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng . In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the

  12. Are predictions of cancer response to targeted drugs, based on effects in unrelated tissues, the 'Black Swan' events?

    Science.gov (United States)

    Kurbel, Beatrica; Golem, Ante Zvonimir; Kurbel, Sven

    2015-01-01

    Adverse effects of targeted drugs on normal tissues can predict the cancer response. Rash correlates with efficacy of erlotinib, cetuximab and gefitinib and onset of arterial hypertension with response to bevacizumab, sunitinib, axitinib and sorafenib, possible examples of 'Black Swan' events, unexpected scientific observations, as described by Karl Popper in 1935. The proposition is that our patients have individual intrinsic variants of cell growth control, important for tumor response and adverse effects on tumor-unrelated tissue. This means that the lack of predictive side effects in healthy tissue is linked with poor results of tumor therapy when tumor resistance is caused by mechanisms that protect all cells of that patient from the targeted drug effects.

  13. Prediction of adverse drug reactions using decision tree modeling.

    Science.gov (United States)

    Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J

    2010-07-01

    Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

  14. Drug target mining and analysis of the Chinese tree shrew for pharmacological testing.

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    Full Text Available The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research.

  15. Brain tumor-targeted drug delivery strategies

    Directory of Open Access Journals (Sweden)

    Xiaoli Wei

    2014-06-01

    Full Text Available Despite the application of aggressive surgery, radiotherapy and chemotherapy in clinics, brain tumors are still a difficult health challenge due to their fast development and poor prognosis. Brain tumor-targeted drug delivery systems, which increase drug accumulation in the tumor region and reduce toxicity in normal brain and peripheral tissue, are a promising new approach to brain tumor treatments. Since brain tumors exhibit many distinctive characteristics relative to tumors growing in peripheral tissues, potential targets based on continuously changing vascular characteristics and the microenvironment can be utilized to facilitate effective brain tumor-targeted drug delivery. In this review, we briefly describe the physiological characteristics of brain tumors, including blood–brain/brain tumor barriers, the tumor microenvironment, and tumor stem cells. We also review targeted delivery strategies and introduce a systematic targeted drug delivery strategy to overcome the challenges.

  16. Application of three-dimensional printing for colon targeted drug delivery systems.

    Science.gov (United States)

    Charbe, Nitin B; McCarron, Paul A; Lane, Majella E; Tambuwala, Murtaza M

    2017-01-01

    Orally administered solid dosage forms currently dominate over all other dosage forms and routes of administrations. However, human gastrointestinal tract (GIT) poses a number of obstacles to delivery of the drugs to the site of interest and absorption in the GIT. Pharmaceutical scientists worldwide have been interested in colon drug delivery for several decades, not only for the delivery of the drugs for the treatment of colonic diseases such as ulcerative colitis and colon cancer but also for delivery of therapeutic proteins and peptides for systemic absorption. Despite extensive research in the area of colon targeted drug delivery, we have not been able to come up with an effective way of delivering drugs to the colon. The current tablets designed for colon drug release depend on either pH-dependent or time-delayed release formulations. During ulcerative colitis the gastric transit time and colon pH-levels is constantly changing depending on whether the patient is having a relapse or under remission. Hence, the current drug delivery system to the colon is based on one-size-fits-all. Fails to effectively deliver the drugs locally to the colon for colonic diseases and delivery of therapeutic proteins and peptides for systemic absorption from the colon. Hence, to overcome the current issues associated with colon drug delivery, we need to provide the patients with personalized tablets which are specifically designed to match the individual's gastric transit time depending on the disease state. Three-dimensional (3D) printing (3DP) technology is getting cheaper by the day and bespoke manufacturing of 3D-printed tablets could provide the solutions in the form of personalized colon drug delivery system. This review provides a bird's eye view of applications and current advances in pharmaceutical 3DP with emphasis on the development of colon targeted drug delivery systems.

  17. Supplementary Material for: DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-01-01

    Abstract Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery

  18. The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development.

    Science.gov (United States)

    Kunz, Meik; Liang, Chunguang; Nilla, Santosh; Cecil, Alexander; Dandekar, Thomas

    2016-01-01

    The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.Database URL:http://drumpid.bioapps.biozentrum.uni-wuerzburg.de. © The Author(s) 2016. Published by Oxford University Press.

  19. Photopatternable Magnetic Hollowbots by Nd-Fe-B Nanocomposite for Potential Targeted Drug Delivery Applications

    Directory of Open Access Journals (Sweden)

    Hui Li

    2018-04-01

    Full Text Available In contrast to traditional drug administration, targeted drug delivery can prolong, localize, target and have a protected drug interaction with the diseased tissue. Drug delivery carriers, such as polymeric micelles, liposomes, dendrimers, nanotubes, and so on, are hard to scale-up, costly, and have short shelf life. Here we show the novel fabrication and characterization of photopatternable magnetic hollow microrobots that can potentially be utilized in microfluidics and drug delivery applications. These magnetic hollowbots can be fabricated using standard ultraviolet (UV lithography with low cost and easily accessible equipment, which results in them being easy to scale up, and inexpensive to fabricate. Contact-free actuation of freestanding magnetic hollowbots were demonstrated by using an applied 900 G external magnetic field to achieve the movement control in an aqueous environment. According to the movement clip, the average speed of the magnetic hollowbots was estimated to be 1.9 mm/s.

  20. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

    KAUST Repository

    Ba Alawi, Wail

    2016-01-01

    -target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all

  1. Target-mediated drug disposition model for drugs with two binding sites that bind to a target with one binding site.

    Science.gov (United States)

    Gibiansky, Leonid; Gibiansky, Ekaterina

    2017-10-01

    The paper extended the TMDD model to drugs with two identical binding sites (2-1 TMDD). The quasi-steady-state (2-1 QSS), quasi-equilibrium (2-1 QE), irreversible binding (2-1 IB), and Michaelis-Menten (2-1 MM) approximations of the model were derived. Using simulations, the 2-1 QSS approximation was compared with the full 2-1 TMDD model. As expected and similarly to the standard TMDD for monoclonal antibodies (mAb), 2-1 QSS predictions were nearly identical to 2-1 TMDD predictions, except for times of fast changes following initiation of dosing, when equilibrium has not yet been reached. To illustrate properties of new equations and approximations, several variations of population PK data for mAbs with soluble (slow elimination of the complex) or membrane-bound (fast elimination of the complex) targets were simulated from a full 2-1 TMDD model and fitted to 2-1 TMDD models, to its approximations, and to the standard (1-1) QSS model. For a mAb with a soluble target, it was demonstrated that the 2-1 QSS model provided nearly identical description of the observed (simulated) free drug and total target concentrations, although there was some minor bias in predictions of unobserved free target concentrations. The standard QSS approximation also provided a good description of the observed data, but was not able to distinguish between free drug concentrations (with no target attached and both binding site free) and partially bound drug concentrations (with one of the binding sites occupied by the target). For a mAb with a membrane-bound target, the 2-1 MM approximation adequately described the data. The 2-1 QSS approximation converged 10 times faster than the full 2-1 TMDD, and its run time was comparable with the standard QSS model.

  2. Preparation of magnetic nanoparticles and their application to magnetic targeting drug delivery

    International Nuclear Information System (INIS)

    Li Guiping; Wang Yongxian

    2006-01-01

    Magnetic nanoparticles barrier is a novel kind of drug delivery system for magnetic targeting drugs, which can effectively deliver the drug to a tumor target site and increase therapeutic benefit, with the side effects minimized. This article summarizes the most outstanding papers on the of magnetic nanoparticles used as the targeting drug's delivery systems. (authors)

  3. Target-mediated drug disposition with drug-drug interaction, Part I: single drug case in alternative formulations.

    Science.gov (United States)

    Koch, Gilbert; Jusko, William J; Schropp, Johannes

    2017-02-01

    Target-mediated drug disposition (TMDD) describes drug binding with high affinity to a target such as a receptor. In application TMDD models are often over-parameterized and quasi-equilibrium (QE) or quasi-steady state (QSS) approximations are essential to reduce the number of parameters. However, implementation of such approximations becomes difficult for TMDD models with drug-drug interaction (DDI) mechanisms. Hence, alternative but equivalent formulations are necessary for QE or QSS approximations. To introduce and develop such formulations, the single drug case is reanalyzed. This work opens the route for straightforward implementation of QE or QSS approximations of DDI TMDD models. The manuscript is the first part to introduce DDI TMDD models with QE or QSS approximations.

  4. Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations.

    Science.gov (United States)

    Vilar, Santiago; Hripcsak, George

    2016-01-01

    Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

  5. The target landscape of clinical kinase drugs.

    Science.gov (United States)

    Klaeger, Susan; Heinzlmeir, Stephanie; Wilhelm, Mathias; Polzer, Harald; Vick, Binje; Koenig, Paul-Albert; Reinecke, Maria; Ruprecht, Benjamin; Petzoldt, Svenja; Meng, Chen; Zecha, Jana; Reiter, Katrin; Qiao, Huichao; Helm, Dominic; Koch, Heiner; Schoof, Melanie; Canevari, Giulia; Casale, Elena; Depaolini, Stefania Re; Feuchtinger, Annette; Wu, Zhixiang; Schmidt, Tobias; Rueckert, Lars; Becker, Wilhelm; Huenges, Jan; Garz, Anne-Kathrin; Gohlke, Bjoern-Oliver; Zolg, Daniel Paul; Kayser, Gian; Vooder, Tonu; Preissner, Robert; Hahne, Hannes; Tõnisson, Neeme; Kramer, Karl; Götze, Katharina; Bassermann, Florian; Schlegl, Judith; Ehrlich, Hans-Christian; Aiche, Stephan; Walch, Axel; Greif, Philipp A; Schneider, Sabine; Felder, Eduard Rudolf; Ruland, Juergen; Médard, Guillaume; Jeremias, Irmela; Spiekermann, Karsten; Kuster, Bernhard

    2017-12-01

    Kinase inhibitors are important cancer therapeutics. Polypharmacology is commonly observed, requiring thorough target deconvolution to understand drug mechanism of action. Using chemical proteomics, we analyzed the target spectrum of 243 clinically evaluated kinase drugs. The data revealed previously unknown targets for established drugs, offered a perspective on the "druggable" kinome, highlighted (non)kinase off-targets, and suggested potential therapeutic applications. Integration of phosphoproteomic data refined drug-affected pathways, identified response markers, and strengthened rationale for combination treatments. We exemplify translational value by discovering SIK2 (salt-inducible kinase 2) inhibitors that modulate cytokine production in primary cells, by identifying drugs against the lung cancer survival marker MELK (maternal embryonic leucine zipper kinase), and by repurposing cabozantinib to treat FLT3-ITD-positive acute myeloid leukemia. This resource, available via the ProteomicsDB database, should facilitate basic, clinical, and drug discovery research and aid clinical decision-making. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  6. QSAR Modeling and Prediction of Drug-Drug Interactions.

    Science.gov (United States)

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  7. DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail; Soufan, Othman; Essack, Magbubah; Kalnis, Panos; Bajic, Vladimir B.

    2016-01-01

    DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://​www.​cbrc.​kaust.​edu.​sa/​daspfind.

  8. Quantitative prediction of drug side effects based on drug-related features.

    Science.gov (United States)

    Niu, Yanqing; Zhang, Wen

    2017-09-01

    Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

  9. Targeted Delivery of Protein Drugs by Nanocarriers

    Directory of Open Access Journals (Sweden)

    Antonella Battisti

    2010-03-01

    Full Text Available Recent advances in biotechnology demonstrate that peptides and proteins are the basis of a new generation of drugs. However, the transportation of protein drugs in the body is limited by their high molecular weight, which prevents the crossing of tissue barriers, and by their short lifetime due to immuno response and enzymatic degradation. Moreover, the ability to selectively deliver drugs to target organs, tissues or cells is a major challenge in the treatment of several human diseases, including cancer. Indeed, targeted delivery can be much more efficient than systemic application, while improving bioavailability and limiting undesirable side effects. This review describes how the use of targeted nanocarriers such as nanoparticles and liposomes can improve the pharmacokinetic properties of protein drugs, thus increasing their safety and maximizing the therapeutic effect.

  10. Tumor targeting using liposomal antineoplastic drugs

    Directory of Open Access Journals (Sweden)

    Jörg Huwyler

    2008-03-01

    Full Text Available Jörg Huwyler1, Jürgen Drewe2, Stephan Krähenbühl21University of Applied Sciences Northwestern Switzerland, Institute of Pharma Technology, Muttenz, Switzerland; 2Department of Research and Division of Clinical Pharmacology, University Hospital Basel, Basel, SwitzerlandAbstract: During the last years, liposomes (microparticulate phospholipid vesicles have beenused with growing success as pharmaceutical carriers for antineoplastic drugs. Fields of application include lipid-based formulations to enhance the solubility of poorly soluble antitumordrugs, the use of pegylated liposomes for passive targeting of solid tumors as well as vector-conjugated liposomal carriers for active targeting of tumor tissue. Such formulation and drug targeting strategies enhance the effectiveness of anticancer chemotherapy and reduce at the same time the risk of toxic side-effects. The present article reviews the principles of different liposomal technologies and discusses current trends in this field of research.Keywords: tumor targeting, antineoplastic drugs, liposomes, pegylation, steric stabilization, immunoliposomes

  11. Exploring drug-target interaction networks of illicit drugs.

    Science.gov (United States)

    Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming

    2013-01-01

    Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. This study presents the first systematic review of the network

  12. Targeted Drug-Carrying Bacteriophages as Antibacterial Nanomedicines▿

    Science.gov (United States)

    Yacoby, Iftach; Bar, Hagit; Benhar, Itai

    2007-01-01

    While the resistance of bacteria to traditional antibiotics is a major public health concern, the use of extremely potent antibacterial agents is limited by their lack of selectivity. As in cancer therapy, antibacterial targeted therapy could provide an opportunity to reintroduce toxic substances to the antibacterial arsenal. A desirable targeted antibacterial agent should combine binding specificity, a large drug payload per binding event, and a programmed drug release mechanism. Recently, we presented a novel application of filamentous bacteriophages as targeted drug carriers that could partially inhibit the growth of Staphylococcus aureus bacteria. This partial success was due to limitations of drug-loading capacity that resulted from the hydrophobicity of the drug. Here we present a novel drug conjugation chemistry which is based on connecting hydrophobic drugs to the phage via aminoglycoside antibiotics that serve as solubility-enhancing branched linkers. This new formulation allowed a significantly larger drug-carrying capacity of the phages, resulting in a drastic improvement in their performance as targeted drug-carrying nanoparticles. As an example for a potential systemic use for potent agents that are limited for topical use, we present antibody-targeted phage nanoparticles that carry a large payload of the hemolytic antibiotic chloramphenicol connected through the aminoglycoside neomycin. We demonstrate complete growth inhibition toward the pathogens Staphylococcus aureus, Streptococcus pyogenes, and Escherichia coli with an improvement in potency by a factor of ∼20,000 compared to the free drug. PMID:17404004

  13. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database.

    Science.gov (United States)

    Barneh, Farnaz; Jafari, Mohieddin; Mirzaie, Mehdi

    2016-11-01

    Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. Collagen like peptide bioconjugates for targeted drug delivery applications

    Science.gov (United States)

    Luo, Tianzhi

    , suggesting that the nanoparticles do not initiate inflammatory response. Endowed with specific collagen binding, controlled thermoresponsiveness, excellent cytocompatibility, and non-immune responsiveness, we believe the ELP-CLP nanoparticles are promising candidates as drug delivery vehicles for targeting collagen containing matrices. Considering the critical role of collagens in extracellular matrix and the unique ability of the CLP to target native collagens, our work offers significant opportunities for the design of collagen-like peptides and their bioconjugates for targeted application in the biomedical arena.

  15. The application of machine learning techniques in the clinical drug therapy.

    Science.gov (United States)

    Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

    2018-05-25

    The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  16. A review on proniosomal drug delivery system for targeted drug action.

    Science.gov (United States)

    Radha, G V; Rani, T Sudha; Sarvani, B

    2013-03-01

    Proniosomes are dry formulation of water soluble carrier particles that are coated with surfactant. They are rehydrated to form niosomal dispersion immediately before use on agitation in hot aqueous media within minutes. Proniosomes are physically stable during the storage and transport. Drug encapsulated in the vesicular structure of proniosomes prolong the existence of drug in the systematic circulation and enhances the penetration into target tissue and reduce toxicity. From a technical point of view, niosomes are promising drug carriers as they possess greater chemical stability and lack of many disadvantages associated with liposomes, such as high- cost and variable purity problems of phospholipids. The present review emphasizes on overall methods of preparation characterization and applicability of proniosomes in targeted drug action.

  17. Organelle targeting: third level of drug targeting

    Directory of Open Access Journals (Sweden)

    Sakhrani NM

    2013-07-01

    Full Text Available Niraj M Sakhrani, Harish PadhDepartment of Cell and Molecular Biology, BV Patel Pharmaceutical Education and Research Development (PERD Centre, Gujarat, IndiaAbstract: Drug discovery and drug delivery are two main aspects for treatment of a variety of disorders. However, the real bottleneck associated with systemic drug administration is the lack of target-specific affinity toward a pathological site, resulting in systemic toxicity and innumerable other side effects as well as higher dosage requirement for efficacy. An attractive strategy to increase the therapeutic index of a drug is to specifically deliver the therapeutic molecule in its active form, not only into target tissue, nor even to target cells, but more importantly, into the targeted organelle, ie, to its intracellular therapeutic active site. This would ensure improved efficacy and minimize toxicity. Cancer chemotherapy today faces the major challenge of delivering chemotherapeutic drugs exclusively to tumor cells, while sparing normal proliferating cells. Nanoparticles play a crucial role by acting as a vehicle for delivery of drugs to target sites inside tumor cells. In this review, we spotlight active and passive targeting, followed by discussion of the importance of targeting to specific cell organelles and the potential role of cell-penetrating peptides. Finally, the discussion will address the strategies for drug/DNA targeting to lysosomes, mitochondria, nuclei and Golgi/endoplasmic reticulum.Keywords: intracellular drug delivery, cancer chemotherapy, therapeutic index, cell penetrating peptides

  18. A General Strategy for Targeting Drugs to Bone.

    Science.gov (United States)

    Jahnke, Wolfgang; Bold, Guido; Marzinzik, Andreas L; Ofner, Silvio; Pellé, Xavier; Cotesta, Simona; Bourgier, Emmanuelle; Lehmann, Sylvie; Henry, Chrystelle; Hemmig, René; Stauffer, Frédéric; Hartwieg, J Constanze D; Green, Jonathan R; Rondeau, Jean-Michel

    2015-11-23

    Targeting drugs to their desired site of action can increase their safety and efficacy. Bisphosphonates are prototypical examples of drugs targeted to bone. However, bisphosphonate bone affinity is often considered too strong and cannot be significantly modulated without losing activity on the enzymatic target, farnesyl pyrophosphate synthase (FPPS). Furthermore, bisphosphonate bone affinity comes at the expense of very low and variable oral bioavailability. FPPS inhibitors were developed with a monophosphonate as a bone-affinity tag that confers moderate affinity to bone, which can furthermore be tuned to the desired level, and the relationship between structure and bone affinity was evaluated by using an NMR-based bone-binding assay. The concept of targeting drugs to bone with moderate affinity, while retaining oral bioavailability, has broad application to a variety of other bone-targeted drugs. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Genomes2Drugs: identifies target proteins and lead drugs from proteome data.

    LENUS (Irish Health Repository)

    Toomey, David

    2009-01-01

    BACKGROUND: Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins\\/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and\\/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials. METHODOLOGY\\/PRINCIPAL FINDINGS: To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins. CONCLUSIONS\\/SIGNIFICANCE: Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under \\'change-of-application\\' patents.

  20. Genomes2Drugs: identifies target proteins and lead drugs from proteome data.

    Directory of Open Access Journals (Sweden)

    David Toomey

    Full Text Available BACKGROUND: Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials. METHODOLOGY/PRINCIPAL FINDINGS: To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i homologous to previously crystallized proteins or (ii targets of known drugs, but are (iii not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins. CONCLUSIONS/SIGNIFICANCE: Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under 'change-of-application' patents.

  1. Targeted drug delivery to magnetic implants for therapeutic applications

    International Nuclear Information System (INIS)

    Yellen, Benjamin B.; Forbes, Zachary G.; Halverson, Derek S.; Fridman, Gregory; Barbee, Kenneth A.; Chorny, Michael; Levy, Robert; Friedman, Gary

    2005-01-01

    A new method for locally targeted drug delivery is proposed that employs magnetic implants placed directly in the cardiovascular system to attract injected magnetic carriers. Theoretical simulations and experimental results support the assumption that using magnetic implants in combination with externally applied magnetic field will optimize the delivery of magnetic drug to selected sites within a subject

  2. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

    Science.gov (United States)

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

  3. Biodegradable polymers for targeted delivery of anti-cancer drugs.

    Science.gov (United States)

    Doppalapudi, Sindhu; Jain, Anjali; Domb, Abraham J; Khan, Wahid

    2016-06-01

    Biodegradable polymers have been used for more than three decades in cancer treatment and have received increased interest in recent years. A range of biodegradable polymeric drug delivery systems designed for localized and systemic administration of therapeutic agents as well as tumor-targeting macromolecules has entered into the clinical phase of development, indicating the significance of biodegradable polymers in cancer therapy. This review elaborates upon applications of biodegradable polymers in the delivery and targeting of anti-cancer agents. Design of various drug delivery systems based on biodegradable polymers has been described. Moreover, the indication of polymers in the targeted delivery of chemotherapeutic drugs via passive, active targeting, and localized drug delivery are also covered. Biodegradable polymer-based drug delivery systems have the potential to deliver the payload to the target and can enhance drug availability at desired sites. Systemic toxicity and serious side effects observed with conventional cancer therapeutics can be significantly reduced with targeted polymeric systems. Still, there are many challenges that need to be met with respect to the degradation kinetics of the system, diffusion of drug payload within solid tumors, targeting tumoral tissue and tumor heterogeneity.

  4. Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

    Science.gov (United States)

    Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

    2018-04-15

    Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen

  5. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.

    Science.gov (United States)

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-07-07

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  6. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    Directory of Open Access Journals (Sweden)

    Xin Deng

    2015-07-01

    Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  7. Applicator for in-vitro ultrasound-activated targeted drug delivery

    Science.gov (United States)

    Gerold, B.; Gourevich, D.; Volovick, A.; Xu, D.; Arditti, F.; Prentice, P.; Cochran, S.; Gnaim, J.; Medan, Y.; Wang, L.; Melzer, A.

    2012-10-01

    Reducing toxicity and improving uptake of cancer drugs in tumors are important goals of targeted drug delivery (TDD). Ultrasonic drug release from various encapsulants has been a focus of many research groups. However, a single standard ultrasonic device, viable for use by biologists, is not currently present in the market. The device reported here is designed to allow investigation of the impact of ultrasound on cellular uptake and cell viability in-vitro. In it, single-element transducers with different operating frequencies are mounted below a standard 96-well plate. The plate is moved above the transducers, such that each line of wells can be sonicated at a different frequency. To assess the device, 96-well plates were seeded with cells and sonicated using different ultrasonic parameters, with and without doxorubicin. Cell viability was measured by colorimetric MTT assay and the uptake of doxorubicin by cells was also determined. The device proved to be highly viable in preliminary tests; it demonstrated that change in ultrasonic parameters produces different effect on cells. For example, increase in uptake of doxorubicin was demonstrated following ultrasound application. The growing interest in ultrasound-activated TDD emphasizes the need for standardization of the ultrasound device and the one reported here may offer some indications of how that may be achieved. It is planned to further improve the prototype by increasing the number of ultrasonic frequencies and degrees of freedom for each transducer.

  8. Targeting Antibacterial Agents by Using Drug-Carrying Filamentous Bacteriophages

    Science.gov (United States)

    Yacoby, Iftach; Shamis, Marina; Bar, Hagit; Shabat, Doron; Benhar, Itai

    2006-01-01

    Bacteriophages have been used for more than a century for (unconventional) therapy of bacterial infections, for half a century as tools in genetic research, for 2 decades as tools for discovery of specific target-binding proteins, and for nearly a decade as tools for vaccination or as gene delivery vehicles. Here we present a novel application of filamentous bacteriophages (phages) as targeted drug carriers for the eradication of (pathogenic) bacteria. The phages are genetically modified to display a targeting moiety on their surface and are used to deliver a large payload of a cytotoxic drug to the target bacteria. The drug is linked to the phages by means of chemical conjugation through a labile linker subject to controlled release. In the conjugated state, the drug is in fact a prodrug devoid of cytotoxic activity and is activated following its dissociation from the phage at the target site in a temporally and spatially controlled manner. Our model target was Staphylococcus aureus, and the model drug was the antibiotic chloramphenicol. We demonstrated the potential of using filamentous phages as universal drug carriers for targetable cells involved in disease. Our approach replaces the selectivity of the drug itself with target selectivity borne by the targeting moiety, which may allow the reintroduction of nonspecific drugs that have thus far been excluded from antibacterial use (because of toxicity or low selectivity). Reintroduction of such drugs into the arsenal of useful tools may help to combat emerging bacterial antibiotic resistance. PMID:16723570

  9. Polymeric micelles as a drug carrier for tumor targeting

    Directory of Open Access Journals (Sweden)

    Neha M Dand

    2013-01-01

    Full Text Available Polymeric micelle can be targeted to tumor site by passive and active mechanism. Some inherent properties of polymeric micelle such as size in nanorange, stability in plasma, longevity in vivo, and pathological characteristics of tumor make polymeric micelles to be targeted at the tumor site by passive mechanism called enhanced permeability and retention effect. Polymeric micelle formed from the amphiphilic block copolymer is suitable for encapsulation of poorly water soluble, hydrophobic anticancer drugs. Other characteristics of polymeric micelles such as separated functionality at the outer shell are useful for targeting the anticancer drug to tumor by active mechanisms. Polymeric micelles can be conjugated with many ligands such as antibodies fragments, epidermal growth factors, α2 -glycoprotein, transferrine, and folate to target micelles to cancer cells. Application of heat and ultrasound are the alternative methods to enhance drug accumulation in tumoral cells. Targeting using micelles can also be done to tumor angiogenesis which is the potentially promising target for anticancer drugs. This review summarizes about recently available information regarding targeting the anticancer drug to the tumor site using polymeric micelles.

  10. Nanomedicine: Drug Delivery Systems and Nanoparticle Targeting

    International Nuclear Information System (INIS)

    Youn, Hye Won; Kang, Keon Wook; Chung, Jun Key; Lee, Dong Soo

    2008-01-01

    Applications of nanotechnology in the medical field have provided the fundamentals of tremendous improvement in precise diagnosis and customized therapy. Recent advances in nanomedicine have led to establish a new concept of theragnosis, which utilizes nanomedicines as a therapeutic and diagnostic tool at the same time. The development of high affinity nanoparticles with large surface area and functional groups multiplies diagnostic and therapeutic capacities. Considering the specific conditions related to the disease of individual patient, customized therapy requires the identification of disease target at the cellular and molecular level for reducing side effects and enhancing therapeutic efficiency. Well-designed nanoparticles can minimize unnecessary exposure of cytotoxic drugs and maximize targeted localization of administrated drugs. This review will focus on major pharmaceutical nanomaterials and nanoparticles as key components of designing and surface engineering for targeted theragnostic drug development

  11. Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

    Science.gov (United States)

    Penrod, Nadia M; Greene, Casey S; Moore, Jason H

    2014-01-01

    Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination

  12. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

    Science.gov (United States)

    Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen

    2014-03-19

    Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called "iNR-Drug" was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  13. iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking

    Directory of Open Access Journals (Sweden)

    Yue-Nong Fan

    2014-03-01

    Full Text Available Nuclear receptors (NRs are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  14. Molecularly targeted drugs for metastatic colorectal cancer

    Directory of Open Access Journals (Sweden)

    Cheng YD

    2013-11-01

    Full Text Available Ying-dong Cheng, Hua Yang, Guo-qing Chen, Zhi-cao Zhang Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing, People's Republic of China Abstract: The survival rate of patients with metastatic colorectal cancer (mCRC has significantly improved with applications of molecularly targeted drugs, such as bevacizumab, and led to a substantial improvement in the overall survival rate. These drugs are capable of specifically targeting the inherent abnormal pathways in cancer cells, which are potentially less toxic than traditional nonselective chemotherapeutics. In this review, the recent clinical information about molecularly targeted therapy for mCRC is summarized, with specific focus on several of the US Food and Drug Administration-approved molecularly targeted drugs for the treatment of mCRC in the clinic. Progression-free and overall survival in patients with mCRC was improved greatly by the addition of bevacizumab and/or cetuximab to standard chemotherapy, in either first- or second-line treatment. Aflibercept has been used in combination with folinic acid (leucovorin–fluorouracil–irinotecan (FOLFIRI chemotherapy in mCRC patients and among patients with mCRC with wild-type KRAS, the outcomes were significantly improved by panitumumab in combination with folinic acid (leucovorin–fluorouracil–oxaliplatin (FOLFOX or FOLFIRI. Because of the new preliminary studies, it has been recommended that regorafenib be used with FOLFOX or FOLFIRI as first- or second-line treatment of mCRC chemotherapy. In summary, an era of new opportunities has been opened for treatment of mCRC and/or other malignancies, resulting from the discovery of new selective targeting drugs. Keywords: metastatic colorectal cancer (mCRC, antiangiogenic drug, bevacizumab, aflibercept, regorafenib, cetuximab, panitumumab, clinical trial, molecularly targeted therapy

  15. TargetSpy: a supervised machine learning approach for microRNA target prediction.

    Science.gov (United States)

    Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

    2010-05-28

    , suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.

  16. TargetSpy: a supervised machine learning approach for microRNA target prediction

    Directory of Open Access Journals (Sweden)

    Langenberger David

    2010-05-01

    mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.

  17. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients.

    Science.gov (United States)

    de Witte, Wilhelmus E A; Wong, Yin Cheong; Nederpelt, Indira; Heitman, Laura H; Danhof, Meindert; van der Graaf, Piet H; Gilissen, Ron A H J; de Lange, Elizabeth C M

    2016-01-01

    Drug-target binding kinetics are major determinants of the time course of drug action for several drugs, as clearly described for the irreversible binders omeprazole and aspirin. This supports the increasing interest to incorporate newly developed high-throughput assays for drug-target binding kinetics in drug discovery. A meaningful application of in vitro drug-target binding kinetics in drug discovery requires insight into the relation between in vivo drug effect and in vitro measured drug-target binding kinetics. In this review, the authors discuss both the relation between in vitro and in vivo measured binding kinetics and the relation between in vivo binding kinetics, target occupancy and effect profiles. More scientific evidence is required for the rational selection and development of drug-candidates on the basis of in vitro estimates of drug-target binding kinetics. To elucidate the value of in vitro binding kinetics measurements, it is necessary to obtain information on system-specific properties which influence the kinetics of target occupancy and drug effect. Mathematical integration of this information enables the identification of drug-specific properties which lead to optimal target occupancy and drug effect in patients.

  18. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

    Science.gov (United States)

    Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

    2010-09-13

    Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

  19. Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

    International Nuclear Information System (INIS)

    Nigsch, Florian; Mitchell, John B.O.

    2008-01-01

    The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

  20. Multi-target drugs: the trend of drug research and development.

    Science.gov (United States)

    Lu, Jin-Jian; Pan, Wei; Hu, Yuan-Jia; Wang, Yi-Tao

    2012-01-01

    Summarizing the status of drugs in the market and examining the trend of drug research and development is important in drug discovery. In this study, we compared the drug targets and the market sales of the new molecular entities approved by the U.S. Food and Drug Administration from January 2000 to December 2009. Two networks, namely, the target-target and drug-drug networks, have been set up using the network analysis tools. The multi-target drugs have much more potential, as shown by the network visualization and the market trends. We discussed the possible reasons and proposed the rational strategies for drug research and development in the future.

  1. Targeting molecular networks for drug research

    Directory of Open Access Journals (Sweden)

    José Pedro Pinto

    2014-06-01

    Full Text Available The study of molecular networks has recently moved into the limelight of biomedical research. While it has certainly provided us with plenty of new insights into cellular mechanisms, the challenge now is how to modify or even restructure these networks. This is especially true for human diseases, which can be regarded as manifestations of distorted states of molecular networks. Of the possible interventions for altering networks, the use of drugs is presently the most feasible. In this mini-review, we present and discuss some exemplary approaches of how analysis of molecular interaction networks can contribute to pharmacology (e.g., by identifying new drug targets or prediction of drug side effects, as well as listing pointers to relevant resources and software to guide future research. We also outline recent progress in the use of drugs for in vitro reprogramming of cells, which constitutes an example par excellence for altering molecular interaction networks with drugs.

  2. Functionalization of protein-based nanocages for drug delivery applications.

    Science.gov (United States)

    Schoonen, Lise; van Hest, Jan C M

    2014-07-07

    Traditional drug delivery strategies involve drugs which are not targeted towards the desired tissue. This can lead to undesired side effects, as normal cells are affected by the drugs as well. Therefore, new systems are now being developed which combine targeting functionalities with encapsulation of drug cargo. Protein nanocages are highly promising drug delivery platforms due to their perfectly defined structures, biocompatibility, biodegradability and low toxicity. A variety of protein nanocages have been modified and functionalized for these types of applications. In this review, we aim to give an overview of different types of modifications of protein-based nanocontainers for drug delivery applications.

  3. Application of RNAi to Genomic Drug Target Validation in Schistosomes.

    Directory of Open Access Journals (Sweden)

    Alessandra Guidi

    2015-05-01

    Full Text Available Concerns over the possibility of resistance developing to praziquantel (PZQ, has stimulated efforts to develop new drugs for schistosomiasis. In addition to the development of improved whole organism screens, the success of RNA interference (RNAi in schistosomes offers great promise for the identification of potential drug targets to initiate drug discovery. In this study we set out to contribute to RNAi based validation of putative drug targets. Initially a list of 24 target candidates was compiled based on the identification of putative essential genes in schistosomes orthologous of C. elegans essential genes. Knockdown of Calmodulin (Smp_026560.2 (Sm-Calm, that topped this list, produced a phenotype characterised by waves of contraction in adult worms but no phenotype in schistosomula. Knockdown of the atypical Protein Kinase C (Smp_096310 (Sm-aPKC resulted in loss of viability in both schistosomula and adults and led us to focus our attention on other kinase genes that were identified in the above list and through whole organism screening of known kinase inhibitor sets followed by chemogenomic evaluation. RNAi knockdown of these kinase genes failed to affect adult worm viability but, like Sm-aPKC, knockdown of Polo-like kinase 1, Sm-PLK1 (Smp_009600 and p38-MAPK, Sm-MAPK p38 (Smp_133020 resulted in an increased mortality of schistosomula after 2-3 weeks, an effect more marked in the presence of human red blood cells (hRBC. For Sm-PLK-1 the same effects were seen with the specific inhibitor, BI2536, which also affected viable egg production in adult worms. For Sm-PLK-1 and Sm-aPKC the in vitro effects were reflected in lower recoveries in vivo. We conclude that the use of RNAi combined with culture with hRBC is a reliable method for evaluating genes important for larval development. However, in view of the slow manifestation of the effects of Sm-aPKC knockdown in adults and the lack of effects of Sm-PLK-1 and Sm-MAPK p38 on adult viability

  4. Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches

    KAUST Repository

    Olayan, Rawan S.

    2017-12-01

    Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient strategy for systematic screening of a large number of drugs in the attempt to identify new DTIs at low cost and with reasonable accuracy. This necessitates development of accurate computational methods that can help focus on the follow-up experimental validation on a smaller number of highly likely targets for a drug. Although many methods have been proposed for computational DTI prediction, they suffer the high false positive prediction rate or they do not predict the effect that drugs exert on targets in DTIs. In this report, first, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithm-centric perspectives. The aim is to provide a comprehensive review of computational methods for identifying DTIs, which could help in constructing more reliable methods. Then, we present DDR, an efficient method to predict the existence of DTIs. DDR achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify 5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a drug on its target. On different representative datasets, under various test setups, and using different performance measures, we show that DDR-FE achieves extremely good performance. Using blind test data, we verified as correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a drug on its target.

  5. Fluid mechanics aspects of magnetic drug targeting.

    Science.gov (United States)

    Odenbach, Stefan

    2015-10-01

    Experiments and numerical simulations using a flow phantom for magnetic drug targeting have been undertaken. The flow phantom is a half y-branched tube configuration where the main tube represents an artery from which a tumour-supplying artery, which is simulated by the side branch of the flow phantom, branches off. In the experiments a quantification of the amount of magnetic particles targeted towards the branch by a magnetic field applied via a permanent magnet is achieved by impedance measurement using sensor coils. Measuring the targeting efficiency, i.e. the relative amount of particles targeted to the side branch, for different field configurations one obtains targeting maps which combine the targeting efficiency with the magnetic force densities in characteristic points in the flow phantom. It could be shown that targeting efficiency depends strongly on the magnetic field configuration. A corresponding numerical model has been set up, which allows the simulation of targeting efficiency for variable field configuration. With this simulation good agreement of targeting efficiency with experimental data has been found. Thus, the basis has been laid for future calculations of optimal field configurations in clinical applications of magnetic drug targeting. Moreover, the numerical model allows the variation of additional parameters of the drug targeting process and thus an estimation of the influence, e.g. of the fluid properties on the targeting efficiency. Corresponding calculations have shown that the non-Newtonian behaviour of the fluid will significantly influence the targeting process, an aspect which has to be taken into account, especially recalling the fact that the viscosity of magnetic suspensions depends strongly on the magnetic field strength and the mechanical load.

  6. Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

    Science.gov (United States)

    Lebedeva, Galina; Sorokin, Anatoly; Faratian, Dana; Mullen, Peter; Goltsov, Alexey; Langdon, Simon P.; Harrison, David J.; Goryanin, Igor

    2012-01-01

    High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a

  7. Retrieval of Enterobacteriaceae drug targets using singular value decomposition.

    Science.gov (United States)

    Silvério-Machado, Rita; Couto, Bráulio R G M; Dos Santos, Marcos A

    2015-04-15

    The identification of potential drug target proteins in bacteria is important in pharmaceutical research for the development of new antibiotics to combat bacterial agents that cause diseases. A new model that combines the singular value decomposition (SVD) technique with biological filters composed of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of Escherichia coli (strain K12) has been created to predict potential antibiotic drug targets in the Enterobacteriaceae family. This model identified 99 potential drug target proteins in the studied family, which exhibit eight different functions and are protein-coding essential genes or similar to protein-coding essential genes of E.coli (strain K12), indicating that the disruption of the activities of these proteins is critical for cells. Proteins from bacteria with described drug resistance were found among the retrieved candidates. These candidates have no similarity to the human proteome, therefore exhibiting the advantage of causing no adverse effects or at least no known adverse effects on humans. rita_silverio@hotmail.com. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  8. The drug target genes show higher evolutionary conservation than non-target genes.

    Science.gov (United States)

    Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

    2016-01-26

    Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

  9. Fragment-based drug discovery and its application to challenging drug targets.

    Science.gov (United States)

    Price, Amanda J; Howard, Steven; Cons, Benjamin D

    2017-11-08

    Fragment-based drug discovery (FBDD) is a technique for identifying low molecular weight chemical starting points for drug discovery. Since its inception 20 years ago, FBDD has grown in popularity to the point where it is now an established technique in industry and academia. The approach involves the biophysical screening of proteins against collections of low molecular weight compounds (fragments). Although fragments bind to proteins with relatively low affinity, they form efficient, high quality binding interactions with the protein architecture as they have to overcome a significant entropy barrier to bind. Of the biophysical methods available for fragment screening, X-ray protein crystallography is one of the most sensitive and least prone to false positives. It also provides detailed structural information of the protein-fragment complex at the atomic level. Fragment-based screening using X-ray crystallography is therefore an efficient method for identifying binding hotspots on proteins, which can then be exploited by chemists and biologists for the discovery of new drugs. The use of FBDD is illustrated here with a recently published case study of a drug discovery programme targeting the challenging protein-protein interaction Kelch-like ECH-associated protein 1:nuclear factor erythroid 2-related factor 2. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

  10. Dendrimers in drug delivery and targeting: Drug-dendrimer interactions and toxicity issues

    Directory of Open Access Journals (Sweden)

    Kanika Madaan

    2014-01-01

    Full Text Available Dendrimers are the emerging polymeric architectures that are known for their defined structures, versatility in drug delivery and high functionality whose properties resemble with biomolecules. These nanostructured macromolecules have shown their potential abilities in entrapping and/or conjugating the high molecular weight hydrophilic/hydrophobic entities by host-guest interactions and covalent bonding (prodrug approach respectively. Moreover, high ratio of surface groups to molecular volume has made them a promising synthetic vector for gene delivery. Owing to these properties dendrimers have fascinated the researchers in the development of new drug carriers and they have been implicated in many therapeutic and biomedical applications. Despite of their extensive applications, their use in biological systems is limited due to toxicity issues associated with them. Considering this, the present review has focused on the different strategies of their synthesis, drug delivery and targeting, gene delivery and other biomedical applications, interactions involved in formation of drug-dendrimer complex along with characterization techniques employed for their evaluation, toxicity problems and associated approaches to alleviate their inherent toxicity.

  11. Dendrimers in drug delivery and targeting: Drug-dendrimer interactions and toxicity issues

    Science.gov (United States)

    Madaan, Kanika; Kumar, Sandeep; Poonia, Neelam; Lather, Viney; Pandita, Deepti

    2014-01-01

    Dendrimers are the emerging polymeric architectures that are known for their defined structures, versatility in drug delivery and high functionality whose properties resemble with biomolecules. These nanostructured macromolecules have shown their potential abilities in entrapping and/or conjugating the high molecular weight hydrophilic/hydrophobic entities by host-guest interactions and covalent bonding (prodrug approach) respectively. Moreover, high ratio of surface groups to molecular volume has made them a promising synthetic vector for gene delivery. Owing to these properties dendrimers have fascinated the researchers in the development of new drug carriers and they have been implicated in many therapeutic and biomedical applications. Despite of their extensive applications, their use in biological systems is limited due to toxicity issues associated with them. Considering this, the present review has focused on the different strategies of their synthesis, drug delivery and targeting, gene delivery and other biomedical applications, interactions involved in formation of drug-dendrimer complex along with characterization techniques employed for their evaluation, toxicity problems and associated approaches to alleviate their inherent toxicity. PMID:25035633

  12. Using human genetics to predict the effects and side-effects of drugs

    DEFF Research Database (Denmark)

    Stender, Stefan; Tybjærg-Hansen, Anne

    2016-01-01

    PURPOSE OF REVIEW: 'Genetic proxies' are increasingly being used to predict the effects of drugs. We present an up-to-date overview of the use of human genetics to predict effects and adverse effects of lipid-targeting drugs. RECENT FINDINGS: LDL cholesterol lowering variants in HMG-Coenzyme A re...

  13. Polymeric micelles for drug targeting.

    Science.gov (United States)

    Mahmud, Abdullah; Xiong, Xiao-Bing; Aliabadi, Hamidreza Montazeri; Lavasanifar, Afsaneh

    2007-11-01

    Polymeric micelles are nano-delivery systems formed through self-assembly of amphiphilic block copolymers in an aqueous environment. The nanoscopic dimension, stealth properties induced by the hydrophilic polymeric brush on the micellar surface, capacity for stabilized encapsulation of hydrophobic drugs offered by the hydrophobic and rigid micellar core, and finally a possibility for the chemical manipulation of the core/shell structure have made polymeric micelles one of the most promising carriers for drug targeting. To date, three generations of polymeric micellar delivery systems, i.e. polymeric micelles for passive, active and multifunctional drug targeting, have arisen from research efforts, with each subsequent generation displaying greater specificity for the diseased tissue and/or targeting efficiency. The present manuscript aims to review the research efforts made for the development of each generation and provide an assessment on the overall success of polymeric micellar delivery system in drug targeting. The emphasis is placed on the design and development of ligand modified, stimuli responsive and multifunctional polymeric micelles for drug targeting.

  14. Identifying problematic drugs based on the characteristics of their targets.

    Science.gov (United States)

    Lopes, Tiago J S; Shoemaker, Jason E; Matsuoka, Yukiko; Kawaoka, Yoshihiro; Kitano, Hiroaki

    2015-01-01

    Identifying promising compounds during the early stages of drug development is a major challenge for both academia and the pharmaceutical industry. The difficulties are even more pronounced when we consider multi-target pharmacology, where the compounds often target more than one protein, or multiple compounds are used together. Here, we address this problem by using machine learning and network analysis to process sequence and interaction data from human proteins to identify promising compounds. We used this strategy to identify properties that make certain proteins more likely to cause harmful effects when targeted; such proteins usually have domains commonly found throughout the human proteome. Additionally, since currently marketed drugs hit multiple targets simultaneously, we combined the information from individual proteins to devise a score that quantifies the likelihood of a compound being harmful to humans. This approach enabled us to distinguish between approved and problematic drugs with an accuracy of 60-70%. Moreover, our approach can be applied as soon as candidate drugs are available, as demonstrated with predictions for more than 5000 experimental drugs. These resources are available at http://sourceforge.net/projects/psin/.

  15. Identifying problematic drugs based on the characteristics of their targets

    Directory of Open Access Journals (Sweden)

    Tiago Jose eDa Silva Lopes

    2015-09-01

    Full Text Available Identifying promising compounds during the early stages of drug development is a major challenge for both academia and the pharmaceutical industry. The difficulties are even more pronounced when we consider multi-target pharmacology, where the compounds often target more than one protein, or multiple compounds are used together. Here, we address this problem by using machine learning and network analysis to process sequence and interaction data from human proteins to identify promising compounds. We used this strategy to identify properties that make certain proteins more likely to cause harmful effects when targeted; such proteins usually have domains commonly found throughout the human proteome. Additionally, since currently marketed drugs hit multiple targets simultaneously, we combined the information from individual proteins to devise a score that quantifies the likelihood of a compound being harmful to humans. This approach enabled us to distinguish between approved and problematic drugs with an accuracy of 60%¬–70%. Moreover, our approach can be applied as soon as candidate drugs are available, as demonstrated with predictions for more than 5000 experimental drugs. These resources are available at http://sourceforge.net/projects/psin/.

  16. The influence of drug distribution and drug-target binding on target occupancy : The rate-limiting step approximation

    NARCIS (Netherlands)

    Witte, de W.E.A.; Vauquelin, G.; Graaf, van der P.H.; Lange, de E.C.M.

    2017-01-01

    The influence of drug-target binding kinetics on target occupancy can be influenced by drug distribution and diffusion around the target, often referred to as "rebinding" or "diffusion-limited binding". This gives rise to a decreased decline of the drug-target complex concentration as a result of a

  17. Pharmacogenomics of GPCR Drug Targets

    DEFF Research Database (Denmark)

    Hauser, Alexander Sebastian; Chavali, Sreenivas; Masuho, Ikuo

    2018-01-01

    Natural genetic variation in the human genome is a cause of individual differences in responses to medications and is an underappreciated burden on public health. Although 108 G-protein-coupled receptors (GPCRs) are the targets of 475 (∼34%) Food and Drug Administration (FDA)-approved drugs...... and account for a global sales volume of over 180 billion US dollars annually, the prevalence of genetic variation among GPCRs targeted by drugs is unknown. By analyzing data from 68,496 individuals, we find that GPCRs targeted by drugs show genetic variation within functional regions such as drug......- and effector-binding sites in the human population. We experimentally show that certain variants of μ-opioid and Cholecystokinin-A receptors could lead to altered or adverse drug response. By analyzing UK National Health Service drug prescription and sales data, we suggest that characterizing GPCR variants...

  18. Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications.

    Science.gov (United States)

    Bian, Yuemin; Xie, Xiang-Qun Sean

    2018-04-09

    Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.

  19. EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

    Science.gov (United States)

    Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

    2018-04-24

    Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

  20. Aptamer-Mediated Polymeric Vehicles for Enhanced Cell-Targeted Drug Delivery.

    Science.gov (United States)

    Tan, Kei X; Danquah, Michael K; Sidhu, Amandeep; Yon, Lau Sie; Ongkudon, Clarence M

    2018-02-08

    The search for smart delivery systems for enhanced pre-clinical and clinical pharmaceutical delivery and cell targeting continues to be a major biomedical research endeavor owing to differences in the physicochemical characteristics and physiological effects of drug molecules, and this affects the delivery mechanisms to elicit maximum therapeutic effects. Targeted drug delivery is a smart evolution essential to address major challenges associated with conventional drug delivery systems. These challenges mostly result in poor pharmacokinetics due to the inability of the active pharmaceutical ingredients to specifically act on malignant cells thus, causing poor therapeutic index and toxicity to surrounding normal cells. Aptamers are oligonucleotides with engineered affinities to bind specifically to their cognate targets. Aptamers have gained significant interests as effective targeting elements for enhanced therapeutic delivery as they can be generated to specifically bind to wide range of targets including proteins, peptides, ions, cells and tissues. Notwithstanding, effective delivery of aptamers as therapeutic vehicles is challenged by cell membrane electrostatic repulsion, endonuclease degradation, low pH cleavage, and binding conformation stability. The application of molecularly engineered biodegradable and biocompatible polymeric particles with tunable features such as surface area and chemistry, particulate size distribution and toxicity creates opportunities to develop smart aptamer-mediated delivery systems for controlled drug release. This article discusses opportunities for particulate aptamer-drug formulations to advance current drug delivery modalities by navigating active ingredients through cellular and biomolecular traffic to target sites for sustained and controlled release at effective therapeutic dosages while minimizing systemic cytotoxic effects. A proposal for a novel drug-polymer-aptamer-polymer (DPAP) design of aptamer-drug formulation with

  1. Melanin targeting for intracellular drug delivery: Quantification of bound and free drug in retinal pigment epithelial cells.

    Science.gov (United States)

    Rimpelä, Anna-Kaisa; Hagström, Marja; Kidron, Heidi; Urtti, Arto

    2018-05-31

    Melanin binding affects drug distribution and retention in pigmented ocular tissues, thereby affecting drug response, duration of activity and toxicity. Therefore, it is a promising possibility for drug targeting and controlled release in the pigmented cells and tissues. Intracellular unbound drug concentrations determine pharmacological and toxicological actions, but analyses of unbound vs. total drug concentrations in pigmented cells are lacking. We studied intracellular binding and cellular drug uptake in pigmented retinal pigment epithelial cells and in non-pigmented ARPE-19 cells with five model drugs (chloroquine, propranolol, timolol, diclofenac, methotrexate). The unbound drug fractions in pigmented cells were 0.00016-0.73 and in non-pigmented cells 0.017-1.0. Cellular uptake (i.e. distribution ratio Kp), ranged from 1.3 to 6300 in pigmented cells and from 1.0 to 25 in non-pigmented cells. Values for intracellular bioavailability, F ic , were similar in both cells types (although larger variation in pigmented cells). In vitro melanin binding parameters were used to predict intracellular unbound drug fraction and cell uptake. Comparison of predictions with experimental data indicates that other factors (e.g. ion-trapping, lipophilicity-related binding to other cell components) also play a role. Melanin binding is a major factor that leads to cellular uptake and unbound drug fractions of a range of 3-4 orders of magnitude indicating that large reservoirs of melanin bound drug can be generated in the cells. Understanding melanin binding has important implications on retinal drug targeting, efficacy and toxicity. Copyright © 2017. Published by Elsevier B.V.

  2. In silico prediction of novel therapeutic targets using gene-disease association data.

    Science.gov (United States)

    Ferrero, Enrico; Dunham, Ian; Sanseau, Philippe

    2017-08-29

    Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target

  3. Targeted drugs in radiation therapy

    International Nuclear Information System (INIS)

    Favaudon, V.; Hennequin, C.; Hennequin, C.

    2004-01-01

    New drugs aiming at the development of targeted therapies have been assayed in combination with ionizing radiation over the past few years. The rationale of this concept comes from the fact that the cytotoxic potential of targeted drugs is limited, thus requiring concomitant association with a cytotoxic agent for the eradication of tumor cells. Conversely a low level of cumulative toxicity is expected from targeted drugs. Most targeted drugs act through inhibition of post-translational modifications of proteins, such as dimerization of growth factor receptors, prenylation reactions, or phosphorylation of tyrosine or serine-threonine residues. Many systems involving the proteasome, neo-angiogenesis promoters, TGF-β, cyclooxygenase or the transcription factor NF-κB, are currently under investigation in hopes they will allow a control of cell proliferation, apoptosis, cell cycle progression, tumor angiogenesis and inflammation. A few drugs have demonstrated an antitumor potential in particular phenotypes. In most instances, however, radiation-drug interactions proved to be strictly additive in terms of cell growth inhibition or induced cell death. Strong potentiation of the response to radiotherapy is expected to require interaction with DNA repair mechanisms. (authors)

  4. Target mediated drug disposition with drug-drug interaction, Part II: competitive and uncompetitive cases.

    Science.gov (United States)

    Koch, Gilbert; Jusko, William J; Schropp, Johannes

    2017-02-01

    We present competitive and uncompetitive drug-drug interaction (DDI) with target mediated drug disposition (TMDD) equations and investigate their pharmacokinetic DDI properties. For application of TMDD models, quasi-equilibrium (QE) or quasi-steady state (QSS) approximations are necessary to reduce the number of parameters. To realize those approximations of DDI TMDD models, we derive an ordinary differential equation (ODE) representation formulated in free concentration and free receptor variables. This ODE formulation can be straightforward implemented in typical PKPD software without solving any non-linear equation system arising from the QE or QSS approximation of the rapid binding assumptions. This manuscript is the second in a series to introduce and investigate DDI TMDD models and to apply the QE or QSS approximation.

  5. Functionalized mesoporous silicon for targeted-drug-delivery.

    Science.gov (United States)

    Tabasi, Ozra; Falamaki, Cavus; Khalaj, Zahra

    2012-10-01

    The present work concerns a preliminary step in the production of anticancer drug loaded porous silicon (PSi) for targeted-drug-delivery applications. A successful procedure for the covalent attachment of folic acid, polyethylene glycol (PEG) and doxorubicin to hydrophilic mesoporous silicon layers is presented. A systematic approach has been followed to obtain the optimal composition of the N,N'-dicyclohexylcarbodiimide (DCC)/N-hydroxysuccimide (NHS) in dimethylsulfoxide (DMSO) solution for the surface activation process of the undecylenic acid (UD) grafted molecules to take place with minimal undesired byproduct formation. The effect of reactant concentration and kind of solvent (aqueous or DMSO) on the attachment of folic acid to the activated PSi layer has been investigated. The covalent attachment of the doxorubicin molecules to the PSi layer functionalized with folic acid and PEG is discussed. The drug release kinetics as a function of pH has been studied. The functionalized PSi particles show a high cytotoxicity compared to the equivalent amount of free drug. Cell toxicity tests show clearly that the incorporation of folate molecules increases substantially the toxicity of the loaded PSi particles. Accordingly this new functionalized PSi may be considered a proper candidate for targeted drug delivery. Copyright © 2012 Elsevier B.V. All rights reserved.

  6. PDTD: a web-accessible protein database for drug target identification

    Directory of Open Access Journals (Sweden)

    Gao Zhenting

    2008-02-01

    unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage or an existing target (i.e. new pharmacological or toxic relevance, thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.

  7. Progress and Challenges in Developing Aptamer-Functionalized Targeted Drug Delivery Systems

    Directory of Open Access Journals (Sweden)

    Feng Jiang

    2015-10-01

    Full Text Available Aptamers, which can be screened via systematic evolution of ligands by exponential enrichment (SELEX, are superior ligands for molecular recognition due to their high selectivity and affinity. The interest in the use of aptamers as ligands for targeted drug delivery has been increasing due to their unique advantages. Based on their different compositions and preparation methods, aptamer-functionalized targeted drug delivery systems can be divided into two main categories: aptamer-small molecule conjugated systems and aptamer-nanomaterial conjugated systems. In this review, we not only summarize recent progress in aptamer selection and the application of aptamers in these targeted drug delivery systems but also discuss the advantages, challenges and new perspectives associated with these delivery systems.

  8. Simulating Serial-Target Antibacterial Drug Synergies Using Flux Balance Analysis

    DEFF Research Database (Denmark)

    Krueger, Andrew S.; Munck, Christian; Dantas, Gautam

    2016-01-01

    Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts cannot predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of t...

  9. Targeted drug delivery using temperature-sensitive liposomes

    International Nuclear Information System (INIS)

    Magin, R.L.; Niesman, M.R.

    1984-01-01

    Liposomes are receiving considerable attention as vehicles for selective drug delivery. One method of targeting liposomal contents involves the combination of local hyperthermia with temperature-sensitive liposomes. Such liposomes have been used to increase the uptake of methotrexate and cis-platinum into locally heated mouse tumors. However, additional information is needed on the mechanism of liposome drug release and the physiologic deposition of liposomes in vivo before clinical trails are begun. Current research is directed at studying the encapsulation and release of water soluble drugs from temperature-sensitive liposomes. The influence of liposome size, structure, and composition on the rapid release in plasma of cytosine arabinoside, cis-platinum, and the radiation sensitizer SR-2508 are described. These results demonstrate potential applications for temperature-sensitive liposomes in selective drug delivery

  10. Magnetic Nanoparticle Facilitated Drug Delivery for Cancer Therapy with Targeted and Image-Guided Approaches.

    Science.gov (United States)

    Huang, Jing; Li, Yuancheng; Orza, Anamaria; Lu, Qiong; Guo, Peng; Wang, Liya; Yang, Lily; Mao, Hui

    2016-06-14

    With rapid advances in nanomedicine, magnetic nanoparticles (MNPs) have emerged as a promising theranostic tool in biomedical applications, including diagnostic imaging, drug delivery and novel therapeutics. Significant preclinical and clinical research has explored their functionalization, targeted delivery, controllable drug release and image-guided capabilities. To further develop MNPs for theranostic applications and clinical translation in the future, we attempt to provide an overview of the recent advances in the development and application of MNPs for drug delivery, specifically focusing on the topics concerning the importance of biomarker targeting for personalized therapy and the unique magnetic and contrast-enhancing properties of theranostic MNPs that enable image-guided delivery. The common strategies and considerations to produce theranostic MNPs and incorporate payload drugs into MNP carriers are described. The notable examples are presented to demonstrate the advantages of MNPs in specific targeting and delivering under image guidance. Furthermore, current understanding of delivery mechanisms and challenges to achieve efficient therapeutic efficacy or diagnostic capability using MNP-based nanomedicine are discussed.

  11. Data-driven prediction of adverse drug reactions induced by drug-drug interactions.

    Science.gov (United States)

    Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Kumar, Kamal; Yu, Xueping; Wallqvist, Anders; Reifman, Jaques

    2017-06-08

    The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects. We hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles. We used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org . We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs

  12. Mononuclear phagocytes as a target, not a barrier, for drug delivery.

    Science.gov (United States)

    Yong, Seok-Beom; Song, Yoonsung; Kim, Hyung Jin; Ain, Qurrat Ul; Kim, Yong-Hee

    2017-08-10

    Mononuclear phagocytes have been generally recognized as a barrier to drug delivery. Recently, a new understanding of mononuclear phagocytes (MPS) ontogeny has surfaced and their functions in disease have been unveiled, demonstrating the need for re-evaluation of perspectives on mononuclear phagocytes in drug delivery. In this review, we described mononuclear phagocyte biology and focus on their accumulation mechanisms in disease sites with explanations of monocyte heterogeneity. In the 'MPS as a barrier' section, we summarized recent studies on mechanisms to avoid phagocytosis based on two different biological principles: protein adsorption and self-recognition. In the 'MPS as a target' section, more detailed descriptions were given on mononuclear phagocyte-targeted drug delivery systems and their applications to various diseases. Collectively, we emphasize in this review that mononuclear phagocytes are potent targets for future drug delivery systems. Mononuclear phagocyte-targeted delivery systems should be created with an understanding of mononuclear phagocyte ontogeny and pathology. Each specific subset of phagocytes should be targeted differently by location and function for improved disease-drug delivery while avoiding RES clearance such as Kupffer cells and splenic macrophages. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Chemical Genomics and Emerging DNA Technologies in the Identification of Drug Mechanisms and Drug Targets

    DEFF Research Database (Denmark)

    Olsen, Louise Cathrine Braun; Færgeman, Nils J.

    2012-01-01

    and validate therapeutic targets and to discover drug candidates for rapidly and effectively generating new interventions for human diseases. The recent emergence of genomic technologies and their application on genetically tractable model organisms like Drosophila melanogaster,Caenorhabditis elegans...... critical roles in the genomic age of biological research and drug discovery. In the present review we discuss how simple biological model organisms can be used as screening platforms in combination with emerging genomic technologies to advance the identification of potential drugs and their molecular...

  14. Drug target ontology to classify and integrate drug discovery data.

    Science.gov (United States)

    Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C

    2017-11-09

    One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic

  15. New approaches to targeted drug delivery to tumour cells

    International Nuclear Information System (INIS)

    Severin, E S

    2015-01-01

    Basic approaches to the design of targeted drugs for the treatment of human malignant tumours have been considered. The stages of the development of these approaches have been described in detail and theoretically substantiated, and basic experimental results have been reported. Considerable attention is paid to the general characteristic of nanopharmacological drugs and to the description of mechanisms of cellular interactions with nanodrugs. The potentialities and limitations of application of nanodrugs for cancer therapy and treatment of other diseases have been considered. The use of nanodrugs conjugated with vector molecules seems to be the most promising trend of targeted therapy of malignant tumours. The bibliography includes 122 references

  16. [Artificial Intelligence in Drug Discovery].

    Science.gov (United States)

    Fujiwara, Takeshi; Kamada, Mayumi; Okuno, Yasushi

    2018-04-01

    According to the increase of data generated from analytical instruments, application of artificial intelligence(AI)technology in medical field is indispensable. In particular, practical application of AI technology is strongly required in "genomic medicine" and "genomic drug discovery" that conduct medical practice and novel drug development based on individual genomic information. In our laboratory, we have been developing a database to integrate genome data and clinical information obtained by clinical genome analysis and a computational support system for clinical interpretation of variants using AI. In addition, with the aim of creating new therapeutic targets in genomic drug discovery, we have been also working on the development of a binding affinity prediction system for mutated proteins and drugs by molecular dynamics simulation using supercomputer "Kei". We also have tackled for problems in a drug virtual screening. Our developed AI technology has successfully generated virtual compound library, and deep learning method has enabled us to predict interaction between compound and target protein.

  17. miR-630 targets IGF1R to regulate response to HER-targeting drugs and overall cancer cell progression in HER2 over-expressing breast cancer.

    Science.gov (United States)

    Corcoran, Claire; Rani, Sweta; Breslin, Susan; Gogarty, Martina; Ghobrial, Irene M; Crown, John; O'Driscoll, Lorraine

    2014-03-24

    While the treatment of HER2 over-expressing breast cancer with recent HER-targeted drugs has been highly effective for some patients, primary (also known as innate) or acquired resistance limits the success of these drugs. microRNAs have potential as diagnostic, prognostic and predictive biomarkers, as well as replacement therapies. Here we investigated the role of microRNA-630 (miR-630) in breast cancer progression and as a predictive biomarker for response to HER-targeting drugs, ultimately yielding potential as a therapeutic approach to add value to these drugs. We investigated the levels of intra- and extracellular miR-630 in cells and conditioned media from breast cancer cell lines with either innate- or acquired- resistance to HER-targeting lapatinib and neratinib, compared to their corresponding drug sensitive cell lines, using qPCR. To support the role of miR-630 in breast cancer, we examined the clinical relevance of this miRNA in breast cancer tumours versus matched peritumours. Transfection of miR-630 mimics and inhibitors was used to manipulate the expression of miR-630 to assess effects on response to HER-targeting drugs (lapatinib, neratinib and afatinib). Other phenotypic changes associated with cellular aggressiveness were evaluated by motility, invasion and anoikis assays. TargetScan prediction software, qPCR, immunoblotting and ELISAs, were used to assess miR-630's regulation of mRNA, proteins and their phosphorylated forms. We established that introducing miR-630 into cells with innate- or acquired- resistance to HER-drugs significantly restored the efficacy of lapatinib, neratinib and afatinib; through a mechanism which we have determined to, at least partly, involve miR-630's regulation of IGF1R. Conversely, we demonstrated that blocking miR-630 induced resistance/insensitivity to these drugs. Cellular motility, invasion, and anoikis were also observed as significantly altered by miR-630 manipulation, whereby introducing miR-630 into cells

  18. Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions.

    Science.gov (United States)

    La, Mary K; Sedykh, Alexander; Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

    2018-06-06

    Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities. This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships. Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for

  19. Rational optimization of drug-target residence time: Insights from inhibitor binding to the S. aureus FabI enzyme-product complex

    Science.gov (United States)

    Chang, Andrew; Schiebel, Johannes; Yu, Weixuan; Bommineni, Gopal R.; Pan, Pan; Baxter, Michael V.; Khanna, Avinash; Sotriffer, Christoph A.; Kisker, Caroline; Tonge, Peter J.

    2013-01-01

    Drug-target kinetics has recently emerged as an especially important facet of the drug discovery process. In particular, prolonged drug-target residence times may confer enhanced efficacy and selectivity in the open in vivo system. However, the lack of accurate kinetic and structural data for series of congeneric compounds hinders the rational design of inhibitors with decreased off-rates. Therefore, we chose the Staphylococcus aureus enoyl-ACP reductase (saFabI) - an important target for the development of new anti-staphylococcal drugs - as a model system to rationalize and optimize the drug-target residence time on a structural basis. Using our new, efficient and widely applicable mechanistically informed kinetic approach, we obtained a full characterization of saFabI inhibition by a series of 20 diphenyl ethers complemented by a collection of 9 saFabI-inhibitor crystal structures. We identified a strong correlation between the affinities of the investigated saFabI diphenyl ether inhibitors and their corresponding residence times, which can be rationalized on a structural basis. Due to its favorable interactions with the enzyme, the residence time of our most potent compound exceeds 10 hours. In addition, we found that affinity and residence time in this system can be significantly enhanced by modifications predictable by a careful consideration of catalysis. Our study provides a blueprint for investigating and prolonging drug-target kinetics and may aid in the rational design of long-residence-time inhibitors targeting the essential saFabI enzyme. PMID:23697754

  20. An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.

    Directory of Open Access Journals (Sweden)

    Qian Wan

    Full Text Available We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

  1. Identification of Multiple Cryptococcal Fungicidal Drug Targets by Combined Gene Dosing and Drug Affinity Responsive Target Stability Screening

    Directory of Open Access Journals (Sweden)

    Yoon-Dong Park

    2016-08-01

    Full Text Available Cryptococcus neoformans is a pathogenic fungus that is responsible for up to half a million cases of meningitis globally, especially in immunocompromised individuals. Common fungistatic drugs, such as fluconazole, are less toxic for patients but have low efficacy for initial therapy of the disease. Effective therapy against the disease is provided by the fungicidal drug amphotericin B; however, due to its high toxicity and the difficulty in administering its intravenous formulation, it is imperative to find new therapies targeting the fungus. The antiparasitic drug bithionol has been recently identified as having potent fungicidal activity. In this study, we used a combined gene dosing and drug affinity responsive target stability (GD-DARTS screen as well as protein modeling to identify a common drug binding site of bithionol within multiple NAD-dependent dehydrogenase drug targets. This combination genetic and proteomic method thus provides a powerful method for identifying novel fungicidal drug targets for further development.

  2. Bitterness prediction in-silico: A step towards better drugs.

    Science.gov (United States)

    Bahia, Malkeet Singh; Nissim, Ido; Niv, Masha Y

    2018-02-05

    Bitter taste is innately aversive and thought to protect against consuming poisons. Bitter taste receptors (Tas2Rs) are G-protein coupled receptors, expressed both orally and extra-orally and proposed as novel targets for several indications, including asthma. Many clinical drugs elicit bitter taste, suggesting the possibility of drugs re-purposing. On the other hand, the bitter taste of medicine presents a major compliance problem for pediatric drugs. Thus, efficient tools for predicting, measuring and masking bitterness of active pharmaceutical ingredients (APIs) are required by the pharmaceutical industry. Here we highlight the BitterDB database of bitter compounds and survey the main computational approaches to prediction of bitter taste based on compound's chemical structure. Current in silico bitterness prediction methods provide encouraging results, can be constantly improved using growing experimental data, and present a reliable and efficient addition to the APIs development toolbox. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Self-assembled peptide-based nanostructures: Smart nanomaterials toward targeted drug delivery.

    Science.gov (United States)

    Habibi, Neda; Kamaly, Nazila; Memic, Adnan; Shafiee, Hadi

    2016-02-01

    Self-assembly of peptides can yield an array of well-defined nanostructures that are highly attractive nanomaterials for many biomedical applications such as drug delivery. Some of the advantages of self-assembled peptide nanostructures over other delivery platforms include their chemical diversity, biocompatibility, high loading capacity for both hydrophobic and hydrophilic drugs, and their ability to target molecular recognition sites. Furthermore, these self-assembled nanostructures could be designed with novel peptide motifs, making them stimuli-responsive and achieving triggered drug delivery at disease sites. The goal of this work is to present a comprehensive review of the most recent studies on self-assembled peptides with a focus on their "smart" activity for formation of targeted and responsive drug-delivery carriers.

  4. Sperm-Hybrid Micromotor for Targeted Drug Delivery.

    Science.gov (United States)

    Xu, Haifeng; Medina-Sánchez, Mariana; Magdanz, Veronika; Schwarz, Lukas; Hebenstreit, Franziska; Schmidt, Oliver G

    2018-01-23

    A sperm-driven micromotor is presented as a targeted drug delivery system, which is appealing to potentially treat diseases in the female reproductive tract. This system is demonstrated to be an efficient drug delivery vehicle by first loading a motile sperm cell with an anticancer drug (doxorubicin hydrochloride), guiding it magnetically, to an in vitro cultured tumor spheroid, and finally freeing the sperm cell to deliver the drug locally. The sperm release mechanism is designed to liberate the sperm when the biohybrid micromotor hits the tumor walls, allowing it to swim into the tumor and deliver the drug through the sperm-cancer cell membrane fusion. In our experiments, the sperm cells exhibited a high drug encapsulation capability and drug carrying stability, conveniently minimizing  toxic side effects and unwanted drug accumulation in healthy tissues. Overall, sperm cells are excellent candidates to operate in physiological environments, as they neither express pathogenic proteins nor proliferate to form undesirable colonies, unlike other cells or microorganisms. This sperm-hybrid micromotor is a biocompatible platform with potential application in gynecological healthcare, treating or detecting cancer or other diseases in the female reproductive system.

  5. ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

    Science.gov (United States)

    Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

    2017-11-27

    Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

  6. Liposomal Tumor Targeting in Drug Delivery Utilizing MMP-2- and MMP-9-Binding Ligands

    Directory of Open Access Journals (Sweden)

    Oula Penate Medina

    2011-01-01

    Full Text Available Nanotechnology offers an alternative to conventional treatment options by enabling different drug delivery and controlled-release delivery strategies. Liposomes being especially biodegradable and in most cases essentially nontoxic offer a versatile platform for several different delivery approaches that can potentially enhance the delivery and targeting of therapies to tumors. Liposomes penetrate tumors spontaneously as a result of fenestrated blood vessels within tumors, leading to known enhanced permeability and subsequent drug retention effects. In addition, liposomes can be used to carry radioactive moieties, such as radiotracers, which can be bound at multiple locations within liposomes, making them attractive carriers for molecular imaging applications. Phage display is a technique that can deliver various high-affinity and selectivity peptides to different targets. In this study, gelatinase-binding peptides, found by phage display, were attached to liposomes by covalent peptide-PEG-PE anchor creating a targeted drug delivery vehicle. Gelatinases as extracellular targets for tumor targeting offer a viable alternative for tumor targeting. Our findings show that targeted drug delivery is more efficient than non-targeted drug delivery.

  7. Magnetically responsive microparticles for targeted drug and radionuclide delivery

    International Nuclear Information System (INIS)

    Kaminski, M. D.; Ghebremeskel, A. N.; Nunez, L.; Kasza, K. E.; Chang, F.; Chien, T.-H.; Fisher, P. F.; Eastman, J. A.; Rosengart, A. J.; McDonald, L.; Xie, Y.; Johns, L.; Pytel, P.; Hafeli, U. O.

    2004-01-01

    We are currently investigating the use of magnetic particles--polymeric-based spheres containing dispersed magnetic nanocrystalline phases--for the precise delivery of drugs via the human vasculature. According to this review, meticulously prepared magnetic drug targeting holds promise as a safe and effective method of delivering drugs to specific organ, tissue or cellular targets. We have critically examined the wide range of approaches in the design and implementation of magnetic-particle-based drug delivery systems to date, including magnetic particle preparation, drug encapsulation, biostability, biocompatibility, toxicity, magnetic field designs, and clinical trials. However, we strongly believe that there are several limitations with past developments that need to be addressed to enable significant strides in the field. First, particle size has to be carefully chosen. Micrometer-sized magnetic particles are better attracted over a distance than nanometer sized magnetic particles by a constant magnetic field gradient, and particle sizes up to 1 (micro)m show a much better accumulation with no apparent side effects in small animal models, since the smallest blood vessels have an inner diameter of 5-7 (micro)m. Nanometer-sized particles <70 nm will accumulate in organ fenestrations despite an effective surface stabilizer. To be suitable for future human applications, our experimental approach synthesizes the magnetic drug carrier according to specific predefined outcome metrics: monodisperse population in a size range of 100 nm to 1.0 (micro)m, non-toxic, with appropriate magnetic properties, and demonstrating successful in vitro and in vivo tests. Another important variable offering possible improvement is surface polarity, which is expected to prolong particle half-life in circulation and modify biodistribution and stability of drugs in the body. The molecules in the blood that are responsible for enhancing the uptake of particles by the reticuloendothelial

  8. Rational polypharmacology: systematically identifying and engaging multiple drug targets to promote axon growth

    Science.gov (United States)

    Al-Ali, Hassan; Lee, Do-Hun; Danzi, Matt C.; Nassif, Houssam; Gautam, Prson; Wennerberg, Krister; Zuercher, Bill; Drewry, David H.; Lee, Jae K.; Lemmon, Vance P.; Bixby, John L.

    2016-01-01

    Mammalian Central Nervous System (CNS) neurons regrow their axons poorly following injury, resulting in irreversible functional losses. Identifying therapeutics that encourage CNS axon repair has been difficult, in part because multiple etiologies underlie this regenerative failure. This suggests a particular need for drugs that engage multiple molecular targets. Although multi-target drugs are generally more effective than highly selective alternatives, we lack systematic methods for discovering such drugs. Target-based screening is an efficient technique for identifying potent modulators of individual targets. In contrast, phenotypic screening can identify drugs with multiple targets; however, these targets remain unknown. To address this gap, we combined the two drug discovery approaches using machine learning and information theory. We screened compounds in a phenotypic assay with primary CNS neurons and also in a panel of kinase enzyme assays. We used learning algorithms to relate the compounds’ kinase inhibition profiles to their influence on neurite outgrowth. This allowed us to identify kinases that may serve as targets for promoting neurite outgrowth, as well as others whose targeting should be avoided. We found that compounds that inhibit multiple targets (polypharmacology) promote robust neurite outgrowth in vitro. One compound with exemplary polypharmacology, was found to promote axon growth in a rodent spinal cord injury model. A more general applicability of our approach is suggested by its ability to deconvolve known targets for a breast cancer cell line, as well as targets recently shown to mediate drug resistance. PMID:26056718

  9. DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

    KAUST Repository

    Olayan, Rawan S.

    2017-11-23

    Motivation Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using five repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new, and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs.

  10. Drug repositioning for enzyme modulator based on human metabolite-likeness.

    Science.gov (United States)

    Lee, Yoon Hyeok; Choi, Hojae; Park, Seongyong; Lee, Boah; Yi, Gwan-Su

    2017-05-31

    Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs. We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for

  11. Recent Trends in Nanotechnology-Based Drugs and Formulations for Targeted Therapeutic Delivery.

    Science.gov (United States)

    Iqbal, Hafiz M N; Rodriguez, Angel M V; Khandia, Rekha; Munjal, Ashok; Dhama, Kuldeep

    2017-01-01

    In the recent past, a wider spectrum of nanotechnologybased drugs or drug-loaded devices and systems has been engineered and investigated with high interests. The key objective is to help for an enhanced/better quality of patient life in a secure way by avoiding/limiting drug abuse, or severe adverse effects of some in practice traditional therapies. Various methodological approaches including in vitro, in vivo, and ex vivo techniques have been exploited, so far. Among them, nanoparticles-based therapeutic agents are of supreme interests for an enhanced and efficient delivery in the current biomedical sector of the modern world. The development of new types of novel, effective and highly reliable therapeutic drug delivery system (DDS) for multipurpose applications is essential and a core demand to tackle many human health related diseases. In this context, nanotechnology-based several advanced DDS have been engineered with novel characteristics for biomedical, pharmaceutical and cosmeceutical applications that include but not limited to the enhanced/improved bioactivity, bioavailability, drug efficacy, targeted delivery, and therapeutically safer with an extra advantage of overcoming demerits of traditional drug formulations/designs. This review work is focused on recent trends/advances in nanotechnology-based drugs and formulations designed for targeted therapeutic delivery. Moreover, information is also reviewed and given from recent patents and summarized or illustrated diagrammatically to depict a better understanding. Recent patents covering various nanotechnology-based approaches for several applications have also been reviewed. The drug-loaded nanoparticles are among versatile candidates with multifunctional characteristics for potential applications in biomedical, and tissue engineering sector. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  12. Drug targets in the cytokine universe for autoimmune disease.

    Science.gov (United States)

    Liu, Xuebin; Fang, Lei; Guo, Taylor B; Mei, Hongkang; Zhang, Jingwu Z

    2013-03-01

    In autoimmune disease, a network of diverse cytokines is produced in association with disease susceptibility to constitute the 'cytokine milieu' that drives chronic inflammation. It remains elusive how cytokines interact in such a complex network to sustain inflammation in autoimmune disease. This has presented huge challenges for successful drug discovery because it has been difficult to predict how individual cytokine-targeted therapy would work. Here, we combine the principles of Chinese Taoism philosophy and modern bioinformatics tools to dissect multiple layers of arbitrary cytokine interactions into discernible interfaces and connectivity maps to predict movements in the cytokine network. The key principles presented here have important implications in our understanding of cytokine interactions and development of effective cytokine-targeted therapies for autoimmune disorders. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Targeting Antibacterial Agents by Using Drug-Carrying Filamentous Bacteriophages

    OpenAIRE

    Yacoby, Iftach; Shamis, Marina; Bar, Hagit; Shabat, Doron; Benhar, Itai

    2006-01-01

    Bacteriophages have been used for more than a century for (unconventional) therapy of bacterial infections, for half a century as tools in genetic research, for 2 decades as tools for discovery of specific target-binding proteins, and for nearly a decade as tools for vaccination or as gene delivery vehicles. Here we present a novel application of filamentous bacteriophages (phages) as targeted drug carriers for the eradication of (pathogenic) bacteria. The phages are genetically modified to d...

  14. In silico modeling predicts drug sensitivity of patient-derived cancer cells.

    Science.gov (United States)

    Pingle, Sandeep C; Sultana, Zeba; Pastorino, Sandra; Jiang, Pengfei; Mukthavaram, Rajesh; Chao, Ying; Bharati, Ila Sri; Nomura, Natsuko; Makale, Milan; Abbasi, Taher; Kapoor, Shweta; Kumar, Ansu; Usmani, Shahabuddin; Agrawal, Ashish; Vali, Shireen; Kesari, Santosh

    2014-05-21

    Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

  15. Mathematical modeling and computational prediction of cancer drug resistance.

    Science.gov (United States)

    Sun, Xiaoqiang; Hu, Bin

    2017-06-23

    Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of

  16. Hot-spot analysis for drug discovery targeting protein-protein interactions.

    Science.gov (United States)

    Rosell, Mireia; Fernández-Recio, Juan

    2018-04-01

    Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

  17. Fast prediction of cytochrome P450 mediated drug metabolism

    DEFF Research Database (Denmark)

    Rydberg, Patrik Åke Anders; Poongavanam, Vasanthanathan; Oostenbrink, Chris

    2009-01-01

    Cytochrome P450 mediated metabolism of drugs is one of the major determinants of their kinetic profile, and prediction of this metabolism is therefore highly relevant during the drug discovery and development process. A new rule-based method, based on results from density functional theory...... calculations, for predicting activation energies for aliphatic and aromatic oxidations by cytochromes P450 is developed and compared with several other methods. Although the applicability of the method is currently limited to a subset of P450 reactions, these reactions describe more than 90...

  18. Retention of ferrofluid aggregates at the target site during magnetic drug targeting

    Energy Technology Data Exchange (ETDEWEB)

    Asfer, Mohammed, E-mail: asfer786@gmail.com [School of Engineering and Technology, BML Munjal University, Haryana (India); Saroj, Sunil Kumar [Department of Mechanical Engineering, IIT Kanpur, Kanpur (India); Panigrahi, Pradipta Kumar, E-mail: panig@iitk.ac.in [Department of Mechanical Engineering, IIT Kanpur, Kanpur (India)

    2017-08-15

    Highlights: • The present in vitro work reports the retention dynamics of ferrofluid aggregates at the target site against a bulk flow of DI water inside a micro capillary during magnetic drug targeting. • The recirculation zone at the downstream of the aggregate is found to be a function of aggregate height, Reynolds number and the degree of surface roughness of the outer boundary of the aggregate. • The reported results of the present work can be used as a guideline for the better design of MDT technique for in vivo applications. - Abstract: The present study reports the retention dynamics of a ferrofluid aggregate localized at the target site inside a glass capillary (500 × 500 µm{sup 2} square cross section) against a bulk flow of DI water (Re = 0.16 and 0.016) during the process of magnetic drug targeting (MDT). The dispersion dynamics of iron oxide nanoparticles (IONPs) into bulk flow for different initial size of aggregate at the target site is reported using the brightfield visualization technique. The flow field around the aggregate during the retention is evaluated using the µPIV technique. IONPs at the outer boundary experience a higher shear force as compared to the magnetic force, resulting in dispersion of IONPs into the bulk flow downstream to the aggregate. The blockage effect and the roughness of the outer boundary of the aggregate resulting from chain like clustering of IONPs contribute to the flow recirculation at the downstream region of the aggregate. The entrapment of seeding particles inside the chain like clusters of IONPs at the outer boundary of the aggregate reduces the degree of roughness resulting in a streamlined aggregate at the target site at later time. The effect of blockage, structure of the aggregate, and disturbed flow such as recirculation around the aggregate are the primary factors, which must be investigated for the effectiveness of the MDT process for in vivo applications.

  19. [Advances of tumor targeting peptides drug delivery system with pH-sensitive activities].

    Science.gov (United States)

    Ma, Yin-yun; Li, Li; Huang, Hai-feng; Gou, San-hu; Ni, Jing-man

    2016-05-01

    The pH-sensitive peptides drug delivery systems, which target to acidic extracellular environment of tumor tissue, have many advantages in drug delivery. They exhibit a high specificity to tumor and low cytotoxicity, which significantly increase the efficacy of traditional anti-cancer drugs. In recent years the systems have received a great attention. The pH-sensitive peptides drug delivery systems can be divided into five types according to the difference in pH-responsive mechanism,type of peptides and carrier materials. This paper summarizes the recent progresses in the field with a focus on the five types of pH-sensitive peptides in drug delivery systems. This may provide a guideline to design and application of tumor targeting drugs.

  20. Individualization of anticancer therapy; molecular targets of novel drugs in oncology

    Directory of Open Access Journals (Sweden)

    Katarzyna Regulska

    2012-11-01

    Full Text Available Deregulation of cellular signal transduction, caused by gene mutations, has been recognized as a basic factor of cancer initiation, promotion and progression. Thus, the ability to control the activity of overstimulated signal molecules by the use of appropriate inhibitors became the idea of targeted cancer therapy, which has provided an effective tool to normalize the molecular disorders in malignant cells and to treat certain types of cancer. The molecularly targeted drugs are divided into two major pharmaceutical classes: monoclonal antibodies and small-molecule kinase inhibitors. This review presents a summary of their characteristics, analyzing their chemical structures, specified molecular targets, mechanisms of action and indications for use. Also the molecules subjected to preclinical trials or phase I, II and III clinical trials evaluating their efficiency and safety are presented. Moreover, the article discusses further perspectives for development of targeted therapies focusing on three major directions: systematic searching and discovery of new targets that are oncogenic drivers, improving the pharmacological properties of currently known drugs, and developing strategies to overcome drug resistance. Finally, the role of proper pharmacodiagnostics as a key to rational anticancer therapy has been emphasized since the verification of reliable predictive biomarkers is a basis of individualized medicine in oncology. 

  1. Magnetic microgels for drug targeting applications: Physical–chemical properties and cytotoxicity evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Turcu, Rodica, E-mail: rodica.turcu@itim-cj.ro [National Institute for Research and Development of Isotopic and Molecular Technologies, 65-103 Donath Street, 400293 Cluj-Napoca (Romania); Craciunescu, Izabell [National Institute for Research and Development of Isotopic and Molecular Technologies, 65-103 Donath Street, 400293 Cluj-Napoca (Romania); Garamus, Vasil M. [Helmholtz-Zentrum Geesthacht, Zentrum für Material- und Küstenforschung GmbH, 21502 Geesthacht (Germany); Janko, Christina; Lyer, Stefan; Tietze, Rainer; Alexiou, Christoph [ENT-Department, Else Kröner-Fresenius Stiftung-Professorship, Section for Experimental Oncology and Nanomedicine (SEON), University Hospital Erlangen (Germany); Vekas, Ladislau, E-mail: vekas@acad-tim.tm.edu.ro [Romanian Academy-Timisoara Branch, CFATR, Laboratory of Magnetic Fluids, Mihai Viteazul Street 24, 300223 Timisoara (Romania)

    2015-04-15

    Magnetoresponsive microgels with high saturation magnetization values have been obtained by a strategy based on the miniemulsion method using high colloidal stability organic carrier ferrofluid as primary material. Hydrophobic nanoparticles Fe{sub 3}O{sub 4}/oleic acid are densely packed into well-defined spherical nanoparticle clusters coated with polymers with sizes in the range 40–350 nm. Physical–chemical characteristics of magnetic microgels were investigated by TEM, SAXS, XPS and VSM measurements with the focus on the structure–properties relationship. The impact of magnetic microgels loaded with anticancer drug mitoxantrone (MTO) on the non-adherent human T cell leukemia line Jurkat was investigated in multiparameter flow cytometry. We showed that both MTO and microgel-loaded MTO penetrate into cells and both induce apoptosis and later secondary necrosis in a time- and dose dependent manner. In contrast, microgels without MTO are not cytotoxic in the corresponding concentrations. Our results show that MTO-loaded microgels are promising structures for application in magnetic drug targeting. - Highlights: • Densely packed spherical clusters of magnetic nanoparticles were obtained. • High magnetization microgels with superparamagnetic behavior are reported. • The facile and reproducible synthesis procedure applied is easy to be up-scaled. • The toxicity tests show that magnetic microgels are not cytotoxic. • We show that mitoxantrone loaded microgels induce death of Jurkat cells.

  2. Drugs and drug delivery systems targeting amyloid-β in Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    Morgan Robinson

    2015-07-01

    Full Text Available Alzheimer's disease (AD is a devastating neurodegenerative disorder with no cure and limited treatment solutions that are unable to target any of the suspected causes. Increasing evidence suggests that one of the causes of neurodegeneration is the overproduction of amyloid beta (Aβ and the inability of Aβ peptides to be cleared from the brain, resulting in self-aggregation to form toxic oligomers, fibrils and plaques. One of the potential treatment options is to target Aβ and prevent self-aggregation to allow for a natural clearing of the brain. In this paper, we review the drugs and drug delivery systems that target Aβ in relation to Alzheimer's disease. Many attempts have been made to use anti-Aβ targeting molecules capable of targeting Aβ (with much success in vitro and in vivo animal models, but the major obstacle to this technique is the challenge posed by the blood brain barrier (BBB. This highly selective barrier protects the brain from toxic molecules and pathogens and prevents the delivery of most drugs. Therefore novel Aβ aggregation inhibitor drugs will require well thought-out drug delivery systems to deliver sufficient concentrations to the brain.

  3. The Research Progress of Targeted Drug Delivery Systems

    Science.gov (United States)

    Zhan, Jiayin; Ting, Xizi Liang; Zhu, Junjie

    2017-06-01

    Targeted drug delivery system (DDS) means to selectively transport drugs to targeted tissues, organs, and cells through a variety of drugs carrier. It is usually designed to improve the pharmacological and therapeutic properties of conventional drugs and to overcome problems such as limited solubility, drug aggregation, poor bio distribution and lack of selectivity, controlling drug release carrier and to reduce normal tissue damage. With the characteristics of nontoxic and biodegradable, it can increase the retention of drug in lesion site and the permeability, improve the concentration of the drug in lesion site. at present, there are some kinds of DDS using at test phase, such as slow controlled release drug delivery system, targeted drug delivery systems, transdermal drug delivery system, adhesion dosing system and so on. This paper makes a review for DDS.

  4. Killing cancer cells by targeted drug-carrying phage nanomedicines

    Directory of Open Access Journals (Sweden)

    Yacoby Iftach

    2008-04-01

    Full Text Available Abstract Background Systemic administration of chemotherapeutic agents, in addition to its anti-tumor benefits, results in indiscriminate drug distribution and severe toxicity. This shortcoming may be overcome by targeted drug-carrying platforms that ferry the drug to the tumor site while limiting exposure to non-target tissues and organs. Results We present a new form of targeted anti-cancer therapy in the form of targeted drug-carrying phage nanoparticles. Our approach is based on genetically-modified and chemically manipulated filamentous bacteriophages. The genetic manipulation endows the phages with the ability to display a host-specificity-conferring ligand. The phages are loaded with a large payload of a cytotoxic drug by chemical conjugation. In the presented examples we used anti ErbB2 and anti ERGR antibodies as targeting moieties, the drug hygromycin conjugated to the phages by a covalent amide bond, or the drug doxorubicin conjugated to genetically-engineered cathepsin-B sites on the phage coat. We show that targeting of phage nanomedicines via specific antibodies to receptors on cancer cell membranes results in endocytosis, intracellular degradation, and drug release, resulting in growth inhibition of the target cells in vitro with a potentiation factor of >1000 over the corresponding free drugs. Conclusion The results of the proof-of concept study presented here reveal important features regarding the potential of filamentous phages to serve as drug-delivery platform, on the affect of drug solubility or hydrophobicity on the target specificity of the platform and on the effect of drug release mechanism on the potency of the platform. These results define targeted drug-carrying filamentous phage nanoparticles as a unique type of antibody-drug conjugates.

  5. Killing cancer cells by targeted drug-carrying phage nanomedicines

    Science.gov (United States)

    Bar, Hagit; Yacoby, Iftach; Benhar, Itai

    2008-01-01

    Background Systemic administration of chemotherapeutic agents, in addition to its anti-tumor benefits, results in indiscriminate drug distribution and severe toxicity. This shortcoming may be overcome by targeted drug-carrying platforms that ferry the drug to the tumor site while limiting exposure to non-target tissues and organs. Results We present a new form of targeted anti-cancer therapy in the form of targeted drug-carrying phage nanoparticles. Our approach is based on genetically-modified and chemically manipulated filamentous bacteriophages. The genetic manipulation endows the phages with the ability to display a host-specificity-conferring ligand. The phages are loaded with a large payload of a cytotoxic drug by chemical conjugation. In the presented examples we used anti ErbB2 and anti ERGR antibodies as targeting moieties, the drug hygromycin conjugated to the phages by a covalent amide bond, or the drug doxorubicin conjugated to genetically-engineered cathepsin-B sites on the phage coat. We show that targeting of phage nanomedicines via specific antibodies to receptors on cancer cell membranes results in endocytosis, intracellular degradation, and drug release, resulting in growth inhibition of the target cells in vitro with a potentiation factor of >1000 over the corresponding free drugs. Conclusion The results of the proof-of concept study presented here reveal important features regarding the potential of filamentous phages to serve as drug-delivery platform, on the affect of drug solubility or hydrophobicity on the target specificity of the platform and on the effect of drug release mechanism on the potency of the platform. These results define targeted drug-carrying filamentous phage nanoparticles as a unique type of antibody-drug conjugates. PMID:18387177

  6. Confidence from uncertainty - A multi-target drug screening method from robust control theory

    Directory of Open Access Journals (Sweden)

    Petzold Linda R

    2010-11-01

    Full Text Available Abstract Background Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty. Results We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty. Conclusions The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.

  7. DNA-templated antibody conjugation for targeted drug delivery to cancer cells

    DEFF Research Database (Denmark)

    Liu, Tianqiang

    2016-01-01

    -templated organic synthesis due to the wide existence of the 3-histidine cluster in most wild-type proteins. In this thesis, three projects that relate to targeted drug delivery to cancer cells based on the DTPC method is described. The first project was a delivery system which uses transferrin as the targeting....... The study shows that DNA is a highly useful tool for the assembly of proteins with potential therapeutic applications. The DNA-templated protein conjugation shows a promising application in constructing antibody-toxin conjugates or antibody-drug conjugates. In addition, DNA strands used for antibody...... either antibody engineering or special expression systems and are both time and labor consuming. To avoid the drawbacks caused by conventional chemical modification and recombinant methodologies, an ideal site specific conjugation technique would use natural amino acid residues to the protein by a new...

  8. Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

    Science.gov (United States)

    García-García, Isabel; Zeighami, Yashar; Dagher, Alain

    2017-06-01

    Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

  9. P-glycoprotein targeted nanoscale drug carriers

    KAUST Repository

    Li, Wengang

    2013-02-01

    Multi-drug resistance (MDR) is a trend whereby tumor cells exposed to one cytotoxic agent develop cross-resistance to a range of structurally and functionally unrelated compounds. P -glycoprotein (P -gp) efflux pump is one of the mostly studied drug carrying processes that shuttle the drugs out of tumor cells. Thus, P -gp inhibitors have attracted a lot of attention as they can stop cancer drugs from being pumped out of target cells with the consumption of ATP. Using quantitive structure activity relationship (QSAR), we have successfully synthesized a series of novel P -gp inhibitors. The obtained dihydropyrroloquinoxalines series were fully characterized and then tested against bacterial and tumor assays with over-expressed P -gps. All compounds were bioactive especially compound 1c that had enhanced antibacterial activity. Furthermore, these compounds were utilized as targeting vectors to direct drug delivery vehicles such as silica nanoparticles (SNPs) to cancerous Hela cells with over expressed P -gps. Cell uptake studies showed a successful accumulation of these decorated SNPs in tumor cells compared to undecorated SNPs. The results obtained show that dihydropyrroloquinoxalines constitute a promising drug candidate for targeting cancers with MDR. Copyright © 2013 American Scientific Publishers All rights reserved.

  10. Expression proteomics study to determine metallodrug targets and optimal drug combinations.

    Science.gov (United States)

    Lee, Ronald F S; Chernobrovkin, Alexey; Rutishauser, Dorothea; Allardyce, Claire S; Hacker, David; Johnsson, Kai; Zubarev, Roman A; Dyson, Paul J

    2017-05-08

    The emerging technique termed functional identification of target by expression proteomics (FITExP) has been shown to identify the key protein targets of anti-cancer drugs. Here, we use this approach to elucidate the proteins involved in the mechanism of action of two ruthenium(II)-based anti-cancer compounds, RAPTA-T and RAPTA-EA in breast cancer cells, revealing significant differences in the proteins upregulated. RAPTA-T causes upregulation of multiple proteins suggesting a broad mechanism of action involving suppression of both metastasis and tumorigenicity. RAPTA-EA bearing a GST inhibiting ethacrynic acid moiety, causes upregulation of mainly oxidative stress related proteins. The approach used in this work could be applied to the prediction of effective drug combinations to test in cancer chemotherapy clinical trials.

  11. Drug targeting and the carriers. Application to chemoembolization and medical imaging

    International Nuclear Information System (INIS)

    Puisieux, F.; Benoit, J.P.; Roblot-Treupel, L.

    1987-01-01

    The last fifteen years have seen an increased interest in drug targeting which can be considered as a new way to control the body distribution of drugs when associated with an appropriate carrier. The systems currently studied possess different structures (macromolecular, vesicular and particular) and can be classified into carriers of first, second and third generation. After a brief review of the three types of carriers, this paper focuses on their respective interest in the different fields of radiology: carriers of first generation (microcapsules, microspheres) in chemoembolization, carriers of second generation (liposomes, nanocapsules, nanospheres) in conventional radiology, in computerized tomography, in scintigraphy, in RMN; carriers of third generation (monoclonal antibodies...) in immunoscintigraphy of tumors [fr

  12. A targeted drug delivery system based on dopamine functionalized nano graphene oxide

    Science.gov (United States)

    Masoudipour, Elham; Kashanian, Soheila; Maleki, Nasim

    2017-01-01

    The cellular targeting property of a biocompatible drug delivery system can widely increase the therapeutic effect against various diseases. Here, we report a dopamine conjugated nano graphene oxide (DA-nGO) carrier for cellular delivery of the anticancer drug, Methotrexate (MTX) into DA receptor positive human breast adenocarcinoma cell line. The material was characterized using scanning electron microscopy, atomic force microscopy, Fourier transform infrared spectroscopy and UV-vis spectroscopy. Furthermore, the antineoplastic action of MTX loaded DA-nGO against DA receptor positive and negative cell lines were explored. The results presented in this article demonstrated that the application of DA functionalized GO as a targeting drug carrier can improve the drug delivery efficacy for DA receptor positive cancer cell lines and promise future designing of carrier conjugates based on it.

  13. Target based drug design - a reality in virtual sphere.

    Science.gov (United States)

    Verma, Saroj; Prabhakar, Yenamandra S

    2015-01-01

    The target based drug design approaches are a series of computational procedures, including visualization tools, to support the decision systems of drug design/discovery process. In the essence of biological targets shaping the potential lead/drug molecules, this review presents a comprehensive position of different components of target based drug design which include target identification, protein modeling, molecular dynamics simulations, binding/catalytic sites identification, docking, virtual screening, fragment based strategies, substructure treatment of targets in tackling drug resistance, in silico ADMET, structural vaccinology, etc along with the key issues involved therein and some well investigated case studies. The concepts and working of these procedures are critically discussed to arouse interest and to advance the drug research.

  14. Anti-malarial Drug Design by Targeting Apicoplasts: New Perspectives

    Directory of Open Access Journals (Sweden)

    Avinaba Mukherjee

    2016-03-01

    Full Text Available Objectives: Malaria has been a major global health problem in recent times with increasing mortality. Current treatment methods include parasiticidal drugs and vaccinations. However, resistance among malarial parasites to the existing drugs has emerged as a significant area of concern in anti-malarial drug design. Researchers are now desperately looking for new targets to develop anti-malarials drug which is more target specific. Malarial parasites harbor a plastid-like organelle known as the ‘apicoplast’, which is thought to provide an exciting new outlook for the development of drugs to be used against the parasite. This review elaborates on the current state of development of novel compounds targeted againstemerging malaria parasites. Methods: The apicoplast, originates by an endosymbiotic process, contains a range of metabolic pathways and housekeeping processes that differ from the host body and thereby presents ideal strategies for anti-malarial drug therapy. Drugs are designed by targeting the unique mechanism of the apicoplasts genetic machinery. Several anabolic and catabolic processes, like fatty acid, isopenetyl diphosphate and heme synthess in this organelle, have also been targeted by drugs. Results: Apicoplasts offer exciting opportunities for the development of malarial treatment specific drugs have been found to act by disrupting this organelle’s function, which wouldimpede the survival of the parasite. Conclusion: Recent advanced drugs, their modes of action, and their advantages in the treatment of malaria by using apicoplasts as a target are discussed in this review which thought to be very useful in desigining anti-malarial drugs. Targetting the genetic machinery of apicoplast shows a great advantange regarding anti-malarial drug design. Critical knowledge of these new drugs would give a healthier understanding for deciphering the mechanism of action of anti-malarial drugs when targeting apicoplasts to overcome drug

  15. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

    Directory of Open Access Journals (Sweden)

    Xuan Xiao

    Full Text Available Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

  16. Species differences in drug glucuronidation: Humanized UDP-glucuronosyltransferase 1 mice and their application for predicting drug glucuronidation and drug-induced toxicity in humans.

    Science.gov (United States)

    Fujiwara, Ryoichi; Yoda, Emiko; Tukey, Robert H

    2018-02-01

    More than 20% of clinically used drugs are glucuronidated by a microsomal enzyme UDP-glucuronosyltransferase (UGT). Inhibition or induction of UGT can result in an increase or decrease in blood drug concentration. To avoid drug-drug interactions and adverse drug reactions in individuals, therefore, it is important to understand whether UGTs are involved in metabolism of drugs and drug candidates. While most of glucuronides are inactive metabolites, acyl-glucuronides that are formed from compounds with a carboxylic acid group can be highly toxic. Animals such as mice and rats are widely used to predict drug metabolism and drug-induced toxicity in humans. However, there are marked species differences in the expression and function of drug-metabolizing enzymes including UGTs. To overcome the species differences, mice in which certain drug-metabolizing enzymes are humanized have been recently developed. Humanized UGT1 (hUGT1) mice were created in 2010 by crossing Ugt1-null mice with human UGT1 transgenic mice in a C57BL/6 background. hUGT1 mice can be promising tools to predict human drug glucuronidation and acyl-glucuronide-associated toxicity. In this review article, studies of drug metabolism and toxicity in the hUGT1 mice are summarized. We further discuss research and strategic directions to advance the understanding of drug glucuronidation in humans. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  17. Application of nanohydrogels in drug delivery systems: recent patents review.

    Science.gov (United States)

    Dalwadi, Chintan; Patel, Gayatri

    2015-01-01

    Nanohydrogel combines the advantages of hydrogel and nano particulate systems. Similar to the hydrogel and macrogel, nanohydrogel can protect the drug and control drug release by stimuli responsive conformation or biodegradable bond into the polymer networks. Nanohydrogel has drawn huge interest due to their potential applications, such as carrier in target-specific controlled drug delivery, absorbents, chemical/biological sensors, and bio-mimetic materials. Similar to the nanoparticles, stimuli responsive nanohydrogel can easily be delivered in the liquid form for parenteral drug delivery application. This review highlights the methods to prepare nanohydrogel based on natural and synthetic polymers for diverse applications in drug delivery. It also encompasses the drug loading and drug release mechanism of the nanohydrogel formulation and patents related to the composition and chemical methods for preparation of nanohydrogel formulation with current status in clinical trials.

  18. Iontophoresis of minoxidil sulphate loaded microparticles, a strategy for follicular drug targeting?

    Science.gov (United States)

    Gelfuso, Guilherme M; Barros, M Angélica de Oliveira; Delgado-Charro, M Begoña; Guy, Richard H; Lopez, Renata F V

    2015-10-01

    The feasibility of targeting drugs to hair follicles by a combination of microencapsulation and iontophoresis has been evaluated. Minoxidil sulphate (MXS), which is used in the treatment of alopecia, was selected as a relevant drug with respect to follicular penetration. The skin permeation and disposition of MXS encapsulated in chitosan microparticles (MXS-MP) was evaluated in vitro after passive and iontophoretic delivery. Uptake of MXS was quantified at different exposure times in the stratum corneum (SC) and hair follicles. Microencapsulation resulted in increased (6-fold) drug accumulation in the hair follicles relative to delivery from a simple MXS solution. Application of iontophoresis enhanced follicular delivery for both the solution and the microparticle formulations. It appears, therefore, that microencapsulation and iontophoresis can act synergistically to enhance topical drug targeting to hair follicles. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Pharmacogenomics of GPCR Drug Targets

    DEFF Research Database (Denmark)

    Hauser, Alexander Sebastian; Chavali, Sreenivas; Masuho, Ikuo

    2018-01-01

    Natural genetic variation in the human genome is a cause of individual differences in responses to medications and is an underappreciated burden on public health. Although 108 G-protein-coupled receptors (GPCRs) are the targets of 475 (∼34%) Food and Drug Administration (FDA)-approved drugs and a...

  20. Peptide- and saccharide-conjugated dendrimers for targeted drug delivery: a concise review

    Science.gov (United States)

    Liu, Jie; Gray, Warren D.; Davis, Michael E.; Luo, Ying

    2012-01-01

    Dendrimers comprise a category of branched materials with diverse functions that can be constructed with defined architectural and chemical structures. When decorated with bioactive ligands made of peptides and saccharides through peripheral chemical groups, dendrimer conjugates are turned into nanomaterials possessing attractive binding properties with the cognate receptors. At the cellular level, bioactive dendrimer conjugates can interact with cells with avidity and selectivity, and this function has particularly stimulated interests in investigating the targeting potential of dendrimer materials for the design of drug delivery systems. In addition, bioactive dendrimer conjugates have so far been studied for their versatile capabilities to enhance stability, solubility and absorption of various types of therapeutics. This review presents a brief discussion on three aspects of the recent studies to use peptide- and saccharide-conjugated dendrimers for drug delivery: (i) synthesis methods, (ii) cell- and tissue-targeting properties and (iii) applications of conjugated dendrimers in drug delivery nanodevices. With more studies to elucidate the structure–function relationship of ligand–dendrimer conjugates in transporting drugs, the conjugated dendrimers hold promise to facilitate targeted delivery and improve drug efficacy for discovery and development of modern pharmaceutics. PMID:23741608

  1. Confirming therapeutic target of protopine using immobilized β2 -adrenoceptor coupled with site-directed molecular docking and the target-drug interaction by frontal analysis and injection amount-dependent method.

    Science.gov (United States)

    Liu, Guangxin; Wang, Pei; Li, Chan; Wang, Jing; Sun, Zhenyu; Zhao, Xinfeng; Zheng, Xiaohui

    2017-07-01

    Drug-protein interaction analysis is pregnant in designing new leads during drug discovery. We prepared the stationary phase containing immobilized β 2 -adrenoceptor (β 2 -AR) by linkage of the receptor on macroporous silica gel surface through N,N'-carbonyldiimidazole method. The stationary phase was applied in identifying antiasthmatic target of protopine guided by the prediction of site-directed molecular docking. Subsequent application of immobilized β 2 -AR in exploring the binding of protopine to the receptor was realized by frontal analysis and injection amount-dependent method. The association constants of protopine to β 2 -AR by the 2 methods were (1.00 ± 0.06) × 10 5 M -1 and (1.52 ± 0.14) × 10 4 M -1 . The numbers of binding sites were (1.23 ± 0.07) × 10 -7 M and (9.09 ± 0.06) × 10 -7 M, respectively. These results indicated that β 2 -AR is the specific target for therapeutic action of protopine in vivo. The target-drug binding occurred on Ser 169 in crystal structure of the receptor. Compared with frontal analysis, injection amount-dependent method is advantageous to drug saving, improvement of sampling efficiency, and performing speed. It has grave potential in high-throughput drug-receptor interaction analysis. Copyright © 2017 John Wiley & Sons, Ltd.

  2. Applications for approval to market a new drug; complete response letter; amendments to unapproved applications. Final rule.

    Science.gov (United States)

    2008-07-10

    The Food and Drug Administration (FDA) is amending its regulations on new drug applications (NDAs) and abbreviated new drug applications (ANDAs) for approval to market new drugs and generic drugs (drugs for which approval is sought in an ANDA). The final rule discontinues FDA's use of approvable letters and not approvable letters when taking action on marketing applications. Instead, we will send applicants a complete response letter to indicate that the review cycle for an application is complete and that the application is not ready for approval. We are also revising the regulations on extending the review cycle due to the submission of an amendment to an unapproved application and starting a new review cycle after the resubmission of an application following receipt of a complete response letter. In addition, we are adding to the regulations on biologics license applications (BLAs) provisions on the issuance of complete response letters to BLA applicants. We are taking these actions to implement the user fee performance goals referenced in the Prescription Drug User Fee Amendments of 2002 (PDUFA III) that address procedures and establish target timeframes for reviewing human drug applications.

  3. Mathematical modeling for novel cancer drug discovery and development.

    Science.gov (United States)

    Zhang, Ping; Brusic, Vladimir

    2014-10-01

    Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

  4. Signaling Network Assessment of Mutations and Copy Number Variations Predict Breast Cancer Subtype-Specific Drug Targets

    Directory of Open Access Journals (Sweden)

    Naif Zaman

    2013-10-01

    Full Text Available Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.

  5. Mitochondrial electron transport is the cellular target of the oncology drug elesclomol.

    Directory of Open Access Journals (Sweden)

    Ronald K Blackman

    Full Text Available Elesclomol is a first-in-class investigational drug currently undergoing clinical evaluation as a novel cancer therapeutic. The potent antitumor activity of the compound results from the elevation of reactive oxygen species (ROS and oxidative stress to levels incompatible with cellular survival. However, the molecular target(s and mechanism by which elesclomol generates ROS and subsequent cell death were previously undefined. The cellular cytotoxicity of elesclomol in the yeast S. cerevisiae appears to occur by a mechanism similar, if not identical, to that in cancer cells. Accordingly, here we used a powerful and validated technology only available in yeast that provides critical insights into the mechanism of action, targets and processes that are disrupted by drug treatment. Using this approach we show that elesclomol does not work through a specific cellular protein target. Instead, it targets a biologically coherent set of processes occurring in the mitochondrion. Specifically, the results indicate that elesclomol, driven by its redox chemistry, interacts with the electron transport chain (ETC to generate high levels of ROS within the organelle and consequently cell death. Additional experiments in melanoma cells involving drug treatments or cells lacking ETC function confirm that the drug works similarly in human cancer cells. This deeper understanding of elesclomol's mode of action has important implications for the therapeutic application of the drug, including providing a rationale for biomarker-based stratification of patients likely to respond in the clinical setting.

  6. Modeling Patient-Specific Magnetic Drug Targeting Within the Intracranial Vasculature

    Directory of Open Access Journals (Sweden)

    Alexander Patronis

    2018-04-01

    Full Text Available Drug targeting promises to substantially enhance future therapies, for example through the focussing of chemotherapeutic drugs at the site of a tumor, thus reducing the exposure of healthy tissue to unwanted damage. Promising work on the steering of medication in the human body employs magnetic fields acting on nanoparticles made of paramagnetic materials. We develop a computational tool to aid in the optimization of the physical parameters of these particles and the magnetic configuration, estimating the fraction of particles reaching a given target site in a large patient-specific vascular system for different physiological states (heart rate, cardiac output, etc.. We demonstrate the excellent computational performance of our model by its application to the simulation of paramagnetic-nanoparticle-laden flows in a circle of Willis geometry obtained from an MRI scan. The results suggest a strong dependence of the particle density at the target site on the strength of the magnetic forcing and the velocity of the background fluid flow.

  7. Modeling Patient-Specific Magnetic Drug Targeting Within the Intracranial Vasculature.

    Science.gov (United States)

    Patronis, Alexander; Richardson, Robin A; Schmieschek, Sebastian; Wylie, Brian J N; Nash, Rupert W; Coveney, Peter V

    2018-01-01

    Drug targeting promises to substantially enhance future therapies, for example through the focussing of chemotherapeutic drugs at the site of a tumor, thus reducing the exposure of healthy tissue to unwanted damage. Promising work on the steering of medication in the human body employs magnetic fields acting on nanoparticles made of paramagnetic materials. We develop a computational tool to aid in the optimization of the physical parameters of these particles and the magnetic configuration, estimating the fraction of particles reaching a given target site in a large patient-specific vascular system for different physiological states (heart rate, cardiac output, etc.). We demonstrate the excellent computational performance of our model by its application to the simulation of paramagnetic-nanoparticle-laden flows in a circle of Willis geometry obtained from an MRI scan. The results suggest a strong dependence of the particle density at the target site on the strength of the magnetic forcing and the velocity of the background fluid flow.

  8. New Equilibrium Models of Drug-Receptor Interactions Derived from Target-Mediated Drug Disposition.

    Science.gov (United States)

    Peletier, Lambertus A; Gabrielsson, Johan

    2018-05-14

    In vivo analyses of pharmacological data are traditionally based on a closed system approach not incorporating turnover of target and ligand-target kinetics, but mainly focussing on ligand-target binding properties. This study incorporates information about target and ligand-target kinetics parallel to binding. In a previous paper, steady-state relationships between target- and ligand-target complex versus ligand exposure were derived and a new expression of in vivo potency was derived for a circulating target. This communication is extending the equilibrium relationships and in vivo potency expression for (i) two separate targets competing for one ligand, (ii) two different ligands competing for a single target and (iii) a single ligand-target interaction located in tissue. The derived expressions of the in vivo potencies will be useful both in drug-related discovery projects and mechanistic studies. The equilibrium states of two targets and one ligand may have implications in safety assessment, whilst the equilibrium states of two competing ligands for one target may cast light on when pharmacodynamic drug-drug interactions are important. The proposed equilibrium expressions for a peripherally located target may also be useful for small molecule interactions with extravascularly located targets. Including target turnover, ligand-target complex kinetics and binding properties in expressions of potency and efficacy will improve our understanding of within and between-individual (and across species) variability. The new expressions of potencies highlight the fact that the level of drug-induced target suppression is very much governed by target turnover properties rather than by the target expression level as such.

  9. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  10. Recent advances in targeted drug therapy for hepatocellular carcinoma

    Directory of Open Access Journals (Sweden)

    FAN Yongqiang

    2018-02-01

    Full Text Available More and more clinical trials have proved the efficacy of targeted drugs in the treatment of hepatocellular carcinoma (HCC. With the development of science and technology, more and more targeted drugs have appeared. In recent years, targeted drugs such as regorafenib and ramucirumab have shown great potential in related clinical trials. In addition, there are ongoing clinical trials for second-line candidate drugs, such as c-Met inhibitors tivantinib and cabozantinib and a VEGFR-2 inhibitor ramucirumab. This article summarizes the advances in targeted drug therapy for HCC and related trial data, which provides a reference for further clinical trials and treatment.

  11. Targeted delivery of anti-tuberculosis drugs to macrophages: targeting mannose receptors

    Science.gov (United States)

    Filatova, L. Yu; Klyachko, N. L.; Kudryashova, E. V.

    2018-04-01

    The development of systems for targeted delivery of anti-tuberculosis drugs is a challenge of modern biotechnology. Currently, these drugs are encapsulated in a variety of carriers such as liposomes, polymers, emulsions and so on. Despite successful in vitro testing of these systems, virtually no success was achieved in vivo, because of low accessibility of the foci of infection located in alveolar macrophage cells. A promising strategy for increasing the efficiency of therapeutic action of anti-tuberculosis drugs is to encapsulate the agents into mannosylated carriers targeting the mannose receptors of alveolar macrophages. The review addresses the methods for modification of drug substance carriers, such as liposomes and biodegradable polymers, with mannose residues. The use of mannosylated carriers to deliver anti-tuberculosis agents increases the drug circulation time in the blood stream and increases the drug concentration in alveolar macrophage cells. The bibliography includes 113 references.

  12. In silico tools used for compound selection during target-based drug discovery and development.

    Science.gov (United States)

    Caldwell, Gary W

    2015-01-01

    The target-based drug discovery process, including target selection, screening, hit-to-lead (H2L) and lead optimization stage gates, is the most common approach used in drug development. The full integration of in vitro and/or in vivo data with in silico tools across the entire process would be beneficial to R&D productivity by developing effective selection criteria and drug-design optimization strategies. This review focuses on understanding the impact and extent in the past 5 years of in silico tools on the various stage gates of the target-based drug discovery approach. There are a large number of in silico tools available for establishing selection criteria and drug-design optimization strategies in the target-based approach. However, the inconsistent use of in vitro and/or in vivo data integrated with predictive in silico multiparameter models throughout the process is contributing to R&D productivity issues. In particular, the lack of reliable in silico tools at the H2L stage gate is contributing to the suboptimal selection of viable lead compounds. It is suggested that further development of in silico multiparameter models and organizing biologists, medicinal and computational chemists into one team with a single accountable objective to expand the utilization of in silico tools in all phases of drug discovery would improve R&D productivity.

  13. RGD-modified lipid disks as drug carriers for tumor targeted drug delivery

    Science.gov (United States)

    Gao, Jie; Xie, Cao; Zhang, Mingfei; Wei, Xiaoli; Yan, Zhiqiang; Ren, Yachao; Ying, Man; Lu, Weiyue

    2016-03-01

    Melittin, the major component of the European bee venom, is a potential anticancer candidate due to its lytic properties. However, in vivo applications of melittin are limited due to its main side effect, hemolysis, especially when applied through intravenous administration. The polyethylene glycol-stabilized lipid disk is a novel type of nanocarrier, and the rim of lipid disks has a high affinity to amphiphilic peptides. In our study, a c(RGDyK) modified lipid disk was developed as a tumor targeted drug delivery system for melittin. Cryo-TEM was used to confirm the shape and size of lipid disks with or without c(RGDyK) modification. In vitro and in vivo hemolysis analyses revealed that the hemolysis effect significantly decreased after melittin associated with lipid disks. Importantly, the results of our in vivo biodistribution and tumor growth inhibitory experiments showed that c(RGDyK) modification increased the distribution of lipid disks in the tumor and the anticancer efficacy of melittin loaded lipid disks. Thus, we successfully achieved a targeted drug delivery system for melittin and other amphiphilic peptides with a good therapeutic effect and low side effects.

  14. Cancer Drug Development: New Targets for Cancer Treatment.

    Science.gov (United States)

    Curt

    1996-01-01

    cancer drug screening and cancer drug development. At the NCI, for example, the old in vivo mouse screen using mouse lymphomas has been shelved; it discovered compounds with some activity in lymphomas, but not the common solid tumors of adulthood. It has been replaced with an initial in vitro screen of some sixty cell lines, representing the common solid tumors-ovary, G.I., lung, breast, CNS, melanoma and others. The idea was to not only discover new drugs with specific anti-tumor activity but also to use the small volumes required for in vitro screening as a medium to screen for new natural product compounds, one of the richest sources of effective chemotherapy. The cell line project had an unexpected dividend. The pattern of sensitivity in the panel predicted the mechanism of action of unknown compounds. An antifolate suppressed cell growth of the different lines like other antifolates, anti-tubulin compounds suppressed like other anti-tubulins, and so on. It now became possible, at a very early stage of cancer drug screening, to select for drugs with unknown-and potentially novel-mechanisms of action. The idea was taken to the next logical step, and that was to characterize the entire panel for important molecular properties of human malignancy: mutations in the tumor suppressor gene p53, expression of important oncogenes like ras or myc, the gp170 gene which confers multiple drug resistance, protein-specific kinases, and others. It now became possible to use the cell line panel as a tool to detect new drugs which targeted a specific genetic property of the tumor cell. Researchers can now ask whether a given drug is likely to inhibit multiple drug resistance or kill cells which over-express specific oncogenes at the earliest phase of drug discovery. In this issue of The Oncologist, Tom Connors celebrates the fiftieth anniversary of cancer chemotherapy. His focus is on the importance of international collaboration in clinical trials and the negative impact of

  15. Carrier-free, functionalized pure drug nanorods as a novel cancer-targeted drug delivery platform

    International Nuclear Information System (INIS)

    Li Yanan; An Feifei; Zhang Xiaohong; Yang Yinlong; Liu Zhuang; Zhang Xiujuan

    2013-01-01

    A one-dimensional drug delivery system (1D DDS) is highly attractive since it has distinct advantages such as enhanced drug efficiency and better pharmacokinetics. However, drugs in 1D DDSs are all encapsulated in inert carriers, and problems such as low drug loading content and possible undesirable side effects caused by the carriers remain a serious challenge. In this paper, a novel, carrier-free, pure drug nanorod-based, tumor-targeted 1D DDS has been developed. Drugs are first prepared as nanorods and then surface functionalized to achieve excellent water dispersity and stability. The resulting drug nanorods show enhanced internalization rates mainly through energy-dependent endocytosis, with the shape-mediated nanorod (NR) diffusion process as a secondary pathway. The multiple endocytotic mechanisms lead to significantly improved drug efficiency of functionalized NRs with nearly ten times higher cytotoxicity than those of free molecules and unfunctionalized NRs. A targeted drug delivery system can be readily achieved through surface functionalization with targeting group linked amphipathic surfactant, which exhibits significantly enhanced drug efficacy and discriminates between cell lines with high selectivity. These results clearly show that this tumor-targeting DDS demonstrates high potential toward specific cancer cell lines. (paper)

  16. Stimulus-responsive liposomes as smart nanoplatforms for drug delivery applications.

    Science.gov (United States)

    Zangabad, Parham Sahandi; Mirkiani, Soroush; Shahsavari, Shayan; Masoudi, Behrad; Masroor, Maryam; Hamed, Hamid; Jafari, Zahra; Taghipour, Yasamin Davatgaran; Hashemi, Hura; Karimi, Mahdi; Hamblin, Michael R

    2018-02-01

    Liposomes are known to be promising nanoparticles (NPs) for drug delivery applications. Among different types of self-assembled NPs, liposomes stand out for their non-toxic nature, and their possession of dual hydrophilic-hydrophobic domains. Advantages of liposomes include the ability to solubilize hydrophobic drugs, the ability to incorporate different hydrophilic and lipophilic drugs at the same time, lessening the exposure of host organs to potentially toxic drugs and allowing modification of the surface by a variety of different chemical groups. This modification of the surface, or of the individual constituents, may be used to achieve two important goals. Firstly, ligands for active targeting can be attached that are recognized by cognate receptors over-expressed on the target cells of tissues. Secondly, modification can be used to impart a stimulus-responsive or "smart" character to the liposomes, whereby the cargo is released on demand only when certain internal stimuli (pH, reducing agents, specific enzymes) or external stimuli (light, magnetic field or ultrasound) are present. Here, we review the field of smart liposomes for drug delivery applications.

  17. Age-Dependent Cellular and Behavioral Deficits Induced by Molecularly Targeted Drugs Are Reversible.

    Science.gov (United States)

    Scafidi, Joseph; Ritter, Jonathan; Talbot, Brooke M; Edwards, Jorge; Chew, Li-Jin; Gallo, Vittorio

    2018-04-15

    Newly developed targeted anticancer drugs inhibit signaling pathways commonly altered in adult and pediatric cancers. However, as these pathways are also essential for normal brain development, concerns have emerged of neurologic sequelae resulting specifically from their application in pediatric cancers. The neural substrates and age dependency of these drug-induced effects in vivo are unknown, and their long-term behavioral consequences have not been characterized. This study defines the age-dependent cellular and behavioral effects of these drugs on normally developing brains and determines their reversibility with post-drug intervention. Mice at different postnatal ages received short courses of molecularly targeted drugs in regimens analagous to clinical treatment. Analysis of rapidly developing brain structures important for sensorimotor and cognitive function showed that, while adult administration was without effect, earlier neonatal administration of targeted therapies attenuated white matter oligodendroglia and hippocampal neuronal development more profoundly than later administration, leading to long-lasting behavioral deficits. This functional impairment was reversed by rehabilitation with physical and cognitive enrichment. Our findings demonstrate age-dependent, reversible effects of these drugs on brain development, which are important considerations as treatment options expand for pediatric cancers. Significance: Targeted therapeutics elicit age-dependent long-term consequences on the developing brain that can be ameliorated with environmental enrichment. Cancer Res; 78(8); 2081-95. ©2018 AACR . ©2018 American Association for Cancer Research.

  18. Data-intensive drug development in the information age: applications of Systems Biology/Pharmacology/Toxicology.

    Science.gov (United States)

    Kiyosawa, Naoki; Manabe, Sunao

    2016-01-01

    Pharmaceutical companies continuously face challenges to deliver new drugs with true medical value. R&D productivity of drug development projects depends on 1) the value of the drug concept and 2) data and in-depth knowledge that are used rationally to evaluate the drug concept's validity. A model-based data-intensive drug development approach is a key competitive factor used by innovative pharmaceutical companies to reduce information bias and rationally demonstrate the value of drug concepts. Owing to the accumulation of publicly available biomedical information, our understanding of the pathophysiological mechanisms of diseases has developed considerably; it is the basis for identifying the right drug target and creating a drug concept with true medical value. Our understanding of the pathophysiological mechanisms of disease animal models can also be improved; it can thus support rational extrapolation of animal experiment results to clinical settings. The Systems Biology approach, which leverages publicly available transcriptome data, is useful for these purposes. Furthermore, applying Systems Pharmacology enables dynamic simulation of drug responses, from which key research questions to be addressed in the subsequent studies can be adequately informed. Application of Systems Biology/Pharmacology to toxicology research, namely Systems Toxicology, should considerably improve the predictability of drug-induced toxicities in clinical situations that are difficult to predict from conventional preclinical toxicology studies. Systems Biology/Pharmacology/Toxicology models can be continuously improved using iterative learn-confirm processes throughout preclinical and clinical drug discovery and development processes. Successful implementation of data-intensive drug development approaches requires cultivation of an adequate R&D culture to appreciate this approach.

  19. Identification of novel small-molecule Ulex europaeus I mimetics for targeted drug delivery.

    Science.gov (United States)

    Hamashin, Christa; Spindler, Lisa; Russell, Shannon; Schink, Amy; Lambkin, Imelda; O'Mahony, Daniel; Houghten, Richard; Pinilla, Clemencia

    2003-11-17

    Lectin mimetics have been identified that may have potential application towards targeted drug delivery. Synthetic multivalent polygalloyl constructs effectively competed with Ulex europaeus agglutinin I (UEA1) for binding to intestinal Caco-2 cell membranes.

  20. A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

    Science.gov (United States)

    Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

    2014-01-01

    Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

  1. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Science.gov (United States)

    Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

    2017-01-01

    Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  2. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Directory of Open Access Journals (Sweden)

    Cai Huang

    Full Text Available Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM algorithm combined with a standard recursive feature elimination (RFE approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60. The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  3. Measurement of drug-target engagement in live cells by two-photon fluorescence anisotropy imaging.

    Science.gov (United States)

    Vinegoni, Claudio; Fumene Feruglio, Paolo; Brand, Christian; Lee, Sungon; Nibbs, Antoinette E; Stapleton, Shawn; Shah, Sunil; Gryczynski, Ignacy; Reiner, Thomas; Mazitschek, Ralph; Weissleder, Ralph

    2017-07-01

    The ability to directly image and quantify drug-target engagement and drug distribution with subcellular resolution in live cells and whole organisms is a prerequisite to establishing accurate models of the kinetics and dynamics of drug action. Such methods would thus have far-reaching applications in drug development and molecular pharmacology. We recently presented one such technique based on fluorescence anisotropy, a spectroscopic method based on polarization light analysis and capable of measuring the binding interaction between molecules. Our technique allows the direct characterization of target engagement of fluorescently labeled drugs, using fluorophores with a fluorescence lifetime larger than the rotational correlation of the bound complex. Here we describe an optimized protocol for simultaneous dual-channel two-photon fluorescence anisotropy microscopy acquisition to perform drug-target measurements. We also provide the necessary software to implement stream processing to visualize images and to calculate quantitative parameters. The assembly and characterization part of the protocol can be implemented in 1 d. Sample preparation, characterization and imaging of drug binding can be completed in 2 d. Although currently adapted to an Olympus FV1000MPE microscope, the protocol can be extended to other commercial or custom-built microscopes.

  4. Di/tri-peptide transporters as drug delivery targets

    DEFF Research Database (Denmark)

    Nielsen, C U; Brodin, Birger

    2003-01-01

    -dependent, and the transporters thus belong to the Proton-dependent Oligopeptide Transporter (POT)-family. The transporters are not drug targets per se, however due to their uniquely broad substrate specificity; they have proved to be relevant drug targets at the level of drug transport. Drug molecules such as oral active beta....../tri-peptide transporters from vesicular storages 3) changes in gene transcription/mRNA stability. The aim of the present review is to discuss physiological, patho-physiological and drug-induced regulation of di/tri-peptide transporter mediated transport....

  5. Smuggling Drugs into the Brain : An Overview of Ligands Targeting Transcytosis for Drug Delivery across the Blood-Brain Barrier

    NARCIS (Netherlands)

    Zuhorn, Inge; Georgieva, Julia V.; Hoekstra, Dick

    2015-01-01

    The blood-brain barrier acts as a physical barrier that prevents free entry of blood-derived substances, including those intended for therapeutic applications. The development of molecular Trojan horses is a promising drug targeting technology that allows for non-invasive delivery of therapeutics

  6. Solution NMR Spectroscopy in Target-Based Drug Discovery.

    Science.gov (United States)

    Li, Yan; Kang, Congbao

    2017-08-23

    Solution NMR spectroscopy is a powerful tool to study protein structures and dynamics under physiological conditions. This technique is particularly useful in target-based drug discovery projects as it provides protein-ligand binding information in solution. Accumulated studies have shown that NMR will play more and more important roles in multiple steps of the drug discovery process. In a fragment-based drug discovery process, ligand-observed and protein-observed NMR spectroscopy can be applied to screen fragments with low binding affinities. The screened fragments can be further optimized into drug-like molecules. In combination with other biophysical techniques, NMR will guide structure-based drug discovery. In this review, we describe the possible roles of NMR spectroscopy in drug discovery. We also illustrate the challenges encountered in the drug discovery process. We include several examples demonstrating the roles of NMR in target-based drug discoveries such as hit identification, ranking ligand binding affinities, and mapping the ligand binding site. We also speculate the possible roles of NMR in target engagement based on recent processes in in-cell NMR spectroscopy.

  7. Self-Assembled Smart Nanocarriers for Targeted Drug Delivery.

    Science.gov (United States)

    Cui, Wei; Li, Junbai; Decher, Gero

    2016-02-10

    Nanostructured drug-carrier systems promise numerous benefits for drug delivery. They can be engineered to precisely control drug-release rates or to target specific sites within the body with a specific amount of therapeutic agent. However, to achieve the best therapeutic effects, the systems should be designed for carrying the optimum amount of a drug to the desired target where it should be released at the optimum rate for a specified time. Despite numerous attempts, fulfilling all of these requirements in a synergistic way remains a huge challenge. The trend in drug delivery is consequently directed toward integrated multifunctional carrier systems, providing selective recognition in combination with sustained or triggered release. Capsules as vesicular systems enable drugs to be confined for controlled release. Furthermore, carriers modified with recognition groups can enhance the capability of encapsulated drug efficacy. Here, recent advances are reviewed regarding designing and preparing assembled capsules with targeting ligands or size controllable for selective recognition in drug delivery. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. A review on target drug delivery: magnetic microspheres

    OpenAIRE

    Amit Chandna; Deepa Batra; Satinder Kakar; Ramandeep Singh

    2013-01-01

    Novel drug delivery system aims to deliver the drug at a rate directed by the needs of the body during the period of treatment, and target the active entity to the site of action. A number of novel drug delivery systems have emerged encompassing various routes of administration, to achieve controlled and targeted drug delivery, magnetic micro carriers being one of them. Magnetic microsphere is newer approach in pharmaceutical field. Magnetic microspheres as an alternative to traditional ra...

  9. MODELING OF TARGETED DRUG DELIVERY PART II. MULTIPLE DRUG ADMINISTRATION

    Directory of Open Access Journals (Sweden)

    A. V. Zaborovskiy

    2017-01-01

    Full Text Available In oncology practice, despite significant advances in early cancer detection, surgery, radiotherapy, laser therapy, targeted therapy, etc., chemotherapy is unlikely to lose its relevance in the near future. In this context, the development of new antitumor agents is one of the most important problems of cancer research. In spite of the importance of searching for new compounds with antitumor activity, the possibilities of the “old” agents have not been fully exhausted. Targeted delivery of antitumor agents can give them a “second life”. When developing new targeted drugs and their further introduction into clinical practice, the change in their pharmacodynamics and pharmacokinetics plays a special role. The paper describes a pharmacokinetic model of the targeted drug delivery. The conditions under which it is meaningful to search for a delivery vehicle for the active substance were described. Primary screening of antitumor agents was undertaken to modify them for the targeted delivery based on underlying assumptions of the model.

  10. Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

    Directory of Open Access Journals (Sweden)

    Rohit Vashisht

    Full Text Available A decade since the availability of Mycobacterium tuberculosis (Mtb genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW, encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

  11. Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis

    Science.gov (United States)

    Rohira, Harsha; Bhat, Ashwini G.; Passi, Anurag; Mukherjee, Keya; Choudhary, Kumari Sonal; Kumar, Vikas; Arora, Anshula; Munusamy, Prabhakaran; Subramanian, Ahalyaa; Venkatachalam, Aparna; S, Gayathri; Raj, Sweety; Chitra, Vijaya; Verma, Kaveri; Zaheer, Salman; J, Balaganesh; Gurusamy, Malarvizhi; Razeeth, Mohammed; Raja, Ilamathi; Thandapani, Madhumohan; Mevada, Vishal; Soni, Raviraj; Rana, Shruti; Ramanna, Girish Muthagadhalli; Raghavan, Swetha; Subramanya, Sunil N.; Kholia, Trupti; Patel, Rajesh; Bhavnani, Varsha; Chiranjeevi, Lakavath; Sengupta, Soumi; Singh, Pankaj Kumar; Atray, Naresh; Gandhi, Swati; Avasthi, Tiruvayipati Suma; Nisthar, Shefin; Anurag, Meenakshi; Sharma, Pratibha; Hasija, Yasha; Dash, Debasis; Sharma, Arun; Scaria, Vinod; Thomas, Zakir; Chandra, Nagasuma; Brahmachari, Samir K.; Bhardwaj, Anshu

    2012-01-01

    A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach. PMID:22808064

  12. ADVERPred-Web Service for Prediction of Adverse Effects of Drugs.

    Science.gov (United States)

    Ivanov, Sergey M; Lagunin, Alexey A; Rudik, Anastasia V; Filimonov, Dmitry A; Poroikov, Vladimir V

    2018-01-22

    Application of structure-activity relationships (SARs) for the prediction of adverse effects of drugs (ADEs) has been reported in many published studies. Training sets for the creation of SAR models are usually based on drug label information which allows for the generation of data sets for many hundreds of drugs. Since many ADEs may not be related to drug consumption, one of the main problems in such studies is the quality of data on drug-ADE pairs obtained from labels. The information on ADEs may be included in three sections of the drug labels: "Boxed warning," "Warnings and Precautions," and "Adverse reactions." The first two sections, especially Boxed warning, usually contain the most frequent and severe ADEs that have either known or probable relationships to drug consumption. Using this information, we have created manually curated data sets for the five most frequent and severe ADEs: myocardial infarction, arrhythmia, cardiac failure, severe hepatotoxicity, and nephrotoxicity, with more than 850 drugs on average for each effect. The corresponding SARs were built with PASS (Prediction of Activity Spectra for Substances) software and had balanced accuracy values of 0.74, 0.7, 0.77, 0.67, and 0.75, respectively. They were implemented in a freely available ADVERPred web service ( http://www.way2drug.com/adverpred/ ), which enables a user to predict five ADEs based on the structural formula of compound. This web service can be applied for estimation of the corresponding ADEs for hits and lead compounds at the early stages of drug discovery.

  13. Drug targeting to tumors: principles, pitfalls and (pre-) clinical progress

    NARCIS (Netherlands)

    Lammers, Twan Gerardus Gertudis Maria; Kiessling, F.; Hennink, W.E.; Storm, Gerrit

    2012-01-01

    Abstract Many different systems and strategies have been evaluated for drug targeting to tumors over the years. Routinely used systems include liposomes, polymers, micelles, nanoparticles and antibodies, and examples of strategies are passive drug targeting, active drug targeting to cancer cells,

  14. Structure and organization of drug-target networks: insights from genomic approaches for drug discovery.

    Science.gov (United States)

    Janga, Sarath Chandra; Tzakos, Andreas

    2009-12-01

    Recent years have seen an explosion in the amount of "omics" data and the integration of several disciplines, which has influenced all areas of life sciences including that of drug discovery. Several lines of evidence now suggest that the traditional notion of "one drug-one protein" for one disease does not hold any more and that treatment for most complex diseases can best be attempted using polypharmacological approaches. In this review, we formalize the definition of a drug-target network by decomposing it into drug, target and disease spaces and provide an overview of our understanding in recent years about its structure and organizational principles. We discuss advances made in developing promiscuous drugs following the paradigm of polypharmacology and reveal their advantages over traditional drugs for targeting diseases such as cancer. We suggest that drug-target networks can be decomposed to be studied at a variety of levels and argue that such network-based approaches have important implications in understanding disease phenotypes and in accelerating drug discovery. We also discuss the potential and scope network pharmacology promises in harnessing the vast amount of data from high-throughput approaches for therapeutic advantage.

  15. Identification of the Schistosoma mansoni Molecular Target for the Antimalarial Drug Artemether

    KAUST Repository

    Lepore, Rosalba

    2011-11-28

    Plasmodium falciparum and Schistosoma mansonii are the parasites responsible for most of the malaria and schistosomiasis cases in the world. Notwithstanding their many differences, the two agents have striking similarities in that they both are blood feeders and are targets of an overlapping set of drugs, including the well-known artemether molecule. Here we explore the possibility of using the known information about the mode of action of artemether in Plasmodium to identify the molecular target of the drug in Schistosoma and provide evidence that artemether binds to SmSERCA, a putative Ca2+-ATPase of Schistosoma. We also predict the putative binding mode of the molecule for both its Plasmodium and Schistosoma targets. Our analysis of the mode of binding of artemether to Ca2+-ATPases also provides an explanation for the apparent paradox that, although the molecule has no side effect in humans, it has been shown to possess antitumoral activity. © 2011 American Chemical Society.

  16. Drug repurposing based on drug-drug interaction.

    Science.gov (United States)

    Zhou, Bin; Wang, Rong; Wu, Ping; Kong, De-Xin

    2015-02-01

    Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug-drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10,835 drug-drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug-drug interaction data. © 2014 John Wiley & Sons A/S.

  17. Enhanced cellular transport and drug targeting using dendritic nanostructures

    Science.gov (United States)

    Kannan, R. M.; Kolhe, Parag; Kannan, Sujatha; Lieh-Lai, Mary

    2003-03-01

    Dendrimers and hyperbranched polymers possess highly branched architectures, with a large number of controllable, tailorable, peripheral' functionalities. Since the surface chemistry of these materials can be modified with relative ease, these materials have tremendous potential in targeted drug delivery. The large density of end groups can also be tailored to create enhanced affinity to targeted cells, and can also encapsulate drugs and deliver them in a controlled manner. We are developing tailor-modified dendritic systems for drug delivery. Synthesis, drug/ligand conjugation, in vitro cellular and in vivo drug delivery, and the targeting efficiency to the cell are being studied systematically using a wide variety of experimental tools. Results on PAMAM dendrimers and polyol hyperbranched polymers suggest that: (1) These materials complex/encapsulate a large number of drug molecules and release them at tailorable rates; (2) The drug-dendrimer complex is transported very rapidly through a A549 lung epithelial cancel cell line, compared to free drug, perhaps by endocytosis. The ability of the drug-dendrimer-ligand complexes to target specific asthma and cancer cells is currently being explored using in vitro and in vivo animal models.

  18. Carbon Nanotubes: An Emerging Drug Carrier for Targeting Cancer Cells

    Science.gov (United States)

    Bhattacharya, Shiv Sankar; Mishra, Arun Kumar; Verma, Navneet; Verma, Anurag; Pandit, Jayanta Kumar

    2014-01-01

    During recent years carbon nanotubes (CNTs) have been attracted by many researchers as a drug delivery carrier. CNTs are the third allotropic form of carbon-fullerenes which were rolled into cylindrical tubes. To be integrated into the biological systems, CNTs can be chemically modified or functionalised with therapeutically active molecules by forming stable covalent bonds or supramolecular assemblies based on noncovalent interactions. Owing to their high carrying capacity, biocompatibility, and specificity to cells, various cancer cells have been explored with CNTs for evaluation of pharmacokinetic parameters, cell viability, cytotoxicty, and drug delivery in tumor cells. This review attempts to highlight all aspects of CNTs which render them as an effective anticancer drug carrier and imaging agent. Also the potential application of CNT in targeting metastatic cancer cells by entrapping biomolecules and anticancer drugs has been covered in this review. PMID:24872894

  19. Drug Repurposing: Far Beyond New Targets for Old Drugs

    DEFF Research Database (Denmark)

    Oprea, Tudor; Mestres, J.

    2012-01-01

    Repurposing drugs requires finding novel therapeutic indications compared to the ones for which they were already approved. This is an increasingly utilized strategy for finding novel medicines, one that capitalizes on previous investments while derisking clinical activities. This approach...... relevance to the disease in question and the intellectual property landscape. These activities go far beyond the identification of new targets for old drugs....

  20. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    OpenAIRE

    Huthmacher, Carola; Hoppe, Andreas; Bulik, Sascha; Holzh?tter, Hermann-Georg

    2010-01-01

    Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicte...

  1. Drug Target Interference in Immunogenicity Assays: Recommendations and Mitigation Strategies.

    Science.gov (United States)

    Zhong, Zhandong Don; Clements-Egan, Adrienne; Gorovits, Boris; Maia, Mauricio; Sumner, Giane; Theobald, Valerie; Wu, Yuling; Rajadhyaksha, Manoj

    2017-11-01

    Sensitive and specific methodology is required for the detection and characterization of anti-drug antibodies (ADAs). High-quality ADA data enables the evaluation of potential impact of ADAs on the drug pharmacokinetic profile, patient safety, and efficacious response to the drug. Immunogenicity assessments are typically initiated at early stages in preclinical studies and continue throughout the drug development program. One of the potential bioanalytical challenges encountered with ADA testing is the need to identify and mitigate the interference mediated by the presence of soluble drug target. A drug target, when present at sufficiently high circulating concentrations, can potentially interfere with the performance of ADA and neutralizing antibody (NAb) assays, leading to either false-positive or, in some cases, false-negative ADA and NAb assay results. This publication describes various mechanisms of assay interference by soluble drug target, as well as strategies to recognize and mitigate such target interference. Pertinent examples are presented to illustrate the impact of target interference on ADA and NAb assays as well as several mitigation strategies, including the use of anti-target antibodies, soluble versions of the receptors, target-binding proteins, lectins, and solid-phase removal of targets. Furthermore, recommendations for detection and mitigation of such interference in different formats of ADA and NAb assays are provided.

  2. Superparamagnetic nanoparticles for biomedical applications: Possibilities and limitations of a new drug delivery system

    Energy Technology Data Exchange (ETDEWEB)

    Neuberger, Tobias [Musculoskeletal Research Unit, Equine Hospital, Vetsuisse Faculty Zurich, University of Zurich, Winterthurerstr. 260, 8057 Zurich (Switzerland); Schoepf, Bernhard [Musculoskeletal Research Unit, Equine Hospital, Vetsuisse Faculty Zurich, University of Zurich, Winterthurerstr. 260, 8057 Zurich (Switzerland); Hofmann, Heinrich [Laboratory of Powder Technology, Institute of Materials, Swiss Federal Institute of Technology, EPFL, 1015 Lausanne (Switzerland); Hofmann, Margarete [MatSearch Pully, Chemin Jean Pavillard, 14, CH-1009 Pully (Switzerland); Rechenberg, Brigitte von [Musculoskeletal Research Unit, Equine Hospital, Vetsuisse Faculty Zurich, University of Zurich, Winterthurerstr. 260, 8057 Zurich (Switzerland)]. E-mail: bvonrechenberg@vetclinics.unizh.ch

    2005-05-15

    Nanoparticles can be used in biomedical applications, where they facilitate laboratory diagnostics, or in medical drug targeting. They are used for in vivo applications such as contrast agent for magnetic resonance imaging (MRI), for tumor therapy or cardiovascular disease. Very promising nanoparticles for these applications are superparamagnetic nanoparticles based on a core consisting of iron oxides (SPION) that can be targeted through external magnets. SPION are coated with biocompatible materials and can be functionalized with drugs, proteins or plasmids. In this review, the characteristics and applications of SPION in the biomedical sector are introduced and discussed.

  3. Superparamagnetic nanoparticles for biomedical applications: Possibilities and limitations of a new drug delivery system

    International Nuclear Information System (INIS)

    Neuberger, Tobias; Schoepf, Bernhard; Hofmann, Heinrich; Hofmann, Margarete; Rechenberg, Brigitte von

    2005-01-01

    Nanoparticles can be used in biomedical applications, where they facilitate laboratory diagnostics, or in medical drug targeting. They are used for in vivo applications such as contrast agent for magnetic resonance imaging (MRI), for tumor therapy or cardiovascular disease. Very promising nanoparticles for these applications are superparamagnetic nanoparticles based on a core consisting of iron oxides (SPION) that can be targeted through external magnets. SPION are coated with biocompatible materials and can be functionalized with drugs, proteins or plasmids. In this review, the characteristics and applications of SPION in the biomedical sector are introduced and discussed

  4. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

    Science.gov (United States)

    Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

    2018-02-05

    Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  5. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

    Directory of Open Access Journals (Sweden)

    Li-Yue Bai

    2018-02-01

    Full Text Available Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  6. Ex vivo investigation of magnetically targeted drug delivery system

    International Nuclear Information System (INIS)

    Yoshida, Y.; Fukui, S.; Fujimoto, S.; Mishima, F.; Takeda, S.; Izumi, Y.; Ohtani, S.; Fujitani, Y.; Nishijima, S.

    2007-01-01

    In conventional systemic drug delivery the drug is administered by intravenous injection; it then travels to the heart from where it is pumped to all regions of the body. When the drug is aimed at a small target region, this method is extremely inefficient and leads to require much larger doses than those being necessary. In order to overcome this problem a number of targeted drug delivery methods are developed. One of these, magnetically targeted drug delivery system (MT-DDS) will be a promising way, which involves binding a drug to small biocompatible magnetic particles, injecting these into the blood stream and using a high gradient magnetic field to pull them out of suspension in the target region. In the present paper, we describe an ex vivo experimental work. It is also reported that navigation and accumulation test of the magnetic particles in the Y-shaped glass tube was performed in order to examine the threshold of the magnetic force for accumulation. It is found that accumulation of the magnetic particles was succeeded in the blood vessel when a permanent magnet was placed at the vicinity of the blood vessel. This result indicates the feasibility of the magnetically drug targeting in the blood vessel

  7. Combinatorial microRNA target predictions

    DEFF Research Database (Denmark)

    Krek, Azra; Grün, Dominic; Poy, Matthew N.

    2005-01-01

    MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript1, 2, 3. Different combinations of microRNAs are expressed...... in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published micro......RNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results...

  8. A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs.

    Directory of Open Access Journals (Sweden)

    Qifan Kuang

    Full Text Available Early and accurate identification of adverse drug reactions (ADRs is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs.In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper.Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

  9. Folate-conjugated boron nitride nanospheres for targeted delivery of anticancer drug

    Directory of Open Access Journals (Sweden)

    Feng S

    2016-09-01

    Full Text Available Shini Feng,1 Huijie Zhang,1 Ting Yan,1 Dandi Huang,1 Chunyi Zhi,2 Hideki Nakanishi,1 Xiao-Dong Gao1 1Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, People’s Republic of China; 2Department of Physics and Materials Science, City University of Hong Kong, Hong Kong SAR, People’s Republic of China Abstract: With its unique physical and chemical properties and structural similarity to carbon, boron nitride (BN has attracted considerable attention and found many applications. Biomedical applications of BN have recently started to emerge, raising great hopes in drug and gene delivery. Here, we developed a targeted anticancer drug delivery system based on folate-conjugated BN nanospheres (BNNS with receptor-mediated targeting. Folic acid (FA was successfully grafted onto BNNS via esterification reaction. In vitro cytotoxicity assay showed that BNNS-FA complexes were non-toxic to HeLa cells up to a concentration of 100 µg/mL. Then, doxorubicin hydrochloride (DOX, a commonly used anticancer drug, was loaded onto BNNS-FA complexes. BNNS-FA/DOX complexes were stable at pH 7.4 but effectively released DOX at pH 5.0, which exhibited a pH sensitive and sustained release pattern. BNNS-FA/DOX complexes could be recognized and specifically internalized by HeLa cells via FA receptor-mediated endocytosis. BNNS-FA/DOX complexes exhibited greater cytotoxicity to HeLa cells than free DOX and BNNS/DOX complexes due to the increased cellular uptake of DOX mediated by the FA receptor. Therefore, BNNS-FA complexes had strong potential for targeted cancer therapy. Keywords: boron nitride nanospheres, folic acid, doxorubicin, targeted delivery, cancer therapy

  10. Targeted Drug-Carrying Bacteriophages as Antibacterial Nanomedicines▿

    OpenAIRE

    Yacoby, Iftach; Bar, Hagit; Benhar, Itai

    2007-01-01

    While the resistance of bacteria to traditional antibiotics is a major public health concern, the use of extremely potent antibacterial agents is limited by their lack of selectivity. As in cancer therapy, antibacterial targeted therapy could provide an opportunity to reintroduce toxic substances to the antibacterial arsenal. A desirable targeted antibacterial agent should combine binding specificity, a large drug payload per binding event, and a programmed drug release mechanism. Recently, w...

  11. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  12. Enhancement of the efficiency of magnetic targeting for drug delivery: Development and evaluation of magnet system

    International Nuclear Information System (INIS)

    Cao Quanliang; Han Xiaotao; Li Liang

    2011-01-01

    Deep magnetic capture and clinical application are the current trends for magnetic targeted drug delivery system. More promising and possible strategies are needed to overcome the current limitations and further improve the magnetic targeting technique. Recent advances in the development of targeting magnet system show promise in progressing this technology from the laboratory to the clinic. Starting from well-known basic concepts, current limitations of magnetic targeted drug delivery system are analyzed. Meanwhile, the design concepts and evaluations of some effective improvements in magnet system are discussed and reviewed with reference to (i) reasonable design of magnet system; (ii) control modes of magnet system used to generate dynamical magnetic fields; and (iii) magnetic field driving types. - Research Highlights: → The current limitations of MTDDS for deep capture and clinical application are analyzed. → The development of magnet system shows promise in progressing MTDDS to clinical application. → The design concepts and evaluations of improvements in magnet system are reviewed and discussed. → The key to improve magnet system lies in controllable magnets and different excitations.

  13. How good are publicly available web services that predict bioactivity profiles for drug repurposing?

    Science.gov (United States)

    Murtazalieva, K A; Druzhilovskiy, D S; Goel, R K; Sastry, G N; Poroikov, V V

    2017-10-01

    Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

  14. PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.

    Science.gov (United States)

    Borrel, Alexandre; Regad, Leslie; Xhaard, Henri; Petitjean, Michel; Camproux, Anne-Claude

    2015-04-27

    Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

  15. Global vision of druggability issues: applications and perspectives.

    Science.gov (United States)

    Abi Hussein, Hiba; Geneix, Colette; Petitjean, Michel; Borrel, Alexandre; Flatters, Delphine; Camproux, Anne-Claude

    2017-02-01

    During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Smuggling Drugs into the Brain: An Overview of Ligands Targeting Transcytosis for Drug Delivery across the Blood-Brain Barrier.

    Science.gov (United States)

    Georgieva, Julia V; Hoekstra, Dick; Zuhorn, Inge S

    2014-11-17

    The blood-brain barrier acts as a physical barrier that prevents free entry of blood-derived substances, including those intended for therapeutic applications. The development of molecular Trojan horses is a promising drug targeting technology that allows for non-invasive delivery of therapeutics into the brain. This concept relies on the application of natural or genetically engineered proteins or small peptides, capable of specifically ferrying a drug-payload that is either directly coupled or encapsulated in an appropriate nanocarrier, across the blood-brain barrier via receptor-mediated transcytosis. Specifically, in this process the nanocarrier-drug system ("Trojan horse complex") is transported transcellularly across the brain endothelium, from the blood to the brain interface, essentially trailed by a native receptor. Naturally, only certain properties would favor a receptor to serve as a transporter for nanocarriers, coated with appropriate ligands. Here we briefly discuss brain microvascular endothelial receptors that have been explored until now, highlighting molecular features that govern the efficiency of nanocarrier-mediated drug delivery into the brain.

  17. Recent advances in dendrimer-based nanovectors for tumor-targeted drug and gene delivery

    Science.gov (United States)

    Kesharwani, Prashant; Iyer, Arun K.

    2015-01-01

    Advances in the application of nanotechnology in medicine have given rise to multifunctional smart nanocarriers that can be engineered with tunable physicochemical characteristics to deliver one or more therapeutic agent(s) safely and selectively to cancer cells, including intracellular organelle-specific targeting. Dendrimers having properties resembling biomolecules, with well-defined 3D nanopolymeric architectures, are emerging as a highly attractive class of drug and gene delivery vector. The presence of numerous peripheral functional groups on hyperbranched dendrimers affords efficient conjugation of targeting ligands and biomarkers that can recognize and bind to receptors overexpressed on cancer cells for tumor-cell-specific delivery. The present review compiles the recent advances in dendrimer-mediated drug and gene delivery to tumors by passive and active targeting principles with illustrative examples. PMID:25555748

  18. Green Toxicology – Application of predictive toxicology

    DEFF Research Database (Denmark)

    Vinggaard, Anne Marie; Wedebye, Eva Bay; Taxvig, Camilla

    2014-01-01

    safer chemicals and to identify problematic compounds already in use such as industrial compounds, drugs, pesticides and cosmetics, is required. Green toxicology is the application of predictive toxicology to the production of chemicals with the specific intent of improving their design for hazard...

  19. Dual responsive PNIPAM–chitosan targeted magnetic nanopolymers for targeted drug delivery

    Energy Technology Data Exchange (ETDEWEB)

    Yadavalli, Tejabhiram, E-mail: tejabhiram@gmail.com [Nanotechnology Research Centre, SRM University, Chennai 603203 (India); Ramasamy, Shivaraman [Nanotechnology Research Centre, SRM University, Chennai 603203 (India); School of Physics, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009 (Australia); Chandrasekaran, Gopalakrishnan; Michael, Isaac; Therese, Helen Annal [Nanotechnology Research Centre, SRM University, Chennai 603203 (India); Chennakesavulu, Ramasamy [Department of Pharmacy practice, SRM College of Pharmacy, Chennai 603203 (India)

    2015-04-15

    A dual stimuli sensitive magnetic hyperthermia based drug delivery system has been developed for targeted cancer treatment. Thermosensitive amine terminated poly-N-isopropylacrylamide complexed with pH sensitive chitosan nanoparticles was prepared as the drug carrier. Folic acid and fluorescein were tagged to the nanopolymer complex via N-hydroxysuccinimide and ethyl-3-(3-dimethylaminopropyl)carbodiimide reaction to form a fluorescent and cancer targeting magnetic carrier system. The formation of the polymer complex was confirmed using infrared spectroscopy. Gadolinium doped nickel ferrite nanoparticles prepared by a hydrothermal method were encapsulated in the polymer complex to form a magnetic drug carrier system. The proton relaxation studies on the magnetic carrier system revealed a 200% increase in the T1 proton relaxation rate. These magnetic carriers were loaded with curcumin using solvent evaporation method with a drug loading efficiency of 86%. Drug loaded nanoparticles were tested for their targeting and anticancer properties on four cancer cell lines with the help of MTT assay. The results indicated apoptosis of cancer cell lines within 3 h of incubation. - Highlights: • The use of gadolinium doped nickel ferrite with the suggested doping level. • The use of PNIPMA–chitosan polymer with folic acid and fluorescein as a drug carrier complex. • Magnetic hyperthermia studies of gadolinium doped nickel ferrites are being reported for the first time. • Proton relaxivity studies which indicate the MRI contrasting properties on the reported system are new. • Use of curcumin, a hydrophobic Indian spice as a cancer killing agent inside the reported magnetic polymer complex.

  20. Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release.

    Science.gov (United States)

    Hosoya, Hitomi; Dobroff, Andrey S; Driessen, Wouter H P; Cristini, Vittorio; Brinker, Lina M; Staquicini, Fernanda I; Cardó-Vila, Marina; D'Angelo, Sara; Ferrara, Fortunato; Proneth, Bettina; Lin, Yu-Shen; Dunphy, Darren R; Dogra, Prashant; Melancon, Marites P; Stafford, R Jason; Miyazono, Kohei; Gelovani, Juri G; Kataoka, Kazunori; Brinker, C Jeffrey; Sidman, Richard L; Arap, Wadih; Pasqualini, Renata

    2016-02-16

    A major challenge of targeted molecular imaging and drug delivery in cancer is establishing a functional combination of ligand-directed cargo with a triggered release system. Here we develop a hydrogel-based nanotechnology platform that integrates tumor targeting, photon-to-heat conversion, and triggered drug delivery within a single nanostructure to enable multimodal imaging and controlled release of therapeutic cargo. In proof-of-concept experiments, we show a broad range of ligand peptide-based applications with phage particles, heat-sensitive liposomes, or mesoporous silica nanoparticles that self-assemble into a hydrogel for tumor-targeted drug delivery. Because nanoparticles pack densely within the nanocarrier, their surface plasmon resonance shifts to near-infrared, thereby enabling a laser-mediated photothermal mechanism of cargo release. We demonstrate both noninvasive imaging and targeted drug delivery in preclinical mouse models of breast and prostate cancer. Finally, we applied mathematical modeling to predict and confirm tumor targeting and drug delivery. These results are meaningful steps toward the design and initial translation of an enabling nanotechnology platform with potential for broad clinical applications.

  1. Applications of polymeric nanocapsules in field of drug delivery systems.

    Science.gov (United States)

    Rong, Xinyu; Xie, Yinghua; Hao, Xiaomei; Chen, Tao; Wang, Yingming; Liu, Yuanyuan

    2011-09-01

    Drug-loaded polymeric nanocapsules have exhibited potential applications in the field of drug delivery systems in recent years. This article entails the biodegradable polymers generally used for preparing nanocapsules, which include both natural polymers and synthetic polymers. Furthermore, the article presents a general review of the different preparation methods: nanoprecipitation method, emulsion-diffusion method, double emulsification method, emulsion-coacervation method, layer-by-layer assembly method. In addition, the analysis methods of nanocapsule characteristics, such as mean size, morphology, surface characteristics, shell thickness, encapsulation efficiency, active substance release, dispersion stability, are mentioned. Also, the applications of nanocapsules as carriers for use in drug delivery systems are reviewed, which primarily involve targeting drug delivery, controlled/sustained release drug delivery systems, transdermal drug delivery systems and improving stability and bioavailability of drugs. Nanocapsules, prepared with different biodegradable polymers, have received more and more attention and have been regarded as one of the most promising drug delivery systems.

  2. Pericyte-targeting drug delivery and tissue engineering

    Directory of Open Access Journals (Sweden)

    Kang E

    2016-05-01

    Full Text Available Eunah Kang,1 Jong Wook Shin2 1School of Chemical Engineering and Material Science, 2Division of Allergic and Pulmonary Medicine, Department of Internal Medicine, College of Medicine, Chung-Ang University, Dongjak-Gu, Seoul, South Korea Abstract: Pericytes are contractile mural cells that wrap around the endothelial cells of capillaries and venules. Depending on the triggers by cellular signals, pericytes have specific functionality in tumor microenvironments, properties of potent stem cells, and plasticity in cellular pathology. These features of pericytes can be activated for the promotion or reduction of angiogenesis. Frontier studies have exploited pericyte-targeting drug delivery, using pericyte-specific peptides, small molecules, and DNA in tumor therapy. Moreover, the communication between pericytes and endothelial cells has been applied to the induction of vessel neoformation in tissue engineering. Pericytes may prove to be a novel target for tumor therapy and tissue engineering. The present paper specifically reviews pericyte-specific drug delivery and tissue engineering, allowing insight into the emerging research targeting pericytes. Keywords: pericytes, pericyte-targeting drug delivery, tissue engineering, platelet-derived growth factor, angiogenesis, vascular remodeling

  3. Poly aspartic acid peptide-linked PLGA based nanoscale particles: potential for bone-targeting drug delivery applications.

    Science.gov (United States)

    Jiang, Tao; Yu, Xiaohua; Carbone, Erica J; Nelson, Clarke; Kan, Ho Man; Lo, Kevin W-H

    2014-11-20

    Delivering drugs specifically to bone tissue is very challenging due to the architecture and structure of bone tissue. Poly(lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) hold great promise for the delivery of therapeutics to bone tissue. The goal of the present research was to formulate a PLGA-based NP drug delivery system for bone tissue exclusively. Since poly-aspartic acids (poly-Asp) peptide sequence has been shown to bind to hydroxyapatite (HA), and has been suggested as a molecular tool for bone-targeting applications, we fabricated PLGA-based NPs linked with poly-Asp peptide sequence. Nanoparticles made of methoxy - poly(ethylene glycol) (PEG)-PLGA and maleimide-PEG-PLGA were prepared using a water-in-oil-in-water double emulsion and solvent evaporation method. Fluorescein isothiocyanate (FITC)-tagged poly-Asp peptide was conjugated to the surface of the nanoparticles via the alkylation reaction between the sulfhydryl groups at the N-terminal of the peptide and the CC double bond of maleimide at one end of the polymer chain to form thioether bonds. The conjugation of FITC-tagged poly-Asp peptide to PLGA NPs was confirmed by NMR analysis and fluorescent microscopy. The developed nanoparticle system is highly aqueous dispersible with an average particle size of ∼80 nm. In vitro binding analyses demonstrated that FITC-poly-Asp NPs were able to bind to HA gel as well as to mineralized matrices produced by human mesenchymal stem cells and mouse bone marrow stromal cells. Using a confocal microscopy technique, an ex vivo binding study of mouse major organ ground sections revealed that the FITC-poly-Asp NPs were able to bind specifically to the bone tissue. In addition, proliferation studies indicated that our FITC-poly-Asp NPs did not induce cytotoxicity to human osteoblast-like MG63 cell lines. Altogether, these promising results indicated that this nanoscale targeting system was able to bind to bone tissue specifically and might have a great

  4. Realizing drug repositioning by adapting a recommendation system to handle the process.

    Science.gov (United States)

    Ozsoy, Makbule Guclin; Özyer, Tansel; Polat, Faruk; Alhajj, Reda

    2018-04-12

    Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases. In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true. Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

  5. Trends in GPCR drug discovery: new agents, targets and indications.

    Science.gov (United States)

    Hauser, Alexander S; Attwood, Misty M; Rask-Andersen, Mathias; Schiöth, Helgi B; Gloriam, David E

    2017-12-01

    G protein-coupled receptors (GPCRs) are the most intensively studied drug targets, mostly due to their substantial involvement in human pathophysiology and their pharmacological tractability. Here, we report an up-to-date analysis of all GPCR drugs and agents in clinical trials, which reveals current trends across molecule types, drug targets and therapeutic indications, including showing that 475 drugs (~34% of all drugs approved by the US Food and Drug Administration (FDA)) act at 108 unique GPCRs. Approximately 321 agents are currently in clinical trials, of which ~20% target 66 potentially novel GPCR targets without an approved drug, and the number of biological drugs, allosteric modulators and biased agonists has increased. The major disease indications for GPCR modulators show a shift towards diabetes, obesity and Alzheimer disease, although several central nervous system disorders are also highly represented. The 224 (56%) non-olfactory GPCRs that have not yet been explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders. Finally, we provide an interactive online resource to analyse and infer trends in GPCR drug discovery.

  6. Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

    Directory of Open Access Journals (Sweden)

    Jennifer D. Atkins

    2015-08-01

    Full Text Available The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.

  7. NCI-MATCH Trial Links Targeted Drugs to Mutations

    Science.gov (United States)

    Investigators for the nationwide trial, NCI-MATCH: Molecular Analysis for Therapy Choice, announced that the trial will seek to determine whether targeted therapies for people whose tumors have specific gene mutations will be effective regardless of their cancer type. NCI-MATCH will incorporate more than 20 different study drugs or drug combinations, each targeting a specific gene mutation, in order to match each patient in the trial with a therapy that targets a molecular abnormality in their tumor.

  8. Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

    DEFF Research Database (Denmark)

    Jonsdottir, Svava Osk; Jorgensen, FS; Brunak, Søren

    2005-01-01

    about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability......MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described....

  9. Drug-target residence time--a case for G protein-coupled receptors.

    Science.gov (United States)

    Guo, Dong; Hillger, Julia M; IJzerman, Adriaan P; Heitman, Laura H

    2014-07-01

    A vast number of marketed drugs act on G protein-coupled receptors (GPCRs), the most successful category of drug targets to date. These drugs usually possess high target affinity and selectivity, and such combined features have been the driving force in the early phases of drug discovery. However, attrition has also been high. Many investigational new drugs eventually fail in clinical trials due to a demonstrated lack of efficacy. A retrospective assessment of successfully launched drugs revealed that their beneficial effects in patients may be attributed to their long drug-target residence times (RTs). Likewise, for some other GPCR drugs short RT could be beneficial to reduce the potential for on-target side effects. Hence, the compounds' kinetics behavior might in fact be the guiding principle to obtain a desired and durable effect in vivo. We therefore propose that drug-target RT should be taken into account as an additional parameter in the lead selection and optimization process. This should ultimately lead to an increased number of candidate drugs moving to the preclinical development phase and on to the market. This review contains examples of the kinetics behavior of GPCR ligands with improved in vivo efficacy and summarizes methods for assessing drug-target RT. © 2014 Wiley Periodicals, Inc.

  10. Structural systems pharmacology: a new frontier in discovering novel drug targets.

    Science.gov (United States)

    Tan, Hepan; Ge, Xiaoxia; Xie, Lei

    2013-08-01

    The modern target-based drug discovery process, characterized by the one-drug-one-gene paradigm, has been of limited success. In contrast, phenotype-based screening produces thousands of active compounds but gives no hint as to what their molecular targets are or which ones merit further research. This presents a question: What is a suitable target for an efficient and safe drug? In this paper, we argue that target selection should take into account the proteome-wide energetic and kinetic landscape of drug-target interactions, as well as their cellular and organismal consequences. We propose a new paradigm of structural systems pharmacology to deconvolute the molecular targets of successful drugs as well as to identify druggable targets and their drug-like binders. Here we face two major challenges in structural systems pharmacology: How do we characterize and analyze the structural and energetic origins of drug-target interactions on a proteome scale? How do we correlate the dynamic molecular interactions to their in vivo activity? We will review recent advances in developing new computational tools for biophysics, bioinformatics, chemoinformatics, and systems biology related to the identification of genome-wide target profiles. We believe that the integration of these tools will realize structural systems pharmacology, enabling us to both efficiently develop effective therapeutics for complex diseases and combat drug resistance.

  11. Drug Elucidation: Invertebrate Genetics Sheds New Light on the Molecular Targets of CNS Drugs

    Directory of Open Access Journals (Sweden)

    Donard S. Dwyer

    2014-07-01

    Full Text Available Many important drugs approved to treat common human diseases were discovered by serendipity, without a firm understanding of their modes of action. As a result, the side effects and interactions of these medications are often unpredictable, and there is limited guidance for improving the design of next-generation drugs. Here, we review the innovative use of simple model organisms, especially Caenorhabditis elegans, to gain fresh insights into the complex biological effects of approved CNS medications. Whereas drug discovery involves the identification of new drug targets and lead compounds/biologics, and drug development spans preclinical testing to FDA approval, drug elucidation refers to the process of understanding the mechanisms of action of marketed drugs by studying their novel effects in model organisms. Drug elucidation studies have revealed new pathways affected by antipsychotic drugs, e.g., the insulin signaling pathway, a trace amine receptor and a nicotinic acetylcholine receptor. Similarly, novel targets of antidepressant drugs and lithium have been identified in C. elegans, including lipid-binding/transport proteins and the SGK-1 signaling pathway, respectively. Elucidation of the mode of action of anesthetic agents has shown that anesthesia can involve mitochondrial targets, leak currents and gap junctions. The general approach reviewed in this article has advanced our knowledge about important drugs for CNS disorders and can guide future drug discovery efforts.

  12. Flexing the PECs: Predicting environmental concentrations of veterinary drugs in Canadian agricultural soils.

    Science.gov (United States)

    Kullik, Sigrun A; Belknap, Andrew M

    2017-03-01

    Veterinary drugs administered to food animals primarily enter ecosystems through the application of livestock waste to agricultural land. Although veterinary drugs are essential for protecting animal health, their entry into the environment may pose a risk for nontarget organisms. A means to predict environmental concentrations of new veterinary drug ingredients in soil is required to assess their environmental fate, distribution, and potential effects. The Canadian predicted environmental concentrations in soil (PECsoil) for new veterinary drug ingredients for use in intensively reared animals is based on the approach currently used by the European Medicines Agency for VICH Phase I environmental assessments. The calculation for the European Medicines Agency PECsoil can be adapted to account for regional animal husbandry and land use practices. Canadian agricultural practices for intensively reared cattle, pigs, and poultry differ substantially from those in the European Union. The development of PECsoil default values and livestock categories representative of typical Canadian animal production methods and nutrient management practices culminates several years of research and an extensive survey and analysis of the scientific literature, Canadian agricultural statistics, national and provincial management recommendations, veterinary product databases, and producers. A PECsoil can be used to rapidly identify new veterinary drugs intended for intensive livestock production that should undergo targeted ecotoxicity and fate testing. The Canadian PECsoil model is readily available, transparent, and requires minimal inputs to generate a screening level environmental assessment for veterinary drugs that can be refined if additional data are available. PECsoil values for a hypothetical veterinary drug dosage regimen are presented and discussed in an international context. Integr Environ Assess Manag 2017;13:331-341. © 2016 Her Majesty the Queen in Right of Canada

  13. Knowledge-based Fragment Binding Prediction

    Science.gov (United States)

    Tang, Grace W.; Altman, Russ B.

    2014-01-01

    Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

  14. Concordance and predictive value of two adverse drug event data sets.

    Science.gov (United States)

    Cami, Aurel; Reis, Ben Y

    2014-08-22

    Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single "gold standard" ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. We systematically evaluated the concordance of two widely used ADE data sets - Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the

  15. Use of machine learning approaches for novel drug discovery.

    Science.gov (United States)

    Lima, Angélica Nakagawa; Philot, Eric Allison; Trossini, Gustavo Henrique Goulart; Scott, Luis Paulo Barbour; Maltarollo, Vinícius Gonçalves; Honorio, Kathia Maria

    2016-01-01

    The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

  16. Methotrexate transport mechanisms: the basis for targeted drug delivery and ß-folate-receptor-specific treatment.

    Science.gov (United States)

    Fiehn, C

    2010-01-01

    Methotrexate (MTX) plays a pivotal role in the treatment of rheumatoid arthritis (RA). The transport mechanisms with which MTX reaches is target after application are an important part of MTX pharmacology and its concentration in target tissue such as RA synovial membrane might strongly influence the effectiveness of the drug. Physiological plasma protein binding of MTX to albumin is important for the distribution of MTX in the body and relative high concentrations of the drug are found in the liver. However, targeted drug delivery into inflamed joints and increased anti-arthritic efficiency can be obtained by covalent coupling of MTX ex-vivo to human serum albumin (MTX-HSA) or in-vivo to endogenous albumin mediated through the MTX-pro-drug AWO54. High expression of the folate receptor β (FR-β) on synovial macrophages of RA patients and its capacity to mediate binding and uptake of MTX has been demonstrated. To further improve drug treatment of RA, FR-β specific drugs have been developed and were characterised for their therapeutic potency in synovial inflammation. Therefore, different approaches to improve folate inhibitory and FR-β specific therapy of RA beyond MTX are in development and will be described.

  17. Increasing the Structural Coverage of Tuberculosis Drug Targets

    Science.gov (United States)

    Baugh, Loren; Phan, Isabelle; Begley, Darren W.; Clifton, Matthew C.; Armour, Brianna; Dranow, David M.; Taylor, Brandy M.; Muruthi, Marvin M.; Abendroth, Jan; Fairman, James W.; Fox, David; Dieterich, Shellie H.; Staker, Bart L.; Gardberg, Anna S.; Choi, Ryan; Hewitt, Stephen N.; Napuli, Alberto J.; Myers, Janette; Barrett, Lynn K.; Zhang, Yang; Ferrell, Micah; Mundt, Elizabeth; Thompkins, Katie; Tran, Ngoc; Lyons-Abbott, Sally; Abramov, Ariel; Sekar, Aarthi; Serbzhinskiy, Dmitri; Lorimer, Don; Buchko, Garry W.; Stacy, Robin; Stewart, Lance J.; Edwards, Thomas E.; Van Voorhis, Wesley C.; Myler, Peter J.

    2015-01-01

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus “homolog-rescue” strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD 85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases. PMID:25613812

  18. Hierarchical pulmonary target nanoparticles via inhaled administration for anticancer drug delivery.

    Science.gov (United States)

    Chen, Rui; Xu, Liu; Fan, Qin; Li, Man; Wang, Jingjing; Wu, Li; Li, Weidong; Duan, Jinao; Chen, Zhipeng

    2017-11-01

    Inhalation administration, compared with intravenous administration, significantly enhances chemotherapeutic drug exposure to the lung tissue and may increase the therapeutic effect for pulmonary anticancer. However, further identification of cancer cells after lung deposition of inhaled drugs is necessary to avoid side effects on normal lung tissue and to maximize drug efficacy. Moreover, as the action site of the major drug was intracellular organelles, drug target to the specific organelle is the final key for accurate drug delivery. Here, we designed a novel multifunctional nanoparticles (MNPs) for pulmonary antitumor and the material was well-designed for hierarchical target involved lung tissue target, cancer cell target, and mitochondrial target. The biodistribution in vivo determined by UHPLC-MS/MS method was employed to verify the drug concentration overwhelmingly increasing in lung tissue through inhaled administration compared with intravenous administration. Cellular uptake assay using A549 cells proved the efficient receptor-mediated cell endocytosis. Confocal laser scanning microscopy observation showed the location of MNPs in cells was mitochondria. All results confirmed the intelligent material can progressively play hierarchical target functions, which could induce more cell apoptosis related to mitochondrial damage. It provides a smart and efficient nanocarrier platform for hierarchical targeting of pulmonary anticancer drug. So far, this kind of material for pulmonary mitochondrial-target has not been seen in other reports.

  19. Magnetic chitosan nanoparticles as a drug delivery system for targeting photodynamic therapy

    International Nuclear Information System (INIS)

    Sun Yun; Chen Zhilong; Yang Xiaoxia; Huang Peng; Zhou Xinping; Du Xiaoxia

    2009-01-01

    Photodynamic therapy (PDT) has become an increasingly recognized alternative to cancer treatment in clinic. However, PDT therapy agents, namely photosensitizer (PS), are limited in application as a result of prolonged cutaneous photosensitivity, poor water solubility and inadequate selectivity, which are encountered by numerous chemical therapies. Magnetic chitosan nanoparticles provide excellent biocompatibility, biodegradability, non-toxicity and water solubility without compromising their magnetic targeting. Nevertheless, no previous attempt has been reported to develop an in vivo magnetic drug delivery system with chitosan nanoparticles for magnetic resonance imaging (MRI) monitored targeting photodynamic therapy. In this study, magnetic targeting chitosan nanoparticles (MTCNPs) were prepared and tailored as a drug delivery system and imaging agents for PS, designated as PHPP. Results showed that PHPP-MTCNPs could be used in MRI monitored targeting PDT with excellent targeting and imaging ability. Non-toxicity and high photodynamic efficacy on SW480 carcinoma cells both in vitro and in vivo were achieved with this method at the level of 0-100 μM. Notably, localization of nanoparticles in skin and hepatic tissue was significantly less than in tumor tissue, therefore photosensitivity and hepatotoxicity can be attenuated.

  20. Macrophages with cellular backpacks for targeted drug delivery to the brain.

    Science.gov (United States)

    Klyachko, Natalia L; Polak, Roberta; Haney, Matthew J; Zhao, Yuling; Gomes Neto, Reginaldo J; Hill, Michael C; Kabanov, Alexander V; Cohen, Robert E; Rubner, Michael F; Batrakova, Elena V

    2017-09-01

    Most potent therapeutics are unable to cross the blood-brain barrier following systemic administration, which necessitates the development of unconventional, clinically applicable drug delivery systems. With the given challenges, biologically active vehicles are crucial to accomplishing this task. We now report a new method for drug delivery that utilizes living cells as vehicles for drug carriage across the blood brain barrier. Cellular backpacks, 7-10 μm diameter polymer patches of a few hundred nanometers in thickness, are a potentially interesting approach, because they can act as drug depots that travel with the cell-carrier, without being phagocytized. Backpacks loaded with a potent antioxidant, catalase, were attached to autologous macrophages and systemically administered into mice with brain inflammation. Using inflammatory response cells enabled targeted drug transport to the inflamed brain. Furthermore, catalase-loaded backpacks demonstrated potent therapeutic effects deactivating free radicals released by activated microglia in vitro. This approach for drug carriage and release can accelerate the development of new drug formulations for all the neurodegenerative disorders. Copyright © 2017. Published by Elsevier Ltd.

  1. In vivo characteristics of targeted drug-carrying filamentous bacteriophage nanomedicines

    Directory of Open Access Journals (Sweden)

    Vaks Lilach

    2011-12-01

    Full Text Available Abstract Background Targeted drug-carrying phage nanomedicines are a new class of nanomedicines that combines biological and chemical components into a modular nanometric drug delivery system. The core of the system is a filamentous phage particle that is produced in the bacterial host Escherichia coli. Target specificity is provided by a targeting moiety, usually an antibody that is displayed on the tip of the phage particle. A large drug payload is chemically conjugated to the protein coat of the phage via a chemically or genetically engineered linker that provides for controlled release of the drug after the particle homed to the target cell. Recently we have shown that targeted drug-carrying phage nanomedicines can be used to eradicate pathogenic bacteria and cultured tumor cells with great potentiation over the activity of the free untargeted drug. We have also shown that poorly water soluble drugs can be efficiently conjugated to the phage coat by applying hydrophilic aminoglycosides as branched solubility-enhancing linkers. Results With an intention to move to animal experimentation of efficacy, we tested anti-bacterial drug-carrying phage nanomedicines for toxicity and immunogenicity and blood pharmacokinetics upon injection into mice. Here we show that anti-bacterial drug-carrying phage nanomedicines that carry the antibiotic chloramphenicol conjugated via an aminoglycoside linker are non-toxic to mice and are greatly reduced in immunogenicity in comparison to native phage particles or particles to which the drug is conjugated directly and are cleared from the blood more slowly in comparison to native phage particles. Conclusion Our results suggest that aminoglycosides may serve as branched solubility enhancing linkers for drug conjugation that also provide for a better safety profile of the targeted nanomedicine.

  2. Modified linear predictive coding approach for moving target tracking by Doppler radar

    Science.gov (United States)

    Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

    2016-07-01

    Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

  3. Nano materials for the Local and Targeted Delivery of Osteoarthritis Drugs

    International Nuclear Information System (INIS)

    Periyasamy, P.C.; Leijten, J.C.H.; Dijkstra, P.J.; Karperien, M.; Post, J.N.

    2012-01-01

    Nano technology has found its potential in every possible field of science and engineering. It offers a plethora of options to design tools at the nanometer scale, which can be expected to function more effectively than micro- and macro systems for specific applications. Although the debate regarding the safety of synthetic nano materials for clinical applications endures, it is a promising technology due to its potential to augment current treatments. Various materials such as synthetic polymer, biopolymers, or naturally occurring materials such as proteins and peptides can serve as building blocks for adaptive nano scale formulations. The choice of materials depends highly on the application. We focus on the use of nanoparticles for the treatment of degenerative cartilage diseases, such as osteoarthritis (OA). Current therapies for OA focus on treating the symptoms rather than modifying the disease. The usefulness of OA disease modifying drugs is hampered by side effects and lack of suitable drug delivery systems that target, deliver, and retain drugs locally. This challenge can be overcome by using nano technological formulations. We describe the different nano drug delivery systems and their potential for cartilage repair. This paper provides the reader basal understanding of nano materials and aims at drawing new perspectives on the use of existing nano technological formulations for the treatment of osteoarthritis.

  4. Mathematical modeling of cell adhesion in shear flow: application to targeted drug delivery in inflammation and cancer metastasis.

    Science.gov (United States)

    Jadhav, Sameer; Eggleton, Charles D; Konstantopoulos, Konstantinos

    2007-01-01

    Cell adhesion plays a pivotal role in diverse biological processes that occur in the dynamic setting of the vasculature, including inflammation and cancer metastasis. Although complex, the naturally occurring processes that have evolved to allow for cell adhesion in the vasculature can be exploited to direct drug carriers to targeted cells and tissues. Fluid (blood) flow influences cell adhesion at the mesoscale by affecting the mechanical response of cell membrane, the intercellular contact area and collisional frequency, and at the nanoscale level by modulating the kinetics and mechanics of receptor-ligand interactions. Consequently, elucidating the molecular and biophysical nature of cell adhesion requires a multidisciplinary approach involving the synthesis of fundamentals from hydrodynamic flow, molecular kinetics and cell mechanics with biochemistry/molecular cell biology. To date, significant advances have been made in the identification and characterization of the critical cell adhesion molecules involved in inflammatory disorders, and, to a lesser degree, in cancer metastasis. Experimental work at the nanoscale level to determine the lifetime, interaction distance and strain responses of adhesion receptor-ligand bonds has been spurred by the advent of atomic force microscopy and biomolecular force probes, although our current knowledge in this area is far from complete. Micropipette aspiration assays along with theoretical frameworks have provided vital information on cell mechanics. Progress in each of the aforementioned research areas is key to the development of mathematical models of cell adhesion that incorporate the appropriate biological, kinetic and mechanical parameters that would lead to reliable qualitative and quantitative predictions. These multiscale mathematical models can be employed to predict optimal drug carrier-cell binding through isolated parameter studies and engineering optimization schemes, which will be essential for developing

  5. Asymmetrical Polymer Vesicles for Drug delivery and Other Applications

    Directory of Open Access Journals (Sweden)

    Yi Zhao

    2017-06-01

    Full Text Available Scientists have been attracted by polymersomes as versatile drug delivery systems since the last two decades. Polymersomes have the potential to be versatile drug delivery systems because of their tunable membrane formulations, stabilities in vivo, various physicochemical properties, controlled release mechanisms, targeting abilities, and capacities to encapsulate a wide range of drugs and other molecules. Asymmetrical polymersomes are nano- to micro-sized polymeric capsules with asymmetrical membranes, which means, they have different outer and inner coronas so that they can exhibit better endocytosis rate and endosomal escape ability than other polymeric systems with symmetrical membranes. Hence, asymmetrical polymersomes are highly promising as self-assembled nano-delivery systems in the future for in vivo therapeutics delivery and diagnostic imaging applications. In this review, we prepared a summary about recent research progresses of asymmetrical polymersomes in the following aspects: synthesis, preparation, applications in drug delivery and others.

  6. Theory and Applications of Covalent Docking in Drug Discovery: Merits and Pitfalls

    Directory of Open Access Journals (Sweden)

    Hezekiel Mathambo Kumalo

    2015-01-01

    Full Text Available he present art of drug discovery and design of new drugs is based on suicidal irreversible inhibitors. Covalent inhibition is the strategy that is used to achieve irreversible inhibition. Irreversible inhibitors interact with their targets in a time-dependent fashion, and the reaction proceeds to completion rather than to equilibrium. Covalent inhibitors possessed some significant advantages over non-covalent inhibitors such as covalent warheads can target rare, non-conserved residue of a particular target protein and thus led to development of highly selective inhibitors, covalent inhibitors can be effective in targeting proteins with shallow binding cleavage which will led to development of novel inhibitors with increased potency than non-covalent inhibitors. Several computational approaches have been developed to simulate covalent interactions; however, this is still a challenging area to explore. Covalent molecular docking has been recently implemented in the computer-aided drug design workflows to describe covalent interactions between inhibitors and biological targets. In this review we highlight: (i covalent interactions in biomolecular systems; (ii the mathematical framework of covalent molecular docking; (iii implementation of covalent docking protocol in drug design workflows; (iv applications covalent docking: case studies and (v shortcomings and future perspectives of covalent docking. To the best of our knowledge; this review is the first account that highlights different aspects of covalent docking with its merits and pitfalls. We believe that the method and applications highlighted in this study will help future efforts towards the design of irreversible inhibitors.

  7. Pharmacological and physical vessel modulation strategies to improve EPR-mediated drug targeting to tumors.

    Science.gov (United States)

    Ojha, Tarun; Pathak, Vertika; Shi, Yang; Hennink, Wim E; Moonen, Chrit T W; Storm, Gert; Kiessling, Fabian; Lammers, Twan

    2017-09-15

    The performance of nanomedicine formulations depends on the Enhanced Permeability and Retention (EPR) effect. Prototypic nanomedicine-based drug delivery systems, such as liposomes, polymers and micelles, aim to exploit the EPR effect to accumulate at pathological sites, to thereby improve the balance between drug efficacy and toxicity. Thus far, however, tumor-targeted nanomedicines have not yet managed to achieve convincing therapeutic results, at least not in large cohorts of patients. This is likely mostly due to high inter- and intra-patient heterogeneity in EPR. Besides developing (imaging) biomarkers to monitor and predict EPR, another strategy to address this heterogeneity is the establishment of vessel modulation strategies to homogenize and improve EPR. Over the years, several pharmacological and physical co-treatments have been evaluated to improve EPR-mediated tumor targeting. These include pharmacological strategies, such as vessel permeabilization, normalization, disruption and promotion, as well as physical EPR enhancement via hyperthermia, radiotherapy, sonoporation and phototherapy. In the present manuscript, we summarize exemplary studies showing that pharmacological and physical vessel modulation strategies can be used to improve tumor-targeted drug delivery, and we discuss how these advanced combination regimens can be optimally employed to enhance the (pre-) clinical performance of tumor-targeted nanomedicines. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.

    Science.gov (United States)

    de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong

    2017-12-01

    CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

  9. Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

    Science.gov (United States)

    Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

    2017-12-12

    Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

  10. Taking aim at a moving target: designing drugs to inhibit drug-resistant HIV-1 reverse transcriptases.

    Science.gov (United States)

    Sarafianos, Stefan G; Das, Kalyan; Hughes, Stephen H; Arnold, Eddy

    2004-12-01

    HIV undergoes rapid genetic variation; this variation is caused primarily by the enormous number of viruses produced daily in an infected individual. Because of this variation, HIV presents a moving target for drug and vaccine development. The variation within individuals has led to the generation of diverse HIV-1 subtypes, which further complicates the development of effective drugs and vaccines. In general, it is more difficult to hit a moving target than a stationary target. Two broad strategies for hitting a moving target (in this case, HIV replication) are to understand the movement and to aim at the portions that move the least. In the case of anti-HIV drug development, the first option can be addressed by understanding the mechanism(s) of drug resistance and developing drugs that effectively inhibit mutant viruses. The second can be addressed by designing drugs that interact with portions of the viral machinery that are evolutionarily conserved, such as enzyme active sites.

  11. A side-effect free method for identifying cancer drug targets.

    Science.gov (United States)

    Ashraf, Md Izhar; Ong, Seng-Kai; Mujawar, Shama; Pawar, Shrikant; More, Pallavi; Paul, Somnath; Lahiri, Chandrajit

    2018-04-27

    Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

  12. Increasing the structural coverage of tuberculosis drug targets.

    Science.gov (United States)

    Baugh, Loren; Phan, Isabelle; Begley, Darren W; Clifton, Matthew C; Armour, Brianna; Dranow, David M; Taylor, Brandy M; Muruthi, Marvin M; Abendroth, Jan; Fairman, James W; Fox, David; Dieterich, Shellie H; Staker, Bart L; Gardberg, Anna S; Choi, Ryan; Hewitt, Stephen N; Napuli, Alberto J; Myers, Janette; Barrett, Lynn K; Zhang, Yang; Ferrell, Micah; Mundt, Elizabeth; Thompkins, Katie; Tran, Ngoc; Lyons-Abbott, Sally; Abramov, Ariel; Sekar, Aarthi; Serbzhinskiy, Dmitri; Lorimer, Don; Buchko, Garry W; Stacy, Robin; Stewart, Lance J; Edwards, Thomas E; Van Voorhis, Wesley C; Myler, Peter J

    2015-03-01

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus "homolog-rescue" strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD 85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Automated applications of sandwich-cultured hepatocytes in the evaluation of hepatic drug transport.

    Science.gov (United States)

    Perry, Cassandra H; Smith, William R; St Claire, Robert L; Brouwer, Kenneth R

    2011-04-01

    Predictions of the absorption, distribution, metabolism, excretion, and toxicity of compounds in pharmaceutical development are essential aspects of the drug discovery process. B-CLEAR is an in vitro system that uses sandwich-cultured hepatocytes to evaluate and predict in vivo hepatobiliary disposition (hepatic uptake, biliary excretion, and biliary clearance), transporter-based hepatic drug-drug interactions, and potential drug-induced hepatotoxicity. Automation of predictive technologies is an advantageous and preferred format in drug discovery. In this study, manual and automated studies are investigated and equivalence is demonstrated. In addition, automated applications using model probe substrates and inhibitors to assess the cholestatic potential of drugs and evaluate hepatic drug transport are examined. The successful automation of this technology provides a more reproducible and less labor-intensive approach, reducing potential operator error in complex studies and facilitating technology transfer.

  14. Smuggling Drugs into the Brain: An Overview of Ligands Targeting Transcytosis for Drug Delivery across the Blood–Brain Barrier

    Directory of Open Access Journals (Sweden)

    Julia V. Georgieva

    2014-11-01

    Full Text Available The blood–brain barrier acts as a physical barrier that prevents free entry of blood-derived substances, including those intended for therapeutic applications. The development of molecular Trojan horses is a promising drug targeting technology that allows for non-invasive delivery of therapeutics into the brain. This concept relies on the application of natural or genetically engineered proteins or small peptides, capable of specifically ferrying a drug-payload that is either directly coupled or encapsulated in an appropriate nanocarrier, across the blood–brain barrier via receptor-mediated transcytosis. Specifically, in this process the nanocarrier–drug system (“Trojan horse complex” is transported transcellularly across the brain endothelium, from the blood to the brain interface, essentially trailed by a native receptor. Naturally, only certain properties would favor a receptor to serve as a transporter for nanocarriers, coated with appropriate ligands. Here we briefly discuss brain microvascular endothelial receptors that have been explored until now, highlighting molecular features that govern the efficiency of nanocarrier-mediated drug delivery into the brain.

  15. MARKETING PREDICTIONS IN ANTI-DRUG SOCIAL PROGRAMS: USE OF CAUSAL METHODS IN THE STUDY AND PREVENTION OF DRUG ABUSE

    Directory of Open Access Journals (Sweden)

    Serban Corina

    2010-12-01

    Full Text Available Drug use is one of the major challenges that todays society faces; its effects are felt at the level of various social, professional and age categories. Over 50 non-profit organizations are involved in the development of anti-drug social programs in Romania. Their role is to improve the degree of awareness of the target population concerning the risks associated with drug use, but also to steer consumers towards healthy areas, beneficial to their future. This paper aims to detail the issue of drug use in Romania, by making predictions based on the evolution of this phenomenon during the next five years. The obtained results have revealed the necessity to increase the number of programs preventing drug use, aswell as the need to continue social programs that have proved effective in previous years.

  16. Trends in GPCR drug discovery: new agents, targets and indications

    DEFF Research Database (Denmark)

    Hauser, Alexander Sebastian; Gloriam, David E.; Attwood, Misty M.

    2017-01-01

    current trends across molecule types, drug targets and therapeutic indications, including showing that 475 drugs (~34% of all drugs approved by the US Food and Drug Administration (FDA)) act at 108 unique GPCRs. Approximately 321 agents are currently in clinical trials, of which ~20% target 66 potentially...... are also highly represented. The 224 (56%) non-olfactory GPCRs that have not yet been explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders. Finally, we provide an interactive online resource to analyse and infer trends in GPCR drug......G protein-coupled receptors (GPCRs) are the most intensively studied drug targets, mostly due to their substantial involvement in human pathophysiology and their pharmacological tractability. Here, we report an up-to-date analysis of all GPCR drugs and agents in clinical trials, which reveals...

  17. Applications of fiber-optics-based nanosensors to drug discovery.

    Science.gov (United States)

    Vo-Dinh, Tuan; Scaffidi, Jonathan; Gregas, Molly; Zhang, Yan; Seewaldt, Victoria

    2009-08-01

    Fiber-optic nanosensors are fabricated by heating and pulling optical fibers to yield sub-micron diameter tips and have been used for in vitro analysis of individual living mammalian cells. Immobilization of bioreceptors (e.g., antibodies, peptides, DNA) selective to targeting analyte molecules of interest provides molecular specificity. Excitation light can be launched into the fiber, and the resulting evanescent field at the tip of the nanofiber can be used to excite target molecules bound to the bioreceptor molecules. The fluorescence or surface-enhanced Raman scattering produced by the analyte molecules is detected using an ultra-sensitive photodetector. This article provides an overview of the development and application of fiber-optic nanosensors for drug discovery. The nanosensors provide minimally invasive tools to probe subcellular compartments inside single living cells for health effect studies (e.g., detection of benzopyrene adducts) and medical applications (e.g., monitoring of apoptosis in cells treated with anticancer drugs).

  18. Theoretical modelling of physiologically stretched vessel in magnetisable stent assisted magnetic drug targeting application

    International Nuclear Information System (INIS)

    Mardinoglu, Adil; Cregg, P.J.; Murphy, Kieran; Curtin, Maurice; Prina-Mello, Adriele

    2011-01-01

    The magnetisable stent assisted magnetic targeted drug delivery system in a physiologically stretched vessel is considered theoretically. The changes in the mechanical behaviour of the vessel are analysed under the influence of mechanical forces generated by blood pressure. In this 2D mathematical model a ferromagnetic, coiled wire stent is implanted to aid collection of magnetic drug carrier particles in an elastic tube, which has similar mechanical properties to the blood vessel. A cyclic mechanical force is applied to the elastic tube to mimic the mechanical stress and strain of both the stent and vessel while in the body due to pulsatile blood circulation. The magnetic dipole-dipole and hydrodynamic interactions for multiple particles are included and agglomeration of particles is also modelled. The resulting collection efficiency of the mathematical model shows that the system performance can decrease by as much as 10% due to the effects of the pulsatile blood circulation. - Research highlights: →Theoretical modelling of magnetic drug targeting on a physiologically stretched stent-vessel system. →Cyclic mechanical force applied to mimic the mechanical stress and strain of both stent and vessel. →The magnetic dipole-dipole and hydrodynamic interactions for multiple particles is modelled. →Collection efficiency of the mathematical model is calculated for different physiological blood flow and magnetic field strength.

  19. Membrane Transporters: Structure, Function and Targets for Drug Design

    Science.gov (United States)

    Ravna, Aina W.; Sager, Georg; Dahl, Svein G.; Sylte, Ingebrigt

    Current therapeutic drugs act on four main types of molecular targets: enzymes, receptors, ion channels and transporters, among which a major part (60-70%) are membrane proteins. This review discusses the molecular structures and potential impact of membrane transporter proteins on new drug discovery. The three-dimensional (3D) molecular structure of a protein contains information about the active site and possible ligand binding, and about evolutionary relationships within the protein family. Transporters have a recognition site for a particular substrate, which may be used as a target for drugs inhibiting the transporter or acting as a false substrate. Three groups of transporters have particular interest as drug targets: the major facilitator superfamily, which includes almost 4000 different proteins transporting sugars, polyols, drugs, neurotransmitters, metabolites, amino acids, peptides, organic and inorganic anions and many other substrates; the ATP-binding cassette superfamily, which plays an important role in multidrug resistance in cancer chemotherapy; and the neurotransmitter:sodium symporter family, which includes the molecular targets for some of the most widely used psychotropic drugs. Recent technical advances have increased the number of known 3D structures of membrane transporters, and demonstrated that they form a divergent group of proteins with large conformational flexibility which facilitates transport of the substrate.

  20. Identification of drug targets by chemogenomic and metabolomic profiling in yeast

    KAUST Repository

    Wu, Manhong

    2012-12-01

    OBJECTIVE: To advance our understanding of disease biology, the characterization of the molecular target for clinically proven or new drugs is very important. Because of its simplicity and the availability of strains with individual deletions in all of its genes, chemogenomic profiling in yeast has been used to identify drug targets. As measurement of drug-induced changes in cellular metabolites can yield considerable information about the effects of a drug, we investigated whether combining chemogenomic and metabolomic profiling in yeast could improve the characterization of drug targets. BASIC METHODS: We used chemogenomic and metabolomic profiling in yeast to characterize the target for five drugs acting on two biologically important pathways. A novel computational method that uses a curated metabolic network was also developed, and it was used to identify the genes that are likely to be responsible for the metabolomic differences found. RESULTS AND CONCLUSION: The combination of metabolomic and chemogenomic profiling, along with data analyses carried out using a novel computational method, could robustly identify the enzymes targeted by five drugs. Moreover, this novel computational method has the potential to identify genes that are causative of metabolomic differences or drug targets. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.

  1. The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects

    OpenAIRE

    de Lange, Elizabeth CM

    2013-01-01

    Despite enormous advances in CNS research, CNS disorders remain the world?s leading cause of disability. This accounts for more hospitalizations and prolonged care than almost all other diseases combined, and indicates a high unmet need for good CNS drugs and drug therapies. Following dosing, not only the chemical properties of the drug and blood?brain barrier (BBB) transport, but also many other processes will ultimately determine brain target site kinetics and consequently the CNS effects. ...

  2. Systems biology-embedded target validation: improving efficacy in drug discovery.

    Science.gov (United States)

    Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter

    2014-01-01

    The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment. © 2013 Wiley Periodicals, Inc.

  3. Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.

    Science.gov (United States)

    Isgut, Monica; Rao, Mukkavilli; Yang, Chunhua; Subrahmanyam, Vangala; Rida, Padmashree C G; Aneja, Ritu

    2018-03-01

    Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product-based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs. © 2017 Wiley Periodicals, Inc.

  4. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

    Science.gov (United States)

    Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

    2018-05-17

    In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Lipid microbubbles as a vehicle for targeted drug delivery using focused ultrasound-induced blood-brain barrier opening.

    Science.gov (United States)

    Sierra, Carlos; Acosta, Camilo; Chen, Cherry; Wu, Shih-Ying; Karakatsani, Maria E; Bernal, Manuel; Konofagou, Elisa E

    2017-04-01

    Focused ultrasound in conjunction with lipid microbubbles has fully demonstrated its ability to induce non-invasive, transient, and reversible blood-brain barrier opening. This study was aimed at testing the feasibility of our lipid-coated microbubbles as a vector for targeted drug delivery in the treatment of central nervous system diseases. These microbubbles were labeled with the fluorophore 5-dodecanoylaminfluorescein. Focused ultrasound targeted mouse brains in vivo in the presence of these microbubbles for trans-blood-brain barrier delivery of 5-dodecanoylaminfluorescein. This new approach, compared to previously studies of our group, where fluorescently labeled dextrans and microbubbles were co-administered, represents an appreciable improvement in safety outcome and targeted drug delivery. This novel technique allows the delivery of 5-dodecanoylaminfluorescein at the region of interest unlike the alternative of systemic exposure. 5-dodecanoylaminfluorescein delivery was assessed by ex vivo fluorescence imaging and by in vivo transcranial passive cavitation detection. Stable and inertial cavitation doses were quantified. The cavitation dose thresholds for estimating, a priori, successful targeted drug delivery were, for the first time, identified with inertial cavitation were concluded to be necessary for successful delivery. The findings presented herein indicate the feasibility and safety of the proposed microbubble-based targeted drug delivery and that, if successful, can be predicted by cavitation detection in vivo.

  6. STOPGAP: a database for systematic target opportunity assessment by genetic association predictions.

    Science.gov (United States)

    Shen, Judong; Song, Kijoung; Slater, Andrew J; Ferrero, Enrico; Nelson, Matthew R

    2017-09-01

    We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications. Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP . Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com . matthew.r.nelson@gsk.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  7. Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application.

    Science.gov (United States)

    Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

    2009-06-16

    microRNAs (miRNAs) are single-stranded RNA molecules of about 20-23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA.

  8. Nanoparticles for intracellular-targeted drug delivery

    International Nuclear Information System (INIS)

    Paulo, Cristiana S O; Pires das Neves, Ricardo; Ferreira, Lino S

    2011-01-01

    Nanoparticles (NPs) are very promising for the intracellular delivery of anticancer and immunomodulatory drugs, stem cell differentiation biomolecules and cell activity modulators. Although initial studies in the area of intracellular drug delivery have been performed in the delivery of DNA, there is an increasing interest in the use of other molecules to modulate cell activity. Herein, we review the latest advances in the intracellular-targeted delivery of short interference RNA, proteins and small molecules using NPs. In most cases, the drugs act at different cellular organelles and therefore the drug-containing NPs should be directed to precise locations within the cell. This will lead to the desired magnitude and duration of the drug effects. The spatial control in the intracellular delivery might open new avenues to modulate cell activity while avoiding side-effects.

  9. Common features of microRNA target prediction tools

    Directory of Open Access Journals (Sweden)

    Sarah M. Peterson

    2014-02-01

    Full Text Available The human genome encodes for over 1800 microRNAs, which are short noncoding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one microRNA to target multiple gene transcripts, microRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of microRNA targets is a critical initial step in identifying microRNA:mRNA target interactions for experimental validation. The available tools for microRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to microRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all microRNA target prediction tools, four main aspects of the microRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MicroRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

  10. Development of novel drug delivery systems using phage display technology for clinical application of protein drugs.

    Science.gov (United States)

    Nagano, Kazuya; Tsutsumi, Yasuo

    2016-01-01

    Attempts are being made to develop therapeutic proteins for cancer, hepatitis, and autoimmune conditions, but their clinical applications are limited, except in the cases of drugs based on erythropoietin, granulocyte colony-stimulating factor, interferon-alpha, and antibodies, owing to problems with fundamental technologies for protein drug discovery. It is difficult to identify proteins useful as therapeutic seeds or targets. Another problem in using bioactive proteins is pleiotropic actions through receptors, making it hard to elicit desired effects without side effects. Additionally, bioactive proteins have poor therapeutic effects owing to degradation by proteases and rapid excretion from the circulatory system. Therefore, it is essential to establish a series of novel drug delivery systems (DDS) to overcome these problems. Here, we review original technologies in DDS. First, we introduce antibody proteomics technology for effective selection of proteins useful as therapeutic seeds or targets and identification of various kinds of proteins, such as cancer-specific proteins, cancer metastasis-related proteins, and a cisplatin resistance-related protein. Especially Ephrin receptor A10 is expressed in breast tumor tissues but not in normal tissues and is a promising drug target potentially useful for breast cancer treatment. Moreover, we have developed a system for rapidly creating functional mutant proteins to optimize the seeds for therapeutic applications and used this system to generate various kinds of functional cytokine muteins. Among them, R1antTNF is a TNFR1-selective antagonistic mutant of TNF and is the first mutein converted from agonist to antagonist. We also review a novel polymer-conjugation system to improve the in vivo stability of bioactive proteins. Site-specific PEGylated R1antTNF is uniform at the molecular level, and its bioactivity is similar to that of unmodified R1antTNF. In the future, we hope that many innovative protein drugs will be

  11. A classification framework for drug relapse prediction | Salleh ...

    African Journals Online (AJOL)

    mining algorithms, Artificial Intelligence Neural Network (ANN) is one of the best algorithms to predict relapse among drug addicts. This may help the rehabilitation center to predict relapse individually and the prediction result is hoped to prevent drug addicts from relapse. Keywords: classification; artificial neural network; ...

  12. Targeting cysteine proteases in trypanosomatid disease drug discovery.

    Science.gov (United States)

    Ferreira, Leonardo G; Andricopulo, Adriano D

    2017-12-01

    Chagas disease and human African trypanosomiasis are endemic conditions in Latin America and Africa, respectively, for which no effective and safe therapy is available. Efforts in drug discovery have focused on several enzymes from these protozoans, among which cysteine proteases have been validated as molecular targets for pharmacological intervention. These enzymes are expressed during the entire life cycle of trypanosomatid parasites and are essential to many biological processes, including infectivity to the human host. As a result of advances in the knowledge of the structural aspects of cysteine proteases and their role in disease physiopathology, inhibition of these enzymes by small molecules has been demonstrated to be a worthwhile approach to trypanosomatid drug research. This review provides an update on drug discovery strategies targeting the cysteine peptidases cruzain from Trypanosoma cruzi and rhodesain and cathepsin B from Trypanosoma brucei. Given that current chemotherapy for Chagas disease and human African trypanosomiasis has several drawbacks, cysteine proteases will continue to be actively pursued as valuable molecular targets in trypanosomatid disease drug discovery efforts. Copyright © 2017. Published by Elsevier Inc.

  13. Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.

    Science.gov (United States)

    Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming

    2018-04-11

    Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree

  14. Nuclear medicine and oncology. Hopes and challenging issues of drugs development: the usefulness of positron emission tomography (PET). An application to targeted therapy

    International Nuclear Information System (INIS)

    Courbon, F.; Delord, J.P.

    2005-01-01

    Thanks to breakthroughs in drug design, new kinds of treatment in oncology have been developed. These new molecules target usually a precise molecular pathway proved to be involved in the development of a malignant disease. This led to the concept of targeted therapy. Therefore, the accurate selection of patients who may experience a clinical benefit of such treatments, and the way to assess the response are still challenging issues. Molecular imaging with radiolabeled compounds seemed to be a very promising tool, as for example PET with F-18 Fluorodeoxyglucose (FDG) which allows to assess and to predict the response to a tyrosine kinase inhibitors more efficiently than conventional imaging tools. FDG is only a surrogate marker of cell proliferation. New F18 radiolabeled molecules provide more specific information about tumor biology, such as receptor expression, DNA and protein synthesis, rate of hypoxia.... The common tools (clinical and radiological assessment) are no longer sufficient to predict the clinical efficacy of these new drugs. Molecular imaging should be added in the design of clinical trials in order to detect earlier pharmaco-dynamic effects, to select responding patients and to provide proofs of efficacy of these non-cytotoxic compounds. Molecular imaging databases have to be created and cross-matched to tumor sample collections, providing consequently new 'dynamic' pathological resources. This requires that all these new F18 radiolabeled molecules have to be readily available and easy to be implemented in clinical trials. (author)

  15. In silico modeling to predict drug-induced phospholipidosis

    International Nuclear Information System (INIS)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

    2013-01-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

  16. Image-guided drug delivery: preclinical applications and clinical translation

    NARCIS (Netherlands)

    Ojha, Tarun; Rizzo, Larissa; Storm, Gerrit; Kiessling, Fabian; Lammers, Twan Gerardus Gertudis Maria

    2015-01-01

    Image-guided drug delivery refers to the combination of drug targeting and imaging. Preclinically, image-guided drug delivery can be used for several different purposes, including for monitoring biodistribution, target site accumulation, off-target localization, drug release and drug efficacy.

  17. A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: advantages of pathway-based discovery.

    Science.gov (United States)

    Hellerstein, Marc K

    2008-01-01

    Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a naïve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.

  18. Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

    Science.gov (United States)

    Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

    2014-01-01

    Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

  19. Self-Assembled Nanocarriers Based on Amphiphilic Natural Polymers for Anti- Cancer Drug Delivery Applications.

    Science.gov (United States)

    Sabra, Sally; Abdelmoneem, Mona; Abdelwakil, Mahmoud; Mabrouk, Moustafa Taha; Anwar, Doaa; Mohamed, Rania; Khattab, Sherine; Bekhit, Adnan; Elkhodairy, Kadria; Freag, May; Elzoghby, Ahmed

    2017-01-01

    Micellization provides numerous merits for the delivery of water insoluble anti-cancer therapeutic agents including a nanosized 'core-shell' drug delivery system. Recently, hydrophobically-modified polysaccharides and proteins are attracting much attention as micelle forming polymers to entrap poorly soluble anti-cancer drugs. By virtue of their small size, the self-assembled micelles can passively target tumor tissues via enhanced permeation and retention effect (EPR). Moreover, the amphiphilic micelles can be exploited for active-targeted drug delivery by attaching specific targeting ligands to the outer micellar hydrophilic surface. Here, we review the conjugation techniques, drug loading methods, physicochemical characteristics of the most important amphiphilic polysaccharides and proteins used as anti-cancer drug delivery systems. Attention focuses on the mechanisms of tumor-targeting and enhanced anti-tumor efficacy of the encapsulated drugs. This review will highlight the remarkable advances of hydrophobized polysaccharide and protein micelles and their potential applications as anti-cancer drug delivery nanosystems. Micellar nanocarriers fabricated from amphiphilic natural polymers hold great promise as vehicles for anti-cancer drugs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Identification of putative drug targets in Vancomycin-resistant Staphylococcus aureus (VRSA) using computer aided protein data analysis.

    Science.gov (United States)

    Hasan, Md Anayet; Khan, Md Arif; Sharmin, Tahmina; Hasan Mazumder, Md Habibul; Chowdhury, Afrin Sultana

    2016-01-01

    Vancomycin-resistant Staphylococcus aureus (VRSA) is a Gram-positive, facultative aerobic bacterium which is evolved from the extensive exposure of Vancomycin to Methicillin resistant S. aureus (MRSA) that had become the most common cause of hospital and community-acquired infections. Due to the emergence of different antibiotic resistance strains, there is an exigency to develop novel drug targets to address the provocation of multidrug-resistant bacteria. In this study, in-silico genome subtraction methodology was used to design potential and pathogen specific drug targets against VRSA. Our study divulged 1987 proteins from the proteome of 34,549 proteins, which have no homologues in human genome after sequential analysis through CD-HIT and BLASTp. The high stringency analysis of the remaining proteins against database of essential genes (DEG) resulted in 169 proteins which are essential for S. aureus. Metabolic pathway analysis of human host and pathogen by KAAS at the KEGG server sorted out 19 proteins involved in unique metabolic pathways. 26 human non-homologous membrane-bound essential proteins including 4 which were also involved in unique metabolic pathway were deduced through PSORTb, CELLO v.2.5, ngLOC. Functional classification of uncharacterized proteins through SVMprot derived 7 human non-homologous membrane-bound hypothetical essential proteins. Study of potential drug target against Drug Bank revealed pbpA-penicillin-binding protein 1 and hypothetical protein MQW_01796 as the best drug target candidate. 2D structure was predicted by PRED-TMBB, 3D structure and functional analysis was also performed. Protein-protein interaction network of potential drug target proteins was analyzed by using STRING. The identified drug targets are expected to have great potential for designing novel drugs against VRSA infections and further screening of the compounds against these new targets may result in the discovery of novel therapeutic compounds that can be

  1. Docking analysis targeted to the whole enzyme: an application to the prediction of inhibition of PTP1B by thiomorpholine and thiazolyl derivatives.

    Science.gov (United States)

    Ganou, C A; Eleftheriou, P Th; Theodosis-Nobelos, P; Fesatidou, M; Geronikaki, A A; Lialiaris, T; Rekka, E A

    2018-02-01

    PTP1b is a protein tyrosine phosphatase involved in the inactivation of insulin receptor. Since inhibition of PTP1b may prolong the action of the receptor, PTP1b has become a drug target for the treatment of type II diabetes. In the present study, prediction of inhibition using docking analysis targeted specifically to the active or allosteric site was performed on 87 compounds structurally belonging to 10 different groups. Two groups, consisting of 15 thiomorpholine and 10 thiazolyl derivatives exhibiting the best prediction results, were selected for in vitro evaluation. All thiomorpholines showed inhibitory action (with IC 50 = 4-45 μΜ, Ki = 2-23 μM), while only three thiazolyl derivatives showed low inhibition (best IC 50 = 18 μΜ, Ki = 9 μΜ). However, free binding energy (E) was in accordance with the IC 50 values only for some compounds. Docking analysis targeted to the whole enzyme revealed that the compounds exhibiting IC 50 values higher than expected could bind to other peripheral sites with lower free energy, E o , than when bound to the active/allosteric site. A prediction factor, E- (Σ Eo × 0.16), which takes into account lower energy binding to peripheral sites, was proposed and was found to correlate well with the IC 50 values following an asymmetrical sigmoidal equation with r 2 = 0.9692.

  2. A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.

    Science.gov (United States)

    Haider, Saad; Rahman, Raziur; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

  3. Hybrid protein-inorganic nanoparticles: From tumor-targeted drug delivery to cancer imaging.

    Science.gov (United States)

    Elzoghby, Ahmed O; Hemasa, Ayman L; Freag, May S

    2016-12-10

    Recently, a great interest has been paid to the development of hybrid protein-inorganic nanoparticles (NPs) for drug delivery and cancer diagnostics in order to combine the merits of both inorganic and protein nanocarriers. This review primarily discusses the most outstanding advances in the applications of the hybrids of naturally-occurring proteins with iron oxide, gadolinium, gold, silica, calcium phosphate NPs, carbon nanotubes, and quantum dots in drug delivery and cancer imaging. Various strategies that have been utilized for the preparation of protein-functionalized inorganic NPs and the mechanisms involved in the drug loading process are discussed. How can the protein functionalization overcome the limitations of colloidal stability, poor dispersibility and toxicity associated with inorganic NPs is also investigated. Moreover, issues relating to the influence of protein hybridization on the cellular uptake, tumor targeting efficiency, systemic circulation, mucosal penetration and skin permeation of inorganic NPs are highlighted. A special emphasis is devoted to the novel approaches utilizing the protein-inorganic nanohybrids in combined cancer therapy, tumor imaging, and theranostic applications as well as stimuli-responsive drug release from the nanohybrids. Copyright © 2016 Elsevier B.V. All rights reserved.

  4. Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction.

    Science.gov (United States)

    Rahman, Raziur; Haider, Saad; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees' prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity prediction problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error.

  5. Aptamers as Both Drugs and Drug-Carriers

    Directory of Open Access Journals (Sweden)

    Md. Ashrafuzzaman

    2014-01-01

    Full Text Available Aptamers are short nucleic acid oligos. They may serve as both drugs and drug-carriers. Their use as diagnostic tools is also evident. They can be generated using various experimental, theoretical, and computational techniques. The systematic evolution of ligands by exponential enrichment which uses iterative screening of nucleic acid libraries is a popular experimental technique. Theory inspired methodology entropy-based seed-and-grow strategy that designs aptamer templates to bind specifically to targets is another one. Aptamers are predicted to be highly useful in producing general drugs and theranostic drugs occasionally for certain diseases like cancer, Alzheimer’s disease, and so on. They bind to various targets like lipids, nucleic acids, proteins, small organic compounds, and even entire organisms. Aptamers may also serve as drug-carriers or nanoparticles helping drugs to get released in specific target regions. Due to better target specific physical binding properties aptamers cause less off-target toxicity effects. Therefore, search for aptamer based drugs, drug-carriers, and even diagnostic tools is expanding fast. The biophysical properties in relation to the target specific binding phenomena of aptamers, energetics behind the aptamer transport of drugs, and the consequent biological implications will be discussed. This review will open up avenues leading to novel drug discovery and drug delivery.

  6. Targeted electrohydrodynamic printing for micro-reservoir drug delivery systems

    International Nuclear Information System (INIS)

    Hwang, Tae Heon; Kim, Jin Bum; Yang, Da Som; Ryu, WonHyoung; Park, Yong-il

    2013-01-01

    Microfluidic drug delivery systems consisting of a drug reservoir and microfluidic channels have shown the possibility of simple and robust modulation of drug release rate. However, the difficulty of loading a small quantity of drug into drug reservoirs at a micro-scale limited further development of such systems. Electrohydrodynamic (EHD) printing was employed to fill micro-reservoirs with controlled amount of drugs in the range of a few hundreds of picograms to tens of micrograms with spatial resolution of as small as 20 µm. Unlike most EHD systems, this system was configured in combination with an inverted microscope that allows in situ targeting of drug loading at micrometer scale accuracy. Methylene blue and rhodamine B were used as model drugs in distilled water, isopropanol and a polymer solution of a biodegradable polymer and dimethyl sulfoxide (DMSO). Also tetracycline-HCl/DI water was used as actual drug ink. The optimal parameters of EHD printing to load an extremely small quantity of drug into microscale drug reservoirs were investigated by changing pumping rates, the strength of an electric field and drug concentration. This targeted EHD technique was used to load drugs into the microreservoirs of PDMS microfluidic drug delivery devices and their drug release performance was demonstrated in vitro. (paper)

  7. The Open Form Inducer Approach for Structure-Based Drug Design.

    Directory of Open Access Journals (Sweden)

    Daniel Ken Inaoka

    Full Text Available Many open form (OF structures of drug targets were obtained a posteriori by analysis of co-crystals with inhibitors. Therefore, obtaining the OF structure of a drug target a priori will accelerate development of potent inhibitors. In addition to its small active site, Trypanosoma cruzi dihydroorotate dehydrogenase (TcDHODH is fully functional in its monomeric form, making drug design approaches targeting the active site and protein-protein interactions unrealistic. Therefore, a novel a priori approach was developed to determination the TcDHODH active site in OF. This approach consists of generating an "OF inducer" (predicted in silico to bind the target and cause steric repulsion with flexible regions proximal to the active site that force it open. We provide the first proof-of-concept of this approach by predicting and crystallizing TcDHODH in complex with an OF inducer, thereby obtaining the OF a priori with its subsequent use in designing potent and selective inhibitors. Fourteen co-crystal structures of TcDHODH with the designed inhibitors are presented herein. This approach has potential to encourage drug design against diseases where the molecular targets are such difficult proteins possessing small AS volume. This approach can be extended to study open/close conformation of proteins in general, the identification of allosteric pockets and inhibitors for other drug targets where conventional drug design approaches are not applicable, as well as the effective exploitation of the increasing number of protein structures deposited in Protein Data Bank.

  8. SVMDLF: A novel R-based Web application for prediction of dipeptidyl peptidase 4 inhibitors.

    Science.gov (United States)

    Chandra, Sharat; Pandey, Jyotsana; Tamrakar, Akhilesh K; Siddiqi, Mohammad Imran

    2017-12-01

    Dipeptidyl peptidase 4 (DPP4) is a well-known target for the antidiabetic drugs. However, currently available DPP4 inhibitor screening assays are costly and labor-intensive. It is important to create a robust in silico method to predict the activity of DPP4 inhibitor for the new lead finding. Here, we introduce an R-based Web application SVMDLF (SVM-based DPP4 Lead Finder) to predict the inhibitor of DPP4, based on support vector machine (SVM) model, predictions of which are confirmed by in vitro biological evaluation. The best model generated by MACCS structure fingerprint gave the Matthews correlation coefficient of 0.87 for the test set and 0.883 for the external test set. We screened Maybridge database consisting approximately 53,000 compounds. For further bioactivity assay, six compounds were shortlisted, and of six hits, three compounds showed significant DPP4 inhibitory activities with IC 50 values ranging from 8.01 to 10.73 μm. This application is an OpenCPU server app which is a novel single-page R-based Web application for the DPP4 inhibitor prediction. The SVMDLF is freely available and open to all users at http://svmdlf.net/ocpu/library/dlfsvm/www/ and http://www.cdri.res.in/svmdlf/. © 2017 John Wiley & Sons A/S.

  9. The exciting potential of nanotherapy in brain-tumor targeted drug delivery approaches

    Directory of Open Access Journals (Sweden)

    Vivek Agrahari

    2017-01-01

    Full Text Available Delivering therapeutics to the central nervous system (CNS and brain-tumor has been a major challenge. The current standard treatment approaches for the brain-tumor comprise of surgical resection followed by immunotherapy, radiotherapy, and chemotherapy. However, the current treatments are limited in providing significant benefits to the patients and despite recent technological advancements; brain-tumor is still challenging to treat. Brain-tumor therapy is limited by the lack of effective and targeted strategies to deliver chemotherapeutic agents across the blood-brain barrier (BBB. The BBB is the main obstacle that must be overcome to allow compounds to reach their targets in the brain. Recent advances have boosted the nanotherapeutic approaches in providing an attractive strategy in improving the drug delivery across the BBB and into the CNS. Compared to conventional formulations, nanoformulations offer significant advantages in CNS drug delivery approaches. Considering the above facts, in this review, the physiological/anatomical features of the brain-tumor and the BBB are briefly discussed. The drug transport mechanisms at the BBB are outlined. The approaches to deliver chemotherapeutic drugs across the CNS into the brain-tumor using nanocarriers are summarized. In addition, the challenges that need to be addressed in nanotherapeutic approaches for their enhanced clinical application in brain-tumor therapy are discussed.

  10. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

    Science.gov (United States)

    Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

    2016-12-01

    Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

  11. Adverse drug reactions in older patients during hospitalisation: are they predictable?

    LENUS (Irish Health Repository)

    O'Connor, Marie N

    2012-11-01

    adverse drug reactions (ADRs) are a major cause of morbidity and healthcare utilisation in older people. The GerontoNet ADR risk score aims to identify older people at risk of ADRs during hospitalisation. We aimed to assess the clinical applicability of this score and identify other variables that predict ADRs in hospitalised older people.

  12. Assessment of deoxyhypusine hydroxylase as a putative, novel drug target.

    Science.gov (United States)

    Kerscher, B; Nzukou, E; Kaiser, A

    2010-02-01

    Antimalarial drug resistance has nowadays reached each drug class on the market for longer than 10 years. The focus on validated, classical targets has severe drawbacks. If resistance is arising or already present in the field, a target-based High-Throughput-Screening (HTS) with the respective target involves the risk of identifying compounds to which field populations are also resistant. Thus, it appears that a rewarding albeit demanding challenge for target-based drug discovery is to identify novel drug targets. In the search for new targets for antimalarials, we have investigated the biosynthesis of hypusine, present in eukaryotic initiation factor 5A (eIF5A). Deoxyhypusine hydroxylase (DOHH), which has recently been cloned and expressed from P. falciparum, completes the modification of eIF5A through hydroxylation. Here, we assess the present druggable data on Plasmodium DOHH and its human counterpart. Plasmodium DOHH arose from a cyanobacterial phycobilin lyase by loss of function. It has a low FASTA score of 27 to its human counterpart. The HEAT-like repeats present in the parasite DOHH differ in number and amino acid identity from its human ortholog and might be of considerable interest for inhibitor design.

  13. Designing multi-targeted agents: An emerging anticancer drug discovery paradigm.

    Science.gov (United States)

    Fu, Rong-Geng; Sun, Yuan; Sheng, Wen-Bing; Liao, Duan-Fang

    2017-08-18

    The dominant paradigm in drug discovery is to design ligands with maximum selectivity to act on individual drug targets. With the target-based approach, many new chemical entities have been discovered, developed, and further approved as drugs. However, there are a large number of complex diseases such as cancer that cannot be effectively treated or cured only with one medicine to modulate the biological function of a single target. As simultaneous intervention of two (or multiple) cancer progression relevant targets has shown improved therapeutic efficacy, the innovation of multi-targeted drugs has become a promising and prevailing research topic and numerous multi-targeted anticancer agents are currently at various developmental stages. However, most multi-pharmacophore scaffolds are usually discovered by serendipity or screening, while rational design by combining existing pharmacophore scaffolds remains an enormous challenge. In this review, four types of multi-pharmacophore modes are discussed, and the examples from literature will be used to introduce attractive lead compounds with the capability of simultaneously interfering with different enzyme or signaling pathway of cancer progression, which will reveal the trends and insights to help the design of the next generation multi-targeted anticancer agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  14. Lipid microbubbles as a vehicle for targeted drug delivery using focused ultrasound-induced blood–brain barrier opening

    Science.gov (United States)

    Sierra, Carlos; Acosta, Camilo; Chen, Cherry; Wu, Shih-Ying; Karakatsani, Maria E; Bernal, Manuel

    2016-01-01

    Focused ultrasound in conjunction with lipid microbubbles has fully demonstrated its ability to induce non-invasive, transient, and reversible blood–brain barrier opening. This study was aimed at testing the feasibility of our lipid-coated microbubbles as a vector for targeted drug delivery in the treatment of central nervous system diseases. These microbubbles were labeled with the fluorophore 5-dodecanoylaminfluorescein. Focused ultrasound targeted mouse brains in vivo in the presence of these microbubbles for trans-blood–brain barrier delivery of 5-dodecanoylaminfluorescein. This new approach, compared to previously studies of our group, where fluorescently labeled dextrans and microbubbles were co-administered, represents an appreciable improvement in safety outcome and targeted drug delivery. This novel technique allows the delivery of 5-dodecanoylaminfluorescein at the region of interest unlike the alternative of systemic exposure. 5-dodecanoylaminfluorescein delivery was assessed by ex vivo fluorescence imaging and by in vivo transcranial passive cavitation detection. Stable and inertial cavitation doses were quantified. The cavitation dose thresholds for estimating, a priori, successful targeted drug delivery were, for the first time, identified with inertial cavitation were concluded to be necessary for successful delivery. The findings presented herein indicate the feasibility and safety of the proposed microbubble-based targeted drug delivery and that, if successful, can be predicted by cavitation detection in vivo. PMID:27278929

  15. Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response.

    Directory of Open Access Journals (Sweden)

    Hugo Geerts

    Full Text Available The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published 'Quantitative Systems Pharmacology' computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D(2 antagonist and ocaperidone, a very high affinity dopamine D(2 antagonist, using only pharmacology and human positron emission tomography (PET imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS total score and the higher extra-pyramidal symptom (EPS liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development.

  16. Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae.

    Science.gov (United States)

    Damte, Dereje; Suh, Joo-Won; Lee, Seung-Jin; Yohannes, Sileshi Belew; Hossain, Md Akil; Park, Seung-Chun

    2013-07-01

    In the present study, a computational comparative and subtractive genomic/proteomic analysis aimed at the identification of putative therapeutic target and vaccine candidate proteins from Kyoto Encyclopedia of Genes and Genomes (KEGG) annotated metabolic pathways of Mycoplasma hyopneumoniae was performed for drug design and vaccine production pipelines against M.hyopneumoniae. The employed comparative genomic and metabolic pathway analysis with a predefined computational systemic workflow extracted a total of 41 annotated metabolic pathways from KEGG among which five were unique to M. hyopneumoniae. A total of 234 proteins were identified to be involved in these metabolic pathways. Although 125 non homologous and predicted essential proteins were found from the total that could serve as potential drug targets and vaccine candidates, additional prioritizing parameters characterize 21 proteins as vaccine candidate while druggability of each of the identified proteins evaluated by the DrugBank database prioritized 42 proteins suitable for drug targets. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. Targeting the latest hallmark of cancer: another attempt at 'magic bullet' drugs targeting cancers' metabolic phenotype.

    Science.gov (United States)

    Cuperlovic-Culf, M; Culf, A S; Touaibia, M; Lefort, N

    2012-10-01

    The metabolism of tumors is remarkably different from the metabolism of corresponding normal cells and tissues. Metabolic alterations are initiated by oncogenes and are required for malignant transformation, allowing cancer cells to resist some cell death signals while producing energy and fulfilling their biosynthetic needs with limiting resources. The distinct metabolic phenotype of cancers provides an interesting avenue for treatment, potentially with minimal side effects. As many cancers show similar metabolic characteristics, drugs targeting the cancer metabolic phenotype are, perhaps optimistically, expected to be 'magic bullet' treatments. Over the last few years there have been a number of potential drugs developed to specifically target cancer metabolism. Several of these drugs are currently in clinical and preclinical trials. This review outlines examples of drugs developed for different targets of significance to cancer metabolism, with a focus on small molecule leads, chemical biology and clinical results for these drugs.

  18. Pros and cons of the liposome platform in cancer drug targeting.

    Science.gov (United States)

    Gabizon, Alberto A; Shmeeda, Hilary; Zalipsky, Samuel

    2006-01-01

    Coating of liposomes with polyethylene-glycol (PEG) by incorporation in the liposome bilayer of PEG-derivatized lipids results in inhibition of liposome uptake by the reticulo-endothelial system and significant prolongation of liposome residence time in the blood stream. Parallel developments in drug loading technology have improved the efficiency and stability of drug entrapment in liposomes, particularly with regard to cationic amphiphiles such as anthracyclines. An example of this new generation of liposomes is a formulation of pegylated liposomal doxorubicin known as Doxil or Caelyx, whose clinical pharmacokinetic profile is characterized by slow plasma clearance and small volume of distribution. A hallmark of these long-circulating liposomal drug carriers is their enhanced accumulation in tumors. The mechanism underlying this passive targeting effect is the phenomenon known as enhanced permeability and retention (EPR) which has been described in a broad variety of experimental tumor types. Further to the passive targeting effect, the liposome drug delivery platform offers the possibility of grafting tumor-specific ligands on the liposome membrane for active targeting to tumor cells, and potentially intracellular drug delivery. The pros and cons of the liposome platform in cancer targeting are discussed vis-à-vis nontargeted drugs, using as an example a liposome drug delivery system targeted to the folate receptor.

  19. Nanostructured materials for selective recognition and targeted drug delivery

    International Nuclear Information System (INIS)

    Kotrotsiou, O; Kotti, K; Dini, E; Kammona, O; Kiparissides, C

    2005-01-01

    Selective recognition requires the introduction of a molecular memory into a polymer matrix in order to make it capable of rebinding an analyte with a very high specificity. In addition, targeted drug delivery requires drug-loaded vesicles which preferentially localize to the sites of injury and avoid uptake into uninvolved tissues. The rapid evolution of nanotechnology is aiming to fulfill the goal of selective recognition and optimal drug delivery through the development of molecularly imprinted polymeric (MIP) nanoparticles, tailor-made for a diverse range of analytes (e.g., pharmaceuticals, pesticides, amino acids, etc.) and of nanostructured targeted drug carriers (e.g., liposomes and micelles) with increased circulation lifetimes. In the present study, PLGA microparticles containing multilamellar vesicles (MLVs), and MIP nanoparticles were synthesized to be employed as drug carriers and synthetic receptors respectively

  20. [Study on liver targeted drug delivery system of the effective anticancer component from Bolbstemma paniculatum].

    Science.gov (United States)

    Sun, Yi-Yi; Ll, Tong-Hui; Tang, Chen-Kang; Zhu, Zi-Ping; Chi, Qun; Hou, Shi-Xiang

    2005-06-01

    To study the liver targeted drug delivery system of TBMS--the effective anticancer component from Bolbstemma paniculatum, and to discuss the system's function of decreasing toxicity. BCA was used as carrier material. The preparation through overall feedback dynamic techniques. The properties of preparation and toxicology were also technology of nanoparticles was optimized studied. Thenanoparticles' targeting in mice vivo was observed with transmission electron microscopy. The function of decreasing toxicity was researched by the XXTX-2000 automatic quantitative analysis management system. D50 was 0.68 microm. Drug-loading rate and entrapment rate were 37.3% and 88.6% respectively. The release in vitro accorded with Weibull equation. The reaching release balance time and the t 1/2 extended 26 times and 19 times respectively comparing with injection. Nanoparticles mainly distributed in liver tissue. Their toxicity to lung and liver was evidently lower than injection. Nanoparticles' LD50 exceeded injection's by 13.5% and their stimulus was much lower than injection. The TBMS can be targeted to liver by liver targeted drug delivery system. At the same time, the problem about the toxicity hindering clinical application could be solved, which lays the foundation for the further studies on TBMS.

  1. Nanoscale drug delivery for targeted chemotherapy.

    Science.gov (United States)

    Xin, Yong; Huang, Qian; Tang, Jian-Qin; Hou, Xiao-Yang; Zhang, Pei; Zhang, Long Zhen; Jiang, Guan

    2016-08-28

    Despite significant improvements in diagnostic methods and innovations in therapies for specific cancers, effective treatments for neoplastic diseases still represent major challenges. Nanotechnology as an emerging technology has been widely used in many fields and also provides a new opportunity for the targeted delivery of cancer drugs. Nanoscale delivery of chemotherapy drugs to the tumor site is highly desirable. Recent studies have shown that nanoscale drug delivery systems not only have the ability to destroy cancer cells but may also be carriers for chemotherapy drugs. Some studies have demonstrated that delivery of chemotherapy via nanoscale carriers has greater therapeutic benefit than either treatment modality alone. In this review, novel approaches to nanoscale delivery of chemotherapy are described and recent progress in this field is discussed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  2. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients.

    NARCIS (Netherlands)

    Witte, W.E.; Wong, Y.C.; Nederpelt, I.; Heitman, L.H.; Danhof, M.; Graaf, van der P.H.; Gilissen, R.A.; de, Lange E.C.

    2016-01-01

    INTRODUCTION Drug-target binding kinetics are major determinants of the time course of drug action for several drugs, as clearly described for the irreversible binders omeprazole and aspirin. This supports the increasing interest to incorporate newly developed high-throughput assays for drug-target

  3. Identification of distant drug off-targets by direct superposition of binding pocket surfaces.

    Science.gov (United States)

    Schumann, Marcel; Armen, Roger S

    2013-01-01

    Correctly predicting off-targets for a given molecular structure, which would have the ability to bind a large range of ligands, is both particularly difficult and important if they share no significant sequence or fold similarity with the respective molecular target ("distant off-targets"). A novel approach for identification of off-targets by direct superposition of protein binding pocket surfaces is presented and applied to a set of well-studied and highly relevant drug targets, including representative kinases and nuclear hormone receptors. The entire Protein Data Bank is searched for similar binding pockets and convincing distant off-target candidates were identified that share no significant sequence or fold similarity with the respective target structure. These putative target off-target pairs are further supported by the existence of compounds that bind strongly to both with high topological similarity, and in some cases, literature examples of individual compounds that bind to both. Also, our results clearly show that it is possible for binding pockets to exhibit a striking surface similarity, while the respective off-target shares neither significant sequence nor significant fold similarity with the respective molecular target ("distant off-target").

  4. Nanotechnology in the targeted drug delivery for bone diseases and bone regeneration

    Directory of Open Access Journals (Sweden)

    Gu W

    2013-06-01

    Full Text Available Wenyi Gu,1,2 Chengtie Wu,3 Jiezhong Chen,1 Yin Xiao1 1Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia; 2Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia; 3State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, People's Republic of China Abstract: Nanotechnology is a vigorous research area and one of its important applications is in biomedical sciences. Among biomedical applications, targeted drug delivery is one of the most extensively studied subjects. Nanostructured particles and scaffolds have been widely studied for increasing treatment efficacy and specificity of present treatment approaches. Similarly, this technique has been used for treating bone diseases including bone regeneration. In this review, we have summarized and highlighted the recent advancement of nanostructured particles and scaffolds for the treatment of cancer bone metastasis, osteosarcoma, bone infections and inflammatory diseases, osteoarthritis, as well as for bone regeneration. Nanoparticles used to deliver deoxyribonucleic acid and ribonucleic acid molecules to specific bone sites for gene therapies are also included. The investigation of the implications of nanoparticles in bone diseases have just begun, and has already shown some promising potential. Further studies have to be conducted, aimed specifically at assessing targeted delivery and bioactive scaffolds to further improve their efficacy before they can be used clinically. Keywords: nanoparticles, nanostructured scaffold, cancer bone metastasis, bone diseases, target drug delivery, bone regeneration

  5. IFE Target Injection Tracking and Position Prediction Update

    International Nuclear Information System (INIS)

    Petzoldt, Ronald W.; Jonestrask, Kevin

    2005-01-01

    To achieve high gain in an inertial fusion energy power plant, driver beams must hit direct drive targets with ±20 μm accuracy (±100 μm for indirect drive). Targets will have to be tracked with even greater accuracy. The conceptual design for our tracking system, which predicts target arrival position and timing based on position measurements outside of the reaction chamber was previously described. The system has been built and has begun tracking targets at the first detector station. Additional detector stations are being modified for increased field of view. After three tracking stations are operational, position predictions at the final station will be compared to position measurements at that station as a measure of target position prediction accuracy.The as-installed design will be described together with initial target tracking and position prediction accuracy results. Design modifications that allow for improved accuracy and/or in-chamber target tracking will also be presented

  6. Iontophoresis on minoxidil sulphate-loaded chitosan nanoparticles accelerates drug release, decreasing their targeting effect to hair follicles

    Directory of Open Access Journals (Sweden)

    Breno N. Matos

    Full Text Available The experiments described in this paper tested the hypothesis whether iontophoresis applied on a chitosan nanoparticle formulation could combine the enhanced drug accumulation into the follicular casts obtained using iontophoresis and the sustained drug release, reducing dermal exposure, provided by nanoparticles. Results showed that even though iontophoresis presented comparable minoxidil targeting potential to hair follicles than passive delivery of chitosan-nanoparticles (4.1 ± 0.9 and 5.3 ± 1.0 µg cm-2, respectively, it was less effective on preventing dermal exposure, since chitosan-nanoparticles presented a drug permeation in the receptor solution of 15.3 ± 4.3 µg cm-2 after 6 h of iontophoresis, while drug amounts from passive nanoparticle delivery were not detected. Drug release experiments showed particles were not able to sustain the drug release under the influence of a potential gradient. In conclusion, the application of MXS-loaded chitosan nanoparticles remains the best way to target MXS to the hair follicles while preventing dermal exposure.

  7. Mitoxantrone Loaded Superparamagnetic Nanoparticles for Drug Targeting: A Versatile and Sensitive Method for Quantification of Drug Enrichment in Rabbit Tissues Using HPLC-UV

    Directory of Open Access Journals (Sweden)

    Rainer Tietze

    2010-01-01

    Full Text Available In medicine, superparamagnetic nanoparticles bound to chemotherapeutics are currently investigated for their feasibility in local tumor therapy. After intraarterial application, these particles can be accumulated in the targeted area by an external magnetic field to increase the drug concentration in the region of interest (Magnetic-Drug-Targeting. We here present an analytical method (HPLC-UV, to detect pure or ferrofluid-bound mitoxantrone in a complex matrix even in trace amounts in order to perform biodistribution studies. Mitoxantrone could be extracted in high yields from different tissues. Recovery of mitoxantrone in liver tissue (5000 ng/g was 76±2%. The limit of quantification of mitoxantrone standard was 10 ng/mL ±12%. Validation criteria such as linearity, precision, and stability were evaluated in ranges achieving the FDA requirements. As shown for pilot samples, biodistribution studies can easily be performed after application of pure or ferrofluid-bound mitoxantrone.

  8. Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance

    Science.gov (United States)

    Gonzalez de Castro, D; Clarke, P A; Al-Lazikani, B; Workman, P

    2013-01-01

    The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution. PMID:23361103

  9. Cost Minimization Analysis of Hypnotic Drug: Target Controlled Inhalation Anesthesia (TCIA Sevoflurane and Target Controlled Infusion (TCI Propofol

    Directory of Open Access Journals (Sweden)

    Made Wiryana

    2016-09-01

    Full Text Available Background: Cost minimization analysis is a pharmaco-economic study used to compare two or more health interventions that have been shown to have the same effect, similar or equivalent. With limited health insurance budget from the Indonesian National Social Security System implementation in 2015, the quality control and the drug cost are two important things that need to be focused. The application of pharmaco-economic study results in the selection and use of drugs more effectively and efficiently. Objective: To determine cost minimization analysis of hypnotic drug between a target controlled inhalation anesthesia (TCIA sevoflurane and a target controlled infusion (TCI propofol in patients underwent a major oncologic surgery in Sanglah General Hospital. Methods: Sixty ASA physical status I-II patients underwent major oncologic surgery were divided into two groups. Group A was using TCIA sevoflurane and group B using TCI propofol. Bispectral index monitor (BIS index was used to evaluate the depth of anesthesia. The statistical tests used are the Shapiro-Wilk test, Lavene test, Mann-Whitney U test and unpaired t-test (α = 0.05. The data analysis used the Statistical Package for Social Sciences (SPSS for Windows. Results: In this study, the rate of drug used per unit time in group A was 0.12 ml sevoflurane per minute (± 0.03 and the group B was 7.25 mg propofol per minute (±0.98. Total cost of hypnotic drug in group A was IDR598.43 (IQR 112.47 per minute, in group B was IDR703.27 (IQR 156.73 per minute (p>0.05. Conclusions: There was no statistically significant difference from the analysis of the drug cost minimization hypnotic drug in a major oncologic surgery using TCIA sevoflurane and TCI propofol.

  10. A comparison of machine learning techniques for detection of drug target articles.

    Science.gov (United States)

    Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

    2010-12-01

    Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

  11. A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

    Science.gov (United States)

    Cheng, Feixiong; Zhao, Junfei; Fooksa, Michaela; Zhao, Zhongming

    2016-07-01

    Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1). In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All

  12. A review on target drug delivery: magnetic microspheres

    Directory of Open Access Journals (Sweden)

    Amit Chandna

    2013-01-01

    Magnetic microsphere is newer approach in pharmaceutical field. Magnetic microspheres as an alternative to traditional radiation methods which use highly penetrating radiation that is absorbed throughout the body. Its use is limited by toxicity and side effects. The aim of the specific targeting is to enhance the efficiency of drug delivery & at the same time to reduce the toxicity & side effects. This kind of delivery system is very much important which localises the drug to the disease site. In this larger amount of freely circulating drug can be replaced by smaller amount of magnetically targeted drug. Magnetic carriers receive magnetic responses to a magnetic field from incorporated materials that are used for magnetic microspheres are chitosan, dextran etc. magnetic microspheres can be prepared from a variety of carrier material. One of the most utilized is serum albumin from human or other appropriate species. Drug release from albumin microspheres can be sustained or controlled by various stabilization procedures generally involving heat or chemical cross-linking of the protein carrier matrix.

  13. Emerging migraine treatments and drug targets

    DEFF Research Database (Denmark)

    Olesen, Jes; Ashina, Messoud

    2011-01-01

    Migraine has a 1-year prevalence of 10% and high socioeconomic costs. Despite recent drug developments, there is a huge unmet need for better pharmacotherapy. In this review we discuss promising anti-migraine strategies such as calcitonin gene-related peptide (CGRP) receptor antagonists and 5....... Tonabersat, a cortical spreading depression inhibitor, has shown efficacy in the prophylaxis of migraine with aura. Several new drug targets such as nitric oxide synthase, the 5-HT(1D) receptor, the prostanoid receptors EP(2) and EP(4), and the pituitary adenylate cyclase receptor PAC1 await development....... The greatest need is for new prophylactic drugs, and it seems likely that such compounds will be developed in the coming decade....

  14. Trusted Allies with New Benefits: Repositioning Existing Drugs

    KAUST Repository

    Gao, Xin

    2016-01-25

    The classical assumption that one drug cures a single disease by binding to a single drug-target has been shown to be inaccurate. Recent studies estimate that each drug on average binds to at least six known and several unknown targets. Identifying the “off-targets” can help understand the side effects and toxicity of the drug. Moreover, off-targets for a given drug may inspire “drug repositioning”, where a drug already approved for one condition is redirected to treat another condition, thereby overcoming delays and costs associated with clinical trials and drug approval. In this talk, I will introduce our work along this direction. We have developed a structural alignment method that can precisely identify structural similarities between arbitrary types of interaction interfaces, such as the drug-target interaction. We have further developed a novel computational framework, iDTP that constructs the structural signatures of approved and experimental drugs, based on which we predict new targets for these drugs. Our method combines information from several sources including sequence independent structural alignment, sequence similarity, drug-target tissue expression data, and text mining. In a cross-validation study, we used iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments.

  15. Ribonucleotide reductase as a drug target against drug resistance Mycobacterium leprae: A molecular docking study.

    Science.gov (United States)

    Mohanty, Partha Sarathi; Bansal, Avi Kumar; Naaz, Farah; Gupta, Umesh Datta; Dwivedi, Vivek Dhar; Yadava, Umesh

    2018-06-01

    Leprosy is a chronic infection of skin and nerve caused by Mycobacterium leprae. The treatment is based on standard multi drug therapy consisting of dapsone, rifampicin and clofazamine. The use of rifampicin alone or with dapsone led to the emergence of rifampicin-resistant Mycobacterium leprae strains. The emergence of drug-resistant leprosy put a hurdle in the leprosy eradication programme. The present study aimed to predict the molecular model of ribonucleotide reductase (RNR), the enzyme responsible for biosynthesis of nucleotides, to screen new drugs for treatment of drug-resistant leprosy. The study was conducted by retrieving RNR of M. leprae from GenBank. A molecular 3D model of M. leprae was predicted using homology modelling and validated. A total of 325 characters were included in the analysis. The predicted 3D model of RNR showed that the ϕ and φ angles of 251 (96.9%) residues were positioned in the most favoured regions. It was also conferred that 18 α-helices, 6 β turns, 2 γ turns and 48 helix-helix interactions contributed to the predicted 3D structure. Virtual screening of Food and Drug Administration approved drug molecules recovered 1829 drugs of which three molecules, viz., lincomycin, novobiocin and telithromycin, were taken for the docking study. It was observed that the selected drug molecules had a strong affinity towards the modelled protein RNR. This was evident from the binding energy of the drug molecules towards the modelled protein RNR (-6.10, -6.25 and -7.10). Three FDA-approved drugs, viz., lincomycin, novobiocin and telithromycin, could be taken for further clinical studies to find their efficacy against drug resistant leprosy. Copyright © 2018 Elsevier B.V. All rights reserved.

  16. Medicinal electronomics bricolage design of hypoxia-targeting antineoplastic drugs and invention of boron tracedrugs as innovative future-architectural drugs.

    Science.gov (United States)

    Hori, Hitoshi; Uto, Yoshihiro; Nakata, Eiji

    2010-09-01

    We describe herein for the first time our medicinal electronomics bricolage design of hypoxia-targeting antineoplastic drugs and boron tracedrugs as newly emerging drug classes. A new area of antineoplastic drugs and treatments has recently focused on neoplastic cells of the tumor environment/microenvironment involving accessory cells. This tumor hypoxic environment is now considered as a major factor that influences not only the response to antineoplastic therapies but also the potential for malignant progression and metastasis. We review our medicinal electronomics bricolage design of hypoxia-targeting drugs, antiangiogenic hypoxic cell radiosensitizers, sugar-hybrid hypoxic cell radiosensitizers, and hypoxia-targeting 10B delivery agents, in which we design drug candidates based on their electronic structures obtained by molecular orbital calculations, not based solely on pharmacophore development. These drugs include an antiangiogenic hypoxic cell radiosensitizer TX-2036, a sugar-hybrid hypoxic cell radiosensitizer TX-2244, new hypoxia-targeting indoleamine 2,3-dioxygenase (IDO) inhibitors, and a hypoxia-targeting BNCT agent, BSH (sodium borocaptate-10B)-hypoxic cytotoxin tirapazamine (TPZ) hybrid drug TX-2100. We then discuss the concept of boron tracedrugs as a new drug class having broad potential in many areas.

  17. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

    2017-01-01

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  18. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim

    2017-06-12

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  19. One For All? Hitting multiple Alzheimer’s Disease targets with one drug

    Directory of Open Access Journals (Sweden)

    Rebecca Ellen Hughes

    2016-04-01

    Full Text Available Alzheimer’s disease is a complex and multifactorial disease for which the mechanism is still not fully understood. As new insights into disease progression are discovered, new drugs must be designed to target those aspects of the disease that cause neuronal damage rather than just the symptoms currently addressed by single target drugs. It is becoming possible to target several aspects of the disease pathology at once using multi-target drugs. Intended as a introduction for non-experts, this review describes the key multi-target drug design approaches, namely structure-based, in silico, and data-mining, to evaluate what is preventing compounds progressing through the clinic to the market. Repurposing current drugs using their off-target effects reduces the cost of development, time to launch and also the uncertainty associated with safety and pharmacokinetics. The most promising drugs currently being investigated for repurposing to Alzheimer’s Disease are rasagiline, originally developed for the treatment of Parkinson’s Disease, and liraglutide, an antidiabetic. Rational drug design can combine pharmacophores of multiple drugs, systematically change functional groups, and rank them by virtual screening. Hits confirmed experimentally are rationally modified to generate an effective multi-potent lead compound. Examples from this approach are ASS234 with properties similar to rasagiline, and donecopride, a hybrid of an acetylcholinesterase inhibitor and a 5-HT4 receptor agonist with pro-cognitive effects. Exploiting these interdisciplinary approaches, public-private collaborative lead factories promise faster delivery of new drugs to the clinic.

  20. Targeted analysis of 116 drugs in hair by UHPLC-MS/MS and its application to forensic cases

    DEFF Research Database (Denmark)

    Wang, Xin; Johansen, Sys Stybe; Nielsen, Marie Katrine Klose

    2017-01-01

    A multi-target method that can detect a broad range of drugs of abuse in human hair, such as hypnotics, anxiolytics, analgesics, benzodiazepines, antihistamines, antidepressants, antipsychotics, and anticonvulsants, was developed based on ultra-high-performance liquid chromatography-tandem mass...... spectrometry (UHPLC-MS/MS). The drugs were extracted from 10 mg of washed hair by incubation for 18 h in a 25:25:50 (v/v/v) mixture of methanol/acetonitrile/2 mM ammonium formate (8% acetonitrile, pH 5.3). For 51% of the basic drugs, the lower limits of quantification (LLOQs) were in the range of 0.05-0.5 pg...... studies, the present method is sensitive enough to detect single dose drug exposure for many of the drugs. The accuracy was within 75-125% for the majority of drugs. Good precision was observed (relative standard deviations [RSD%] 

  1. Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies.

    Science.gov (United States)

    Williamson, Beth; Riley, Robert J

    2017-12-01

    Drug-drug interactions (DDIs) continue to account for 5% of hospital admissions and therefore remain a major regulatory concern. Effective, quantitative prediction of DDIs will reduce unexpected clinical findings and encourage projects to frontload DDI investigations rather than concentrating on risk management ('manage the baggage') later in drug development. A key challenge in DDI prediction is the discrepancies between reported models. Areas covered: The current synopsis focuses on four recent influential publications on hepatic drug transporter DDIs using static models that tackle interactions with individual transporters and in combination with other drug transporters and metabolising enzymes. These models vary in their assumptions (including input parameters), transparency, reproducibility and complexity. In this review, these facets are compared and contrasted with recommendations made as to their application. Expert opinion: Over the past decade, static models have evolved from simple [I]/k i models to incorporate victim and perpetrator disposition mechanisms including the absorption rate constant, the fraction of the drug metabolised/eliminated and/or clearance concepts. Nonetheless, models that comprise additional parameters and complexity do not necessarily out-perform simpler models with fewer inputs. Further, consideration of the property space to exploit some drug target classes has also highlighted the fine balance required between frontloading and back-loading studies to design out or 'manage the baggage'.

  2. A review on proniosomal drug delivery system for targeted drug action

    OpenAIRE

    Radha, G. V.; Rani, T. Sudha; Sarvani, B.

    2013-01-01

    Proniosomes are dry formulation of water soluble carrier particles that are coated with surfactant. They are rehydrated to form niosomal dispersion immediately before use on agitation in hot aqueous media within minutes. Proniosomes are physically stable during the storage and transport. Drug encapsulated in the vesicular structure of proniosomes prolong the existence of drug in the systematic circulation and enhances the penetration into target tissue and reduce toxicity. From a technical po...

  3. Bioactivity of Hybrid Polymeric Magnetic Nanoparticles and Their Applications in Drug Delivery.

    Science.gov (United States)

    Mohammed, Leena; Ragab, Doaa; Gomaa, Hassan

    2016-01-01

    Engineered magnetic nanoparticles (MNPs) possess unique properties and hold great potential in biomedicine and clinical applications. With their magnetic properties and their ability to work at cellular and molecular level, MNP have been applied both in-vitro and in-vivo in targeted drug delivery and imaging. Focusing on Iron Oxide Superparamagnetic nanoparticles (SPIONs), this paper elaborates on the recent advances in development of hybrid polymeric-magnetic nanoparticles. Their main applications in drug delivery include Chemotherapeutics, Hyperthermia treatment, Radio-therapeutics, Gene delivary, and Biotheraputics. Physiochemical properties such as size, shape, surface and magnetic properties are key factors in determining their behavior. Additionally tailoring SPIONs surface is often vital for desired cell targetting and improved efficiency. Polymer coating is specifically reviewed with brief discussion of SPIONs administration routes. Commonly used drug release models for describing release mechanisms and the nanotoxicity aspects are also discussed. This review focus on superparamagnetic nanoparticles coated with different types of polymers starting with the key physiochemical features that dominate their behavior. The importance of surface modification is addressed. Subsequently, the major classes of polymer modified iron oxide nanoparticles is demonstrated according to their clinical use and application. Clinically approved nanoparticles are then addressed and the different routes of administration are mentioned. Lastly, mathematical models of drug release profile of the common used nanoparticles are addressed. MNPs emerging in recent medicine are remarkable for both imaging and therapeutics, particularly, as drug carriers for their great potential in targeted delivery and cancer treatment. Targeting ability and biocompatibility can be improved though surface coating which provides a mean to alter the surface features including physical characteristics and

  4. Doxorubicin loaded PEG-b-poly(4-vinylbenzylphosphonate) coated magnetic iron oxide nanoparticles for targeted drug delivery

    Energy Technology Data Exchange (ETDEWEB)

    Hałupka-Bryl, Magdalena, E-mail: magdalenahalupka@op.pl [The NanoBioMedical Centre, Adam Mickiewicz University, Poznań (Poland); Division of Medical Physics, Faculty of Physics, Adam Mickiewicz University, Poznań (Poland); Department of Materials Sciences, Graduate School of Pure and Applied Sciences, University of Tsukuba (Japan); Bednarowicz, Magdalena [The NanoBioMedical Centre, Adam Mickiewicz University, Poznań (Poland); Division of Medical Physics, Faculty of Physics, Adam Mickiewicz University, Poznań (Poland); Department of Materials Sciences, Graduate School of Pure and Applied Sciences, University of Tsukuba (Japan); Dobosz, Bernadeta; Krzyminiewski, Ryszard [The NanoBioMedical Centre, Adam Mickiewicz University, Poznań (Poland); Division of Medical Physics, Faculty of Physics, Adam Mickiewicz University, Poznań (Poland); Zalewski, Tomasz [The NanoBioMedical Centre, Adam Mickiewicz University, Poznań (Poland); Wereszczyńska, Beata [Department of Macromolecular Physics, Adam Mickiewicz University, Poznań (Poland); Nowaczyk, Grzegorz; Jarek, Marcin [The NanoBioMedical Centre, Adam Mickiewicz University, Poznań (Poland); Nagasaki, Yukio [Department of Materials Sciences, Graduate School of Pure and Applied Sciences, University of Tsukuba (Japan); Master’s School of Medicinal Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba (Japan); International Centre for Materials Nanoarchitectonics Satellite (WPI-MANA), National Institute For Materials Sciences (NIMS) and University of Tsukuba (Japan)

    2015-06-15

    Due to their unique physical properties, superparamagnetic iron oxide nanoparticles are increasingly used in medical applications. They are very useful carriers for delivering antitumor drugs in targeted cancer treatment. Magnetic nanoparticles with chemiotherapeutic were synthesized by coprecipitation method followed by coating with biocompatible polymer. The aim of this work is to characterize physical and magnetic properties of synthesized nanoparicles. Characterization was carried out using EPR, HRTEM, X-ray diffraction, SQUID and NMR methods. The present findings show that synthesized nanosystem is promising tool for potential magnetic drug delivery. - Highlights: • Synthesized PEG-PIONs/DOX have excellent physical properties. • PEG-PIONs/DOX have a potential to in vivo application. • PEG-PIONs/DOX could be used as drug delivery system as well as contrast agents.

  5. Physics considerations in targeted anticancer drug delivery by magnetoelectric nanoparticles

    Science.gov (United States)

    Stimphil, Emmanuel; Nagesetti, Abhignyan; Guduru, Rakesh; Stewart, Tiffanie; Rodzinski, Alexandra; Liang, Ping; Khizroev, Sakhrat

    2017-06-01

    In regard to cancer therapy, magnetoelectric nanoparticles (MENs) have proven to be in a class of its own when compared to any other nanoparticle type. Like conventional magnetic nanoparticles, they can be used for externally controlled drug delivery via application of a magnetic field gradient and image-guided delivery. However, unlike conventional nanoparticles, due to the presence of a non-zero magnetoelectric effect, MENs provide a unique mix of important properties to address key challenges in modern cancer therapy: (i) a targeting mechanism driven by a physical force rather than antibody matching, (ii) a high-specificity delivery to enhance the cellular uptake of therapeutic drugs across the cancer cell membranes only, while sparing normal cells, (iii) an externally controlled mechanism to release drugs on demand, and (iv) a capability for image guided precision medicine. These properties separate MEN-based targeted delivery from traditional biotechnology approaches and lay a foundation for the complementary approach of technobiology. The biotechnology approach stems from the underlying biology and exploits bioinformatics to find the right therapy. In contrast, the technobiology approach is geared towards using the physics of molecular-level interactions between cells and nanoparticles to treat cancer at the most fundamental level and thus can be extended to all the cancers. This paper gives an overview of the current state of the art and presents an ab initio model to describe the underlying mechanisms of cancer treatment with MENs from the perspective of basic physics.

  6. Changing paradigm from one target one ligand towards multi target directed ligand design for key drug targets of Alzheimer disease: An important role of Insilco methods in multi target directed ligands design.

    Science.gov (United States)

    Kumar, Akhil; Tiwari, Ashish; Sharma, Ashok

    2018-03-15

    Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis is not able to provide complete solution of AD due to multifactorial nature of disease and one target one drug seems to fail to provide better treatment against AD. Moreover, current available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So the current AD drug discovery research shifting towards new approach for better solution that simultaneously modulate more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs. Drug discovery project is tedious, costly and long term project. Moreover, multi target AD drug discovery added extra challenges such as good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off target side effect and crossing of the blood brain barrier. These hurdles may be addressed by insilico methods for efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here we are summarizing some of the most prominent and computationally explored single target against AD and further we discussed successful example of dual or multiple inhibitors for same targets. Moreover we focused on ligand and structure based computational approach to design MTDL against AD. However is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy are useful in future MTDLs drug discovery alone or in combination with fragment based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug

  7. [Targeting high-risk drugs to optimize clinical pharmacists' intervention].

    Science.gov (United States)

    Mouterde, Anne-Laure; Bourdelin, Magali; Maison, Ophélie; Coursier, Sandra; Bontemps, Hervé

    2016-12-01

    By the Order of 6 April 2011, the pharmacist must validate all the prescriptions containing "high-risk drugs" or those of "patients at risk". To optimize this clinical pharmacy activity, we identified high-risk drugs. A list of high-risk drugs has been established using literature, pharmacists' interventions (PI) performed in our hospital and a survey sent to hospital pharmacists. In a prospective study (analysis of 100 prescriptions for each high-risk drug selected), we have identified the most relevant to target. We obtained a statistically significant PI rate (P<0.05) for digoxin, oral anticoagulants direct, oral methotrexate and colchicine. This method of targeted pharmaceutical validation based on high-risk drugs is relevant to detect patients with high risk of medicine-related illness. Copyright © 2016 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.

  8. Visualization of network target crosstalk optimizes drug synergism in myocardial ischemia.

    Directory of Open Access Journals (Sweden)

    Xiaojing Wan

    Full Text Available Numerous drugs and compounds have been validated as protecting against myocardial ischemia (MI, a leading cause of heart failure; however, synergistic possibilities among them have not been systematically explored. Thus, there appears to be significant room for optimization in the field of drug combination therapy for MI. Here, we propose an easy approach for the identification and optimization of MI-related synergistic drug combinations via visualization of the crosstalk between networks of drug targets corresponding to different drugs (each drug has a unique network of targets. As an example, in the present study, 28 target crosstalk networks (TCNs of random pairwise combinations of 8 MI-related drugs (curcumin, capsaicin, celecoxib, raloxifene, silibinin, sulforaphane, tacrolimus, and tamoxifen were established to illustrate the proposed method. The TCNs revealed a high likelihood of synergy between curcumin and the other drugs, which was confirmed by in vitro experiments. Further drug combination optimization showed a synergistic protective effect of curcumin, celecoxib, and sililinin in combination against H₂O₂-induced ischemic injury of cardiomyocytes at a relatively low concentration of 500 nM. This result is in agreement with the earlier finding of a denser and modular functional crosstalk between their networks of targets in the regulation of cell apoptosis. Our study offers a simple approach to rapidly search for and optimize potent synergistic drug combinations, which can be used for identifying better MI therapeutic strategies. Some new light was also shed on the characteristic features of drug synergy, suggesting that it is possible to apply this method to other complex human diseases.

  9. Visualization of network target crosstalk optimizes drug synergism in myocardial ischemia.

    Science.gov (United States)

    Wan, Xiaojing; Meng, Jia; Dai, Yingnan; Zhang, Yina; Yan, Shuang

    2014-01-01

    Numerous drugs and compounds have been validated as protecting against myocardial ischemia (MI), a leading cause of heart failure; however, synergistic possibilities among them have not been systematically explored. Thus, there appears to be significant room for optimization in the field of drug combination therapy for MI. Here, we propose an easy approach for the identification and optimization of MI-related synergistic drug combinations via visualization of the crosstalk between networks of drug targets corresponding to different drugs (each drug has a unique network of targets). As an example, in the present study, 28 target crosstalk networks (TCNs) of random pairwise combinations of 8 MI-related drugs (curcumin, capsaicin, celecoxib, raloxifene, silibinin, sulforaphane, tacrolimus, and tamoxifen) were established to illustrate the proposed method. The TCNs revealed a high likelihood of synergy between curcumin and the other drugs, which was confirmed by in vitro experiments. Further drug combination optimization showed a synergistic protective effect of curcumin, celecoxib, and sililinin in combination against H₂O₂-induced ischemic injury of cardiomyocytes at a relatively low concentration of 500 nM. This result is in agreement with the earlier finding of a denser and modular functional crosstalk between their networks of targets in the regulation of cell apoptosis. Our study offers a simple approach to rapidly search for and optimize potent synergistic drug combinations, which can be used for identifying better MI therapeutic strategies. Some new light was also shed on the characteristic features of drug synergy, suggesting that it is possible to apply this method to other complex human diseases.

  10. High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics

    Energy Technology Data Exchange (ETDEWEB)

    Pan, Jian-Bo [Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005 (China); Ji, Nan; Pan, Wen; Hong, Ru [State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102 (China); Wang, Hao [Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005 (China); Ji, Zhi-Liang, E-mail: appo@xmu.edu.cn [State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102 (China); Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005 (China)

    2014-01-01

    Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPs that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing. - Highlights: • A novel computational framework was developed for mechanistic study of SADRs. • Off-targets of drugs were identified in large scale and in a high-throughput manner. • SADRs like cardiac disorders were systematically explored in molecular networks. • A number of ADR-associated proteins were identified.

  11. High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics

    International Nuclear Information System (INIS)

    Pan, Jian-Bo; Ji, Nan; Pan, Wen; Hong, Ru; Wang, Hao; Ji, Zhi-Liang

    2014-01-01

    Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPs that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing. - Highlights: • A novel computational framework was developed for mechanistic study of SADRs. • Off-targets of drugs were identified in large scale and in a high-throughput manner. • SADRs like cardiac disorders were systematically explored in molecular networks. • A number of ADR-associated proteins were identified

  12. An efficient targeted drug delivery through apotransferrin loaded nanoparticles.

    Directory of Open Access Journals (Sweden)

    Athuluri Divakar Sai Krishna

    Full Text Available BACKGROUND: Cancerous state is a highly stimulated environment of metabolically active cells. The cells under these conditions over express selective receptors for assimilation of factors essential for growth and transformation. Such receptors would serve as potential targets for the specific ligand mediated transport of pharmaceutically active molecules. The present study demonstrates the specificity and efficacy of protein nanoparticle of apotransferrin for targeted delivery of doxorubicin. METHODOLOGY/PRINCIPAL FINDINGS: Apotransferrin nanoparticles were developed by sol-oil chemistry. A comparative analysis of efficiency of drug delivery in conjugated and non-conjugated forms of doxorubicin to apotransferrin nanoparticle is presented. The spherical shaped apotransferrin nanoparticles (nano have diameters of 25-50 etam, which increase to 60-80 etam upon direct loading of drug (direct-nano, and showed further increase in dimension (75-95 etam in conjugated nanoparticles (conj-nano. The competitive experiments with the transferrin receptor specific antibody showed the entry of both conj-nano and direct-nano into the cells through transferrin receptor mediated endocytosis. Results of various studies conducted clearly establish the superiority of the direct-nano over conj-nano viz. (a localization studies showed complete release of drug very early, even as early as 30 min after treatment, with the drug localizing in the target organelle (nucleus (b pharmacokinetic studies showed enhanced drug concentrations, in circulation with sustainable half-life (c the studies also demonstrated efficient drug delivery, and an enhanced inhibition of proliferation in cancer cells. Tissue distribution analysis showed intravenous administration of direct nano lead to higher drug localization in liver, and blood as compared to relatively lesser localization in heart, kidney and spleen. Experiments using rat cancer model confirmed the efficacy of the formulation in

  13. Magnetic field pattern synthesis and its application in targeted drug delivery: Design and implementation.

    Science.gov (United States)

    Hajiaghajani, Amirhossein; Abdolali, Ali

    2018-05-01

    In cancer therapy, magnetic drug targeting is considered as an effective treatment to reduce chemotherapy's side effects. The accurate design and shaping of magnetic fields are crucial for healthy cells to be immune from chemotherapeutics. In this paper, arbitrary 2-dimensional spatial patterns of magnetic fields from DC to megahertz are represented in terms of spatial Fourier spectra with sinusoidal eigenfunctions. Realization of each spatial frequency was investigated by a set of elliptical coils. Therefore, it is shown that the desired pattern was synthesized by simultaneous use of coil sets. Currents running on each set were obtained via fast and straightforward analytical Fourier series calculation. Experimentally scanned sample patterns were in close agreement with full wave analysis. Discussions include the evaluation of the Fourier series approximation error and cross-polarization of produced magnetic fields. It was observed that by employing the controlled magnetic field produced by the proposed setup, we were able to steer therapeutic particles toward the right or left half-spheres of the breast, with an efficiency of 90%. Such a pattern synthesizer may be employed in numerous human arteries as well as other bioelectromagnetic patterning applications, e.g., wireless power transfer, magnetic innervation, and tomography. Bioelectromagnetics. 39:325-338, 2018. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

  14. Protein and Peptide in Drug Targeting and its Therapeutic Approach

    Directory of Open Access Journals (Sweden)

    Raj K. Keservani

    2015-09-01

    Full Text Available Aim: The main aim of this review article is to provide information like advantages of protein and peptides via different routes of drug administration, targeted to a particular site and its implication in drug delivery system. Methods: To that aim, from the web sites of PubMed, HCAplus, Thomson, and Registry were used as the main sources to perform the search for the most significant research articles published on the subject. The information was then carefully analyzed, highlighting the most important results in the development of protein and peptide drug targeting as well as its therapeutic activity. Results: In recent years many researchers use protein and peptide as a target site of drug by a different delivery system. Proteins and peptides are used as specific and effective therapeutic agents, due to instability and side effects their use is complicated. Protein kinases are important regulators of most, if not all, biological processes. Abnormal activity of proteins and peptides has been implicated in many human diseases, such as diabetes, cancer and neurodegenerative disorders. Conclusions: It is concluded that the protein and peptide were used in drug targeting to specific site and also used in different diseased states like cancer, diabetes, immunomodulating, neurodegenerative effects and antimicrobial activity.

  15. Orexin Receptor Targets for Anti-Relapse Medication Development in Drug Addiction

    Directory of Open Access Journals (Sweden)

    Ronald E. See

    2011-06-01

    Full Text Available Drug addiction is a chronic illness characterized by high rates of relapse. Relapse to drug use can be triggered by re-exposure to drug-associated cues, stressful events, or the drug itself after a period of abstinence. Pharmacological intervention to reduce the impact of relapse-instigating factors offers a promising target for addiction treatment. Growing evidence has implicated an important role of the orexin/hypocretin system in drug reward and drug-seeking, including animal models of relapse. Here, we review the evidence for the role of orexins in modulating reward and drug-seeking in animal models of addiction and the potential for orexin receptors as specific targets for anti-relapse medication approaches.

  16. The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness

    Science.gov (United States)

    Mangus, J Michael; Turner, Benjamin O

    2017-01-01

    Abstract While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. PMID:29140500

  17. Progress and perspectives on targeting nanoparticles for brain drug delivery

    Directory of Open Access Journals (Sweden)

    Huile Gao

    2016-07-01

    Full Text Available Due to the ability of the blood–brain barrier (BBB to prevent the entry of drugs into the brain, it is a challenge to treat central nervous system disorders pharmacologically. The development of nanotechnology provides potential to overcome this problem. In this review, the barriers to brain-targeted drug delivery are reviewed, including the BBB, blood–brain tumor barrier (BBTB, and nose-to-brain barrier. Delivery strategies are focused on overcoming the BBB, directly targeting diseased cells in the brain, and dual-targeted delivery. The major concerns and perspectives on constructing brain-targeted delivery systems are discussed.

  18. Progress and perspectives on targeting nanoparticles for brain drug delivery

    Institute of Scientific and Technical Information of China (English)

    Huile Gao

    2016-01-01

    Due to the ability of the blood–brain barrier(BBB) to prevent the entry of drugs into the brain, it is a challenge to treat central nervous system disorders pharmacologically. The development of nanotechnology provides potential to overcome this problem. In this review, the barriers to brain-targeted drug delivery are reviewed, including the BBB, blood–brain tumor barrier(BBTB), and nose-to-brain barrier. Delivery strategies are focused on overcoming the BBB, directly targeting diseased cells in the brain, and dual-targeted delivery. The major concerns and perspectives on constructing brain-targeted delivery systems are discussed.

  19. Increasing the Structural Coverage of Tuberculosis Drug Targets

    OpenAIRE

    Baugh, Loren; Phan, Isabelle; Begley, Darren W.; Clifton, Matthew C.; Armour, Brianna; Dranow, David M.; Taylor, Brandy M.; Muruthi, Marvin M.; Abendroth, Jan; Fairman, James W.; Fox, David; Dieterich, Shellie H.; Staker, Bart L.; Gardberg, Anna S.; Choi, Ryan

    2014-01-01

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus “homolog-rescue” strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal s...

  20. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

    International Nuclear Information System (INIS)

    Poulin, Patrick; Ekins, Sean; Theil, Frank-Peter

    2011-01-01

    A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V ss ) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V ss for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V ss of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.

  1. Encapsulation of methotrexate loaded magnetic microcapsules for magnetic drug targeting and controlled drug release

    Energy Technology Data Exchange (ETDEWEB)

    Chakkarapani, Prabu [Department of Pharmaceutical Technology & Centre for Excellence in Nanobio Translational Research, Anna University, Bharathidasan Institute of Technology Campus, Tiruchirappalli 620024, Tamil Nadu (India); Subbiah, Latha, E-mail: lathasuba2010@gmail.com [Department of Pharmaceutical Technology & Centre for Excellence in Nanobio Translational Research, Anna University, Bharathidasan Institute of Technology Campus, Tiruchirappalli 620024, Tamil Nadu (India); Palanisamy, Selvamani; Bibiana, Arputha [Department of Pharmaceutical Technology & Centre for Excellence in Nanobio Translational Research, Anna University, Bharathidasan Institute of Technology Campus, Tiruchirappalli 620024, Tamil Nadu (India); Ahrentorp, Fredrik; Jonasson, Christian; Johansson, Christer [Acreo Swedish ICT AB, Arvid Hedvalls backe 4, SE-411 33 Göteborg (Sweden)

    2015-04-15

    We report on the development and evaluation of methotrexate magnetic microcapsules (MMC) for targeted rheumatoid arthritis therapy. Methotrexate was loaded into CaCO{sub 3}-PSS (poly (sodium 4-styrenesulfonate)) doped microparticles that were coated successively with poly (allylamine hydrochloride) and poly (sodium 4-styrenesulfonate) by layer-by-layer technique. Ferrofluid was incorporated between the polyelectrolyte layers. CaCO{sub 3}-PSS core was etched by incubation with EDTA yielding spherical MMC. The MMC were evaluated for various physicochemical, pharmaceutical parameters and magnetic properties. Surface morphology, crystallinity, particle size, zeta potential, encapsulation efficiency, loading capacity, drug release pattern, release kinetics and AC susceptibility studies revealed spherical particles of ~3 µm size were obtained with a net zeta potential of +24.5 mV, 56% encapsulation and 18.6% drug loading capacity, 96% of cumulative drug release obeyed Hixson-Crowell model release kinetics. Drug excipient interaction, surface area, thermal and storage stability studies for the prepared MMC was also evaluated. The developed MMC offer a promising mode of targeted and sustained release drug delivery for rheumatoid arthritis therapy. - Highlights: • Development of methotrexate magnetic microcapsules (MMC) by layer-by-layer method. • Characterization of physicochemical, pharmaceutical and magnetic properties of MMC. • Multiple layers of alternative polyelectrolytes prolongs methotrexate release time. • MMC is capable for targeted and sustained release rheumatoid arthritis therapy.

  2. The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

    Science.gov (United States)

    Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

    2012-01-01

    In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

  3. Mechanism and kinetics of the loss of poorly soluble drugs from liposomal carriers studied by a novel flow field-flow fractionation-based drug release-/transfer-assay

    DEFF Research Database (Denmark)

    Hinna, Askell Hvid; Hupfeld, Stefan; Kuntsche, Judith

    2016-01-01

    Liposomes represent a versatile drug formulation approach e.g. for improving the water-solubility of poorly soluble drugs but also to achieve drug targeting and controlled release. For the latter applications it is essential that the drug remains associated with the liposomal carrier during transit...... in the vascular bed. A range of in vitro test methods has been suggested over the years for prediction of the release of drug from liposomal carriers. The majority of these fail to give a realistic prediction for poorly water-soluble drugs due to the intrinsic tendency of such compounds to remain associated...... the amount of drug remaining associated with the liposomal drug carrier as well as that transferred to the acceptor liposomes at distinct times of incubation, boththe kinetics of drug transfer and release to the water phase could be established for the model drug p-THPP (5,10,15,20-tetrakis(4-hydroxyphenyl...

  4. Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

    Science.gov (United States)

    Abdolmaleki, Azizeh; Ghasemi, Jahan B

    2017-01-01

    Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. Targeted Learning in Healthcare Research.

    Science.gov (United States)

    Gruber, Susan

    2015-12-01

    The increasing availability of Big Data in healthcare encourages investigators to seek answers to big questions. However, nonparametric approaches to analyzing these data can suffer from the curse of dimensionality, and traditional parametric modeling does not necessarily scale. Targeted learning (TL) combines semiparametric methodology with advanced machine learning techniques to provide a sound foundation for extracting information from data. Predictive models, variable importance measures, and treatment benefits and risks can all be addressed within this framework. TL has been applied in a broad range of healthcare settings, including genomics, precision medicine, health policy, and drug safety. This article provides an introduction to the two main components of TL, targeted minimum loss-based estimation and super learning, and gives examples of applications in predictive modeling, variable importance ranking, and comparative effectiveness research.

  6. A study on accumulation of magnetic drug in the capillary vessel of target organ using superconducting MDDS

    International Nuclear Information System (INIS)

    Mishima, F.; Akiyama, Y.; Nishijima, S.

    2010-01-01

    Magnetic Drug Delivery System (MDDS) is one of the drug therapy technologies to accumulate the drug at the targeted part efficiently. The ferromagnetic particle is attached to the medicine, antibody, hormones and so on. The magnetic seeded drug is injected into the blood vessel, and then is accumulated in capillary vessel of target organ by magnetic field generated by the superconducting magnet placed outside of the body. The technology is great prospective for not only human medical treatment but also stockbreeding field. Treatment for cow ovarian diseases (decay of ovarian hormone secretion) requires an improvement in suppression of the drug diffusion to non-diseased part by the blood flow. In order to solve the problem, the applicability of the MDDS was examined. The behavior of the magnetic drug under the magnetic field generated by high temperature superconducting (HTS) bulk magnet were studied by the model experiment and computer simulation with the capillary model of the corpus luteum. As a result, it was shown that MDDS is able to apply to the capillaries of the corpus luteum (yellow body).

  7. Contact-facilitated drug delivery with Sn2 lipase labile prodrugs optimize targeted lipid nanoparticle drug delivery.

    Science.gov (United States)

    Pan, Dipanjan; Pham, Christine T N; Weilbaecher, Katherine N; Tomasson, Michael H; Wickline, Samuel A; Lanza, Gregory M

    2016-01-01

    Sn2 lipase labile phospholipid prodrugs in conjunction with contact-facilitated drug delivery offer an important advancement in Nanomedicine. Many drugs incorporated into nanosystems, targeted or not, are substantially lost during circulation to the target. However, favorably altering the pharmacokinetics and volume of distribution of systemic drug delivery can offer greater efficacy with lower toxicity, leading to new prolonged-release nanoexcipients. However, the concept of achieving Paul Erhlich's inspired vision of a 'magic bullet' to treat disease has been largely unrealized due to unstable nanomedicines, nanosystems achieving low drug delivery to target cells, poor intracellular bioavailability of endocytosed nanoparticle payloads, and the substantial biological barriers of extravascular particle penetration into pathological sites. As shown here, Sn2 phospholipid prodrugs in conjunction with contact-facilitated drug delivery prevent premature drug diffusional loss during circulation and increase target cell bioavailability. The Sn2 phospholipid prodrug approach applies equally well for vascular constrained lipid-encapsulated particles and micelles the size of proteins that penetrate through naturally fenestrated endothelium in the bone marrow or thin-walled venules of an inflamed microcirculation. At one time Nanomedicine was considered a 'Grail Quest' by its loyal opposition and even many in the field adsorbing the pains of a long-learning curve about human biology and particles. However, Nanomedicine with innovations like Sn2 phospholipid prodrugs has finally made 'made the turn' toward meaningful translational success. © 2015 The Authors. WIREs Nanomedicine and Nanobiotechnology published by Wiley Periodicals, Inc.

  8. Targeted proteins for diabetes drug design

    Science.gov (United States)

    Doan Trang Nguyen, Ngoc; Thi Le, Ly

    2012-03-01

    Type 2 diabetes mellitus is a common metabolism disorder characterized by high glucose in the bloodstream, especially in the case of insulin resistance and relative insulin deficiency. Nowadays, it is very common in middle-aged people and involves such dangerous symptoms as increasing risk of stroke, obesity and heart failure. In Vietnam, besides the common treatment of insulin injection, some herbal medication is used but no unified optimum remedy for the disease yet exists and there is no production of antidiabetic drugs in the domestic market yet. In the development of nanomedicine at the present time, drug design is considered as an innovative tool for researchers to study the mechanisms of diseases at the molecular level. The aim of this article is to review some common protein targets involved in type 2 diabetes, offering a new idea for designing new drug candidates to produce antidiabetic drugs against type 2 diabetes for Vietnamese people.

  9. Targeted proteins for diabetes drug design

    International Nuclear Information System (INIS)

    Trang Nguyen, Ngoc Doan; Le, Ly Thi

    2012-01-01

    Type 2 diabetes mellitus is a common metabolism disorder characterized by high glucose in the bloodstream, especially in the case of insulin resistance and relative insulin deficiency. Nowadays, it is very common in middle-aged people and involves such dangerous symptoms as increasing risk of stroke, obesity and heart failure. In Vietnam, besides the common treatment of insulin injection, some herbal medication is used but no unified optimum remedy for the disease yet exists and there is no production of antidiabetic drugs in the domestic market yet. In the development of nanomedicine at the present time, drug design is considered as an innovative tool for researchers to study the mechanisms of diseases at the molecular level. The aim of this article is to review some common protein targets involved in type 2 diabetes, offering a new idea for designing new drug candidates to produce antidiabetic drugs against type 2 diabetes for Vietnamese people. (review)

  10. Quantitative modeling of selective lysosomal targeting for drug design

    DEFF Research Database (Denmark)

    Trapp, Stefan; Rosania, G.; Horobin, R.W.

    2008-01-01

    log K ow. These findings were validated with experimental results and by a comparison to the properties of antimalarial drugs in clinical use. For ten active compounds, nine were predicted to accumulate to a greater extent in lysosomes than in other organelles, six of these were in the optimum range...... predicted by the model and three were close. Five of the antimalarial drugs were lipophilic weak dibasic compounds. The predicted optimum properties for a selective accumulation of weak bivalent bases in lysosomes are consistent with experimental values and are more accurate than any prior calculation...

  11. Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.

    Science.gov (United States)

    Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru

    2018-02-01

    Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  12. Emerging drugs which target the renin-angiotensin-aldosterone system.

    Science.gov (United States)

    Steckelings, Ulrike Muscha; Paulis, Ludovit; Unger, Thomas; Bader, Michael

    2011-12-01

    The renin-angiotensin-aldosterone system (RAAS) is already the most important target for drugs in the cardiovascular system. However, still new developments are underway to interfere with the system on different levels. The novel strategies to interfere with RAAS aim to reduce the synthesis of the two major RAAS effector hormones, angiotensin (Ang) II and aldosterone, or interfere with their receptors, AT1 and mineralocorticoid receptor, respectively. Moreover, novel targets have been identified in RAAS, such as the (pro)renin receptor, and molecules, which counteract the classical actions of Ang II and are therefore beneficial in cardiovascular diseases. These include the AT2 receptor and the ACE2/Ang-(1-7)/Mas axis. The search for drugs activating these tissue-protective arms of RAAS is therefore the most innovative field in RAAS pharmacology. Most of the novel pharmacological strategies to inhibit the classical RAAS need to prove their superiority above the existing treatment in clinical trials and then have to compete against these now quite cheap drugs in a competitive market. The newly discovered targets have functions beyond the cardiovascular system opening up novel therapeutic areas for drugs interfering with RAAS components.

  13. Literature-based condition-specific miRNA-mRNA target prediction.

    Directory of Open Access Journals (Sweden)

    Minsik Oh

    Full Text Available miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction

  14. Nano-Star-Shaped Polymers for Drug Delivery Applications.

    Science.gov (United States)

    Yang, Da-Peng; Oo, Ma Nwe Nwe Linn; Deen, Gulam Roshan; Li, Zibiao; Loh, Xian Jun

    2017-11-01

    With the advancement of polymer engineering, complex star-shaped polymer architectures can be synthesized with ease, bringing about a host of unique properties and applications. The polymer arms can be functionalized with different chemical groups to fine-tune the response behavior or be endowed with targeting ligands or stimuli responsive moieties to control its physicochemical behavior and self-organization in solution. Rheological properties of these solutions can be modulated, which also facilitates the control of the diffusion of the drug from these star-based nanocarriers. However, these star-shaped polymers designed for drug delivery are still in a very early stage of development. Due to the sheer diversity of macromolecules that can take on the star architectures and the various combinations of functional groups that can be cross-linked together, there remain many structure-property relationships which have yet to be fully established. This review aims to provide an introductory perspective on the basic synthetic methods of star-shaped polymers, the properties which can be controlled by the unique architecture, and also recent advances in drug delivery applications related to these star candidates. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Inhaled Micro/Nanoparticulate Anticancer Drug Formulations: An Emerging Targeted Drug Delivery Strategy for Lung Cancers.

    Science.gov (United States)

    Islam, Nazrul; Richard, Derek

    2018-05-24

    Local delivery of drug to the target organ via inhalation offers enormous benefits in the management of many diseases. Lung cancer is the most common of all cancers and it is the leading cause of death worldwide. Currently available treatment systems (intravenous or oral drug delivery) are not efficient in accumulating the delivered drug into the target tumor cells and are usually associated with various systemic and dose-related adverse effects. The pulmonary drug delivery technology would enable preferential accumulation of drug within the cancer cell and thus be superior to intravenous and oral delivery in reducing cancer cell proliferation and minimising the systemic adverse effects. Site-specific drug delivery via inhalation for the treatment of lung cancer is both feasible and efficient. The inhaled drug delivery system is non-invasive, produces high bioavailability at low dose and avoids first pass metabolism of the delivered drug. Various anticancer drugs including chemotherapeutics, proteins and genes have been investigated for inhalation in lung cancers with significant outcomes. Pulmonary delivery of drugs from dry powder inhaler (DPI) formulation is stable and has high patient compliance. Herein, we report the potential of pulmonary drug delivery from dry powder inhaler (DPI) formulations inhibiting lung cancer cell proliferation at very low dose with reduced unwanted adverse effects. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  16. Predicting and detecting adverse drug reactions in old age: challenges and opportunities.

    Science.gov (United States)

    Mangoni, Arduino A

    2012-05-01

    Increased, often inappropriate, drug exposure, pharmacokinetic and pharmacodynamic changes, reduced homeostatic reserve and frailty increase the risk of adverse drug reactions (ADRs) in the older population, thereby imposing a significant public health burden. Predicting and diagnosing ADRs in old age presents significant challenges for the clinician, even when specific risk scoring systems are available. The picture is further compounded by the potential adverse impact of several drugs on more 'global' health indicators, for example, physical function and independence, and the fragmentation of care (e.g., increased number of treating doctors and care transitions) experienced by older patients during their clinical journey. The current knowledge of drug safety in old age is also curtailed by the lack of efficacy and safety data from pre-marketing studies. Moreover, little consideration is given to individual patients' experiences and reporting of specific ADRs, particularly in the presence of cognitive impairment. Pending additional data on these issues, the close review and monitoring of individual patients' drug prescribing, clinical status and biochemical parameters remain essential to predict and detect ADRs in old age. Recently developed strategies, for example, medication reconciliation and trigger tool methodology, have the potential for ADRs risk mitigation in this population. However, more information is required on their efficacy and applicability in different healthcare settings.

  17. Sodium dependent multivitamin transporter (SMVT): a potential target for drug delivery.

    Science.gov (United States)

    Vadlapudi, Aswani Dutt; Vadlapatla, Ramya Krishna; Mitra, Ashim K

    2012-06-01

    Sodium dependent multivitamin transporter (SMVT; product of the SLC5A6 gene) is an important transmembrane protein responsible for translocation of vitamins and other essential cofactors such as biotin, pantothenic acid and lipoic acid. Hydropathy plot (Kyte-Dolittle algorithm) revealed that human SMVT protein consists of 635 amino acids and 12 transmembrane domains with both amino and carboxyl termini oriented towards the cytoplasm. SMVT is expressed in various tissues such as placenta, intestine, brain, liver, lung, kidney, cornea, retina and heart. This transporter displays broad substrate specificity and excellent capacity for utilization in drug delivery. Drug absorption is often limited by the presence of physiological (epithelial tight junctions), biochemical (efflux transporters and enzymatic degradation) and chemical (size, lipophilicity, molecular weight, charge etc.) barriers. These barriers may cause many potential therapeutics to be dropped from the preliminary screening portfolio and subsequent entry into the market. Transporter targeted delivery has become a powerful approach to deliver drugs to target tissues because of the ability of the transporter to translocate the drug to intracellular organelles at a higher rate. This review highlights studies employing SMVT transporter as a target for drug delivery to improve bioavailability and investigate the feasibility of developing SMVT targeted drug delivery systems.

  18. Predicting Drug Recalls From Internet Search Engine Queries.

    Science.gov (United States)

    Yom-Tov, Elad

    2017-01-01

    Batches of pharmaceuticals are sometimes recalled from the market when a safety issue or a defect is detected in specific production runs of a drug. Such problems are usually detected when patients or healthcare providers report abnormalities to medical authorities. Here, we test the hypothesis that defective production lots can be detected earlier by monitoring queries to Internet search engines. We extracted queries from the USA to the Bing search engine, which mentioned one of the 5195 pharmaceutical drugs during 2015 and all recall notifications issued by the Food and Drug Administration (FDA) during that year. By using attributes that quantify the change in query volume at the state level, we attempted to predict if a recall of a specific drug will be ordered by FDA in a time horizon ranging from 1 to 40 days in future. Our results show that future drug recalls can indeed be identified with an AUC of 0.791 and a lift at 5% of approximately 6 when predicting a recall occurring one day ahead. This performance degrades as prediction is made for longer periods ahead. The most indicative attributes for prediction are sudden spikes in query volume about a specific medicine in each state. Recalls of prescription drugs and those estimated to be of medium-risk are more likely to be identified using search query data. These findings suggest that aggregated Internet search engine data can be used to facilitate in early warning of faulty batches of medicines.

  19. Colon-targeted oral drug delivery systems: design trends and approaches.

    Science.gov (United States)

    Amidon, Seth; Brown, Jack E; Dave, Vivek S

    2015-08-01

    Colon-specific drug delivery systems (CDDS) are desirable for the treatment of a range of local diseases such as ulcerative colitis, Crohn's disease, irritable bowel syndrome, chronic pancreatitis, and colonic cancer. In addition, the colon can be a potential site for the systemic absorption of several drugs to treat non-colonic conditions. Drugs such as proteins and peptides that are known to degrade in the extreme gastric pH, if delivered to the colon intact, can be systemically absorbed by colonic mucosa. In order to achieve effective therapeutic outcomes, it is imperative that the designed delivery system specifically targets the drugs into the colon. Several formulation approaches have been explored in the development colon-targeted drug delivery systems. These approaches involve the use of formulation components that interact with one or more aspects of gastrointestinal (GI) physiology, such as the difference in the pH along the GI tract, the presence of colonic microflora, and enzymes, to achieve colon targeting. This article highlights the factors influencing colon-specific drug delivery and colonic bioavailability, and the limitations associated with CDDS. Further, the review provides a systematic discussion of various conventional, as well as relatively newer formulation approaches/technologies currently being utilized for the development of CDDS.

  20. 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.

    Science.gov (United States)

    Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Sobarzo-Sánchez, Eduardo; Yañez, Matilde; Riera-Fernandez, Pablo; González-Díaz, Humberto

    2011-12-01

    There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore

  1. Biodegradable Drug-Loaded Hydroxyapatite Nanotherapeutic Agent for Targeted Drug Release in Tumors.

    Science.gov (United States)

    Sun, Wen; Fan, Jiangli; Wang, Suzhen; Kang, Yao; Du, Jianjun; Peng, Xiaojun

    2018-03-07

    Tumor-targeted drug delivery systems have been increasingly used to improve the therapeutic efficiency of anticancer drugs and reduce their toxic side effects in vivo. Focused on this point, doxorubicin (DOX)-loaded hydroxyapatite (HAP) nanorods consisting of folic acid (FA) modification (DOX@HAP-FA) were developed for efficient antitumor treatment. The DOX-loaded nanorods were synthesized through in situ coprecipitation and hydrothermal method with a DOX template, demonstrating a new procedure for drug loading in HAP materials. DOX could be efficiently released from DOX@HAP-FA within 24 h in weakly acidic buffer solution (pH = 6.0) because of the degradation of HAP nanorods. With endocytosis under the mediation of folate receptors, the nanorods exhibited enhanced cellular uptake and further degraded, and consequently, the proliferation of targeted cells was inhibited. More importantly, in a tumor-bearing mouse model, DOX@HAP-FA treatment demonstrated excellent tumor growth inhibition. In addition, no apparent side effects were observed during the treatment. These results suggested that DOX@HAP-FA may be a promising nanotherapeutic agent for effective cancer treatment in vivo.

  2. Nanotechnology in the targeted drug delivery for bone diseases and bone regeneration

    Science.gov (United States)

    Gu, Wenyi; Wu, Chengtie; Chen, Jiezhong; Xiao, Yin

    2013-01-01

    Nanotechnology is a vigorous research area and one of its important applications is in biomedical sciences. Among biomedical applications, targeted drug delivery is one of the most extensively studied subjects. Nanostructured particles and scaffolds have been widely studied for increasing treatment efficacy and specificity of present treatment approaches. Similarly, this technique has been used for treating bone diseases including bone regeneration. In this review, we have summarized and highlighted the recent advancement of nanostructured particles and scaffolds for the treatment of cancer bone metastasis, osteosarcoma, bone infections and inflammatory diseases, osteoarthritis, as well as for bone regeneration. Nanoparticles used to deliver deoxyribonucleic acid and ribonucleic acid molecules to specific bone sites for gene therapies are also included. The investigation of the implications of nanoparticles in bone diseases have just begun, and has already shown some promising potential. Further studies have to be conducted, aimed specifically at assessing targeted delivery and bioactive scaffolds to further improve their efficacy before they can be used clinically. PMID:23836972

  3. Nanotechnology in the targeted drug delivery for bone diseases and bone regeneration.

    Science.gov (United States)

    Gu, Wenyi; Wu, Chengtie; Chen, Jiezhong; Xiao, Yin

    2013-01-01

    Nanotechnology is a vigorous research area and one of its important applications is in biomedical sciences. Among biomedical applications, targeted drug delivery is one of the most extensively studied subjects. Nanostructured particles and scaffolds have been widely studied for increasing treatment efficacy and specificity of present treatment approaches. Similarly, this technique has been used for treating bone diseases including bone regeneration. In this review, we have summarized and highlighted the recent advancement of nanostructured particles and scaffolds for the treatment of cancer bone metastasis, osteosarcoma, bone infections and inflammatory diseases, osteoarthritis, as well as for bone regeneration. Nanoparticles used to deliver deoxyribonucleic acid and ribonucleic acid molecules to specific bone sites for gene therapies are also included. The investigation of the implications of nanoparticles in bone diseases have just begun, and has already shown some promising potential. Further studies have to be conducted, aimed specifically at assessing targeted delivery and bioactive scaffolds to further improve their efficacy before they can be used clinically.

  4. Assessment of berberine as a multi-target antimicrobial: a multi-omics study for drug discovery and repositioning.

    Science.gov (United States)

    Karaosmanoglu, Kubra; Sayar, Nihat Alpagu; Kurnaz, Isil Aksan; Akbulut, Berna Sariyar

    2014-01-01

    Postgenomics drug development is undergoing major transformation in the age of multi-omics studies and drug repositioning. Rather than applications solely in personalized medicine, omics science thus additionally offers a better understanding of a broader range of drug targets and drug repositioning. Berberine is an isoquinoline alkaloid found in many medicinal plants. We report here a whole genome microarray study in tandem with proteomics techniques for mining the plethora of targets that are putatively involved in the antimicrobial activity of berberine against Escherichia coli. We found DNA replication/repair and transcription to be triggered by berberine, indicating that nucleic acids, in general, are among its targets. Our combined transcriptomics and proteomics multi-omics findings underscore that, in the presence of berberine, cell wall or cell membrane transport and motility-related functions are also specifically regulated. We further report a general decline in metabolism, as seen by repression of genes in carbohydrate and amino acid metabolism, energy production, and conversion. An involvement of multidrug efflux pumps, as well as reduced membrane permeability for developing resistance against berberine in E. coli was noted. Collectively, these findings offer original and significant leads for omics-guided drug discovery and future repositioning approaches in the postgenomics era, using berberine as a multi-omics case study.

  5. Polyethyleneimine-modified iron oxide nanoparticles for brain tumor drug delivery using magnetic targeting and intra-carotid administration

    OpenAIRE

    Chertok, Beata; David, Allan E.; Yang, Victor C.

    2010-01-01

    This study aimed to examine the applicability of polyethyleneimine (PEI)-modified magnetic nanoparticles (GPEI) as a potential vascular drug/gene carrier to brain tumors. In vitro, GPEI exhibited high cell association and low cell toxicity – properties which are highly desirable for intracellular drug/gene delivery. In addition, a high saturation magnetization of 93 emu/g Fe was expected to facilitate magnetic targeting of GPEI to brain tumor lesions. However, following intravenous administra...

  6. Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

    Science.gov (United States)

    Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

    2017-10-01

    Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Peptide drugs to target G protein-coupled receptors.

    Science.gov (United States)

    Bellmann-Sickert, Kathrin; Beck-Sickinger, Annette G

    2010-09-01

    Major indications for use of peptide-based therapeutics include endocrine functions (especially diabetes mellitus and obesity), infectious diseases, and cancer. Whereas some peptide pharmaceuticals are drugs, acting as agonists or antagonists to directly treat cancer, others (including peptide diagnostics and tumour-targeting pharmaceuticals) use peptides to 'shuttle' a chemotherapeutic agent or a tracer to the tumour and allow sensitive imaging or targeted therapy. Significant progress has been made in the last few years to overcome disadvantages in peptide design such as short half-life, fast proteolytic cleavage, and low oral bioavailability. These advances include peptide PEGylation, lipidisation or multimerisation; the introduction of peptidomimetic elements into the sequences; and innovative uptake strategies such as liposomal, capsule or subcutaneous formulations. This review focuses on peptides targeting G protein-coupled receptors that are promising drug candidates or that have recently entered the pharmaceutical market. Copyright 2010 Elsevier Ltd. All rights reserved.

  8. ORAL COLON TARGETED DRUG DELIVERY SYSTEM: A REVIEW ON CURRENT AND NOVEL PERSPECTIVES

    OpenAIRE

    Asija Rajesh; Chaudhari Bharat; Asija Sangeeta

    2012-01-01

    Small intestine is mostly the site for drug absorption but in some cases the drug needs to be targeted to colon due to some factors like local colonic disease, degradation related conditions, delayed release of drugs, systemic delivery of protein and peptide drugs etc. Colon targeted drug delivery is important and relatively new concept for the absorption of drugs because it offers almost neutral pH and long residence time, thereby increasing the drug absorption. Colon has proved to be a site...

  9. hTe exciting potential of nanotherapy in brain-tumor targeted drug delivery approaches

    Institute of Scientific and Technical Information of China (English)

    Vivek Agrahari

    2017-01-01

    Delivering therapeutics to the central nervous system (CNS) and brain-tumor has been a major challenge. hTe current standard treatment approaches for the brain-tumor comprise of surgical resection followed by immunotherapy, radiotherapy, and chemotherapy. However, the current treatments are limited in provid-ing signiifcant beneifts to the patients and despite recent technological advancements; brain-tumor is still challenging to treat. Brain-tumor therapy is limited by the lack of effective and targeted strategies to deliver chemotherapeutic agents across the blood-brain barrier (BBB). hTe BBB is the main obstacle that must be overcome to allow compounds to reach their targets in the brain. Recent advances have boosted the nan-otherapeutic approaches in providing an attractive strategy in improving the drug delivery across the BBB and into the CNS. Compared to conventional formulations, nanoformulations offer signiifcant ad vantages in CNS drug delivery approaches. Considering the above facts, in this review, the physiological/anatomical features of the brain-tumor and the BBB are brielfy discussed. hTe drug transport mechanisms at the BBB are outlined. hTe approaches to deliver chemotherapeutic drugs across the CNS into the brain-tumor using nanocarriers are summarized. In addition, the challenges that need to be addressed in nanotherapeutic ap-proaches for their enhanced clinical application in brain-tumor therapy are discussed.

  10. Oncogenic targets, magnitude of benefit, and market pricing of antineoplastic drugs.

    Science.gov (United States)

    Amir, Eitan; Seruga, Bostjan; Martinez-Lopez, Joaquin; Kwong, Ryan; Pandiella, Atanasio; Tannock, Ian F; Ocaña, Alberto

    2011-06-20

    The relationship between market pricing of new anticancer drugs and the magnitude of clinical benefit caused by them has not been reported. Randomized clinical trials (RCTs) that evaluated approved new agents for solid tumors by the U.S. Food and Drug administration since the year 2000 were assessed. Hazard ratios (HRs) and 95% CIs were extracted for time-to-event end points described for each RCT. HRs were pooled for three groups: agents directed against a specific molecular target, for which the target population is selected by a biomarker (group A); less specific biologic targeted agents (group B); and chemotherapeutic agents (group C). Monthly market prices of these different drugs were compared. For overall survival (OS), the pooled HR was 0.69 (95% CI, 0.59 to 0.81) for group A (six drugs, six trials); it was 0.78 (95% CI, 0.74 to 0.83) for group B (seven drugs, 14 trials); and it was 0.84 (95% CI, 0.79 to 0.90) for group C (eight drugs, 12 trials). For progression-free survival (PFS), the pooled HR was 0.42 (95% CI, 0.36 to 0.49) for group A (six drugs, seven trials); it was 0.57 (95% CI, 0.51 to 0.64) for group B (seven drugs, 14 trials); and it was 0.75 (95% CI, 0.66 to 0.85) for group C (six drugs, 10 trials). Tests for heterogeneity between subgroups were highly significant for PFS (P targets are clinically the most beneficial, but their monthly market prices are not significantly different from those of other anticancer agents.

  11. Virus-encoded chemokine receptors--putative novel antiviral drug targets

    DEFF Research Database (Denmark)

    Rosenkilde, Mette M

    2005-01-01

    Large DNA viruses, in particular herpes- and poxviruses, have evolved proteins that serve as mimics or decoys for endogenous proteins in the host. The chemokines and their receptors serve key functions in both innate and adaptive immunity through control of leukocyte trafficking, and have...... receptors belong to the superfamily of G-protein coupled 7TM receptors that per se are excellent drug targets. At present, non-peptide antagonists have been developed against many chemokine receptors. The potentials of the virus-encoded chemokine receptors as drug targets--ie. as novel antiviral strategies...

  12. Drug target ontology to classify and integrate drug discovery data

    DEFF Research Database (Denmark)

    Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande

    2017-01-01

    using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem...... of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target...... characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein...

  13. Drug Delivery and Cosmeceutical Applications of Poly- Lactic Acid Based Novel Constructs - A Review.

    Science.gov (United States)

    Ruiz-Ruiz, Federico; Mancera-Andrade, Elena Ivonne; Parra-Saldivar, Roberto; Keshavarz, Tajalli; Iqbal, Hafiz M N

    2017-01-01

    Poly (lactic acid) (PLA) based novel constructs have been engineered for targeted applications in various biomedical sectors of the modern world. In this context, a special focus has been given to pharmaceutical and cosmeceutical industries. In this review, we extensively reviewed, analyzed and compiled salient information from the authentic bibliographic sources including PubMed, Scopus, Elsevier, Springer, Bentham Science and other scientific databases. A focused review question and inclusion/exclusion criterion were adopted to appraise the quality of retrieved peer-reviewed research literature. Recently, bio-based constructs are being engineered for target applications in different bio- and non-bio sectors of the modern world to address the growing human health-related serious concerns. The utilization of properly designed and structured materials thus allows the creation of a well-defined environment that induces a series of directed measures, and so on. Over the last few years, PLA-based novel constructs have received exceptional attention as potential candidates for various biotechnological and biomedical applications at large and drug delivery in particular. Owing to their unique characteristics including biocompatibility, together with the adjustable thermomechanical and tunable control drug release, PLA has raised interesting applications in many sectors of the medical world. So far, many of such PLA-based bio-constructs have been exploited in drug delivery systems, cosmeceutical products, and therapeutic uses. In recent years, many new applications have been reported for PLA-based materials at the micro- and nano- level, resulting in novel requests for specific drug delivery and cosmeceutical sectors. In summary, this review summarizes recent research on different aspects of PLA and PLA-based novel constructs and their potential biomedical applications. Moreover, with the aid of nanotechnology, PLA has made a positive impact in emerging sectors such as

  14. Self-assembled silk sericin/poloxamer nanoparticles as nanocarriers of hydrophobic and hydrophilic drugs for targeted delivery

    International Nuclear Information System (INIS)

    Mandal, Biman B; Kundu, S C

    2009-01-01

    In recent times self-assembled micellar nanoparticles have been successfully employed in tissue engineering for targeted drug delivery applications. In this review, silk sericin protein from non-mulberry Antheraea mylitta tropical tasar silk cocoons was blended with pluronic F-127 and F-87 in the presence of solvents to achieve self-assembled micellar nanostructures capable of carrying both hydrophilic (FITC-inulin) and hydrophobic (anticancer drug paclitaxel) drugs. The fabricated nanoparticles were subsequently characterized for their size distribution, drug loading capability, cellular uptake and cytotoxicity. Nanoparticle sizes ranged between 100 and 110 nm in diameter as confirmed by dynamic light scattering. Rapid uptake of these particles into cells was observed in in vitro cellular uptake studies using breast cancer MCF-7 cells. In vitro cytotoxicity assay using paclitaxel-loaded nanoparticles against breast cancer cells showed promising results comparable to free paclitaxel drugs. Drug-encapsulated nanoparticle-induced apoptosis in MCF-7 cells was confirmed by FACS and confocal microscopic studies using Annexin V staining. Up-regulation of pro-apoptotic protein Bax, down-regulation of anti-apoptotic protein Bcl-2 and cleavage of regulatory protein PARP through Western blot analysis suggested further drug-induced apoptosis in cells. This study projects silk sericin protein as an alternative natural biomaterial for fabrication of self-assembled nanoparticles in the presence of poloxamer for successful delivery of both hydrophobic and hydrophilic drugs to target sites.

  15. Self-assembled silk sericin/poloxamer nanoparticles as nanocarriers of hydrophobic and hydrophilic drugs for targeted delivery

    Energy Technology Data Exchange (ETDEWEB)

    Mandal, Biman B; Kundu, S C, E-mail: kundu@hijli.iitkgp.ernet.i [Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302 (India)

    2009-09-02

    In recent times self-assembled micellar nanoparticles have been successfully employed in tissue engineering for targeted drug delivery applications. In this review, silk sericin protein from non-mulberry Antheraea mylitta tropical tasar silk cocoons was blended with pluronic F-127 and F-87 in the presence of solvents to achieve self-assembled micellar nanostructures capable of carrying both hydrophilic (FITC-inulin) and hydrophobic (anticancer drug paclitaxel) drugs. The fabricated nanoparticles were subsequently characterized for their size distribution, drug loading capability, cellular uptake and cytotoxicity. Nanoparticle sizes ranged between 100 and 110 nm in diameter as confirmed by dynamic light scattering. Rapid uptake of these particles into cells was observed in in vitro cellular uptake studies using breast cancer MCF-7 cells. In vitro cytotoxicity assay using paclitaxel-loaded nanoparticles against breast cancer cells showed promising results comparable to free paclitaxel drugs. Drug-encapsulated nanoparticle-induced apoptosis in MCF-7 cells was confirmed by FACS and confocal microscopic studies using Annexin V staining. Up-regulation of pro-apoptotic protein Bax, down-regulation of anti-apoptotic protein Bcl-2 and cleavage of regulatory protein PARP through Western blot analysis suggested further drug-induced apoptosis in cells. This study projects silk sericin protein as an alternative natural biomaterial for fabrication of self-assembled nanoparticles in the presence of poloxamer for successful delivery of both hydrophobic and hydrophilic drugs to target sites.

  16. Off-target effects of psychoactive drugs revealed by genome-wide assays in yeast.

    Directory of Open Access Journals (Sweden)

    Elke Ericson

    2008-08-01

    Full Text Available To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac interfered with establishment of cell polarity, cyproheptadine (Periactin targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol and pimozide (Orap. Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.

  17. A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity

    Directory of Open Access Journals (Sweden)

    Ted G Laderas

    2015-12-01

    Full Text Available Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, among other cancer hallmarks. High throughput omics techniques are used in precision medicine, allowing identification of these mutations with the goal of identifying treatments that target them. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to its dysregulation, a new genomic feature that we term surrogate oncogenes. By mapping mutations to a protein/protein interaction network, we can determine significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified significant surrogate oncogenes in oncogenes such as BRCA1 and ESR1. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations on an individual level. Our model has the potential for integrating patient-unique mutations in predicting drug-sensitivity, suggesting a potential new direction in precision medicine, as well as a new approach for drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers within the Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue types.

  18. Vaccines targeting drugs of abuse: is the glass half-empty or half-full?

    Science.gov (United States)

    Janda, Kim D; Treweek, Jennifer B

    2011-12-16

    The advent of vaccines targeting drugs of abuse heralded a fundamentally different approach to treating substance-related disorders. In contrast to traditional pharmacotherapies for drug abuse, vaccines act by sequestering circulating drugs and terminating the drug-induced 'high' without inducing unwanted neuromodulatory effects. Drug-targeting vaccines have entered clinical evaluation, and although these vaccines show promise from a biomedical viewpoint, the ethical and socioeconomic implications of vaccinating patients against drugs of abuse merit discussion within the scientific community.

  19. Transferrin-conjugated magnetic dextran-spermine nanoparticles for targeted drug transport across blood-brain barrier.

    Science.gov (United States)

    Ghadiri, Maryam; Vasheghani-Farahani, Ebrahim; Atyabi, Fatemeh; Kobarfard, Farzad; Mohamadyar-Toupkanlou, Farzaneh; Hosseinkhani, Hossein

    2017-10-01

    Application of many vital hydrophilic medicines have been restricted by blood-brain barrier (BBB) for treatment of brain diseases. In this study, a targeted drug delivery system based on dextran-spermine biopolymer was developed for drug transport across BBB. Drug loaded magnetic dextran-spermine nanoparticles (DS-NPs) were prepared via ionic gelation followed by transferrin (Tf) conjugation as targeting moiety. The characteristics of Tf conjugated nanoparticles (TDS-NPs) were analyzed by different methods and their cytotoxicity effects on U87MG cells were tested. The superparamagnetic characteristic of TDS-NPs was verified by vibration simple magnetometer. Capecitabine loaded TDS-NPs exhibited pH-sensitive release behavior with enhanced cytotoxicity against U87MG cells, compared to DS-NPs and free capecitabine. Prussian-blue staining and TEM-imaging showed the significant cellular uptake of TDS-NPs. Furthermore, a remarkable increase of Fe concentrations in brain was observed following their biodistribution and histological studies in vivo, after 1 and 7 days of post-injection. Enhanced drug transport across BBB and pH-triggered cellular uptake of TDS-NPs indicated that these theranostic nanocarriers are promising candidate for the brain malignance treatment. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 2851-2864, 2017. © 2017 Wiley Periodicals, Inc.

  20. Relationship among reaction rate, release rate and efficiency of nanomachine-based targeted drug delivery.

    Science.gov (United States)

    Zhao, Qingying; Li, Min; Luo, Jun

    2017-12-04

    In nanomachine applications towards targeted drug delivery, drug molecules released by nanomachines propagate and chemically react with tumor cells in aqueous environment. If the nanomachines release drug molecules faster than the tumor cells react, it will result in loss and waste of drug molecules. It is a potential issue associated with the relationship among reaction rate, release rate and efficiency. This paper aims to investigate the relationship among reaction rate, release rate and efficiency based on two drug reception models. We expect to pave a way for designing a control method of drug release. We adopted two analytical methods that one is drug reception process based on collision with tumors and another is based on Michaelis Menten enzymatic kinetics. To evaluate the analytical formulations, we used the well-known simulation framework N3Sim to establish simulations. The analytical results of the relationship among reaction rate, release rate and efficiency is obtained, which match well with the numerical simulation results in a 3-D environment. Based upon two drug reception models, the results of this paper would be beneficial for designing a control method of nanomahine-based drug release.

  1. The Nicotinic Acetylcholine Receptor as a Target for Antidepressant Drug Development

    Directory of Open Access Journals (Sweden)

    Noah S. Philip

    2012-01-01

    Full Text Available An important new area of antidepressant drug development involves targeting the nicotinic acetylcholine receptor (nAChR. This receptor, which is distributed widely in regions of the brain associated with depression, is also implicated in other important processes that are relevant to depression, such as stress and inflammation. The two classes of drugs that target nAChRs can be broadly divided into mecamylamine- and cytisine-based compounds. These drugs probably exert their effects via antagonism at α4β2 nAChRs, and strong preclinical data support the antidepressant efficacy of both classes when used in conjunction with other primary antidepressants (e.g., monoamine reuptake inhibitors. Although clinical data remain limited, preliminary results in this area constitute a compelling argument for further evaluation of the nAChR as a target for future antidepressant drug development.

  2. The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness.

    Science.gov (United States)

    Huskey, Richard; Mangus, J Michael; Turner, Benjamin O; Weber, René

    2017-12-01

    While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. © The Author (2017). Published by Oxford University Press.

  3. Quantification of pancreatic cancer proteome and phosphorylome: indicates molecular events likely contributing to cancer and activity of drug targets.

    Directory of Open Access Journals (Sweden)

    David Britton

    Full Text Available LC-MS/MS phospho-proteomics is an essential technology to help unravel the complex molecular events that lead to and propagate cancer. We have developed a global phospho-proteomic workflow to determine activity of signaling pathways and drug targets in pancreatic cancer tissue for clinical application.Peptides resulting from tryptic digestion of proteins extracted from frozen tissue of pancreatic ductal adenocarcinoma and background pancreas (n = 12, were labelled with tandem mass tags (TMT 8-plex, separated by strong cation exchange chromatography, then were analysed by LC-MS/MS directly or first enriched for phosphopeptides using IMAC and TiO2, prior to analysis. In-house, commercial and freeware bioinformatic platforms were used to identify relevant biological events from the complex dataset.Of 2,101 proteins identified, 152 demonstrated significant difference in abundance between tumor and non-tumor tissue. They included proteins that are known to be up-regulated in pancreatic cancer (e.g. Mucin-1, but the majority were new candidate markers such as HIPK1 & MLCK. Of the 6,543 unique phosphopeptides identified (6,284 unique phosphorylation sites, 635 showed significant regulation, particularly those from proteins involved in cell migration (Rho guanine nucleotide exchange factors & MRCKα and formation of focal adhesions. Activator phosphorylation sites on FYN, AKT1, ERK2, HDAC1 and other drug targets were found to be highly modulated (≥2 fold in different cases highlighting their predictive power.Here we provided critical information enabling us to identify the common and unique molecular events likely contributing to cancer in each case. Such information may be used to help predict more bespoke therapy suitable for an individual case.

  4. Quantification of pancreatic cancer proteome and phosphorylome: indicates molecular events likely contributing to cancer and activity of drug targets.

    Science.gov (United States)

    Britton, David; Zen, Yoh; Quaglia, Alberto; Selzer, Stefan; Mitra, Vikram; Löβner, Christopher; Jung, Stephan; Böhm, Gitte; Schmid, Peter; Prefot, Petra; Hoehle, Claudia; Koncarevic, Sasa; Gee, Julia; Nicholson, Robert; Ward, Malcolm; Castellano, Leandro; Stebbing, Justin; Zucht, Hans Dieter; Sarker, Debashis; Heaton, Nigel; Pike, Ian

    2014-01-01

    LC-MS/MS phospho-proteomics is an essential technology to help unravel the complex molecular events that lead to and propagate cancer. We have developed a global phospho-proteomic workflow to determine activity of signaling pathways and drug targets in pancreatic cancer tissue for clinical application. Peptides resulting from tryptic digestion of proteins extracted from frozen tissue of pancreatic ductal adenocarcinoma and background pancreas (n = 12), were labelled with tandem mass tags (TMT 8-plex), separated by strong cation exchange chromatography, then were analysed by LC-MS/MS directly or first enriched for phosphopeptides using IMAC and TiO2, prior to analysis. In-house, commercial and freeware bioinformatic platforms were used to identify relevant biological events from the complex dataset. Of 2,101 proteins identified, 152 demonstrated significant difference in abundance between tumor and non-tumor tissue. They included proteins that are known to be up-regulated in pancreatic cancer (e.g. Mucin-1), but the majority were new candidate markers such as HIPK1 & MLCK. Of the 6,543 unique phosphopeptides identified (6,284 unique phosphorylation sites), 635 showed significant regulation, particularly those from proteins involved in cell migration (Rho guanine nucleotide exchange factors & MRCKα) and formation of focal adhesions. Activator phosphorylation sites on FYN, AKT1, ERK2, HDAC1 and other drug targets were found to be highly modulated (≥2 fold) in different cases highlighting their predictive power. Here we provided critical information enabling us to identify the common and unique molecular events likely contributing to cancer in each case. Such information may be used to help predict more bespoke therapy suitable for an individual case.

  5. Contrast ultrasound targeted treatment of gliomas in mice via drug-bearing nanoparticle delivery and microvascular ablation.

    Science.gov (United States)

    Burke, Caitlin W; Price, Richard J

    2010-12-15

    We are developing minimally-invasive contrast agent microbubble based therapeutic approaches in which the permeabilization and/or ablation of the microvasculature are controlled by varying ultrasound pulsing parameters. Specifically, we are testing whether such approaches may be used to treat malignant brain tumors through drug delivery and microvascular ablation. Preliminary studies have been performed to determine whether targeted drug-bearing nanoparticle delivery can be facilitated by the ultrasound mediated destruction of "composite" delivery agents comprised of 100nm poly(lactide-co-glycolide) (PLAGA) nanoparticles that are adhered to albumin shelled microbubbles. We denote these agents as microbubble-nanoparticle composite agents (MNCAs). When targeted to subcutaneous C6 gliomas with ultrasound, we observed an immediate 4.6-fold increase in nanoparticle delivery in MNCA treated tumors over tumors treated with microbubbles co-administered with nanoparticles and a 8.5 fold increase over non-treated tumors. Furthermore, in many cancer applications, we believe it may be desirable to perform targeted drug delivery in conjunction with ablation of the tumor microcirculation, which will lead to tumor hypoxia and apoptosis. To this end, we have tested the efficacy of non-theramal cavitation-induced microvascular ablation, showing that this approach elicits tumor perfusion reduction, apoptosis, significant growth inhibition, and necrosis. Taken together, these results indicate that our ultrasound-targeted approach has the potential to increase therapeutic efficiency by creating tumor necrosis through microvascular ablation and/or simultaneously enhancing the drug payload in gliomas.

  6. Computer Aided Drug Design: Success and Limitations.

    Science.gov (United States)

    Baig, Mohammad Hassan; Ahmad, Khurshid; Roy, Sudeep; Ashraf, Jalaluddin Mohammad; Adil, Mohd; Siddiqui, Mohammad Haris; Khan, Saif; Kamal, Mohammad Amjad; Provazník, Ivo; Choi, Inho

    2016-01-01

    Over the last few decades, computer-aided drug design has emerged as a powerful technique playing a crucial role in the development of new drug molecules. Structure-based drug design and ligand-based drug design are two methods commonly used in computer-aided drug design. In this article, we discuss the theory behind both methods, as well as their successful applications and limitations. To accomplish this, we reviewed structure based and ligand based virtual screening processes. Molecular dynamics simulation, which has become one of the most influential tool for prediction of the conformation of small molecules and changes in their conformation within the biological target, has also been taken into account. Finally, we discuss the principles and concepts of molecular docking, pharmacophores and other methods used in computer-aided drug design.

  7. Many particle magnetic dipole-dipole and hydrodynamic interactions in magnetizable stent assisted magnetic drug targeting

    International Nuclear Information System (INIS)

    Cregg, P.J.; Murphy, Kieran; Mardinoglu, Adil; Prina-Mello, Adriele

    2010-01-01

    The implant assisted magnetic targeted drug delivery system of Aviles, Ebner and Ritter is considered both experimentally (in vitro) and theoretically. The results of a 2D mathematical model are compared with 3D experimental results for a magnetizable wire stent. In this experiment a ferromagnetic, coiled wire stent is implanted to aid collection of particles which consist of single domain magnetic nanoparticles (radius ∼10nm). In order to model the agglomeration of particles known to occur in this system, the magnetic dipole-dipole and hydrodynamic interactions for multiple particles are included. Simulations based on this mathematical model were performed using open source C++ code. Different initial positions are considered and the system performance is assessed in terms of collection efficiency. The results of this model show closer agreement with the measured in vitro experimental results and with the literature. The implications in nanotechnology and nanomedicine are based on the prediction of the particle efficiency, in conjunction with the magnetizable stent, for targeted drug delivery.

  8. Blueprint for antimicrobial hit discovery targeting metabolic networks.

    Science.gov (United States)

    Shen, Y; Liu, J; Estiu, G; Isin, B; Ahn, Y-Y; Lee, D-S; Barabási, A-L; Kapatral, V; Wiest, O; Oltvai, Z N

    2010-01-19

    Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.

  9. Multifunctional Polymer Nanoparticles for Dual Drug Release and Cancer Cell Targeting

    Directory of Open Access Journals (Sweden)

    Yu-Han Wen

    2017-06-01

    Full Text Available Multifunctional polymer nanoparticles have been developed for cancer treatment because they could be easily designed to target cancer cells and to enhance therapeutic efficacy according to cancer hallmarks. In this study, we synthesized a pH-sensitive polymer, poly(methacrylic acid-co-histidine/doxorubicin/biotin (HBD in which doxorubicin (DOX was conjugated by a hydrazone bond to encapsulate an immunotherapy drug, imiquimod (IMQ, to form dual cancer-targeting and dual drug-loaded nanoparticles. At low pH, polymeric nanoparticles could disrupt and simultaneously release DOX and IMQ. Our experimental results show that the nanoparticles exhibited pH-dependent drug release behavior and had an ability to target cancer cells via biotin and protonated histidine.

  10. Combinatorial Approaches for the Identification of Brain Drug Delivery Targets

    Science.gov (United States)

    Stutz, Charles C.; Zhang, Xiaobin; Shusta, Eric V.

    2018-01-01

    The blood-brain barrier (BBB) represents a large obstacle for the treatment of central nervous system diseases. Targeting endogenous nutrient transporters that transcytose the BBB is one promising approach to selectively and noninvasively deliver a drug payload to the brain. The main limitations of the currently employed transcytosing receptors are their ubiquitous expression in the peripheral vasculature and the inherent low levels of transcytosis mediated by such systems. In this review, approaches designed to increase the repertoire of transcytosing receptors which can be targeted for the purpose of drug delivery are discussed. In particular, combinatorial protein libraries can be screened on BBB cells in vitro or in vivo to isolate targeting peptides or antibodies that can trigger transcytosis. Once these targeting reagents are discovered, the cognate BBB transcytosis system can be identified using techniques such as expression cloning or immunoprecipitation coupled with mass spectrometry. Continued technological advances in BBB genomics and proteomics, membrane protein manipulation, and in vitro BBB technology promise to further advance the capability to identify and optimize peptides and antibodies capable of mediating drug transport across the BBB. PMID:23789958

  11. Drug Target Identification and Elucidation of Natural Inhibitors for : An Study

    Directory of Open Access Journals (Sweden)

    Surya Narayan Rath

    2016-12-01

    Full Text Available Environmental microbes like Bordetella petrii has been established as a causative agent for various infectious diseases in human. Again, development of drug resistance in B. petrii challenged to combat against the infection. Identification of potential drug target and proposing a novel lead compound against the pathogen has a great aid and value. In this study, bioinformatics tools and technology have been applied to suggest a potential drug target by screening the proteome information of B. petrii DSM 12804 (accession No. PRJNA28135 from genome database of National Centre for Biotechnology information. In this regards, the inhibitory effect of nine natural compounds like ajoene (Allium sativum, allicin (A. sativum, cinnamaldehyde (Cinnamomum cassia, curcumin (Curcuma longa, gallotannin (active component of green tea and red wine, isoorientin (Anthopterus wardii, isovitexin (A. wardii, neral (Melissa officinalis, and vitexin (A. wardii have been acknowledged with anti-bacterial properties and hence tested against identified drug target of B. petrii by implicating computational approach. The in silico studies revealed the hypothesis that lpxD could be a potential drug target and with recommendation of a strong inhibitory effect of selected natural compounds against infection caused due to B. petrii, would be further validated through in vitro experiments.

  12. PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.

    Science.gov (United States)

    Hussein, Hiba Abi; Borrel, Alexandre; Geneix, Colette; Petitjean, Michel; Regad, Leslie; Camproux, Anne-Claude

    2015-07-01

    Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose 'PockDrug-Server' to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  13. Peptide and low molecular weight proteins based kidney targeted drug delivery systems.

    Science.gov (United States)

    Xu, Pengfei; Zhang, Hailiang; Dang, Ruili; Jiang, Pei

    2018-05-30

    Renal disease is a worldwide public health problem, and unfortunately, the therapeutic index of regular drugs is limited. Thus, it is a great need to develop effective treatment strategies. Among the reported strategies, kidney-targeted drug delivery system is a promising method to increase renal efficacy and reduce extra-renal toxicity. In recent years, working as vehicles for targeted drug delivery, low molecular weight proteins (LMWP) and peptide have received immense attention due to their many advantages, such as selective accumulation in kidney, high drug loading capability, control over routes of biodegradation, convenience in modification at the amino terminus, and good biocompatibility. In this review, we describe the current LMWP and peptide carriers for kidney targeted drug delivery systems. In addition, we discuss different linking strategies between carriers and drugs. Furthermore, we briefly outline the current status and attempt to give an outlook on the further study. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  14. PCSK9: Regulation and Target for Drug Development for Dyslipidemia.

    Science.gov (United States)

    Burke, Amy C; Dron, Jacqueline S; Hegele, Robert A; Huff, Murray W

    2017-01-06

    Proprotein convertase subtilisin/kexin type-9 (PCSK9) is a secreted zymogen expressed primarily in the liver. PCSK9 circulates in plasma, binds to cell surface low-density lipoprotein (LDL) receptors, is internalized, and then targets the receptors to lysosomal degradation. Studies of naturally occurring PCSK9 gene variants that caused extreme plasma LDL cholesterol (LDL-C) deviations and altered atherosclerosis risk unleashed a torrent of biological and pharmacological research. Rapid progress in understanding the physiological regulation of PCSK9 was soon translated into commercially available biological inhibitors of PCSK9 that reduced LDL-C levels and likely also cardiovascular outcomes. Here we review the swift evolution of PCSK9 from novel gene to drug target, to animal and human testing, and finally to outcome trials and clinical applications. In addition, we explore how the genetics-guided path to PCSK9 inhibitor development exemplifies a new paradigm in pharmacology. Finally, we consider some potential challenges as PCSK9 inhibition becomes established in the clinic.

  15. Reverse screening methods to search for the protein targets of chemopreventive compounds

    Science.gov (United States)

    Huang, Hongbin; Zhang, Guigui; Zhou, Yuquan; Lin, Chenru; Chen, Suling; Lin, Yutong; Mai, Shangkang; Huang, Zunnan

    2018-05-01

    This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and

  16. Artificial neural network study on organ-targeting peptides

    Science.gov (United States)

    Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

    2010-01-01

    We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

  17. Antibody-drug conjugates: Promising and efficient tools for targeted cancer therapy.

    Science.gov (United States)

    Nasiri, Hadi; Valedkarimi, Zahra; Aghebati-Maleki, Leili; Majidi, Jafar

    2018-09-01

    Over the recent decades, the use of antibody-drug conjugates (ADCs) has led to a paradigm shift in cancer chemotherapy. Antibody-based treatment of various human tumors has presented dramatic efficacy and is now one of the most promising strategies used for targeted therapy of patients with a variety of malignancies, including hematological cancers and solid tumors. Monoclonal antibodies (mAbs) are able to selectively deliver cytotoxic drugs to tumor cells, which express specific antigens on their surface, and has been suggested as a novel category of agents for use in the development of anticancer targeted therapies. In contrast to conventional treatments that cause damage to healthy tissues, ADCs use mAbs to specifically attach to antigens on the surface of target cells and deliver their cytotoxic payloads. The therapeutic success of future ADCs depends on closely choosing the target antigen, increasing the potency of the cytotoxic cargo, improving the properties of the linker, and reducing drug resistance. If appropriate solutions are presented to address these issues, ADCs will play a more important role in the development of targeted therapeutics against cancer in the next years. We review the design of ADCs, and focus on how ADCs can be exploited to overcome multiple drug resistance (MDR). © 2018 Wiley Periodicals, Inc.

  18. Meningococcal disease and future drug targets

    DEFF Research Database (Denmark)

    Gammelgaard, L K; Colding, H; Hartzen, S H

    2011-01-01

    recent data and current knowledge on molecular mechanisms of meningococcal disease and explains how host immune responses ultimately may aggravate neuropathology and the clinical prognosis. Within this context, particular importance is paid to the endotoxic components that provide potential drug targets...... for novel neuroprotective adjuvants, which are needed in order to improve the clinical management of meningoencephalitis and patient prognosis....

  19. HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

    Science.gov (United States)

    Ahmadi, Hamed; Ahmadi, Ali; Azimzadeh-Jamalkandi, Sadegh; Shoorehdeli, Mahdi Aliyari; Salehzadeh-Yazdi, Ali; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

    2013-02-01

    MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates. Copyright © 2012 Elsevier Inc. All rights reserved.

  20. Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts.

    Science.gov (United States)

    Liggi, Sonia; Drakakis, Georgios; Koutsoukas, Alexios; Cortes-Ciriano, Isidro; Martínez-Alonso, Patricia; Malliavin, Thérèse E; Velazquez-Campoy, Adrian; Brewerton, Suzanne C; Bodkin, Michael J; Evans, David A; Glen, Robert C; Carrodeguas, José Alberto; Bender, Andreas

    2014-01-01

    An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.

  1. The History of Target-Controlled Infusion

    NARCIS (Netherlands)

    Struys, Michel M. R. F.; De Smet, Tom; Glen, John (Iain) B.; Vereecke, Hugo E. M.; Absalom, Anthony R.; Schnider, Thomas W.

    Target-controlled infusion (TCI) is a technique of infusing IV drugs to achieve a user-defined predicted (target) drug concentration in a specific body compartment or tissue of interest. In this review, we describe the pharmacokinetic principles of TCI, the development of TCI systems, and technical

  2. Pharmacokinetics in Drug Discovery: An Exposure-Centred Approach to Optimising and Predicting Drug Efficacy and Safety.

    Science.gov (United States)

    Reichel, Andreas; Lienau, Philip

    2016-01-01

    The role of pharmacokinetics (PK) in drug discovery is to support the optimisation of the absorption, distribution, metabolism and excretion (ADME) properties of lead compounds with the ultimate goal to attain a clinical candidate which achieves a concentration-time profile in the body that is adequate for the desired efficacy and safety profile. A thorough characterisation of the lead compounds aiming at the identification of the inherent PK liabilities also includes an early generation of PK/PD relationships linking in vitro potency and target exposure/engagement with expression of pharmacological activity (mode-of-action) and efficacy in animal studies. The chapter describes an exposure-centred approach to lead generation, lead optimisation and candidate selection and profiling that focuses on a stepwise generation of an understanding between PK/exposure and PD/efficacy relationships by capturing target exposure or surrogates thereof and cellular mode-of-action readouts in vivo. Once robust PK/PD relationship in animal PD models has been constructed, it is translated to anticipate the pharmacologically active plasma concentrations in patients and the human therapeutic dose and dosing schedule which is also based on the prediction of the PK behaviour in human as described herein. The chapter outlines how the level of confidence in the predictions increases with the level of understanding of both the PK and the PK/PD of the new chemical entities (NCE) in relation to the disease hypothesis and the ability to propose safe and efficacious doses and dosing schedules in responsive patient populations. A sound identification of potential drug metabolism and pharmacokinetics (DMPK)-related development risks allows proposing of an effective de-risking strategy for the progression of the project that is able to reduce uncertainties and to increase the probability of success during preclinical and clinical development.

  3. Prediction methodologies for target scene generation in the aerothermal targets analysis program (ATAP)

    Science.gov (United States)

    Hudson, Douglas J.; Torres, Manuel; Dougherty, Catherine; Rajendran, Natesan; Thompson, Rhoe A.

    2003-09-01

    The Air Force Research Laboratory (AFRL) Aerothermal Targets Analysis Program (ATAP) is a user-friendly, engineering-level computational tool that features integrated aerodynamics, six-degree-of-freedom (6-DoF) trajectory/motion, convective and radiative heat transfer, and thermal/material response to provide an optimal blend of accuracy and speed for design and analysis applications. ATAP is sponsored by the Kinetic Kill Vehicle Hardware-in-the-Loop Simulator (KHILS) facility at Eglin AFB, where it is used with the CHAMP (Composite Hardbody and Missile Plume) technique for rapid infrared (IR) signature and imagery predictions. ATAP capabilities include an integrated 1-D conduction model for up to 5 in-depth material layers (with options for gaps/voids with radiative heat transfer), fin modeling, several surface ablation modeling options, a materials library with over 250 materials, options for user-defined materials, selectable/definable atmosphere and earth models, multiple trajectory options, and an array of aerodynamic prediction methods. All major code modeling features have been validated with ground-test data from wind tunnels, shock tubes, and ballistics ranges, and flight-test data for both U.S. and foreign strategic and theater systems. Numerous applications include the design and analysis of interceptors, booster and shroud configurations, window environments, tactical missiles, and reentry vehicles.

  4. A Bioinorganic Approach to Fragment-Based Drug Discovery Targeting Metalloenzymes.

    Science.gov (United States)

    Cohen, Seth M

    2017-08-15

    Metal-dependent enzymes (i.e., metalloenzymes) make up a large fraction of all enzymes and are critically important in a wide range of biological processes, including DNA modification, protein homeostasis, antibiotic resistance, and many others. Consequently, metalloenzymes represent a vast and largely untapped space for drug development. The discovery of effective therapeutics that target metalloenzymes lies squarely at the interface of bioinorganic and medicinal chemistry and requires expertise, methods, and strategies from both fields to mount an effective campaign. In this Account, our research program that brings together the principles and methods of bioinorganic and medicinal chemistry are described, in an effort to bridge the gap between these fields and address an important class of medicinal targets. Fragment-based drug discovery (FBDD) is an important drug discovery approach that is particularly well suited for metalloenzyme inhibitor development. FBDD uses relatively small but diverse chemical structures that allow for the assembly of privileged molecular collections that focus on a specific feature of the target enzyme. For metalloenzyme inhibition, the specific feature is rather obvious, namely, a metal-dependent active site. Surprisingly, prior to our work, the exploration of diverse molecular fragments for binding the metal active sites of metalloenzymes was largely unexplored. By assembling a modest library of metal-binding pharmacophores (MBPs), we have been able to find lead hits for many metalloenzymes and, from these hits, develop inhibitors that act via novel mechanisms of action. A specific case study on the use of this strategy to identify a first-in-class inhibitor of zinc-dependent Rpn11 (a component of the proteasome) is highlighted. The application of FBDD for the development of metalloenzyme inhibitors has raised several other compelling questions, such as how the metalloenzyme active site influences the coordination chemistry of bound

  5. Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

    Science.gov (United States)

    Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

    2017-11-15

    cell types. Despite this extensive lysosomal sequestration in the individual cells types, the maximal change in the overall predicted tissue Kpu was model input parameters, in particular lysosomal pH and fraction of the cellular volume occupied by the lysosomes, only partially explained discrepancies between observed and predicted Kpu data in the lung. Improved understanding of the system properties, e.g., cell/organelle composition is required to support further development of mechanistic equations for the prediction of drug tissue distribution. Application of this revised mechanistic model is recommended for prediction of Kpu in lysosome-rich tissue to facilitate the advancement of physiologically-based prediction of volume of distribution and drug exposure in the tissues. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Nanotechnology-based polymeric bio(muco)adhesive platforms for controlling drug delivery - properties, methodologies and applications

    International Nuclear Information System (INIS)

    Carvalho, Flavia Chiva; Chorilli, Marlus; Gremiao, Maria Palmira Daflon

    2014-01-01

    Studies using bio(muco)adhesive drug delivery systems have recently gained great interest, which can promote drug targeting and more specific contact of the drug delivery system with the various absorptive membranes of the body. This technological platform associated with nanotechnology offers potential for controlling drug delivery; therefore, they are excellent strategies to increase the bioavailability of drugs. The objective of this work was to study nanotechnology-based polymeric bio(muco)adhesive platforms for controlling drug delivery, highlighting their properties, how the bio(muco)adhesion can be measured and their potential applications for different routes of administration. (author)

  7. Accurate microRNA target prediction correlates with protein repression levels

    Directory of Open Access Journals (Sweden)

    Simossis Victor A

    2009-09-01

    Full Text Available Abstract Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

  8. Inhibition of Glutamine Synthetase: A Potential Drug Target in Mycobacterium tuberculosis

    Directory of Open Access Journals (Sweden)

    Sherry L. Mowbray

    2014-08-01

    Full Text Available Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Globally, tuberculosis is second only to AIDS in mortality and the disease is responsible for over 1.3 million deaths each year. The impractically long treatment schedules (generally 6–9 months and unpleasant side effects of the current drugs often lead to poor patient compliance, which in turn has resulted in the emergence of multi-, extensively- and totally-drug resistant strains. The development of new classes of anti-tuberculosis drugs and new drug targets is of global importance, since attacking the bacterium using multiple strategies provides the best means to prevent resistance. This review presents an overview of the various strategies and compounds utilized to inhibit glutamine synthetase, a promising target for the development of drugs for TB therapy.

  9. Condensational growth of combination drug-excipient submicrometer particles for targeted high efficiency pulmonary delivery: comparison of CFD predictions with experimental results.

    Science.gov (United States)

    Longest, P Worth; Hindle, Michael

    2012-03-01

    The objective of this study was to investigate the hygroscopic growth of combination drug and excipient submicrometer aerosols for respiratory drug delivery using in vitro experiments and a newly developed computational fluid dynamics (CFD) model. Submicrometer combination drug and excipient particles were generated experimentally using both the capillary aerosol generator and the Respimat inhaler. Aerosol hygroscopic growth was evaluated in vitro and with CFD in a coiled tube geometry designed to provide residence times and thermodynamic conditions consistent with the airways. The in vitro results and CFD predictions both indicated that the initially submicrometer particles increased in mean size to a range of 1.6-2.5 μm for the 50:50 combination of a non-hygroscopic drug (budesonide) and different hygroscopic excipients. CFD results matched the in vitro predictions to within 10% and highlighted gradual and steady size increase of the droplets, which will be effective for minimizing extrathoracic deposition and producing deposition deep within the respiratory tract. Enhanced excipient growth (EEG) appears to provide an effective technique to increase pharmaceutical aerosol size, and the developed CFD model will provide a powerful design tool for optimizing this technique to produce high efficiency pulmonary delivery.

  10. Nanodiamond and its application to drug delivery

    Directory of Open Access Journals (Sweden)

    Eiji Osawa

    2012-08-01

    Full Text Available Quasi-spherical diamond crystals having an average diameter of 3.7±0.6 nm are attracting much attention as an ideal material in carbon nanotechnology. In contrast to the other popular nanocarbons including fullerenes, carbon nanotubes and graphenes, our single-nanodiamond can be produced in uniform shape/size on industrial scale. Thus, the most serious problem in nanocarbon industry that persisted in the past 25 years, namely the technical failure to produce highly crystalline nanocarbons in narrow shape/size range does not exist in our diamond from the beginning. Among potential applications of the single-nanodiamond under development, this review concentrates on its highly promising role as a drug carrier, especially for therapeutic-resistant cancer. An interesting possibility of intercalation is proposed as the mechanism of drug transport through blood, which takes into accounts of the spontaneous formation of nanographene layer on the [111] facets, which is then extensively oxidized during oxidative soot removal process to give nanographene oxide partial surface, capable of intercalating drug molecules to prevent them from leaking and causing undesirable side effects during transportation to target malignant cells. A perspective of quantifying the drug delivery process by anticipating orders of magnitude in the number of administered detonation nanodiamond (DND particles is suggested.

  11. EphB1 as a Novel Drug Target to Combat Pain and Addiction

    Science.gov (United States)

    2017-09-01

    AWARD NUMBER: W81XWH-14-1-0220 TITLE: EphB1 as a Novel Drug Target to Combat Pain and Addiction PRINCIPAL INVESTIGATOR: Mark Henkemeyer...as a Novel Drug Target to Combat Pain and Ad 5a. CONTRACT NUMBER EphB1 as a Novel Drug Target to Combat Pain and Addiction 5b. GRANT NUMBER W81XWH...neuronal and has functions in vascular endothelial cells. 6. We have also carried out computational analysis of potantial docking/binding of chemical

  12. Ginger components as new leads for the design and development of novel multi-targeted anti-Alzheimer’s drugs: a computational investigation

    Directory of Open Access Journals (Sweden)

    Azam F

    2014-10-01

    Full Text Available Faizul Azam,1,2 Abdualrahman M Amer,1 Abdullah R Abulifa,1 Mustafa M Elzwawi1 1Faculty of Pharmacy, Misurata University, Misurata, Libya; 2Department of Pharmaceutical Chemistry, Nims Institute of Pharmacy, Nims University, Jaipur, Rajasthan, India Abstract: Ginger (Zingiber officinale, despite being a common dietary adjunct that contributes to the taste and flavor of foods, is well known to contain a number of potentially bioactive phytochemicals having valuable medicinal properties. Although recent studies have emphasized their benefits in Alzheimer’s disease, limited information is available on the possible mechanism by which it renders anti-Alzheimer activity. Therefore, the present study seeks to employ molecular docking studies to investigate the binding interactions between active ginger components and various anti-Alzheimer drug targets. Lamarckian genetic algorithm methodology was employed for docking of 12 ligands with 13 different target proteins using AutoDock 4.2 program. Docking protocol was validated by re-docking of all native co-crystallized ligands into their original binding cavities exhibiting a strong correlation coefficient value (r2=0.931 between experimentally reported and docking predicted activities. This value suggests that the approach could be a promising computational tool to aid optimization of lead compounds obtained from ginger. Analysis of binding energy, predicted inhibition constant, and hydrophobic/hydrophilic interactions of ligands with target receptors revealed acetylcholinesterase as most promising, while c-Jun N-terminal kinase was recognized as the least favorable anti-Alzheimer’s drug target. Common structural requirements include hydrogen bond donor/acceptor area, hydrophobic domain, carbon spacer, and distal hydrophobic domain flanked by hydrogen bond donor/acceptor moieties. In addition, drug-likeness score and molecular properties responsible for a good pharmacokinetic profile were calculated

  13. A smart polymeric platform for multistage nucleus-targeted anticancer drug delivery.

    Science.gov (United States)

    Zhong, Jiaju; Li, Lian; Zhu, Xi; Guan, Shan; Yang, Qingqing; Zhou, Zhou; Zhang, Zhirong; Huang, Yuan

    2015-10-01

    Tumor cell nucleus-targeted delivery of antitumor agents is of great interest in cancer therapy, since the nucleus is one of the most frequent targets of drug action. Here we report a smart polymeric conjugate platform, which utilizes stimulus-responsive strategies to achieve multistage nuclear drug delivery upon systemic administration. The conjugates composed of a backbone based on N-(2-hydroxypropyl) methacrylamide (HPMA) copolymer and detachable nucleus transport sub-units that sensitive to lysosomal enzyme. The sub-units possess a biforked structure with one end conjugated with the model drug, H1 peptide, and the other end conjugated with a novel pH-responsive targeting peptide (R8NLS) that combining the strength of cell penetrating peptide and nuclear localization sequence. The conjugates exhibited prolonged circulation time and excellent tumor homing ability. And the activation of R8NLS in acidic tumor microenvironment facilitated tissue penetration and cellular internalization. Once internalized into the cell, the sub-units were unleashed for nuclear transport through nuclear pore complex. The unique features resulted in 50-fold increase of nuclear drug accumulation relative to the original polymer-drug conjugates in vitro, and excellent in vivo nuclear drug delivery efficiency. Our report provides a strategy in systemic nuclear drug delivery by combining the microenvironment-responsive structure and detachable sub-units. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. A unified frame of predicting side effects of drugs by using linear neighborhood similarity.

    Science.gov (United States)

    Zhang, Wen; Yue, Xiang; Liu, Feng; Chen, Yanlin; Tu, Shikui; Zhang, Xining

    2017-12-14

    Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

  15. Toxins and derivatives in molecular pharmaceutics: Drug delivery and targeted therapy.

    Science.gov (United States)

    Zhan, Changyou; Li, Chong; Wei, Xiaoli; Lu, Wuyuan; Lu, Weiyue

    2015-08-01

    Protein and peptide toxins offer an invaluable source for the development of actively targeted drug delivery systems. They avidly bind to a variety of cognate receptors, some of which are expressed or even up-regulated in diseased tissues and biological barriers. Protein and peptide toxins or their derivatives can act as ligands to facilitate tissue- or organ-specific accumulation of therapeutics. Some toxins have evolved from a relatively small number of structural frameworks that are particularly suitable for addressing the crucial issues of potency and stability, making them an instrumental source of leads and templates for targeted therapy. The focus of this review is on protein and peptide toxins for the development of targeted drug delivery systems and molecular therapies. We summarize disease- and biological barrier-related toxin receptors, as well as targeted drug delivery strategies inspired by those receptors. The design of new therapeutics based on protein and peptide toxins is also discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue

    OpenAIRE

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algori...

  17. Technological advances and proteomic applications in drug discovery and target deconvolution: identification of the pleiotropic effects of statins.

    Science.gov (United States)

    Banfi, Cristina; Baetta, Roberta; Gianazza, Erica; Tremoli, Elena

    2017-06-01

    Proteomic-based techniques provide a powerful tool for identifying the full spectrum of protein targets of a drug, elucidating its mechanism(s) of action, and identifying biomarkers of its efficacy and safety. Herein, we outline the technological advancements in the field, and illustrate the contribution of proteomics to the definition of the pharmacological profile of statins, which represent the cornerstone of the prevention and treatment of cardiovascular diseases (CVDs). Statins act by inhibiting 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase, thus reducing cholesterol biosynthesis and consequently enhancing the clearance of low-density lipoproteins from the blood; however, HMG-CoA reductase inhibition can result in a multitude of additional effects beyond lipid lowering, known as 'pleiotropic effects'. The case of statins highlights the unique contribution of proteomics to the target profiling of a drug molecule. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Physical property control in core/shell inorganic nanostructures for fluorescence and magnetic targeting applications

    Science.gov (United States)

    Roberts, Stephen K.

    Nanomaterials show immense promise for the future in numerous areas of application. Properties that are unique from the bulk material and are tunable allow for innovation in material design. This thesis will focus on controlling the physical properties of core/shell nanostructures to enhance the utility of the materials. The first focus is on the impact of different solvent mixtures during the shell growth phase of SILAR based core/shell quantum dot synthesis is studied. Gaining insight into the mechanism for SILAR growth of core/shell nanoparticles allows improved synthetic yields and precursor binding, providing enhanced control to synthesis of core/shell nanoparticles. The second focus of this thesis is exploring the use of magnetic nanoparticles for magnetic drug targeting for cardiovascular conditions. Magnetic targeting for drug delivery enables increased local drug concentration, while minimizing non-specific interactions. In order to be effective for magnetic targeting, it must be shown that low magnetic strength is sufficient to capture flowing nanoparticles. By demonstrating the binding of a therapeutic agent to the surface at medicinal levels, the viability for use as a nanoparticle drug delivery system is improved.

  19. Multi-targeted therapy for leprosy: insilico strategy to overcome multi drug resistance and to improve therapeutic efficacy.

    Science.gov (United States)

    Anusuya, Shanmugam; Natarajan, Jeyakumar

    2012-12-01

    Leprosy remains a major public health problem, since single and multi-drug resistance has been reported worldwide over the last two decades. In the present study, we report the novel multi-targeted therapy for leprosy to overcome multi drug resistance and to improve therapeutic efficacy. If multiple enzymes of an essential metabolic pathway of a bacterium were targeted, then the therapy would become more effective and can prevent the occurrence of drug resistance. The MurC, MurD, MurE and MurF enzymes of peptidoglycan biosynthetic pathway were selected for multi targeted therapy. The conserved or class specific active site residues important for function or stability were predicted using evolutionary trace analysis and site directed mutagenesis studies. Ten such residues which were present in at least any three of the four Mur enzymes (MurC, MurD, MurE and MurF) were identified. Among the ten residues G125, K126, T127 and G293 (numbered based on their position in MurC) were found to be conserved in all the four Mur enzymes of the entire bacterial kingdom. In addition K143, T144, T166, G168, H234 and Y329 (numbered based on their position in MurE) were significant in binding substrates and/co-factors needed for the functional events in any three of the Mur enzymes. These are the probable residues for designing newer anti-leprosy drugs in an attempt to reduce drug resistance. Copyright © 2012 Elsevier B.V. All rights reserved.

  20. Spherical and cylindrical cavity expansion models based prediction of penetration depths of concrete targets.

    Directory of Open Access Journals (Sweden)

    Xiaochao Jin

    Full Text Available The cavity expansion theory is most widely used to predict the depth of penetration of concrete targets. The main purpose of this work is to clarify the differences between the spherical and cylindrical cavity expansion models and their scope of application in predicting the penetration depths of concrete targets. The factors that influence the dynamic cavity expansion process of concrete materials were first examined. Based on numerical results, the relationship between expansion pressure and velocity was established. Then the parameters in the Forrestal's formula were fitted to have a convenient and effective prediction of the penetration depth. Results showed that both the spherical and cylindrical cavity expansion models can accurately predict the depth of penetration when the initial velocity is lower than 800 m/s. However, the prediction accuracy decreases with the increasing of the initial velocity and diameters of the projectiles. Based on our results, it can be concluded that when the initial velocity is higher than the critical velocity, the cylindrical cavity expansion model performs better than the spherical cavity expansion model in predicting the penetration depth, while when the initial velocity is lower than the critical velocity the conclusion is quite the contrary. This work provides a basic principle for selecting the spherical or cylindrical cavity expansion model to predict the penetration depth of concrete targets.

  1. Targeted and non-targeted drug screening in whole blood by UHPLC-TOF-MS with data-independent acquisition

    DEFF Research Database (Denmark)

    Mollerup, Christian Brinch; Dalsgaard, Petur Weihe; Mardal, Marie

    2017-01-01

    of peaks to inspect by three orders of magnitude, down to four peaks per DUID sample. The screening allowed for tentative identification of metabolites and drugs not included in the initial screening, and three drugs and thirteen metabolites were tentatively identified in the authentic DUID samples......High-resolution mass spectrometry (HRMS) is widely used for the drug screening of biological samples in clinical and forensic laboratories. With the continuous addition of new psychoactive substances (NPS), keeping such methods updated is challenging. HRMS allows for combined targeted and non......-targeted screening; first, peaks are identified by software algorithms, and identifications are based on reference standard data. Remaining unknown peaks are attempted identified with in silico and literature data. However, several thousand peaks remain where most are unidentifiable or uninteresting in drug...

  2. RecA: a universal drug target in pathogenic bacteria.

    Science.gov (United States)

    Pavlopoulou, Athanasia

    2018-01-01

    The spread of bacterial infectious diseases due to the development of resistance to antibiotic drugs in pathogenic bacteria is an emerging global concern. Therefore, the efficacious management and prevention of bacterial infections are major public health challenges. RecA is a pleiotropic recombinase protein that has been demonstrated to be implicated strongly in the bacterial drug resistance, survival and pathogenicity. In this minireview, RecA's role in the development of antibiotic resistance and its potential as an antimicrobial drug target are discussed.

  3. Cavitation damage prediction for the JSNS mercury target vessel

    Energy Technology Data Exchange (ETDEWEB)

    Naoe, Takashi, E-mail: naoe.takashi@jaea.go.jp; Kogawa, Hiroyuki; Wakui, Takashi; Haga, Katsuhiro; Teshigawara, Makoto; Kinoshita, Hidetaka; Takada, Hiroshi; Futakawa, Masatoshi

    2016-01-15

    The liquid mercury target system for the Japan Spallation Neutron Source (JSNS) at the Materials and Life science experimental Facility (MLF) in the Japan Proton Accelerator Research Complex (J-PARC) is designed to produce pulsed neutrons. The mercury target vessel in this system, which is made of type 316L stainless steel, is damaged by pressure wave-induced cavitation due to proton beam bombardment. Currently, cavitation damage is considered to be the dominant factor influencing the service life of the target vessel rather than radiation damage. In this study, cavitation damage to the interior surface of the target vessel was predicted on the basis of accumulated damage data from off-beam and on-beam experiments. The predicted damage was compared with the damage observed in a used target vessel. Furthermore, the effect of injecting gas microbubbles on cavitation damage was predicted through the measurement of the acoustic vibration of the target vessel. It was shown that the predicted depth of cavitation damage is reasonably coincident with the observed results. Moreover, it was confirmed that the injection of gas microbubbles had an effect on cavitation damage.

  4. Multifunctional Nanocarriers for diagnostics, drug delivery and targeted treatment across blood-brain barrier: perspectives on tracking and neuroimaging

    Directory of Open Access Journals (Sweden)

    Estrada Giovani

    2010-03-01

    Full Text Available Abstract Nanotechnology has brought a variety of new possibilities into biological discovery and clinical practice. In particular, nano-scaled carriers have revolutionalized drug delivery, allowing for therapeutic agents to be selectively targeted on an organ, tissue and cell specific level, also minimizing exposure of healthy tissue to drugs. In this review we discuss and analyze three issues, which are considered to be at the core of nano-scaled drug delivery systems, namely functionalization of nanocarriers, delivery to target organs and in vivo imaging. The latest developments on highly specific conjugation strategies that are used to attach biomolecules to the surface of nanoparticles (NP are first reviewed. Besides drug carrying capabilities, the functionalization of nanocarriers also facilitate their transport to primary target organs. We highlight the leading advantage of nanocarriers, i.e. their ability to cross the blood-brain barrier (BBB, a tightly packed layer of endothelial cells surrounding the brain that prevents high-molecular weight molecules from entering the brain. The BBB has several transport molecules such as growth factors, insulin and transferrin that can potentially increase the efficiency and kinetics of brain-targeting nanocarriers. Potential treatments for common neurological disorders, such as stroke, tumours and Alzheimer's, are therefore a much sought-after application of nanomedicine. Likewise any other drug delivery system, a number of parameters need to be registered once functionalized NPs are administered, for instance their efficiency in organ-selective targeting, bioaccumulation and excretion. Finally, direct in vivo imaging of nanomaterials is an exciting recent field that can provide real-time tracking of those nanocarriers. We review a range of systems suitable for in vivo imaging and monitoring of drug delivery, with an emphasis on most recently introduced molecular imaging modalities based on optical

  5. PBPK Modeling - A Predictive, Eco-Friendly, Bio-Waiver Tool for Drug Research.

    Science.gov (United States)

    De, Baishakhi; Bhandari, Koushik; Mukherjee, Ranjan; Katakam, Prakash; Adiki, Shanta K; Gundamaraju, Rohit; Mitra, Analava

    2017-01-01

    The world has witnessed growing complexities in disease scenario influenced by the drastic changes in host-pathogen- environment triadic relation. Pharmaceutical R&Ds are in constant search of novel therapeutic entities to hasten transition of drug molecules from lab bench to patient bedside. Extensive animal studies and human pharmacokinetics are still the "gold standard" in investigational new drug research and bio-equivalency studies. Apart from cost, time and ethical issues on animal experimentation, burning questions arise relating to ecological disturbances, environmental hazards and biodiversity issues. Grave concerns arises when the adverse outcomes of continued studies on one particular disease on environment gives rise to several other pathogenic agents finally complicating the total scenario. Thus Pharma R&Ds face a challenge to develop bio-waiver protocols. Lead optimization, drug candidate selection with favorable pharmacokinetics and pharmacodynamics, toxicity assessment are vital steps in drug development. Simulation tools like Gastro Plus™, PK Sim®, SimCyp find applications for the purpose. Advanced technologies like organ-on-a chip or human-on-a chip where a 3D representation of human organs and systems can mimic the related processes and activities, thereby linking them to major features of human biology can be successfully incorporated in the drug development tool box. PBPK provides the State of Art to serve as an optional of animal experimentation. PBPK models can successfully bypass bio-equivalency studies, predict bioavailability, drug interactions and on hyphenation with in vitro-in vivo correlation can be extrapolated to humans thus serving as bio-waiver. PBPK can serve as an eco-friendly bio-waiver predictive tool in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  6. Structural genomics of infectious disease drug targets: the SSGCID

    International Nuclear Information System (INIS)

    Stacy, Robin; Begley, Darren W.; Phan, Isabelle; Staker, Bart L.; Van Voorhis, Wesley C.; Varani, Gabriele; Buchko, Garry W.; Stewart, Lance J.; Myler, Peter J.

    2011-01-01

    An introduction and overview of the focus, goals and overall mission of the Seattle Structural Genomics Center for Infectious Disease (SSGCID) is given. The Seattle Structural Genomics Center for Infectious Disease (SSGCID) is a consortium of researchers at Seattle BioMed, Emerald BioStructures, the University of Washington and Pacific Northwest National Laboratory that was established to apply structural genomics approaches to drug targets from infectious disease organisms. The SSGCID is currently funded over a five-year period by the National Institute of Allergy and Infectious Diseases (NIAID) to determine the three-dimensional structures of 400 proteins from a variety of Category A, B and C pathogens. Target selection engages the infectious disease research and drug-therapy communities to identify drug targets, essential enzymes, virulence factors and vaccine candidates of biomedical relevance to combat infectious diseases. The protein-expression systems, purified proteins, ligand screens and three-dimensional structures produced by SSGCID constitute a valuable resource for drug-discovery research, all of which is made freely available to the greater scientific community. This issue of Acta Crystallographica Section F, entirely devoted to the work of the SSGCID, covers the details of the high-throughput pipeline and presents a series of structures from a broad array of pathogenic organisms. Here, a background is provided on the structural genomics of infectious disease, the essential components of the SSGCID pipeline are discussed and a survey of progress to date is presented

  7. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

    Science.gov (United States)

    Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

    2017-12-21

    In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

  8. EphB1 as a Novel Drug Target to Combat Pain and Addiction

    Science.gov (United States)

    2015-09-01

    EphB1  as  a  Novel  Drug  Target  to  Combat  Pain  and   Addiction   Principal  Investigator  Name:   Mark...Principal  Investigator   Phone  and  Email:   Phone :  214-­‐645-­‐5916   Email:  Mark.Henkemeyer@UTSouthwestern.edu   Report  Date...31 Aug 2015 4. TITLE AND SUBTITLE EphB1 as a Novel Drug Target to Combat Pain and Addiction 5a. CONTRACT NUMBER EphB1 as a Novel Drug Target to

  9. Cancer therapy with drug loaded magnetic nanoparticles-magnetic drug targeting

    International Nuclear Information System (INIS)

    Alexiou, Christoph; Tietze, Rainer; Schreiber, Eveline; Jurgons, Roland; Richter, Heike; Trahms, Lutz; Rahn, Helene; Odenbach, Stefan; Lyer, Stefan

    2011-01-01

    The aim of magnetic drug targeting (MDT) in cancer therapy is to concentrate chemotherapeutics to a tumor region while simultaneously the overall dose is reduced. This can be achieved with coated superparamagnetic nanoparticles bound to a chemotherapeutic agent. These particles are applied intra arterially close to the tumor region and focused to the tumor by a strong external magnetic field. The interaction of the particles with the field gradient leads to an accumulation in the region of interest (i.e. tumor). The particle enrichment and thereby the drug-load in the tumor during MDT has been proven by several analytical and imaging methods. Moreover, in pilot studies we investigated in an experimental in vivo tumor model the effectiveness of this approach. Complete tumor regressions without any negative side effects could be observed. - Research Highlights: →Iron oxide nanoparticles can be enriched in tumors by external magnetic fields. → Histology evidences the intravasation of particles enter the intracellular space. → Non-invasive imaging techniques can display the spatial arrangement of particles. → HPLC-analysis show outstanding drug enrichment in tumors after MDT.

  10. Cancer therapy with drug loaded magnetic nanoparticles-magnetic drug targeting

    Energy Technology Data Exchange (ETDEWEB)

    Alexiou, Christoph, E-mail: c.alexiou@web.d [Department of Oto-rhino-laryngology, Head and Neck Surgery, University Hospital Erlangen, Section for Experimental Oncology and Nanomedicine at the Else Kroener-Fresenius-Stiftung-Professorship (Germany); Tietze, Rainer; Schreiber, Eveline [Department of Oto-rhino-laryngology, Head and Neck Surgery, University Hospital Erlangen, Section for Experimental Oncology and Nanomedicine at the Else Kroener-Fresenius-Stiftung-Professorship (Germany); Jurgons, Roland [Franz Penzoldt Center, University Hospital Erlangen (Germany); Richter, Heike; Trahms, Lutz [PTB Berlin (Germany); Rahn, Helene; Odenbach, Stefan [TU Dresden, Chair of Magnetofluiddynamics, 01062 Dresden (Germany); Lyer, Stefan [Department of Oto-rhino-laryngology, Head and Neck Surgery, University Hospital Erlangen, Section for Experimental Oncology and Nanomedicine at the Else Kroener-Fresenius-Stiftung-Professorship (Germany)

    2011-05-15

    The aim of magnetic drug targeting (MDT) in cancer therapy is to concentrate chemotherapeutics to a tumor region while simultaneously the overall dose is reduced. This can be achieved with coated superparamagnetic nanoparticles bound to a chemotherapeutic agent. These particles are applied intra arterially close to the tumor region and focused to the tumor by a strong external magnetic field. The interaction of the particles with the field gradient leads to an accumulation in the region of interest (i.e. tumor). The particle enrichment and thereby the drug-load in the tumor during MDT has been proven by several analytical and imaging methods. Moreover, in pilot studies we investigated in an experimental in vivo tumor model the effectiveness of this approach. Complete tumor regressions without any negative side effects could be observed. - Research Highlights: Iron oxide nanoparticles can be enriched in tumors by external magnetic fields. Histology evidences the intravasation of particles enter the intracellular space. Non-invasive imaging techniques can display the spatial arrangement of particles. HPLC-analysis show outstanding drug enrichment in tumors after MDT.

  11. Internal Targeting and External Control: Phototriggered Targeting in Nanomedicine.

    Science.gov (United States)

    Arrue, Lily; Ratjen, Lars

    2017-12-07

    The photochemical control of structure and reactivity bears great potential for chemistry, biology, and life sciences. A key feature of photochemistry is the spatiotemporal control over secondary events. Well-established applications of photochemistry in medicine are photodynamic therapy (PDT) and photopharmacology (PP). However, although both are highly localizable through the application of light, they lack cell- and tissue-specificity. The combination of nanomaterial-based drug delivery and targeting has the potential to overcome limitations for many established therapy concepts. Even more privileged seems the merger of nanomedicine and cell-specific targeting (internal targeting) controlled by light (external control), as it can potentially be applied to many different areas of medicine and pharmaceutical research, including the aforementioned PDT and PP. In this review a survey of the interface of photochemistry, medicine and targeted drug delivery is given, especially focusing on phototriggered targeting in nanomedicine. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Neutrophil targeted nano-drug delivery system for chronic obstructive lung diseases.

    Science.gov (United States)

    Vij, Neeraj; Min, Taehong; Bodas, Manish; Gorde, Aakruti; Roy, Indrajit

    2016-11-01

    The success of drug delivery to target airway cell(s) remains a significant challenge due to the limited ability of nanoparticle (NP) systems to circumvent protective airway-defense mechanisms. The size, density, surface and physical-chemical properties of nanoparticles are the key features that determine their ability to navigate across the airway-barrier. We evaluated here the efficacy of a PEGylated immuno-conjugated PLGA-nanoparticle (PINP) to overcome this challenge and selectively deliver drug to specific inflammatory cells (neutrophils). We first characterized the size, shape, surface-properties and neutrophil targeting using dynamic laser scattering, transmission electron microscopy and flow cytometry. Next, we assessed the efficacy of neutrophil-targeted PINPs in transporting through the airway followed by specific binding and release of drug to neutrophils. Finally, our results demonstrate the efficacy of PINP mediated non-steroidal anti-inflammatory drug-(ibuprofen) delivery to neutrophils in murine models of obstructive lung diseases, based on its ability to control neutrophilic-inflammation and resulting lung disease. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Condensational Growth of Combination Drug-Excipient Submicrometer Particles for Targeted High Efficiency Pulmonary Delivery: Comparison of CFD Predictions with Experimental Results

    Science.gov (United States)

    Hindle, Michael

    2011-01-01

    Purpose The objective of this study was to investigate the hygroscopic growth of combination drug and excipient submicrometer aerosols for respiratory drug delivery using in vitro experiments and a newly developed computational fluid dynamics (CFD) model. Methods Submicrometer combination drug and excipient particles were generated experimentally using both the capillary aerosol generator and the Respimat inhaler. Aerosol hygroscopic growth was evaluated in vitro and with CFD in a coiled tube geometry designed to provide residence times and thermodynamic conditions consistent with the airways. Results The in vitro results and CFD predictions both indicated that the initially submicrometer particles increased in mean size to a range of 1.6–2.5 µm for the 50:50 combination of a non-hygroscopic drug (budesonide) and different hygroscopic excipients. CFD results matched the in vitro predictions to within 10% and highlighted gradual and steady size increase of the droplets, which will be effective for minimizing extrathoracic deposition and producing deposition deep within the respiratory tract. Conclusions Enhanced excipient growth (EEG) appears to provide an effective technique to increase pharmaceutical aerosol size, and the developed CFD model will provide a powerful design tool for optimizing this technique to produce high efficiency pulmonary delivery. PMID:21948458

  14. The application of drug delivery system about nanoparticles in nuclear medicine

    International Nuclear Information System (INIS)

    Yao Ning; Wang Rongfu

    2013-01-01

    The development of nuclear medicine relies on the advancement of precise probes at the cellular and molecular levels. Nanoparticle as a new molecular probe, is mainly consists of the targeting groups, imaging groups, the superb biocompatible 'shells' and the modify groups. These nanoparticles have the better image contrast by targeting positioning in the target tissues and cells. At the same time, because of the diversity of the materials and the uniqueness of the structures, the nanoparticles can realize multimodal imaging at molecular level, which complement each other's advantages of different imaging modals. If the treatment groups are joined into the nanoparticles, a new nanoparticles are formed-the theranosis nanoparticles, which have realized the diagnosis and therapy at the molecular level synchronously. In addition, the application of intelligent nanoprobes can achieve the smart control of drug release and reduce the side effects of cancer treatment. Anyhow, the development of this new drug delivery system about nanoparticles has brought about a new breakthrough on the nuclear medicine. (authors)

  15. Application of optical action potentials in human induced pluripotent stem cells-derived cardiomyocytes to predict drug-induced cardiac arrhythmias.

    Science.gov (United States)

    Lu, H R; Hortigon-Vinagre, M P; Zamora, V; Kopljar, I; De Bondt, A; Gallacher, D J; Smith, G

    2017-09-01

    Human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) are emerging as new and human-relevant source in vitro model for cardiac safety assessment that allow us to investigate a set of 20 reference drugs for predicting cardiac arrhythmogenic liability using optical action potential (oAP) assay. Here, we describe our examination of the oAP measurement using a voltage sensitive dye (Di-4-ANEPPS) to predict adverse compound effects using hiPS-CMs and 20 cardioactive reference compounds. Fluorescence signals were digitized at 10kHz and the records subsequently analyzed off-line. Cells were exposed to 30min incubation to vehicle or compound (n=5/dose, 4 doses/compound) that were blinded to the investigating laboratory. Action potential parameters were measured, including rise time (T rise ) of the optical action potential duration (oAPD). Significant effects on oAPD were sensitively detected with 11 QT-prolonging drugs, while oAPD shortening was observed with I Ca -antagonists, I Kr -activator or ATP-sensitive K + channel (K ATP )-opener. Additionally, the assay detected varied effects induced by 6 different sodium channel blockers. The detection threshold for these drug effects was at or below the published values of free effective therapeutic plasma levels or effective concentrations by other studies. The results of this blinded study indicate that OAP is a sensitive method to accurately detect drug-induced effects (i.e., duration/QT-prolongation, shortening, beat rate, and incidence of early after depolarizations) in hiPS-CMs; therefore, this technique will potentially be useful in predicting drug-induced arrhythmogenic liabilities in early de-risking within the drug discovery phase. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Quantitative PET Imaging in Drug Development: Estimation of Target Occupancy.

    Science.gov (United States)

    Naganawa, Mika; Gallezot, Jean-Dominique; Rossano, Samantha; Carson, Richard E

    2017-12-11

    Positron emission tomography, an imaging tool using radiolabeled tracers in humans and preclinical species, has been widely used in recent years in drug development, particularly in the central nervous system. One important goal of PET in drug development is assessing the occupancy of various molecular targets (e.g., receptors, transporters, enzymes) by exogenous drugs. The current linear mathematical approaches used to determine occupancy using PET imaging experiments are presented. These algorithms use results from multiple regions with different target content in two scans, a baseline (pre-drug) scan and a post-drug scan. New mathematical estimation approaches to determine target occupancy, using maximum likelihood, are presented. A major challenge in these methods is the proper definition of the covariance matrix of the regional binding measures, accounting for different variance of the individual regional measures and their nonzero covariance, factors that have been ignored by conventional methods. The novel methods are compared to standard methods using simulation and real human occupancy data. The simulation data showed the expected reduction in variance and bias using the proper maximum likelihood methods, when the assumptions of the estimation method matched those in simulation. Between-method differences for data from human occupancy studies were less obvious, in part due to small dataset sizes. These maximum likelihood methods form the basis for development of improved PET covariance models, in order to minimize bias and variance in PET occupancy studies.

  17. Systematic identification of proteins that elicit drug side effects

    DEFF Research Database (Denmark)

    Kuhn, Michael; Al Banchaabouchi, Mumna; Campillos, Monica

    2013-01-01

    Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins...... that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause......) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations....

  18. An introduction to predictive modelling of drug concentration in anaesthesia monitors.

    Science.gov (United States)

    DeCou, J; Johnson, K

    2017-01-01

    A significant amount of anaesthetists' work involves the prediction of drug effects and interactions to produce a smooth general anaesthetic that minimises drug side effects and promotes rapid emergence. Successfully managing this process requires a basic understanding of drug effects, experience and inevitably some guesswork, since it is difficult (and in some cases impossible) to anticipate all relevant patient and surgical factors. Although data are generally available to allow calculation of plasma drug and effect site concentrations, this is often difficult to apply in complex clinical contexts, particularly when multiple drug types are used. In recent years, manufacturers have developed and incorporated into anaesthetic workstations technologies that use drug pharmacodynamic and pharmacokinetic data to predict drug effects and interactions. Such systems can predict the duration and effects of drugs during anaesthesia and assist the anaesthetist to understand complex drug interactions. With this information available, different drug types, doses and combinations may be tailored in a scientific way to maximise useful effects whilst minimising overdose and side-effects, particularly in high-risk patients. Examples are used to illustrate how such systems can be used in practice, and how drug effects and interactions can be simulated to "rehearse" an anaesthetic before any drugs are actually administered. At present only a small number of anaesthetic workstations use this technology, and as yet they are not able to manage all drugs used in anaesthetic practice. However, such systems have the potential to help anaesthetists manage the complexity of their work, and to provide information on predicted drug effects in a way that is useful and relevant to both experienced anaesthetists and trainees. © 2017 The Association of Anaesthetists of Great Britain and Ireland.

  19. Novel Antibacterial Compounds and their Drug Targets - Successes and Challenges.

    Science.gov (United States)

    Kaczor, Agnieszka A; Polski, Andrzej; Sobótka-Polska, Karolina; Pachuta-Stec, Anna; Makarska-Bialokoz, Magdalena; Pitucha, Monika

    2017-01-01

    Infectious diseases are one of the most important and urgent health problems in the world. According to the World Health Organization (WHO) statistics, infectious and parasitic diseases are a cause of about 16% of all deaths worldwide and over 40% of deaths in Africa. A considerable progress that has been made during last hundred years in the fight against infectious diseases, in particular bacterial infections, can be attributed mainly to three factors: (1) the general improvement of living conditions, in particular sanitation; (2) development of vaccines and (3) development of efficient antibacterial drugs. Although considerable progress in reduction of the number of cases of bacterial infections, especially in lethal cases, has been made, continued cases and outbreaks of these diseases persist, which is caused by different contributing factors. Indeed, during last sixty years antibacterial drugs were used against various infectious diseases caused by bacterial pathogens with an undoubtable success. The most fruitful period for antibiotic development lasted from 40's to 60's of the last century and resulted in the majority of antibiotics currently on the market, which were obtained by screening actinomycetes derived from soil. Although the market for antibacterial drugs is nowadays greater than 25 billion US dollars per year, novel antibacterial drugs are still demanded due to developed resistance of many pathogenic bacteria against current antibiotics. In the last five years, one can observe a dramatic increase in cases of resistant bacteria strains (e.g. Klebsiella pneumoniae and E. coli) which are responsible for difficult to treat pneumonia and infections of urinary tract. The development of resistant bacteria strains is a side effect of antibiotic application for treatment: the infections become untreatable as a result of the existence of antibiotic-tolerant persisters. In this review, we discuss the challenges in antibacterial drug discovery, including the

  20. A conceptual framework for the identification of candidate drugs and drug targets in acute promyelocytic leukemia

    DEFF Research Database (Denmark)

    Marstrand, T T; Borup, R; Willer, A

    2010-01-01

    regulation, and (ii) the identification of candidate drugs and drug targets for therapeutic interventions. Significantly, our study provides a conceptual framework that can be applied to any subtype of AML and cancer in general to uncover novel information from published microarray data sets at low cost...

  1. Ultrasound-sensitive nanoparticle aggregates for targeted drug delivery.

    Science.gov (United States)

    Papa, Anne-Laure; Korin, Netanel; Kanapathipillai, Mathumai; Mammoto, Akiko; Mammoto, Tadanori; Jiang, Amanda; Mannix, Robert; Uzun, Oktay; Johnson, Christopher; Bhatta, Deen; Cuneo, Garry; Ingber, Donald E

    2017-09-01

    Here we describe injectable, ultrasound (US)-responsive, nanoparticle aggregates (NPAs) that disintegrate into slow-release, nanoscale, drug delivery systems, which can be targeted to selective sites by applying low-energy US locally. We show that, unlike microbubble based drug carriers which may suffer from stability problems, the properties of mechanical activated NPAs, composed of polymer nanoparticles, can be tuned by properly adjusting the polymer molecular weight, the size of the nanoparticle precursors as well as the percentage of excipient utilized to hold the NPA together. We then apply this concept to practice by fabricating NPAs composed of nanoparticles loaded with Doxorubicin (Dox) and tested their ability to treat tumors via ultrasound activation. Mouse studies demonstrated significantly increased efficiency of tumor targeting of the US-activated NPAs compared to PLGA nanoparticle controls (with or without US applied) or intact NPAs. Importantly, when the Dox-loaded NPAs were injected and exposed to US energy locally, this increased ability to concentrate nanoparticles at the tumor site resulted in a significantly greater reduction in tumor volume compared to tumors treated with a 20-fold higher dose of the free drug. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. NSAIDs: Old Drugs Reveal New Anticancer Targets

    Directory of Open Access Journals (Sweden)

    Gary A. Piazza

    2010-05-01

    Full Text Available There is compelling evidence that nonsteroidal anti-inflammatory drugs (NSAIDs and cyclooxygenase-2 selective inhibitors have antineoplastic activity, but toxicity from cyclooxygenase (COX inhibition and the suppression of physiologically important prostaglandins limits their use for cancer chemoprevention. Previous studies as reviewed here suggest that the mechanism for their anticancer properties does not require COX inhibition, but instead involves an off-target effect. In support of this possibility, recent molecular modeling studies have shown that the NSAID sulindac can be chemically modified to selectively design out its COX-1 and COX-2 inhibitory activity. Unexpectedly, certain derivatives that were synthesized based on in silico modeling displayed increased potency to inhibit tumor cell growth. Other experiments have shown that sulindac can inhibit phosphodiesterase to increase intracellular cyclic GMP levels and that this activity is closely associated with its ability to selectively induce apoptosis of tumor cells. Together, these studies suggest that COX-independent mechanisms can be targeted to develop safer and more efficacious drugs for cancer chemoprevention.

  3. Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery

    Directory of Open Access Journals (Sweden)

    Neil O Carragher

    2011-04-01

    Full Text Available Dynamic regulation of specific molecular processes and cellular phenotypes in live cell systems reveal unique insights into cell fate and drug pharmacology that are not gained from traditional fixed endpoint assays. Recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and sophisticated image analysis solutions have increased the scope of live cell imaging applications in drug discovery. We highlight recent literature examples where live cell imaging has uncovered novel insight into biological mechanism or drug mode-of-action. We survey distinct types of optical biosensors and associated analytical methods for monitoring molecular dynamics, in vitro and in vivo. We describe the recent expansion of live cell imaging into automated target validation and drug screening activities through the development of dedicated brightfield and fluorescence kinetic imaging platforms. We provide specific examples of how temporal profiling of phenotypic response signatures using such kinetic imaging platforms can increase the value of in vitro high-content screening. Finally, we offer a prospective view of how further application and development of live cell imaging technology and reagents can accelerate preclinical lead optimization cycles and enhance the in vitro to in vivo translation of drug candidates.

  4. Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

    Science.gov (United States)

    Wacker, Soren; Noskov, Sergei Yu

    2018-05-01

    Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost

  5. Implant-assisted magnetic drug targeting in permeable microvessels: Comparison of two-fluid statistical transport model with experiment

    Energy Technology Data Exchange (ETDEWEB)

    ChiBin, Zhang; XiaoHui, Lin, E-mail: lxh60@seu.edu.cn; ZhaoMin, Wang; ChangBao, Wang

    2017-03-15

    In experiments and theoretical analyses, this study examines the capture efficiency (CE) of magnetic drug carrier particles (MDCPs) for implant-assisted magnetic drug targeting (IA-MDT) in microvessels. It also proposes a three-dimensional statistical transport model of MDCPs for IA-MDT in permeable microvessels, which describes blood flow by the two-fluid (Casson and Newtonian) model. The model accounts for the permeable effect of the microvessel wall and the coupling effect between the blood flow and tissue fluid flow. The MDCPs move randomly through the microvessel, and their transport state is described by the Boltzmann equation. The regulated changes and factors affecting the CE of the MDCPs in the assisted magnetic targeting were obtained by solving the theoretical model and by experimental testing. The CE was negatively correlated with the blood flow velocity, and positively correlated with the external magnetic field intensity and microvessel permeability. The predicted CEs of the MDCPs were consistent with the experimental results. Additionally, under the same external magnetic field, the predicted CE was 5–8% higher in the IA-MDT model than in the model ignoring the permeability effect of the microvessel wall. - Highlights: • A model of MDCPs for IA-MDT in permeable microvessels was established. • An experimental device was established, the CE of MDCPs was measured. • The predicted CE of MDCPs was 5–8% higher in the IA-MDT model.

  6. Rho-Kinase/ROCK as a Potential Drug Target for Vitreoretinal Diseases

    Directory of Open Access Journals (Sweden)

    Muneo Yamaguchi

    2017-01-01

    Full Text Available Rho-associated kinase (Rho-kinase/ROCK was originally identified as an effector protein of the G protein Rho. Its involvement in various diseases, particularly cancer and cardiovascular disease, has been elucidated, and ROCK inhibitors have already been applied clinically for cerebral vasospasm and glaucoma. Vitreoretinal diseases including diabetic retinopathy, age-related macular degeneration, and proliferative vitreoretinoapthy are still a major cause of blindness. While anti-VEGF therapy has recently been widely used for vitreoretinal disorders due to its efficacy, attention has been drawn to new unmet needs. The importance of ROCK in pathological vitreoretinal conditions has also been elucidated and is attracting attention as a potential therapeutic target. ROCK is involved in angiogenesis and hyperpermeability and also in the pathogenesis of various pathologies such as inflammation and fibrosis. It has been expected that ROCK inhibitors will become new molecular target drugs for vitreoretinal diseases. This review summarizes the recent progress on the mechanisms of action of ROCK and their applications in disease treatment.

  7. Specific Cell Targeting Therapy Bypasses Drug Resistance Mechanisms in African Trypanosomiasis.

    Directory of Open Access Journals (Sweden)

    Juan D Unciti-Broceta

    2015-06-01

    Full Text Available African trypanosomiasis is a deadly neglected disease caused by the extracellular parasite Trypanosoma brucei. Current therapies are characterized by high drug toxicity and increasing drug resistance mainly associated with loss-of-function mutations in the transporters involved in drug import. The introduction of new antiparasitic drugs into therapeutic use is a slow and expensive process. In contrast, specific targeting of existing drugs could represent a more rapid and cost-effective approach for neglected disease treatment, impacting through reduced systemic toxicity and circumventing resistance acquired through impaired compound uptake. We have generated nanoparticles of chitosan loaded with the trypanocidal drug pentamidine and coated by a single domain nanobody that specifically targets the surface of African trypanosomes. Once loaded into this nanocarrier, pentamidine enters trypanosomes through endocytosis instead of via classical cell surface transporters. The curative dose of pentamidine-loaded nanobody-chitosan nanoparticles was 100-fold lower than pentamidine alone in a murine model of acute African trypanosomiasis. Crucially, this new formulation displayed undiminished in vitro and in vivo activity against a trypanosome cell line resistant to pentamidine as a result of mutations in the surface transporter aquaglyceroporin 2. We conclude that this new drug delivery system increases drug efficacy and has the ability to overcome resistance to some anti-protozoal drugs.

  8. TAPIR, a web server for the prediction of plant microRNA targets, including target mimics.

    Science.gov (United States)

    Bonnet, Eric; He, Ying; Billiau, Kenny; Van de Peer, Yves

    2010-06-15

    We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir. Supplementary data are available at Bioinformatics online.

  9. AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Jiansong Fang

    Full Text Available Alzheimer's disease (AD is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs to delay disease progression. The in silico prediction of chemical-protein interactions (CPI can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB and recursive partitioning (RP algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.

  10. Application of PK/PD Modeling in Veterinary Field: Dose Optimization and Drug Resistance Prediction

    Directory of Open Access Journals (Sweden)

    Ijaz Ahmad

    2016-01-01

    Full Text Available Among veterinary drugs, antibiotics are frequently used. The true mean of antibiotic treatment is to administer dose of drug that will have enough high possibility of attaining the preferred curative effect, with adequately low chance of concentration associated toxicity. Rising of antibacterial resistance and lack of novel antibiotic is a global crisis; therefore there is an urgent need to overcome this problem. Inappropriate antibiotic selection, group treatment, and suboptimal dosing are mostly responsible for the mentioned problem. One approach to minimizing the antibacterial resistance is to optimize the dosage regimen. PK/PD model is important realm to be used for that purpose from several years. PK/PD model describes the relationship between drug potency, microorganism exposed to drug, and the effect observed. Proper use of the most modern PK/PD modeling approaches in veterinary medicine can optimize the dosage for patient, which in turn reduce toxicity and reduce the emergence of resistance. The aim of this review is to look at the existing state and application of PK/PD in veterinary medicine based on in vitro, in vivo, healthy, and disease model.

  11. A mapping of drug space from the viewpoint of small molecule metabolism.

    Directory of Open Access Journals (Sweden)

    James Corey Adams

    2009-08-01

    Full Text Available Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the "effect space" comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.

  12. A mapping of drug space from the viewpoint of small molecule metabolism.

    Science.gov (United States)

    Adams, James Corey; Keiser, Michael J; Basuino, Li; Chambers, Henry F; Lee, Deok-Sun; Wiest, Olaf G; Babbitt, Patricia C

    2009-08-01

    Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the "effect space" comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.

  13. Application of PBPK modelling in drug discovery and development at Pfizer.

    Science.gov (United States)

    Jones, Hannah M; Dickins, Maurice; Youdim, Kuresh; Gosset, James R; Attkins, Neil J; Hay, Tanya L; Gurrell, Ian K; Logan, Y Raj; Bungay, Peter J; Jones, Barry C; Gardner, Iain B

    2012-01-01

    Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

  14. Ocular drug delivery targeted by iontophoresis in the suprachoroidal space using a microneedle.

    Science.gov (United States)

    Jung, Jae Hwan; Chiang, Bryce; Grossniklaus, Hans E; Prausnitz, Mark R

    2018-05-10

    Treatment of many posterior-segment ocular indications would benefit from improved targeting of drug delivery to the back of the eye. Here, we propose the use of iontophoresis to direct delivery of negatively charged nanoparticles through the suprachoroidal space (SCS) toward the posterior pole of the eye. Injection of nanoparticles into the SCS of the rabbit eye ex vivo without iontophoresis led to a nanoparticle distribution mostly localized at the site of injection near the limbus and 9 mm from the limbus). Iontophoresis using a novel microneedle-based device increased posterior targeting with >30% of nanoparticles in the most posterior region of SCS. Posterior targeting increased with increasing iontophoresis current and increasing application time up to 3 min, but further increasing to 5 min was not better, probably due to the observed collapse of the SCS within 5 min after injection ex vivo. Reversing the direction of iontophoretic flow inhibited posterior targeting, with just ~5% of nanoparticles reaching the most posterior region of SCS. In the rabbit eye in vivo, iontophoresis at 0.14 mA for 3 min after injection of a 100 μL suspension of nanoparticles resulted in ~30% of nanoparticles delivered to the most posterior region of the SCS, which was consistent with ex vivo findings. The procedure was well tolerated, with only mild, transient tissue effects at the site of injection. We conclude that iontophoresis in the SCS using a microneedle has promise as a method to target ocular drug delivery within the eye, especially toward the posterior pole. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

    Science.gov (United States)

    Wagner, Christian; Pan, Yuzhuo; Hsu, Vicky; Grillo, Joseph A; Zhang, Lei; Reynolds, Kellie S; Sinha, Vikram; Zhao, Ping

    2015-01-01

    The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI. This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors' PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration-time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]). In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range. These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

  16. Advances in research of targeting delivery and controlled release of drug-loaded nanoparticles

    International Nuclear Information System (INIS)

    Tan Zhonghua

    2003-01-01

    Biochemistry drug, at present, is still the main tool that human struggle to defeat the diseases. So, developing safe and efficacious technique of drug targeting delivery and controlled release is key to enhance curative effect, decrease drug dosage, and lessen its side effect. Drug-loaded nanoparticles, which is formed by conjugate between nanotechnology and modern pharmaceutics, is a new fashioned pharmic delivery carrier. Because of advantages in pharmic targeting transport and controlled or slow release and improving bioavailability, it has been one of developing trend of modern pharmaceutical dosage forms

  17. DIANA-microT web server: elucidating microRNA functions through target prediction.

    Science.gov (United States)

    Maragkakis, M; Reczko, M; Simossis, V A; Alexiou, P; Papadopoulos, G L; Dalamagas, T; Giannopoulos, G; Goumas, G; Koukis, E; Kourtis, K; Vergoulis, T; Koziris, N; Sellis, T; Tsanakas, P; Hatzigeorgiou, A G

    2009-07-01

    Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

  18. Trusted Allies with New Benefits: Repositioning Existing Drugs

    KAUST Repository

    Gao, Xin

    2016-01-01

    types of interaction interfaces, such as the drug-target interaction. We have further developed a novel computational framework, iDTP that constructs the structural signatures of approved and experimental drugs, based on which we predict new targets

  19. Doxorubicin loaded PVA coated iron oxide nanoparticles for targeted drug delivery

    International Nuclear Information System (INIS)

    Kayal, S.; Ramanujan, R.V.

    2010-01-01

    Magnetic drug targeting is a drug delivery system that can be used in locoregional cancer treatment. Coated magnetic particles, called carriers, are very useful for delivering chemotherapeutic drugs. Magnetic carriers were synthesized by coprecipitation of iron oxide followed by coating with polyvinyl alcohol (PVA). Characterization was carried out using X-ray diffraction, TEM, TGA, FTIR and VSM techniques. The magnetic core of the carriers was magnetite (Fe 3 O 4 ), with average size of 10 nm. The room temperature VSM measurements showed that magnetic particles were superparamagnetic. The amount of PVA bound to the iron oxide nanoparticles were estimated by thermogravimetric analysis (TGA) and the attachment of PVA to the iron oxide nanoparticles was confirmed by FTIR analysis. Doxorubicin (DOX) drug loading and release profiles of PVA coated iron oxide nanoparticles showed that up to 45% of adsorbed drug was released in 80 h, the drug release followed the Fickian diffusion-controlled process. The binding of DOX to the PVA was confirmed by FTIR analysis. The present findings show that DOX loaded PVA coated iron oxide nanoparticles are promising for magnetically targeted drug delivery.

  20. PoSSuM v.2.0: data update and a new function for investigating ligand analogs and target proteins of small-molecule drugs.

    Science.gov (United States)

    Ito, Jun-ichi; Ikeda, Kazuyoshi; Yamada, Kazunori; Mizuguchi, Kenji; Tomii, Kentaro

    2015-01-01

    PoSSuM (http://possum.cbrc.jp/PoSSuM/) is a database for detecting similar small-molecule binding sites on proteins. Since its initial release in 2011, PoSSuM has grown to provide information related to 49 million pairs of similar binding sites discovered among 5.5 million known and putative binding sites. This enlargement of the database is expected to enhance opportunities for biological and pharmaceutical applications, such as predictions of new functions and drug discovery. In this release, we have provided a new service named PoSSuM drug search (PoSSuMds) at http://possum.cbrc.jp/PoSSuM/drug_search/, in which we selected 194 approved drug compounds retrieved from ChEMBL, and detected their known binding pockets and pockets that are similar to them. Users can access and download all of the search results via a new web interface, which is useful for finding ligand analogs as well as potential target proteins. Furthermore, PoSSuMds enables users to explore the binding pocket universe within PoSSuM. Additionally, we have improved the web interface with new functions, including sortable tables and a viewer for visualizing and downloading superimposed pockets. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.