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

Science.gov (United States)

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.

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

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

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.

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

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.

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.

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

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.

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.

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

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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

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.

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....

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.

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.

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.

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

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.

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

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.

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.

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.

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.

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.

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

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.

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

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.

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

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.

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.

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.

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.

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.

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...

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

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.

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

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.

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.

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.

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.

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.

Drug-targeting methodologies with applications: A review

Science.gov (United States)

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

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...

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.

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/.

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/.

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

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...

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.

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.

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.

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

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.

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)

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.

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...

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.

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

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

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

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.

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

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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

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. 

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

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.

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

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

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...

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.

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.

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.

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

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.

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.

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.

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.

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.

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)

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.

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.

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....

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.

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.

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...

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.

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.

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

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.

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,

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.

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.

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.

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.

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

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....

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...

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.

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.

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.

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.

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

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...

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.

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...

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...

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.

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.

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.

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

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.

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.

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

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

Directory of Open Access Journals (Sweden)

Gao Zhenting

2008-02-01

Full Text Available Abstract Background Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Target Fishing Docking http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a protein target database, Potential Drug Target Database (PDTD, and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. Description PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. Conclusion PDTD serves as a comprehensive and

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.

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.

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....

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.

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.

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.

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

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila

TargetSpy: a supervised machine learning approach for microRNA target prediction

Directory of Open Access Journals (Sweden)

Langenberger David

2010-05-01

Full Text Available Abstract Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I no seed match requirement, II seed match requirement, and III conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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...

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.

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.

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.

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. ...

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.

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.

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.

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.

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.

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)

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; ...

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.

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

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

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.

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

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.

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)

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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

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").

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

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

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.

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

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.

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....

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.

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.

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.

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.

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.

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.

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.

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...

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

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

[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.

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.

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.

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.

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.

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

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

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.

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.

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

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.

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.

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...

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.

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.

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

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

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.

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.

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)

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...

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.

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

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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...

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.

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...

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...

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

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.

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.

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.

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.

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.

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.

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...

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.

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.

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

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.

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.

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.

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.

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.

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....

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.

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

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.

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.

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.

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

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.

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.

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

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

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

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.

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.

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.

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...

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.

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

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

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...

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.

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.