Trypanosoma cruzi benznidazole susceptibility in vitro does not predict the therapeutic outcome of human Chagas disease

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Margoth Moreno

2010-11-01

Full Text Available Therapeutic failure of benznidazole (BZ is widely documented in Chagas disease and has been primarily associated with variations in the drug susceptibility of Trypanosoma cruzi strains. In humans, therapeutic success has been assessed by the negativation of anti-T. cruzi antibodies, a process that may take up to 10 years. A protocol for early screening of the drug resistance of infective strains would be valuable for orienting physicians towards alternative therapies, with a combination of existing drugs or new anti-T. cruzi agents. We developed a procedure that couples the isolation of parasites by haemoculture with quantification of BZ susceptibility in the resultant epimastigote forms. BZ activity was standardized with reference strains, which showed IC50 to BZ between 7.6-32 µM. The assay was then applied to isolates from seven chronic patients prior to administration of BZ therapy. The IC50 of the strains varied from 15.6 ± 3-51.4 ± 1 µM. Comparison of BZ susceptibility of the pre-treatment isolates of patients considered cured by several criteria and of non-cured patients indicates that the assay does not predict therapeutic outcome. A two-fold increase in BZ resistance in the post-treatment isolates of two patients was verified. Based on the profile of nine microsatellite loci, sub-population selection in non-cured patients was ruled out.

CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy.

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Ma, Leyuan; Boucher, Jeffrey I; Paulsen, Janet; Matuszewski, Sebastian; Eide, Christopher A; Ou, Jianhong; Eickelberg, Garrett; Press, Richard D; Zhu, Lihua Julie; Druker, Brian J; Branford, Susan; Wolfe, Scot A; Jensen, Jeffrey D; Schiffer, Celia A; Green, Michael R; Bolon, Daniel N

2017-10-31

Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene BCR-ABL1 Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics. Published under the PNAS license.

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

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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

Mycobacterium abscessus subsp. abscessus Lung Disease: Drug Susceptibility Testing in Sputum Culture Negative Conversion

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Takehiko Kobayashi

2018-01-01

Full Text Available Background: Among Mycobacterium abscessus complex infections, patients with M. abscessus subsp. abscessus (MAA lung disease are difficult to treat and no standard therapy has been established. Few reports have investigated the drug susceptibility of these strains. We retrospectively investigated how in vitro drug susceptibility testing (DST of MAA affects the induction of sputum conversion using pharmacotherapy. Methods: Patients with MAA lung disease diagnosed and treated between 2010 and 2014 at our hospital were enrolled and divided into Group A (sputum conversion without relapse within 1 year and Group B (persistent positive cultured or negative conversion with relapse. MAA was identified in M. abscessus using sequence with genotyping, and DST of MAA was performed. Results: We assessed 23 patients (9 males and 14 females. There were 8 patients in Group A and 15 in Group B. Higher prevalence of susceptible isolates for clarithromycin (CAM susceptibility on day 14 was noted in Group A than in Group B (P = 0.03 and no significant difference observed in the two groups for other drugs. Conclusions: In vitro DST of MAA, especially CAM susceptibility on day 14, affected the results of negative conversion. No other drugs were found to affect sputum culture negative conversion.

Microbial sensor for drug susceptibility testing of Mycobacterium tuberculosis.

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Zhang, Z-T; Wang, D-B; Li, C-Y; Deng, J-Y; Zhang, J-B; Bi, L-J; Zhang, X-E

2018-01-01

Drug susceptibility testing (DST) of clinical isolates of Mycobacterium tuberculosis is critical in treating tuberculosis. We demonstrate the possibility of using a microbial sensor to perform DST of M. tuberculosis and shorten the time required for DST. The sensor is made of an oxygen electrode with M. tuberculosis cells attached to its surface. This sensor monitors the residual oxygen consumption of M. tuberculosis cells after treatment with anti-TB drugs with glycerine as a carbon source. In principle, after drug pretreatment for 4-5 days, the response differences between the sensors made of drug-sensitive isolates are distinguishable from the sensors made of drug-resistant isolates. The susceptibility of the M. tuberculosis H37Ra strain, its mutants and 35 clinical isolates to six common anti-TB drugs: rifampicin, isoniazid, streptomycin, ethambutol, levofloxacin and para-aminosalicylic acid were tested using the proposed method. The results agreed well with the gold standard method (LJ) and were determined in significantly less time. The whole procedure takes approximately 11 days and therefore has the potential to inform clinical decisions. To our knowledge, this is the first study that demonstrates the possible application of a dissolved oxygen electrode-based microbial sensor in M. tuberculosis drug resistance testing. This study used the microbial sensor to perform DST of M. tuberculosis and shorten the time required for DST. The overall detection result of the microbial sensor agreed well with that of the conventional LJ proportion method and takes less time than the existing phenotypic methods. In future studies, we will build an O 2 electrode array microbial sensor reactor to enable a high-throughput drug resistance analysis. © 2017 The Authors. Journal of Applied Microbiology published by John Wiley & Sons Ltd on behalf of The Society for Applied Microbiology.

Antimicrobial drug susceptibility of Neisseria meningitidis strains isolated from carriers

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Dayamí García

2000-06-01

Full Text Available When it is necessary to determine the susceptibility of Neisseria meningitidis (Nm strains to antimicrobial drugs, it is important to consider that it should be analyzed in a double context. One of them related to the use of drugs in a specific medical treatment; and the other; to chemoprophylatic drugs, both with the same purpose: the accurate selection of the “in vivo” antimicrobial agent. This requires the study of the sensitivity and resistance of strains isolated in both carriers and patients. With the aim of further studying the behavior of the strains that currently circulate in Cuba, an antimicrobial drug susceptibility study was conducted in 90 strains isolated from carriers during the first half of 1998. The agar dilution method was used to determine the minimum inhibitory concentrations (MICs to: penicillin, ampicillin, rifampin, sulfadiazine, chloramphenicol, ciprofloxacin, ceftriaxone, cefotaxime. The study of the three latter drugs was done for the first time in our country. The search for β- lactamase-producer strains was also performed. There was a predominance of penicillin sensitive strains (82,2% with an intermediate sensitivity to ampicillin (57,8%, while 70% of the strains were sensitive to sulfadiazine. Regarding the rest of the antimicrobial drugs, 100% of the strains were sensitive. The paper shows the MICs for each drug as well as the phenotypic characteristics of the strains with the penicillin and sulfadiazine sensitivity and resistance patterns. No β-lactamase-producer strains were found.

Bacterial isolates and drug susceptibility patterns of urinary tract ...

African Journals Online (AJOL)

Urinary tract infection (UTI) during pregnancy may cause complications such as pyelonephritis, hypertensive disease of pregnancy, anaemia, chronic renal failure, premature delivery and foetal mortality. This study aimed to identify the etiologic agents of UTI and to determine the patterns of antimicrobial drug susceptibility ...

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

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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.

In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool

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The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functiona...

QSAR Modeling and Prediction of Drug-Drug Interactions.

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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.

The usefulness of microscopic observation for drug susceptibility of ...

African Journals Online (AJOL)

The usefulness of microscopic observation for drug susceptibility of. Mycobacterium tuberculosis complex in routine clinical microbiology laboratory. S.E. MSHANA1,2*, C. IMIRZALIOGLU2, E. DOMANN2 and T. CHAKRABORTY2. 1 We l Bugando Un vers ty College of Health Sc ences, P.O. Box 1464, Mwanza, Tanzan a.

[Circadian rhythm in susceptibility of mice to the anti-tumor drug carboplatin].

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Lu, X H; Yin, L J

1994-12-01

The platinum-containing compounds has become a major chemical agent in the treatment of cancer. A circadian rhythm in the susceptibility of rodents and human being to cisplatin has been demonstrated, the maximal tolerance being found in the animal's active phase. Carboplatin is a second generation analog. Two studies were performed on mice with carboplatin under 12:12 light dark cycle to study its chronotoxicity and chronoeffectiveness. In study I, single intraperitoneal injection of 192mg/kg (LD50) carboplatin was given to four groups of mice at four different circadian stage. It was found that at 50% the overall mortality of mice, there was a mortality difference of 28% for mice receiving the drug at 9 a.m. to 71% for mice receiving drug at 9 p.m. It demonstrated that carboplatin was better tolerated in the animal's early sleep phase. In study II, S180 tumor-bearing mice were treated with 50mg/kg of carboplatin. The longest mean survival time and the lowest marrow toxicity occurred in the group which received the drug at the beginning of the sleep phase. It showed that the susceptibility of mice to carboplatin is circadian stage dependent. These data clearly demonstrate that, by timing the administration of drugs according to body rhythms, such as the host susceptibility-resistance rhythm to a drug, one can gain a therapeutic advantage over an approach which ignores such rhythms.

Drug Susceptibility of Mycobacterium tuberculosis Beijing Genotype and Association with MDR TB

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ten Kate, Marian T.; de Knegt, Gerjo J.; Kremer, Kristin; Aarnoutse, Rob E.; Boeree, Martin J.; Verbrugh, Henri A.; van Soolingen, Dick; Bakker-Woudenberg, Irma A.J.M.

2012-01-01

To determine differences in the ability of Mycobacterium tuberculosis strains to withstand antituberculosis drug treatment, we compared the activity of antituberculosis drugs against susceptible Beijing and East-African/Indian genotype M. tuberculosis strains. Beijing genotype strains showed high rates of mutation within a wide range of drug concentrations, possibly explaining this genotype’s association with multidrug-resistant tuberculosis. PMID:22469099

In-vitro antimycobacterial drug susceptibility testing of non-tubercular mycobacteria by tetrazolium microplate assay.

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Sankar, Manimuthu Mani; Gopinath, Krishnamoorthy; Singla, Roopak; Singh, Sarman

2008-07-11

Non-tubercular mycobacteria (NTM) has not been given due attention till the recent epidemic of HIV. This has led to increasing interest of health care workers in their biology, epidemiology and drug resistance. However, timely detection and drug susceptibility profiling of NTM isolates are always difficult in resource poor settings like India. Furthermore, no standardized methodology or guidelines are available to reproduce the results with clinical concordance. To find an alternative and rapid method for performing the drug susceptibility assay in a resource limited settings like India, we intended to evaluate the utility of Tetrazolium microplate assay (TEMA) in comparison with proportion method for reporting the drug resistance in clinical isolates of NTM. A total of fifty-five NTM isolates were tested for susceptibility against Streptomycin, Rifampicin, Ethambutol, Ciprofloxacin, Ofloxacin, Azithromycin, and Clarithromycin by TEMA and the results were compared with agar proportion method (APM). Of the 55 isolates, 23 (41.8%) were sensitive to all the drugs and the remaining 32 (58.2%) were resistant to at least one drug. TEMA had very good concordance with APM except with minor discrepancies. Susceptibility results were obtained in the median of 5 to 9 days by TEMA. The NTM isolates were highly sensitive against Ofloxacin (98.18% sensitive) and Ciprofloxacin (90.09% sensitive). M. mucogenicum was sensitive only to Clarithromycin and resistant to all the other drugs tested. The concordance between TEMA and APM ranged between 96.4 - 100%. Tetrazolium Microplate Assay is a rapid and highly reproducible method. However, it must be performed only in tertiary level Mycobacteriology laboratories with proper bio-safety conditions.

Prediction Equations Overestimate the Energy Requirements More for Obesity-Susceptible Individuals.

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McLay-Cooke, Rebecca T; Gray, Andrew R; Jones, Lynnette M; Taylor, Rachael W; Skidmore, Paula M L; Brown, Rachel C

2017-09-13

Predictive equations to estimate resting metabolic rate (RMR) are often used in dietary counseling and by online apps to set energy intake goals for weight loss. It is critical to know whether such equations are appropriate for those susceptible to obesity. We measured RMR by indirect calorimetry after an overnight fast in 26 obesity susceptible (OSI) and 30 obesity resistant (ORI) individuals, identified using a simple 6-item screening tool. Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations. Absolute measured RMR did not differ significantly between OSI versus ORI (6339 vs. 5893 kJ·d -1 , p = 0.313). All three prediction equations over-estimated RMR for both OSI and ORI when measured RMR was ≤5000 kJ·d -1 . For measured RMR ≤7000 kJ·d -1 there was statistically significant evidence that the equations overestimate RMR to a greater extent for those classified as obesity susceptible with biases ranging between around 10% to nearly 30% depending on the equation. The use of prediction equations may overestimate RMR and energy requirements particularly in those who self-identify as being susceptible to obesity, which has implications for effective weight management.

Estimating Fitness by Competition Assays between Drug Susceptible and Resistant Mycobacterium tuberculosis of Predominant Lineages in Mumbai, India

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Bhatter, Purva; Chatterjee, Anirvan; D'souza, Desiree; Tolani, Monica; Mistry, Nerges

2012-01-01

Background Multi Drug Resistant Tuberculosis (MDR TB) is a threat to global tuberculosis control. A significant fitness cost has been associated with DR strains from specific lineages. Evaluation of the influence of the competing drug susceptible strains on fitness of drug resistant strains may have an important bearing on understanding the spread of MDR TB. The aim of this study was to evaluate the fitness of MDR TB strains, from a TB endemic region of western India: Mumbai, belonging to 3 predominant lineages namely CAS, Beijing and MANU in the presence of drug susceptible strains from the same lineages. Methodology Drug susceptible strains from a single lineage were mixed with drug resistant strain, bearing particular non synonymous mutation (rpoB D516V; inhA, A16G; katG, S315T1/T2) from the same or different lineages. Fitness of M.tuberculosis (M.tb) strains was evaluated using the difference in growth rates obtained by using the CFU assay system. Conclusion/Significance While MANU were most fit amongst the drug susceptible strains of the 3 lineages, only Beijing MDR strains were found to grow in the presence of any of the competing drug susceptible strains. A disproportionate increase in Beijing MDR could be an alarm for an impending epidemic in this locale. In addition to particular non synonymous substitutions, the competing strains in an environment may impact the fitness of circulating drug resistant strains. PMID:22479407

Miltefosine and antimonial drug susceptibility of Leishmania Viannia species and populations in regions of high transmission in Colombia.

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Olga Lucía Fernández

2014-05-01

Full Text Available Pentavalent antimonials have been the first line treatment for dermal leishmaniasis in Colombia for over 30 years. Miltefosine is administered as second line treatment since 2005. The susceptibility of circulating populations of Leishmania to these drugs is unknown despite clinical evidence supporting the emergence of resistance.In vitro susceptibility was determined for intracellular amastigotes of 245 clinical strains of the most prevalent Leishmania Viannia species in Colombia to miltefosine (HePC and/or meglumine antimoniate (Sb(V; 163, (80% were evaluated for both drugs. Additionally, susceptibility to Sb(V was examined in two cohorts of 85 L. V. panamensis strains isolated between 1980-1989 and 2000-2009 in the municipality of Tumaco. Susceptibility to each drug differed among strains of the same species and between species. Whereas 68% of L. V. braziliensis strains presented in vitro resistance to HePC, 69% were sensitive to Sb(V. Resistance to HePC and Sb(V occurred respectively, in 20% y 21% of L. panamensis strains. Only 3% of L. V. guyanensis were resistant to HePC, and none to Sb(V. Drug susceptibility differed between geographic regions and time periods. Subpopulations having disparate susceptibility to Sb(V were discerned among L. V. panamensis strains isolated during 1980-1990 in Tumaco where resistant strains belonged to zymodeme 2.3, and sensitive strains to zymodeme 2.2.Large scale evaluation of clinical strains of Leishmania Viannia species demonstrated species, population, geographic, and epidemiologic differences in susceptibility to meglumine antimoniate and miltefosine, and provided baseline information for monitoring susceptibility to these drugs. Sensitive and resistant clinical strains within each species, and zymodeme as a proxy marker of antimony susceptibility for L. V. panamensis, will be useful in deciphering factors involved in susceptibility and the distribution of sensitive and resistant populations.

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

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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.

Evaluation of rapid radiometric method for drug susceptibility testing of Mycobacterium tuberculosis

International Nuclear Information System (INIS)

Siddiqi, S.H.; Libonati, J.P.; Middlebrook, G.

1981-01-01

A total of 106 isolates of Mycobacterium tuberculosis were tested for drug susceptibility by the conventional 7H11 plate method and by a new rapid radiometric method using special 7H12 liquid medium with 14 C-labeled substrate. Results obtained by the two methods were compared for rapidity, sensitivity, and specificity of the new test method. There was 98% overall agreement between the results obtained by the two methods. Of a total of 424 drug tests, only 8 drug results did not agree, mostly in the case of streptomycin. This new procedure was found to be rapid, with 87% of the tests results reportable within 4 days and 98% reportable within 5 days as compared to the usual 3 weeks required with the conventional indirect susceptibility test method. The results of this preliminary study indicate that the rapid radiometric method seems to have the potential for routine laboratory use and merits further investigations

Susceptibility profiles of Nocardia isolates based on current taxonomy.

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Schlaberg, Robert; Fisher, Mark A; Hanson, Kimberley E

2014-01-01

The genus Nocardia has undergone rapid taxonomic expansion in recent years, and an increasing number of species are recognized as human pathogens. Many established species have predictable antimicrobial susceptibility profiles, but sufficient information is often not available for recently described organisms. Additionally, the effectiveness of sulfonamides as first-line drugs for Nocardia has recently been questioned. This led us to review antimicrobial susceptibility patterns for a large number of molecularly identified clinical isolates. Susceptibility results were available for 1,299 isolates representing 39 different species or complexes, including 11 that were newly described, during a 6-year study period. All tested isolates were susceptible to linezolid. Resistance to trimethoprim-sulfamethoxazole (TMP-SMX) was rare (2%) except among Nocardia pseudobrasiliensis (31%) strains and strains of the N. transvalensis complex (19%). Imipenem susceptibility varied for N. cyriacigeorgica and N. farcinica, as did ceftriaxone susceptibility of the N. nova complex. Resistance to more than one of the most commonly used drugs (amikacin, ceftriaxone, TMP-SMX, and imipenem) was highest for N. pseudobrasiliensis (100%), N. transvalensis complex (83%), N. farcinica (68%), N. puris (57%), N. brasiliensis (51%), N. aobensis (50%), and N. amikacinitolerans (43%). Thus, while antimicrobial resistance can often be predicted, susceptibility testing should still be considered when combination therapy is warranted, for less well characterized species or those with variable susceptibility profiles, and for patients with TMP-SMX intolerance.

Deep-Learning-Based Drug-Target Interaction Prediction.

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

2017-04-07

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

Multiple drug-susceptibility screening in Mycobacterium bovis: new nucleotide polymorphisms in the embB gene among ethambutol susceptible strains

Directory of Open Access Journals (Sweden)

Cinzia Marianelli

2015-04-01

Conclusion: All M. bovis isolates were sensitive to the most common antituberculosis drugs used for treatment. There was a good agreement between the d-REMA assay and the agar based reference method. Among ethambutol susceptible isolates, four new embB mutations were found.

What is the mechanism for persistent coexistence of drug-susceptible and drug-resistant strains of Streptococcus pneumoniae?

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Colijn, Caroline; Cohen, Ted; Fraser, Christophe; Hanage, William; Goldstein, Edward; Givon-Lavi, Noga; Dagan, Ron; Lipsitch, Marc

2010-01-01

The rise of antimicrobial resistance in many pathogens presents a major challenge to the treatment and control of infectious diseases. Furthermore, the observation that drug-resistant strains have risen to substantial prevalence but have not replaced drug-susceptible strains despite continuing (and even growing) selective pressure by antimicrobial use presents an important problem for those who study the dynamics of infectious diseases. While simple competition models predict the exclusion of one strain in favour of whichever is ‘fitter’, or has a higher reproduction number, we argue that in the case of Streptococcus pneumoniae there has been persistent coexistence of drug-sensitive and drug-resistant strains, with neither approaching 100 per cent prevalence. We have previously proposed that models seeking to understand the origins of coexistence should not incorporate implicit mechanisms that build in stable coexistence ‘for free’. Here, we construct a series of such ‘structurally neutral’ models that incorporate various features of bacterial spread and host heterogeneity that have been proposed as mechanisms that may promote coexistence. We ask to what extent coexistence is a typical outcome in each. We find that while coexistence is possible in each of the models we consider, it is relatively rare, with two exceptions: (i) allowing simultaneous dual transmission of sensitive and resistant strains lets coexistence become a typical outcome, as does (ii) modelling each strain as competing more strongly with itself than with the other strain, i.e. self-immunity greater than cross-immunity. We conclude that while treatment and contact heterogeneity can promote coexistence to some extent, the in-host interactions between strains, particularly the interplay between coinfection, multiple infection and immunity, play a crucial role in the long-term population dynamics of pathogens with drug resistance. PMID:19940002

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

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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.

Predicting drug-target interactions using restricted Boltzmann machines.

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Wang, Yuhao; Zeng, Jianyang

2013-07-01

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

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...

Rapid drug susceptibility test of mycobacterium tuberculosis by bioluminescence sensor

Science.gov (United States)

Lu, Bin; Xu, Shunqing; Chen, Zifei; Zhou, Yikai

2001-09-01

With the persisting increase of drug-resistant stains of M. Tuberculosis around the world, rapid and sensitive detection of antibiotic of M. Tuberculosis is becoming more and more important. In the present study, drug susceptibility of M. tuberculosis were detected by recombination mycobacteriophage combined with bioluminescence sensor. It is based on the use of recombination mycobacteriophage which can express firefly luciferase when it infects viable mycobacteria, and can effectively produce quantifiable photon. Meanwhile, in mycobacterium cells treated with active antibiotic, no light is observed. The emitted light is recorded by a bioluminscence sensor, so the result of drug-resistant test can be determined by the naked eye. 159 stains of M. tuberculosis were applied to this test on their resistant to rifampin, streptomycin and isoniazid. It is found that the agreement of this assay with Liewenstein- Jensen slat is: rifampin 95.60 percent, isoniazid 91.82 percent, streptomycin 88.68 percent, which showed that it is a fast and practical method to scene and detect drug resistant of mycobacterium stains.

Mortality among MDR-TB cases: comparison with drug-susceptible tuberculosis and associated factors.

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Kocfa Chung-Delgado

Full Text Available An increase in multidrug-resistant tuberculosis (MDR-TB cases is evident worldwide. Its management implies a complex treatment, high costs, more toxic anti-tuberculosis drug use, longer treatment time and increased treatment failure and mortality. The aims of this study were to compare mortality between MDR and drug-susceptible cases of tuberculosis, and to determine risk factors associated with mortality among MDR-TB cases.A retrospective cohort study was performed using data from clinical records of the National Strategy for Prevention and Control of Tuberculosis in Lima, Peru. In the first objective, MDR-TB, compared to drug-susceptible cases, was the main exposure variable and time to death, censored at 180 days, the outcome of interest. For the second objective, different variables obtained from clinical records were assessed as potential risk factors for death among MDR-TB cases. Cox regression analysis was used to determine hazard ratios (HR and 95% confidence intervals (95%CI. A total of 1,232 patients were analyzed: mean age 30.9 ±14.0 years, 60.0% were males. 61 patients (5.0% died during treatment, whereas the MDR-TB prevalence was 19.2%. MDR-TB increased the risk of death during treatment (HR = 7.5; IC95%: 4.1-13.4 when compared to presumed drug-susceptible cases after controlling for potential confounders. Education level (p = 0.01, previous TB episodes (p<0.001, diabetes history (p<0.001 and HIV infection (p = 0.04 were factors associated with mortality among MDR-TB cases.MDR-TB is associated with an increased risk of death during treatment. Lower education, greater number of previous TB episodes, diabetes history, and HIV infection were independently associated with mortality among MDR-TB cases. New strategies for appropriate MDR-TB detection and management should be implemented, including drug sensitivity tests, diabetes and HIV screening, as well as guarantee for a complete adherence to therapy.

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....

Predictive susceptibility analysis of typhoon induced landslides in Central Taiwan

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Shou, Keh-Jian; Lin, Zora

2017-04-01

Climate change caused by global warming affects Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary, such as 2004 Mindulle and 2009 Morakot, hit Taiwan and induced serious flooding and landslides. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the adopted Wu River watershed in Central Taiwan. To assess the spatial hazard of the landslides, landslide susceptibility analysis was also applied. Different types of rainfall factors were tested in the susceptibility models for a better accuracy. In addition, the routes of typhoons were also considered in the predictive analysis. The results of predictive analysis can be applied for risk prevention and management in the study area.

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

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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.

Drug susceptibility testing in microaerophilic parasites: Cysteine strongly affects the effectivities of metronidazole and auranofin, a novel and promising antimicrobial

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David Leitsch

2017-12-01

Full Text Available The microaerophilic parasites Entamoeba histolytica, Trichomonas vaginalis, and Giardia lamblia annually cause hundreds of millions of human infections which are treated with antiparasitic drugs. Metronidazole is the most often prescribed drug but also other drugs are in use, and novel drugs with improved characteristics are constantly being developed. One of these novel drugs is auranofin, originally an antirheumatic which has been relabelled for the treatment of parasitic infections. Drug effectivity is arguably the most important criterion for its applicability and is commonly assessed in susceptibility assays using in vitro cultures of a given pathogen. However, drug susceptibility assays can be strongly affected by certain compounds in the growth media. In the case of microaerophilic parasites, cysteine which is added in large amounts as an antioxidant is an obvious candidate because it is highly reactive and known to modulate the toxicity of metronidazole in several microaerophilic parasites.In this study, it was attempted to reduce cysteine concentrations as far as possible without affecting parasite viability by performing drug susceptibility assays under strictly anaerobic conditions in an anaerobic cabinet. Indeed, T. vaginalis and E. histolytica could be grown without any cysteine added and the cysteine concentration necessary to maintain G. lamblia could be reduced to 20%. Susceptibilities to metronidazole were found to be clearly reduced in the presence of cysteine. With auranofin the protective effect of cysteine was extreme, providing protection to concentrations up to 100-fold higher as observed in the absence of cysteine. With three other drugs tested, albendazole, furazolidone and nitazoxanide, all in use against G. lamblia, the effect of cysteine was less pronounced. Oxygen was found to have a less marked impact on metronidazole and auranofin than cysteine but bovine bile which is standardly used in growth media for G

Profiling gene expression of antimony response genes in Leishmania (Viannia) panamensis and infected macrophages and its relationship with drug susceptibility.

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Barrera, Maria Claudia; Rojas, Laura Jimena; Weiss, Austin; Fernandez, Olga; McMahon-Pratt, Diane; Saravia, Nancy G; Gomez, Maria Adelaida

2017-12-01

The mechanisms of Leishmania resistance to antimonials have been primarily determined in experimentally derived Leishmania strains. However, their participation in the susceptibility phenotype in field isolates has not been conclusively established. Being an intracellular parasite, the activity of antileishmanials is dependent on internalization of drugs into host cells and effective delivery to the intracellular compartments inhabited by the parasite. In this study we quantified and comparatively analyzed the gene expression of nine molecules involved in mechanisms of xenobiotic detoxification and Leishmania resistance to antimonial drugs in resistant and susceptible laboratory derived and clinical L.(Viannia) panamensis strains(n=19). In addition, we explored the impact of Leishmania susceptibility to antimonials on the expression of macrophage gene products having putative functions in transport, accumulation and metabolism of antimonials. As previously shown for other Leishmania species, a trend of increased abcc3 and lower aqp-1 expression was observed in the laboratory derived Sb-resistant L.(V.) panamensis line. However, this was not found in clinical strains, in which the expression of abca2 was significantly higher in resistant strains as both, promastigotes and intracellular amastigotes. The effect of drug susceptibility on host cell gene expression was evaluated on primary human macrophages from patients with cutaneous leishmaniasis (n=17) infected ex-vivo with the matched L.(V.) panamensis strains isolated at diagnosis, and in THP-1 cells infected with clinical strains (n=6) and laboratory adapted L.(V.) panamensis lines. Four molecules, abcb1 (p-gp), abcb6, aqp-9 and mt2a were differentially modulated by drug resistant and susceptible parasites, and among these, a consistent and significantly increased expression of the xenobiotic scavenging molecule mt2a was observed in macrophages infected with Sb-susceptible L. (V.) panamensis. Our results

Drug-Target Interactions: Prediction Methods and Applications.

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

2018-01-01

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

In vitro antimalarial drug susceptibility in Thai border areas from 1998–2003

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Mungthin Mathirut

2005-08-01

Full Text Available Abstract Background The Thai-Myanmar and Thai-Cambodia borders have been historically linked with the emergence and spread of Plasmodium falciparum parasites resistant to antimalarial drugs. Indeed, the areas are often described as harbouring multi-drug resistant parasites. These areas of Thailand have experienced significant changes in antimalarial drug exposure patterns over the past decade. This study describes the in vitro antimalarial susceptibility patterns of 95 laboratory-adapted P. falciparum isolates, collected between 1998 and 2003,. Methods Ninety five P. falciparum isolates were collected from five sites in Thailand between 1998 and 2003. After laboratory adaptation to in vitro culture, the susceptibility of these parasites to a range of established antimalarial drugs (chloroquine [CQ], mefloquine [MQ], quinine [QN] and dihydroartemisinin [DHA] was determined by the isotopic microtest. Results Mefloquine (MQ sensitivity remained poorest in areas previously described as MQ-resistant areas. Sensitivity to MQ of parasites from this area was significantly lower than those from areas reported to harbour moderate (p = 0.002 of low level MQ resistance (p = 000001. Importantly for all drugs tested, there was a considerable range in absolute parasite sensitivities. There was a weak, but statistically positive correlation between parasite sensitivity to CQ and sensitivity to both QN and MQ and a positive correlation between MQ and QN. In terms of geographical distribution, parasites from the Thai-Cambodia were tended to be less sensitive to all drugs tested compared to the Thai-Myanmar border. Parasite sensitivity to all drugs was stable over the 6-year collection period with the exception of QN. Conclusion This study highlights the high degree of variability in parasite drug sensitivity in Thailand. There were geographical differences in the pattern of resistance which might reflect differences in drug usage in each area. In contrast to many

A cost-effective smartphone-based antimicrobial susceptibility test reader for drug resistance testing (Conference Presentation)

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Feng, Steve W.; Tseng, Derek; Di Carlo, Dino; Garner, Omai B.; Ozcan, Aydogan

2017-03-01

Antimicrobial susceptibility testing (AST) is commonly used for determining microbial drug resistance, but routine testing, which can significantly reduce the spread of multi-drug resistant organisms, is not regularly performed in resource-limited and field-settings due to technological challenges and lack of trained diagnosticians. We developed a portable cost-effective smartphone-based colorimetric 96-well microtiter plate (MTP) reader capable of automated AST without the need for a trained diagnostician. This system is composed of a smartphone used in conjunction with a 3D-printed opto-mechanical attachment, which holds a set of inexpensive light-emitting-diodes and fiber-optic cables coupled to the 96-well MTP for enabling the capture of the transmitted light through each well by the smartphone camera. Images of the MTP plate are captured at multiple exposures and uploaded to a local or remote server (e.g., a laptop) for automated processing/analysis of the results using a custom-designed smartphone application. Each set of images are combined to generate a high dynamic-range image and analyzed for well turbidity (indicative of bacterial growth), followed by interpretative analysis per plate to determine minimum inhibitory concentration (MIC) and drug susceptibility for the specific bacterium. Results are returned to the originating device within 1 minute and shown to the user in tabular form. We demonstrated the capability of this platform using MTPs prepared with 17 antibiotic drugs targeting Gram-negative bacteria and tested 82 patient isolate MTPs of Klebsiella pneumoniae, achieving well turbidity accuracy of 98.19%, MIC accuracy of 95.15%, and drug susceptibility interpretation accuracy of 99.06%, meeting the FDA defined criteria for AST.

Drug-target interaction prediction: A Bayesian ranking approach.

Science.gov (United States)

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

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

Drug susceptibility testing of Mycobacterium Avium subsp. Avium isolates from naturally infected domestic pigeons to avian tuberculosis

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Kaveh Parvandar

2016-01-01

Conclusion: We suggest drug susceptibility testing for more nontuberculous mycobateria, particularly M. avium complex isolated from infected birds and humans, as well as molecular basics of drug sensitivity in order to detect resistance genes of pathogenic M. avium subsp. avium.

Evaluation of the microscopic observational drug susceptibility assay for rapid and efficient diagnosis of multi-drug resistant tuberculosis

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R P Lazarus

2012-01-01

Full Text Available Purpose: Tuberculosis (TB is endemic in India and the burden of multi-drug-resistant tuberculosis (MDR-TB is high. Early detection of MDR-TB is of primary importance in controlling the spread of TB. The microscopic observational drug susceptibility (MODS assay has been described as a cost-effective and rapid method by which mycobacterial culture and the drug susceptibility test (DST can be done at the same time. Materials and Methods: A total of 302 consecutive sputum samples that were received in an accredited mycobacteriology laboratory for conventional culture and DST were evaluated by the MODS assay. Results: In comparison with conventional culture on Lowenstein Jensen (LJ media, the MODS assay showed a sensitivity of 94.12% and a specificity of 89.39% and its concordance with the DST by the proportion method on LJ media to isoniazid and rifampicin was 90.8% and 91.5%, respectively. The turnaround time for results by MODS was 9 days compared to 21 days by culture on LJ media and an additional 42 days for DST by the 1% proportion method. The cost of performing a single MODS assay was Rs. 250/-, compared to Rs. 950/- for culture and 1st line DST on LJ. Conclusion: MODS was found to be a sensitive and rapid alternative method for performing culture and DST to identify MDR-TB in resource poor settings.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

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Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

(fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

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Elisa Passini

2017-09-01

(fast/late Na+ and Ca2+ currents exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca2+-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

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; ...

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2012-11-08

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

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

Susceptibility Testing by Polymerase Chain Reaction DNA Quantitation: A Method to Measure Drug Resistance of Human Immunodeficiency Virus Type 1 Isolates

Science.gov (United States)

Eron, Joseph J.; Gorczyca, Paul; Kaplan, Joan C.; D'Aquila, Richard T.

1992-04-01

Polymerase chain reaction (PCR) DNA quantitation (PDQ) susceptibility testing rapidly and directly measures nucleoside sensitivity of human immunodeficiency virus type 1 (HIV-1) isolates. PCR is used to quantitate the amount of HIV-1 DNA synthesized after in vitro infection of peripheral blood mononuclear cells. The relative amounts of HIV-1 DNA in cell lysates from cultures maintained at different drug concentrations reflect drug inhibition of virus replication. The results of PDQ susceptibility testing of 2- or 3-day cultures are supported by assays measuring HIV-1 p24 antigen production in supernatants of 7- or 10-day cultures. DNA sequence analyses to identify mutations in the reverse transcriptase gene that cause resistance to 3'-azido-3'-deoxythymidine also support the PDQ results. With the PDQ method, both infectivity titration and susceptibility testing can be performed on supernatants from primary cultures of peripheral blood mononuclear cells. PDQ susceptibility testing should facilitate epidemiologic studies of the clinical significance of drug-resistant HIV-1 isolates.

The value of microscopic-observation drug susceptibility assay in the diagnosis of tuberculosis and detection of multidrug resistance.

Science.gov (United States)

Sertel Şelale, Denİz; Uzun, Meltem

2018-01-01

Inexpensive, rapid, and reliable tests for detecting the presence and drug susceptibility of Mycobacterium tuberculosis complex (MTBC) are urgently needed to control the transmission of tuberculosis. In this study, we aimed to assess the accuracy and speed of the microscopic-observation drug susceptibility (MODS) assay in the identification of MTBC and detection of multidrug resistance. Sputum samples from patients suspected to have tuberculosis were simultaneously tested with MODS and conventional culture [Löwenstein-Jensen (LJ) culture, BACTEC MGIT™ 960 (MGIT) system], and drug susceptibility testing (MGIT system) methods. A total of 331 sputum samples were analyzed. Sensitivity and specificity of MODS assay for detection of MTBC strains were 96% and 98.8%, respectively. MODS assay detected multidrug resistant MTBC isolates with 92.3% sensitivity and 96.6% specificity. Median time to culture positivity was similar for MGIT (8 days) and MODS culture (8 days), but was significantly longer with LJ culture (20 days) (p tuberculosis and detection of multidrug resistance. © 2017 APMIS. Published by John Wiley & Sons Ltd.

[Antimicrobial susceptibility and drug-resistance genes of Yersinia spp. of retailed poultry in 4 provinces of China].

Science.gov (United States)

Peng, Z X; Zou, M Y; Xu, J; Guan, W Y; Li, Y; Liu, D R; Zhang, S S; Hao, Q; Yan, S F; Wang, W; Yu, D M; Li, F Q

2018-04-06

Objective: To monitor the antimicrobial resistance and drug-resistance genes of Yersinia enterocolitis , Y. intermedia and Y. frederiksenii recovered from retailed fresh poultry of 4 provinces of China. Methods: The susceptibility of 25 isolated Yersinia spp. to 14 classes and 25 kinds of antibiotics was determined by broth microdilution method according to CLSI (Clinical and Laboratory Standards Institute). The antibiotic resistance genes were predicted with antibiotic resistance genes database (ARDB) using whole genome sequences of Yersinia spp. Results: In all 22 Y. enterocolitis tested, 63.7% (14 isolates), 22.8% (5 isolates), 4.6% and 4.6% of 1 isolates exhibited the resistance to cefoxitin, ampicillin-sulbactam, nitrofurantoin and trimethoprim-sulfamethoxazole, respectively. All the 25 isolates were multi-drug resistant to more than 3 antibiotics, while 64.0% of isolates were resistant to more than 4 antibiotics. A few Y. enterocolitis isolates of this study were intermediate to ceftriaxone and ciprofloxacin. Most Yersinia spp. isolates contained antibiotic resistance genes mdtG, ksgA, bacA, blaA, rosAB and acrB , and 5 isolates recovered from fresh chicken also contained dfrA 1, catB 2 and ant 3 ia . Conclusion: The multi-drug resistant Yersinia spp. isolated from retailed fresh poultry is very serious in the 4 provinces of China, and their contained many kinds of drug-resistance genes.

In vitro susceptibility of nematophagous fungi to antiparasitic drugs: interactions and implications for biological control

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J. N. Vieira

Full Text Available Abstract The fast anthelmintic resistance development has shown a limited efficiency in the control of animal’s endoparasitosis and has promoted research using alternative control methods. The use of chemicals in animal anthelmintic treatment, in association with nematophagous fungi used for biological control, is a strategy that has proven to be effective in reducing the nematode population density in farm animals. This study aims to verify the in vitro susceptibility of the nematophagous fungi Arthrobotrys oligospora, Duddingtonia flagrans and Paecilomyces lilacinus against the antiparasitic drugs albendazole, thiabendazole, ivermectin, levamisole and closantel by using the Minimum Inhibitory Concentration (MIC. MICs ranged between 4.0 and 0.031 µg/mL for albendazole, thiabendazole and ivermectin, between 0.937 and 0.117 µg/mL for levamisole, and between 0.625 and 0.034 µg/mL for closantel. The results showed that all antiparasitic drugs had an in vitro inhibitory effect on nematophagous fungi, which could compromise their action as agents of biological control. D. flagrans was the most susceptible species to all drugs.

Curiosity predicts smoking experimentation independent of susceptibility in a US national sample.

Science.gov (United States)

Nodora, Jesse; Hartman, Sheri J; Strong, David R; Messer, Karen; Vera, Lisa E; White, Martha M; Portnoy, David B; Choiniere, Conrad J; Vullo, Genevieve C; Pierce, John P

2014-12-01

To improve smoking prevention efforts, better methods for identifying at-risk youth are needed. The widely used measure of susceptibility to smoking identifies at-risk adolescents; however, it correctly identifies only about one third of future smokers. Adding curiosity about smoking to this susceptibility index may allow us to identify a greater proportion of future smokers while they are still pre-teens. We use longitudinal data from a recent national study on parenting to prevent problem behaviors. Only oldest children between 10 and 13years of age were eligible. Participants were identified by RDD survey and followed for 6years. All baseline never smokers with at least one follow-up assessment were included (n=878). The association of curiosity about smoking with future smoking behavior was assessed. Then, curiosity was added to form an enhanced susceptibility index and sensitivity, specificity and positive predictive value were calculated. Among committed never smokers at baseline, those who were 'definitely not curious' were less likely to progress toward smoking than both those who were 'probably not curious' (ORadj=1.89; 95% CI=1.03-3.47) or 'probably/definitely curious' (ORadj=2.88; 95% CI=1.11-7.45). Incorporating curiosity into the susceptibility index increased the proportion identified as at-risk to smoke from 25.1% to 46.9%. The sensitivity (true positives) for this enhanced susceptibility index for both experimentation and established smoking increased from 37-40% to over 50%, although the positive predictive value did not improve. The addition of curiosity significantly improves the identification and classification of which adolescents will experiment with smoking or become established smokers. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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

2017-09-18

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

Candida Species From Eye Infections: Drug Susceptibility, Virulence Factors, and Molecular Characterization.

Science.gov (United States)

Ranjith, Konduri; Sontam, Bhavani; Sharma, Savitri; Joseph, Joveeta; Chathoth, Kanchana N; Sama, Kalyana C; Murthy, Somasheila I; Shivaji, Sisinthy

2017-08-01

To determine the type of Candida species in ocular infections and to investigate the relationship of antifungal susceptibility profile to virulence factors. Fifty isolates of yeast-like fungi from patients with keratitis, endophthalmitis, and orbital cellulitis were identified by Vitek-2 compact system and DNA sequencing of ITS1-5.8S-ITS2 regions of the rRNA gene, followed by phylogenetic analysis for phenotypic and genotypic identification, respectively. Minimum inhibitory concentration of six antifungal drugs was determined by E test/microbroth dilution methods. Phenotypic and genotypic methods were used to determine the virulence factors. Phylogenetic analysis showed the clustering of all isolates into eight distinct groups with a major cluster formed Candida parapsilosis (n = 21), which was the most common species by both Vitek 2 and DNA sequencing. Using χ2 test no significant difference was noted between the techniques except that Vitek 2 did not identify C. viswanathii, C. orthopsilosis, and two non-Candida genera. Of 43 tested Candida isolates high susceptibility to amphotericin B (39/43, 90.6%) and natamycin (43/43, 100%) was noted. While none of the isolates produced coagulase, all produced esterase and catalase. The potential to form biofilm was detected in 23/43 (53.4%) isolates. Distribution of virulence factors by heat map analysis showed difference in metabolic activity of biofilm producers from nonbiofilm producers. Identified by Vitek 2 and DNA sequencing methods C. parapsilosis was the most common species associated with eye infections. Irrespective of the virulence factors elaborated, the Candida isolates were susceptible to commonly used antifungal drugs such as amphotericin B and natamycin.

Prediction of potential drug targets based on simple sequence properties

Directory of Open Access Journals (Sweden)

Lai Luhua

2007-09-01

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

Differential Response of Coral Assemblages to Thermal Stress Underscores the Complexity in Predicting Bleaching Susceptibility.

Science.gov (United States)

Chou, Loke Ming; Toh, Tai Chong; Toh, Kok Ben; Ng, Chin Soon Lionel; Cabaitan, Patrick; Tun, Karenne; Goh, Eugene; Afiq-Rosli, Lutfi; Taira, Daisuke; Du, Rosa Celia Poquita; Loke, Hai Xin; Khalis, Aizat; Li, Jinghan; Song, Tiancheng

2016-01-01

Coral bleaching events have been predicted to occur more frequently in the coming decades with global warming. The susceptibility of corals to bleaching during thermal stress episodes is dependent on many factors and an understanding of these underlying drivers is crucial for conservation management. In 2013, a mild bleaching episode ensued in response to elevated sea temperature on the sediment-burdened reefs in Singapore. Surveys of seven sites highlighted variable bleaching susceptibility among coral genera-Pachyseris and Podabacia were the most impacted (31% of colonies of both genera bleached). The most susceptible genera such as Acropora and Pocillopora, which were expected to bleach, did not. Susceptibility varied between less than 6% and more than 11% of the corals bleached, at four and three sites respectively. Analysis of four of the most bleached genera revealed that a statistical model that included a combination of the factors (genus, colony size and site) provided a better explanation of the observed bleaching patterns than any single factor alone. This underscored the complexity in predicting the coral susceptibility to future thermal stress events and the importance of monitoring coral bleaching episodes to facilitate more effective management of coral reefs under climate change.

Differential Response of Coral Assemblages to Thermal Stress Underscores the Complexity in Predicting Bleaching Susceptibility

Science.gov (United States)

Toh, Kok Ben; Ng, Chin Soon Lionel; Cabaitan, Patrick; Tun, Karenne; Goh, Eugene; Afiq-Rosli, Lutfi; Taira, Daisuke; Du, Rosa Celia Poquita; Loke, Hai Xin; Khalis, Aizat; Li, Jinghan; Song, Tiancheng

2016-01-01

Coral bleaching events have been predicted to occur more frequently in the coming decades with global warming. The susceptibility of corals to bleaching during thermal stress episodes is dependent on many factors and an understanding of these underlying drivers is crucial for conservation management. In 2013, a mild bleaching episode ensued in response to elevated sea temperature on the sediment-burdened reefs in Singapore. Surveys of seven sites highlighted variable bleaching susceptibility among coral genera–Pachyseris and Podabacia were the most impacted (31% of colonies of both genera bleached). The most susceptible genera such as Acropora and Pocillopora, which were expected to bleach, did not. Susceptibility varied between less than 6% and more than 11% of the corals bleached, at four and three sites respectively. Analysis of four of the most bleached genera revealed that a statistical model that included a combination of the factors (genus, colony size and site) provided a better explanation of the observed bleaching patterns than any single factor alone. This underscored the complexity in predicting the coral susceptibility to future thermal stress events and the importance of monitoring coral bleaching episodes to facilitate more effective management of coral reefs under climate change. PMID:27438593

Radiometric studies on the oxidation of (I-14C) fatty acids by drug-susceptible and drug-resistant mycobacteria

International Nuclear Information System (INIS)

Camargo, E.E.; Kopajtic, T.M.; Hopkins, G.K.; Cannon, N.P.; Wagner Junior, H.N.

1987-01-01

A radiometric assay system has been used to study oxidation patterns of (l - 14 C) fatty acids by drug-susceptible and drug-resistant organisms of the genus Mycobacterium (M. tuberculosis - H 37 Rv and Erdman, M. bovis, M. avium, M. intracellulare, M.Kansasii and M. chelonei). The organisms were inoculated in sterile reaction vials containing liquid 7H9 medium, 10% ADC enrichment and 1.0 uli of one of the (l- 14 C) fatty acids (butyric, hexanoic, octanoic, decanoic, lauric, myristic, palmitic, stearic, oleic, linoleic, linolenic). Vials were incubated at 37 0 C and the 14 CO 2 envolved was measured daily for 3 days with a Bactec R-301 instrument. (M.A.C.) [pt

[Identification and drug susceptibility testing of Mycobacterium thermoresistibile and Mycobacterium elephantis isolated from a cow with mastitis].

Science.gov (United States)

Li, W B; Ji, L Y; Xu, D L; Liu, H C; Zhao, X Q; Wu, Y M; Wan, K L

2018-05-10

Objective: To understand the etiological characteristics and drug susceptibility of Mycobacterium thermoresistibile and Mycobacterium elephantis isolated from a cow with mastitis and provide evidence for the prevention and control of infectious mastitis in cows. Methods: The milk sample was collected from a cow with mastitis, which was pretreated with 4 % NaOH and inoculated with L-J medium for Mycobacterium isolation. The positive cultures were initially identified by acid-fast staining and multi-loci PCR, then Mycobacterium species was identified by the multiple loci sequence analysis (MLSA) with 16S rRNA , hsp65 , ITS and SodA genes. The drug sensitivity of the isolates to 27 antibiotics was tested by alamar blue assay. Results: Two anti-acid stain positive strains were isolated from the milk of a cow with mastitis, which were identified as non- tuberculosis mycobacterium by multi-loci PCR, and multi-loci nucleic acid sequence analysis indicated that one strain was Mycobacterium thermoresistibile and another one was Mycobacterium elephantis . The results of the drug susceptibility test showed that the two strains were resistant to most antibiotics, including rifampicin and isoniazid, but they were sensitive to amikacin, moxifloxacin, levofloxacin, ethambutol, streptomycin, tobramycin, ciprofloxacin and linezolid. Conclusions: Mycobacterium thermoresistibile and Mycobacterium elephantis were isolated in a cow with mastitis and the drug susceptibility spectrum of the pathogens were unique. The results of the study can be used as reference for the prevention and control the infection in cows.

Drug Susceptibility Testing of 31 Antimicrobial Agents on Rapidly Growing Mycobacteria Isolates from China.

Science.gov (United States)

Pang, Hui; Li, Guilian; Zhao, Xiuqin; Liu, Haican; Wan, Kanglin; Yu, Ping

2015-01-01

Several species of rapidly growing mycobacteria (RGM) are now recognized as human pathogens. However, limited data on effective drug treatments against these organisms exists. Here, we describe the species distribution and drug susceptibility profiles of RGM clinical isolates collected from four southern Chinese provinces from January 2005 to December 2012. Clinical isolates (73) were subjected to in vitro testing with 31 antimicrobial agents using the cation-adjusted Mueller-Hinton broth microdilution method. The isolates included 55 M. abscessus, 11 M. fortuitum, 3 M. chelonae, 2 M. neoaurum, and 2 M. septicum isolates. M. abscessus (75.34%) and M. fortuitum (15.07%), the most common species, exhibited greater antibiotic resistance than the other three species. The isolates had low resistance to amikacin, linezolid, and tigecycline, and high resistance to first-line antituberculous agents, amoxicillin-clavulanic acid, rifapentine, dapsone, thioacetazone, and pasiniazid. M. abscessus and M. fortuitum were highly resistant to ofloxacin and rifabutin, respectively. The isolates showed moderate resistance to the other antimicrobial agents. Our results suggest that tigecycline, linezolid, clofazimine, and cefmetazole are appropriate choices for M. abscessus infections. Capreomycin, sulfamethoxazole, tigecycline, clofazimine, and cefmetazole are potentially good choices for M. fortuitum infections. Our drug susceptibility data should be useful to clinicians.

Drug Susceptibility Testing of 31 Antimicrobial Agents on Rapidly Growing Mycobacteria Isolates from China

Directory of Open Access Journals (Sweden)

Hui Pang

2015-01-01

Full Text Available Objectives. Several species of rapidly growing mycobacteria (RGM are now recognized as human pathogens. However, limited data on effective drug treatments against these organisms exists. Here, we describe the species distribution and drug susceptibility profiles of RGM clinical isolates collected from four southern Chinese provinces from January 2005 to December 2012. Methods. Clinical isolates (73 were subjected to in vitro testing with 31 antimicrobial agents using the cation-adjusted Mueller-Hinton broth microdilution method. The isolates included 55 M. abscessus, 11 M. fortuitum, 3 M. chelonae, 2 M. neoaurum, and 2 M. septicum isolates. Results. M. abscessus (75.34% and M. fortuitum (15.07%, the most common species, exhibited greater antibiotic resistance than the other three species. The isolates had low resistance to amikacin, linezolid, and tigecycline, and high resistance to first-line antituberculous agents, amoxicillin-clavulanic acid, rifapentine, dapsone, thioacetazone, and pasiniazid. M. abscessus and M. fortuitum were highly resistant to ofloxacin and rifabutin, respectively. The isolates showed moderate resistance to the other antimicrobial agents. Conclusions. Our results suggest that tigecycline, linezolid, clofazimine, and cefmetazole are appropriate choices for M. abscessus infections. Capreomycin, sulfamethoxazole, tigecycline, clofazimine, and cefmetazole are potentially good choices for M. fortuitum infections. Our drug susceptibility data should be useful to clinicians.

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.

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

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

2016-12-22

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

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.

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.

Gaussian interaction profile kernels for predicting drug-target interaction.

Science.gov (United States)

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

2011-11-01

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

Differential Response of Coral Assemblages to Thermal Stress Underscores the Complexity in Predicting Bleaching Susceptibility.

Directory of Open Access Journals (Sweden)

Loke Ming Chou

Full Text Available Coral bleaching events have been predicted to occur more frequently in the coming decades with global warming. The susceptibility of corals to bleaching during thermal stress episodes is dependent on many factors and an understanding of these underlying drivers is crucial for conservation management. In 2013, a mild bleaching episode ensued in response to elevated sea temperature on the sediment-burdened reefs in Singapore. Surveys of seven sites highlighted variable bleaching susceptibility among coral genera-Pachyseris and Podabacia were the most impacted (31% of colonies of both genera bleached. The most susceptible genera such as Acropora and Pocillopora, which were expected to bleach, did not. Susceptibility varied between less than 6% and more than 11% of the corals bleached, at four and three sites respectively. Analysis of four of the most bleached genera revealed that a statistical model that included a combination of the factors (genus, colony size and site provided a better explanation of the observed bleaching patterns than any single factor alone. This underscored the complexity in predicting the coral susceptibility to future thermal stress events and the importance of monitoring coral bleaching episodes to facilitate more effective management of coral reefs under climate change.

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

Science.gov (United States)

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

2015-07-15

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

Rapid, radiolabeled-microculture method that uses macrophages for in vitro evaluation of Mycobacterium leprae viability and drug susceptibility.

Science.gov (United States)

Mittal, A; Sathish, M; Seshadri, P S; Nath, I

1983-04-01

This paper describes a microculture rapid assay using radiolabeling and mouse macrophages to determine the viability and the drug susceptibility or resistance of Mycobacterium leprae. Comparison of M. leprae resident macrophage cultures maintained in 96-well flat-bottomed plates showed results for viability and susceptibility or resistance to dapsone that were similar to results for concurrent cultures in Leighton tubes with greater numbers of bacilli and macrophages.

Rapid, Radiolabeled-Microculture Method That Uses Macrophages for In Vitro Evaluation of Mycobacterium leprae Viability and Drug Susceptibility

OpenAIRE

Mittal, A.; Sathish, M.; Seshadri, P. S.; Nath, Indira

1983-01-01

This paper describes a microculture rapid assay using radiolabeling and mouse macrophages to determine the viability and the drug susceptibility or resistance of Mycobacterium leprae. Comparison of M. leprae resident macrophage cultures maintained in 96-well flat-bottomed plates showed results for viability and susceptibility or resistance to dapsone that were similar to results for concurrent cultures in Leighton tubes with greater numbers of bacilli and macrophages.

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

Science.gov (United States)

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

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

Exploring the Predictive Validity of the Susceptibility to Smoking Construct for Tobacco Cigarettes, Alternative Tobacco Products, and E-Cigarettes.

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Cole, Adam G; Kennedy, Ryan David; Chaurasia, Ashok; Leatherdale, Scott T

2017-12-06

Within tobacco prevention programming, it is useful to identify youth that are at risk for experimenting with various tobacco products and e-cigarettes. The susceptibility to smoking construct is a simple method to identify never-smoking students that are less committed to remaining smoke-free. However, the predictive validity of this construct has not been tested within the Canadian context or for the use of other tobacco products and e-cigarettes. This study used a large, longitudinal sample of secondary school students that reported never using tobacco cigarettes and non-current use of alternative tobacco products or e-cigarettes at baseline in Ontario, Canada. The sensitivity, specificity, and positive and negative predictive values of the susceptibility construct for predicting tobacco cigarette, e-cigarette, cigarillo or little cigar, cigar, hookah, and smokeless tobacco use one and two years after baseline measurement were calculated. At baseline, 29.4% of the sample was susceptible to future tobacco product or e-cigarette use. The sensitivity of the construct ranged from 43.2% (smokeless tobacco) to 59.5% (tobacco cigarettes), the specificity ranged from 70.9% (smokeless tobacco) to 75.9% (tobacco cigarettes), and the positive predictive value ranged from 2.6% (smokeless tobacco) to 32.2% (tobacco cigarettes). Similar values were calculated for each measure of the susceptibility construct. A significant number of youth that did not currently use tobacco products or e-cigarettes at baseline reported using tobacco products and e-cigarettes over a two-year follow-up period. The predictive validity of the susceptibility construct was high and the construct can be used to predict other tobacco product and e-cigarette use among youth. This study presents the predictive validity of the susceptibility construct for the use of tobacco cigarettes among secondary school students in Ontario, Canada. It also presents a novel use of the susceptibility construct for

A New Criterion for Prediction of Hot Tearing Susceptibility of Cast Alloys

Science.gov (United States)

Nasresfahani, Mohamad Reza; Niroumand, Behzad

2014-08-01

A new criterion for prediction of hot tearing susceptibility of cast alloys is suggested which takes into account the effects of both important mechanical and metallurgical factors and is believed to be less sensitive to the presence of volume defects such as bifilms and inclusions. The criterion was validated by studying the hot tearing tendency of Al-Cu alloy. In conformity with the experimental results, the new criterion predicted reduction of hot tearing tendency with increasing the copper content.

The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

OpenAIRE

Mahmoud Shirazi; Monavar Janfaza

2015-01-01

Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of t...

Genotyping and drug susceptibility testing of mycobacterial isolates from population-based tuberculosis prevalence survey in Ghana.

Science.gov (United States)

Addo, Kennedy Kwasi; Addo, Samuel Ofori; Mensah, Gloria Ivy; Mosi, Lydia; Bonsu, Frank Adae

2017-12-02

Mycobacterium tuberculosis complex (MTBC) and Non-tuberculosis Mycobacterium (NTM) infections differ clinically, making rapid identification and drug susceptibility testing (DST) very critical for infection control and drug therapy. This study aims to use World Health Organization (WHO) approved line probe assay (LPA) to differentiate mycobacterial isolates obtained from tuberculosis (TB) prevalence survey in Ghana and to determine their drug resistance patterns. A retrospective study was conducted whereby a total of 361 mycobacterial isolates were differentiated and their drug resistance patterns determined using GenoType Mycobacterium Assays: MTBC and CM/AS for differentiating MTBC and NTM as well MTBDRplus and NTM-DR for DST of MTBC and NTM respectively. Out of 361 isolates, 165 (45.7%) MTBC and 120 (33.2%) NTM (made up of 14 different species) were identified to the species levels whiles 76 (21.1%) could not be completely identified. The MTBC comprised 161 (97.6%) Mycobacterium tuberculosis and 4 (2.4%) Mycobacterium africanum. Isoniazid and rifampicin monoresistant MTBC isolates were 18/165 (10.9%) and 2/165(1.2%) respectively whiles 11/165 (6.7%) were resistant to both drugs. Majority 42/120 (35%) of NTM were M. fortuitum. DST of 28 M. avium complex and 8 M. abscessus complex species revealed that all were susceptible to macrolides (clarithromycin, azithromycin) and aminoglycosides (kanamycin, amikacin, and gentamicin). Our research signifies an important contribution to TB control in terms of knowledge of the types of mycobacterium species circulating and their drug resistance patterns in Ghana.

Novel Methods for Drug-Target Interaction Prediction using Graph Mining

KAUST Repository

Ba Alawi, Wail

2016-08-31

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

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.

Drug-target interaction prediction from PSSM based evolutionary information.

Science.gov (United States)

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

2016-01-01

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

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.

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization.

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

2017-01-01

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

Characterization of drug susceptibility of Mycobacterium tuberculosis isolated from new cases of tuberculosis concurrent with HIV infection

Directory of Open Access Journals (Sweden)

G. V. Panov

2015-01-01

Full Text Available The paper characterizes drug susceptibility in Mycobacterium tuberculosis isolated from new cases of tuberculosis concurrent with HIV infection. The investigators have studied the spectrum of drug resistance in Mycobacterium tuberculosis isolated from new cases of tuberculosis concurrent with and without HIV infection (172 and 309 clinical isolates, respectively. There are differences in the rate of primary drug resistance to antituberculosis drugs in patients with and without HIV infection (59 and 43.5% of the cases, respectively. The HIV-infected have also shown high rifampicin resistance rates in Mycobacterium tuberculosis (41.7%. The reasons for these differences are as yet unknown and call for further investigation.

Analysis of metal and biocides resistance genes in drug resistance and susceptible Salmonella enterica from food animals

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Background Generally drug resistant bacteria carry antibiotic resistance genes and heavy metal and biocide resistance genes on large conjugative plasmids. The presence of these metal and biocide resistance genes in susceptible bacteria are not assessed comprehensively. Hence, WGS data of susceptib...

Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

Science.gov (United States)

Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

2017-10-01

In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

Pathogens causing blood stream infections and their drug susceptibility profile in immunocompromised patients.

Science.gov (United States)

Fayyaz, Muhammad; Mirza, Irfan Ali; Ikram, Aamer; Hussain, Aamir; Ghafoor, Tahir; Shujat, Umer

2013-12-01

To determine the types of pathogens causing blood stream infections and their drug susceptibility profile in immunocompromised patients. Cross-sectional, observational study. Department of Microbiology, Armed Forces Institute of Pathology, Rawalpindi, from January to September 2012. Blood culture bottles received from immunocompromised patients were dealt by two methods, brain heart infusion (BHI) broth based manual method and automated BACTEC system. The samples yielding positive growth from either of two methods were further analyzed. The identification of isolates was done with the help of biochemical reactions and rapid tests. Antimicrobial susceptibility of the isolates was carried out as per recommendations of Clinical and Laboratory Standards Institute (CLSI). Out of the 938 blood culture specimens received from immunocompromised patients, 188 (20%) yielded positive growth. Out of these, 89 (47.3%) isolates were Gram positive and Gram negative each, while 10 (5.3%) isolates were fungi (Candida spp.). In case of Gram positive isolates, 75 (84.3%) were Staphylococcus spp. and 51 (67%) were Methicillin resistant. Amongst Gram negative group 49 (55.1%) isolates were of enterobacteriaceae family, while 40 (44.9%) were non-lactose fermenters (NLF). In vitro antimicrobial susceptibility of Staphylococci revealed 100% susceptibility to vancomycin and linezolid. The enterobacteriaceae isolates had better susceptibility against amikacin 85.7% compared to tigecycline 61.2% and imipenem 59.2%. For NLF, the in vitro efficacy of aminoglycosides was 72.5%. The frequency of Gram positive and Gram negative organisms causing blood stream infections in immunocompromised patients was equal. Vancomycin in case of Gram positive and amikacin for Gram negative organisms revealed better in vitro efficacy as compared to other antibiotics.

Pathogens Causing Blood Stream Infections and their Drug Susceptibility Profile in Immunocompromised Patients

International Nuclear Information System (INIS)

Fayyaz, M.; Mirza, I.A.; Ikram, A.; Hussain, A.; Ghafoor, T.; Shujat, U.

2013-01-01

Objective: To determine the types of pathogens causing blood stream infections and their drug susceptibility profile in immunocompromised patients. Study Design: Cross-sectional, observational study. Place and Duration of Study: Department of Microbiology, Armed Forces Institute of Pathology, Rawalpindi, from January to September 2012. Methodology: Blood culture bottles received from immunocompromised patients were dealt by two methods, brain heart infusion (BHI) broth based manual method and automated BACTEC system. The samples yielding positive growth from either of two methods were further analyzed. The identification of isolates was done with the help of biochemical reactions and rapid tests. Antimicrobial susceptibility of the isolates was carried out as per recommendations of Clinical and Laboratory Standards Institute (CLSI). Results: Out of the 938 blood culture specimens received from immunocompromised patients, 188 (20%) yielded positive growth. Out of these, 89 (47.3%) isolates were Gram positive and Gram negative each, while 10 (5.3%) isolates were fungi (Candida spp.). In case of Gram positive isolates, 75 (84.3%) were Staphylococcus spp. and 51 (67%) were Methicillin resistant. Amongst Gram negative group 49 (55.1%) isolates were of enterobacteriaceae family, while 40 (44.9%) were non-lactose fermenters (NLF). In vitro antimicrobial susceptibility of Staphylococci revealed 100% susceptibility to vancomycin and linezolid. The enterobacteriaceae isolates had better susceptibility against amikacin 85.7% compared to tigecycline 61.2% and imipenem 59.2%. For NLF, the in vitro efficacy of aminoglycosides was 72.5%. Conclusion: The frequency of Gram positive and Gram negative organisms causing blood stream infections in immunocompromised patients was equal. Vancomycin in case of Gram positive and amikacin for Gram negative organisms revealed better in vitro efficacy as compared to other antibiotics. (author)

Ertapenem susceptibility of extended spectrum beta-lactamase-producing organisms

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Selby Edward B

2007-06-01

Full Text Available Abstract Background Infections caused by multiply drug resistant organisms such as extended spectrum beta-lactamase (ESBL-producing Escherichia coli and Klebsiella pneumoniae are increasing. Carbapenems (imipenem and meropenem are the antibiotics commonly used to treat these agents. There is limited clinical data regarding the efficacy of the newest carbapenem, ertapenem, against these organisms. Ertapenem susceptibility of ESBL-producing E. coli and K. pneumoniae clinical isolates were evaluated and compared to imipenem to determine if imipenem susceptibility could be used as a surrogate for ertapenem susceptibility. Methods 100 ESBL isolates (n = 34 E. coli and n = 66 K. pneumoniae collected from 2005–2006 clinical specimens at WRAMC were identified and tested for susceptibility by Vitek Legacy [bioMerieux, Durham, NC]. Ertapenem susceptibility was performed via epsilometer test (E-test [AB Biodisk, Solna, Sweden]. Results 100% of ESBL isolates tested were susceptible to ertapenem. 100% of the same isolates were also susceptible to imipenem. Conclusion These results, based on 100% susceptibility, suggest that ertapenem may be an alternative to other carbapenems for the treatment of infections caused by ESBL-producing E. coli and K. pneumoniae. Clinical outcomes studies are needed to determine if ertapenem is effective for the treatment of infection caused by these organisms. However, due to lack of resistant isolates, we are unable to conclude whether imipenem susceptibility accurately predicts ertapenem susceptibility.

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

Science.gov (United States)

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

2016-02-01

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

A simple field kit for the determination of drug susceptibility in Plasmodium falciparum.

Science.gov (United States)

Nguyen-Dinh, P; Magloire, R; Chin, W

1983-05-01

A field kit has been developed which greatly simplifies the performance of the 48-hour in vitro test for drug resistance in Plasmodium falciparum. The kit uses an easily reconstituted lyophilized culture medium, and requires only a fingerprick blood sample. In parallel tests with 13 isolates of P. falciparum in Haiti, the new technique had a success rate equal to that of the previously described method, with comparable results in terms of parasite susceptibility in vitro to chloroquine and pyrimethamine.

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.

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.

Molecular characterization and drug susceptibility profile of a Mycobacterium avium subspecies avium isolate from a dog with disseminated infection.

Science.gov (United States)

Armas, Federica; Furlanello, Tommaso; Camperio, Cristina; Trotta, Michele; Novari, Gianluca; Marianelli, Cinzia

2016-01-12

Mycobacterium avium complex (MAC) infections have been described in many mammalian species including humans and pets. We isolated and molecularly typed the causative agent of a rare case of disseminated mycobacteriosis in a dog. We identified the pathogen as a M. avium subspecies avium by sequencing the partial genes gyrB and rpsA. Considering the zoonotic potential of this infection, and in an attempt to ensure the most effective treatment for the animal, we also determined the drug susceptibility profile of the isolate to the most common drugs used to treat MAC disease in humans. The pathogen was tested in vitro against the macrolide clarithromycin, as well as against amikacin, ciprofloxacin, rifampicin, ethambutol and linezolid by the resazurin microdilution assay. It was found to be sensitive to all tested drugs save ethambutol. Despite the fact that the pathogen was sensitive to the therapies administered, the dog's overall clinical status worsened, and the animal died shortly after antimicrobial susceptibility results became available. Nucleotide sequencing of the embB gene, the target gene most commonly associated with ethambutol resistance, showed new missense mutations when compared to sequences available in public databases. In conclusion, we molecularly identified the MAC pathogen and determined its drug susceptibility profile in a relatively short period of time (seven days). We also characterized new genetic mutations likely to have been involved in the observed ethambutol resistance. Our results confirm the usefulness of both the gyrB and the rpsA genes as biomarkers for an accurate identification and differentiation of MAC pathogens.

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...

Polymorphism in Mitochondrial Group I Introns among Cryptococcus neoformans and Cryptococcus gattii Genotypes and Its Association with Drug Susceptibility

Directory of Open Access Journals (Sweden)

Felipe E. E. S. Gomes

2018-02-01

Full Text Available Cryptococcosis, one of the most important systemic mycosis in the world, is caused by different genotypes of Cryptococcus neoformans and Cryptococcus gattii, which differ in their ecology, epidemiology, and antifungal susceptibility. Therefore, the search for new molecular markers for genotyping, pathogenicity and drug susceptibility is necessary. Group I introns fulfill the requisites for such task because (i they are polymorphic sequences; (ii their self-splicing is inhibited by some drugs; and (iii their correct splicing under parasitic conditions is indispensable for pathogen survival. Here, we investigated the presence of group I introns in the mitochondrial LSU rRNA gene in 77 Cryptococcus isolates and its possible relation to drug susceptibility. Sequencing revealed two new introns in the LSU rRNA gene. All the introns showed high sequence similarity to other mitochondrial introns from distinct fungi, supporting the hypothesis of an ancient non-allelic invasion. Intron presence was statistically associated with those genotypes reported to be less pathogenic (p < 0.001. Further virulence assays are needed to confirm this finding. In addition, in vitro antifungal tests indicated that the presence of LSU rRNA introns may influence the minimum inhibitory concentration (MIC of amphotericin B and 5-fluorocytosine. These findings point to group I introns in the mitochondrial genome of Cryptococcus as potential molecular markers for antifungal resistance, as well as therapeutic targets.

An in vitro study of antifungal drug susceptibility of Candida species isolated from human immunodeficiency virus seropositive and human immunodeficiency virus seronegative individuals in Lucknow population Uttar Pradesh.

Science.gov (United States)

Dar, Mohammad Shafi; Sreedar, Gadiputi; Shukla, Abhilasha; Gupta, Prashant; Rehan, Ahmad Danish; George, Jiji

2015-01-01

Candidiasis is the most common opportunistic infection in human immunodeficiency virus (HIV) seropositive patients, starting from asymptomatic colonization to pathogenic forms and gradual colonization of non-albicans in patients with advanced immunosuppression leads to resistance for azole group of antifungal drugs with high rate of morbidity and mortality. To isolate the Candida species and determine of antifungal drug susceptibility against fluconazole, itraconazole, nystatin, amphotericin B, and clotrimazolein HIV seropositive and control individuals, with or without clinical oropharyngeal candidiasis (OPC). Includes samples from faucial region of 70 subjects with and without clinical candidiasis in HIV seropositive and controls were aseptically inoculated onto Sabaraud's Dextrose Agar media and yeasts were identified for the specific species by Corn Meal Agar, sugar fermentation and heat tolerance tests. Antifungal drug susceptibility of the isolated species was done against above-mentioned drugs by E-test and disc diffusion method. The commonly isolated species in HIV seropositive and controls were Candida albicans, Candida glabrata and Candida tropicalis Candida guilliermondii and Candida dubliniensis isolated only in HIV seropositive patients. Susceptibility against selected antifungal drugs was observed more in HIV-negative individuals whereas susceptible dose-dependent and resistance were predominant in HIV-positive patients. Resistance is the major problem in the therapy of OPC, especially in HIV seropositive patients due to aggressive and prolonged use of antifungal agents, therefore, our study emphasizes the need for antifungal drug susceptibility testing whenever antifungal treatment is desired, especially in HIV-infected subjects.

Drug susceptibility testing of Mycobacterium tuberculosis to fluoroquinolones

DEFF Research Database (Denmark)

Johansen, I S; Larsen, A R; Sandven, P

2003-01-01

In the first attempt to establish a quality assurance programme for susceptibility testing of Mycobacterium tuberculosis to fluoroquinolones, 20 strains with different fluoroquinolone susceptibility patterns were distributed by the Supranational Reference Laboratory in Stockholm to the other...

Factors affecting drug-induced liver injury: antithyroid drugs as instances

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Reza Heidari

2014-09-01

Full Text Available Methimazole and propylthiouracil have been used in the management of hyperthyroidism for more than half a century. However, hepatotoxicity is one of the most deleterious side effects associated with these medications. The mechanism(s of hepatic injury induced by antithyroid agents is not fully recognized yet. Furthermore, there are no specific tools for predicting the occurrence of hepatotoxicity induced by these drugs. The purpose of this article is to give an overview on possible susceptibility factors in liver injury induced by antithyroid agents. Age, gender, metabolism characteristics, alcohol consumption, underlying diseases, immunologic mechanisms, and drug interactions are involved in enhancing antithyroid drugs-induced hepatic damage. An outline on the clinically used treatments for antithyroid drugs-induced hepatotoxicity and the potential therapeutic strategies found to be effective against this complication are also discussed.

An introduction to predictive modelling of drug concentration in anaesthesia monitors.

Science.gov (United States)

DeCou, J; Johnson, K

2017-01-01

A significant amount of anaesthetists' work involves the prediction of drug effects and interactions to produce a smooth general anaesthetic that minimises drug side effects and promotes rapid emergence. Successfully managing this process requires a basic understanding of drug effects, experience and inevitably some guesswork, since it is difficult (and in some cases impossible) to anticipate all relevant patient and surgical factors. Although data are generally available to allow calculation of plasma drug and effect site concentrations, this is often difficult to apply in complex clinical contexts, particularly when multiple drug types are used. In recent years, manufacturers have developed and incorporated into anaesthetic workstations technologies that use drug pharmacodynamic and pharmacokinetic data to predict drug effects and interactions. Such systems can predict the duration and effects of drugs during anaesthesia and assist the anaesthetist to understand complex drug interactions. With this information available, different drug types, doses and combinations may be tailored in a scientific way to maximise useful effects whilst minimising overdose and side-effects, particularly in high-risk patients. Examples are used to illustrate how such systems can be used in practice, and how drug effects and interactions can be simulated to "rehearse" an anaesthetic before any drugs are actually administered. At present only a small number of anaesthetic workstations use this technology, and as yet they are not able to manage all drugs used in anaesthetic practice. However, such systems have the potential to help anaesthetists manage the complexity of their work, and to provide information on predicted drug effects in a way that is useful and relevant to both experienced anaesthetists and trainees. © 2017 The Association of Anaesthetists of Great Britain and Ireland.

Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models

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S. Pereira

2012-04-01

Full Text Available The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km² located in the Northern Portugal.

In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use and was compared with a model developed using all seven landslide predisposing factors.

Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.

[Drug susceptibility test guided therapy and novel empirical quadruple therapy for Helicobacter pylori infection: a network Meta-analysis].

Science.gov (United States)

Gou, Q Y; Yu, R B; Shi, R H

2017-05-10

Objective: To compare the efficacy and the risk of adverse effect of drug susceptibility test guided therapy and novel empirical quadruple therapy for Helicobacter ( H .) pylori infection. Methods: Literature retrieval was conducted by using major databases. Related papers published up to June 2015 were considered eligible if they were randomized control trials comparing different pharmacological formulations for H. pylori infection and used in a network Meta-analysis and a single rate Meta-analysis to evaluate the relative and absolute rates of H. pylori eradication and the risk of adverse effect. The Jadad score was used to evaluate the methodological quality. Funnel plot was constructed to evaluate the risk of publication bias. Begg's rank correlation test or Egger's regression intercept test was done for the asymmetry of funnel plot. Results: Twenty randomized control trials for the treatment of 6 753 initial treated patients with H. pylori infection were included. Drug susceptibility test guided therapy was significantly superior to concomitant therapy, hybrid therapy, sequential therapy and bismuth quadruple therapy. The culture-based therapy had the highest likelihood of improving clinical efficacy, with lowest risk of adverse effect. Concomitant therapy had the highest probability of causing adverse effect despite its effectiveness. Hybrid therapy and bismuth quadruple therapy were associated with lower risk of adverse effect and higher effectiveness. Conclusion: Drug susceptibility test guided therapy showed superiority to other 4 interventions for H. pylori eradication mentioned above. Hybrid therapy and bismuth quadruple therapy might be applied in the settings where the culture-based strategy is not available.

An analytical method for assessing stage-specific drug activity in Plasmodium vivax malaria: implications for ex vivo drug susceptibility testing.

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Douglas H Kerlin

Full Text Available The emergence of highly chloroquine (CQ resistant P. vivax in Southeast Asia has created an urgent need for an improved understanding of the mechanisms of drug resistance in these parasites, the development of robust tools for defining the spread of resistance, and the discovery of new antimalarial agents. The ex vivo Schizont Maturation Test (SMT, originally developed for the study of P. falciparum, has been modified for P. vivax. We retrospectively analysed the results from 760 parasite isolates assessed by the modified SMT to investigate the relationship between parasite growth dynamics and parasite susceptibility to antimalarial drugs. Previous observations of the stage-specific activity of CQ against P. vivax were confirmed, and shown to have profound consequences for interpretation of the assay. Using a nonlinear model we show increased duration of the assay and a higher proportion of ring stages in the initial blood sample were associated with decreased effective concentration (EC(50 values of CQ, and identify a threshold where these associations no longer hold. Thus, starting composition of parasites in the SMT and duration of the assay can have a profound effect on the calculated EC(50 for CQ. Our findings indicate that EC(50 values from assays with a duration less than 34 hours do not truly reflect the sensitivity of the parasite to CQ, nor an assay where the proportion of ring stage parasites at the start of the assay does not exceed 66%. Application of this threshold modelling approach suggests that similar issues may occur for susceptibility testing of amodiaquine and mefloquine. The statistical methodology which has been developed also provides a novel means of detecting stage-specific drug activity for new antimalarials.

Molecular detection methods of resistance to antituberculosis drugs in Mycobacterium tuberculosis.

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Brossier, F; Sougakoff, W

2017-09-01

Molecular methods predict drug resistance several weeks before phenotypic methods and enable rapid implementation of appropriate therapeutic treatment. We aimed to detail the most representative molecular tools used in routine practice for the rapid detection of resistance to antituberculosis drugs among Mycobacterium tuberculosis strains. The molecular diagnosis of resistance to antituberculosis drugs in clinical samples or from in vitro cultures is based on the detection of the most common mutations in the genes involved in the development of resistance in M. tuberculosis strains (encoding either protein targets of antibiotics, or antibiotic activating enzymes) by commercial molecular kits or by sequencing. Three hypotheses could explain the discrepancies between the genotypic results and the phenotypic drug susceptibility testing results: a low percentage of resistant mutants precluding the detection by genotypic methods on the primary culture; a low level of resistance not detected by phenotypic testing; and other resistance mechanisms not yet characterized. Molecular methods have varying sensitivity with regards to detecting antituberculosis drug resistance; that is why phenotypic susceptibility testing methods are mandatory for detecting antituberculosis drug-resistant isolates that have not been detected by molecular methods. The questionable ability of existing phenotypic and genotypic drug susceptibility testing to properly classify strains as susceptible or resistant, and at what level of resistance, was raised for several antituberculosis agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Drug susceptibility to etravirine and darunavir among Human Immunodeficiency Virus Type 1-derived pseudoviruses in treatment-experienced patients with HIV/AIDS in South Korea.

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Kwon, Oh-Kyung; Kim, Sung Soon; Rhee, Jee Eun; Kee, Mee-Kyung; Park, Mina; Oh, Hye-Ri; Choi, Ju-Yeon

2015-04-09

In South Korea, about 20 types of antiretroviral drugs are used in the treatment of patients with human immunodeficiency virus/acquired immune deficiency syndrome. Since 2010, raltegravir, etravirine, and darunavir have been spotlighted as new drugs for highly active antiretroviral therapy (HAART)-experienced adults with resistant HIV-1 in South Korea. In this study, we investigated potential susceptibility of pseudoviruses derived from treatment-experienced Korean patients to etravirine vs efavirenz and to darunavir vs amprenavir and indinavir using a modified single-round assay. Pseudoviruses derived from nine treatment-experienced patients infected with HIV-1 were investigated by comparison with the wild-type strain pNL4-3. The 50% inhibitory concentration (IC50) values were calculated and drug susceptibility was compared. The intensity of genotypic drug resistance was classified based on the 'SIR' interpretation of the Stanford data base. Drug susceptibility was generally higher for etravirine and darunavir compared with efavirenz, amprenavir, and indinavir in pseudoviruses derived from treatment-experienced patients. Pseudoviruses derived from patients KRB4025 and KRB8014, who exhibited long-term use of protease inhibitors, showed an outside of tested drug concentration, especially for amprenavir and indinavir. However, they exhibited a lower fold-change in resistance to darunavir. Etravirine and darunavir have been used in HAART since 2010 in South Korea. Therefore, these antiretroviral drugs together with other newly introduced antiretroviral drugs are interesting for the optimal treatment of patients with treatment failure. This study may help to find a more effective HAART in the case of HIV-1 infected patients that have difficulty being treated.

Bacterial profile and drug susceptibility pattern of urinary tract infection in pregnant women at University of Gondar Teaching Hospital, Northwest Ethiopia.

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Alemu, Agersew; Moges, Feleke; Shiferaw, Yitayal; Tafess, Ketema; Kassu, Afework; Anagaw, Belay; Agegn, Abebe

2012-04-25

Urinary tract infection (UTI) is a common health problem among pregnant women. Proper investigation and prompt treatment are needed to prevent serious life threatening condition and morbidity due to urinary tract infection that can occur in pregnant women. Recent report in Addis Ababa, Ethiopia indicated the prevalence of UTI in pregnant women was 11.6% and Gram negative bacteria was the predominant isolates and showed multi drug resistance. This study aimed to assess bacterial profile that causes urinary tract infection and their antimicrobial susceptibility pattern among pregnant women visiting antenatal clinic at University of Gondar Teaching Hospital, Northwest Ethiopia. A cross-sectional study was conducted at University of Gondar Teaching Hospital from March 22 to April 30, 2011. Mid stream urine samples were collected and inoculated into Cystine Lactose Electrolyte Deficient medium (CLED). Colony counts yielding bacterial growth of 105/ml of urine or more of pure isolates were regarded as significant bacteriuria for infection. Colony from CLED was sub cultured onto MacConkey agar and blood agar plates. Identification was done using cultural characteristics and a series of biochemical tests. A standard method of agar disc diffusion susceptibility testing method was used to determine susceptibility patterns of the isolates. The overall prevalence of UTI in pregnant women was 10.4%. The predominant bacterial pathogens were Escherichia coli 47.5% followed by coagulase-negative staphylococci 22.5%, Staphylococcus aureus 10%, and Klebsiella pneumoniae 10%. Gram negative isolates were resulted low susceptibility to co-trimoxazole (51.9%) and tetracycline (40.7%) whereas Gram positive showed susceptibility to ceftriaxon (84.6%) and amoxicillin-clavulanic acid (92.3%). Multiple drug resistance (resistance to two or more drugs) was observed in 95% of the isolates. Significant bacteriuria was observed in asymptomatic pregnant women. Periodic studies are recommended to

Bacterial profile and drug susceptibility pattern of urinary tract infection in pregnant women at University of Gondar Teaching Hospital, Northwest Ethiopia

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Alemu Agersew

2012-04-01

Full Text Available Abstract Background Urinary tract infection (UTI is a common health problem among pregnant women. Proper investigation and prompt treatment are needed to prevent serious life threatening condition and morbidity due to urinary tract infection that can occur in pregnant women. Recent report in Addis Ababa, Ethiopia indicated the prevalence of UTI in pregnant women was 11.6 % and Gram negative bacteria was the predominant isolates and showed multi drug resistance. This study aimed to assess bacterial profile that causes urinary tract infection and their antimicrobial susceptibility pattern among pregnant women visiting antenatal clinic at University of Gondar Teaching Hospital, Northwest Ethiopia. Methods A cross-sectional study was conducted at University of Gondar Teaching Hospital from March 22 to April 30, 2011. Mid stream urine samples were collected and inoculated into Cystine Lactose Electrolyte Deficient medium (CLED. Colony counts yielding bacterial growth of 105/ml of urine or more of pure isolates were regarded as significant bacteriuria for infection. Colony from CLED was sub cultured onto MacConkey agar and blood agar plates. Identification was done using cultural characteristics and a series of biochemical tests. A standard method of agar disc diffusion susceptibility testing method was used to determine susceptibility patterns of the isolates. Results The overall prevalence of UTI in pregnant women was 10.4 %. The predominant bacterial pathogens were Escherichia coli 47.5 % followed by coagulase-negative staphylococci 22.5 %, Staphylococcus aureus 10 %, and Klebsiella pneumoniae 10 %. Gram negative isolates were resulted low susceptibility to co-trimoxazole (51.9 % and tetracycline (40.7 % whereas Gram positive showed susceptibility to ceftriaxon (84.6 % and amoxicillin–clavulanic acid (92.3 %. Multiple drug resistance (resistance to two or more drugs was observed in 95 % of the isolates. Conclusion

Interventions on Metabolism: Making Antibiotic-Susceptible Bacteria

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Fernando Baquero

2017-11-01

Full Text Available Antibiotics act on bacterial metabolism, and antibiotic resistance involves changes in this metabolism. Interventions on metabolism with drugs might therefore modify drug susceptibility and drug resistance. In their recent article, Martin Vestergaard et al. (mBio 8:e01114-17, 2017, https://doi.org/10.1128/mBio.01114-17 illustrate the possibility of converting intrinsically resistant bacteria into susceptible ones. They reported that inhibition of a central metabolic enzyme, ATP synthase, allows otherwise ineffective polymyxin antibiotics to act on Staphylococcus aureus. The study of the intrinsic resistome of bacterial pathogens has shown that several metabolic genes, including multigene transcriptional regulators, contribute to antibiotic resistance. In some cases, these genes only marginally increase antibiotic resistance, but reduced levels of susceptibility might be critical in the evolution or resistance under low antibiotic concentrations or in the clinical response of highly resistant bacteria. Drug interventions on bacterial metabolism might constitute a critical adjuvant therapy in combination with antibiotics to ensure susceptibility of pathogens with intrinsic or acquired antimicrobial resistance.

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

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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.

A method for evaluating antiviral drug susceptibility of Epstein-Barr virus

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Charlotte A Romain

2010-01-01

Full Text Available Charlotte A Romain1, Henry H Balfour Jr1,2, Heather E Vezina1,3, Carol J Holman11Department of Laboratory Medicine and Pathology, 2Department of Pediatrics, 3Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USAAbstract: We developed an in vitro Epstein-Barr virus (EBV drug susceptibility assay using P3HR1 cells or lymphoblastoid cells from subjects with infectious mononucleosis, which were grown in the presence of various concentrations of acyclovir (ACV, ganciclovir (GCV or R-9-[4-hydroxy-2-(hydroxymethylbutyl]guanine (H2G and 12-O-tetradecanoyl-phorbol-13-acetate (TPA. On day 7, total cellular DNA was extracted and EBV DNA was detected using an in-house quantitative real-time polymerase chain reaction (PCR method. All three drugs had in vitro activity against EBV in both the laboratory standard producer cell line P3HR1 and in subject-derived lymphoblastoid cell lines. The median 50% inhibitory concentrations (IC50s in P3HR1 cells were: ACV, 3.4 μM; GCV, 2.6 μM; and H2G, 2.7 μM and in 3 subject-derived cells were: ACV, 2.5 μM; GCV, 1.7 μM; and H2G, 1.9 μM. Our assay can be used to screen candidate anti-EBV drugs. Because we can measure the IC50 of patients’ strains of EBV, this assay may also be useful for monitoring viral resistance especially in immunocompomised hosts receiving antiviral drugs for prevention or treatment of EBV diseases.Keywords: Epstein-Barr virus, ganciclovir, acyclovir, valomaciclovir, H2G, antivirals

Renal endothelial function and blood flow predict the individual susceptibility to adriamycin-induced renal damage

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Ochodnicky, Peter; Henning, Robert H.; Buikema, Hendrik; Kluppel, Alex C. A.; van Wattum, Marjolein; de Zeeuw, Dick; van Dokkum, Richard P. E.

Background. Susceptibility to renal injury varies among individuals. Previously, we found that individual endothelial function of healthy renal arteries in vitro predicted severity of renal damage after 5/6 nephrectomy. Here we hypothesized that individual differences in endothelial function in

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

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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.

Design of a tripartite network for the prediction of drug targets

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Kunimoto, Ryo; Bajorath, Jürgen

2018-02-01

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

Evaluation of MGIT 960 System for the Second-Line Drugs Susceptibility Testing of Mycobacterium tuberculosis

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Hyejin Kim

2013-01-01

Full Text Available Many laboratories validate DST of the second-line drugs by BACTEC MGIT 960 system. The objective of this study is to evaluate the critical concentration and perform DST for the 2nd line drugs. We evaluated 193 clinical strains of M. tuberculosis isolated from patients in South Korea. Testing the critical concentration of six second-line drugs was performed by MGIT 960 and compared with L-J proportion method. The critical concentration was determined to establish the most one that gave the difference between drug resistance and susceptibility in MGIT960 system. Good agreement of the following concentrations was found: Concordance was 95% for 0.5 μg/mL of moxifloxacin; 93.6%, 1.0 μg/mL of levofloxacin; 97.5%, 2.5 μg/mL of kanamycin; 90.6%, 2.5 μg/mL of capreomycin; 86.2%, 5.0 μg/mL of ethionamide; and 90.8%, 2.0 μg/mL of ρ-aminosalicylic acid. The critical concentrations of the four drugs, moxifloxacin, levofloxacin, kanamycin, and capreomycin, were concordant and reliable for testing 2nd line drug resistance. Further study of ethionamide and ρ-aminosalicylic acid is required.

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

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

2018-01-22

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

Susceptibility of Aeromonas Hydophila Isolates to Antimicrobial Drugs

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Igor Stojanov

2010-05-01

Full Text Available Aeromonas hydrophila is a microorganism widely distributed in nature: in water, soil, food. It is also part of the normal bacterial flora of many animals. As an opportune microorganism it is a secondary biological agent that contributes to the occurrence of a fish disease and its deterioration. Frequently, its presence is an indication of bad zoohygiene and zootechnical conditions in fish ponds. Reduced quality and quantity of feed, mechanical injuries, parasitosis, seasonal oscillation in temperature present some of the factors that produce favorable conditions for bacterial proliferation of aeromonas in fish ponds, so clinical symptoms of the disease occur. Aeromonas is almost always present in clinical isolates and may be unjustly accused for bad health of fish. Antibiotic therapy is applied even when the clinical findings are clear, what certainly effects the susceptibility to chemotherapeutics. The subject of our work was bacteriological examination of the material obtained from the carps with the observed skin changes and the carps without these changes. Also, antimicrobial susceptibility of Aeromonas hydrophila was tested. The aim of this research was to determined the presence of Aeromonas hydrophilia in the carp ponds and to test antibiotic susceptibility. The material consisted of the samples from the fish ponds where the carps were with and without changed skin. The method the isolation of Aeromonas hydrophila was used. The diffusion disk technique was used for testing antibiotic susceptibility. The isolates were tested for their susceptibility to Florephenikol, Flumequine, Olaqindox and Oxitetracycline. The obtained results point that antimicrobial susceptibility was the same regardless of the origin of the samples, i.e. the resistance was the same for both groups of samples (the strains isolated from the fish with skin changes and the strains from fish without changes on skin. The strains were highly resistant: 35% were resistant to

Trends of Mycobacterium bovis Isolation and First-Line Anti-tuberculosis Drug Susceptibility Profile: A Fifteen-Year Laboratory-Based Surveillance.

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Bobadilla-del Valle, Miriam; Torres-González, Pedro; Cervera-Hernández, Miguel Enrique; Martínez-Gamboa, Areli; Crabtree-Ramirez, Brenda; Chávez-Mazari, Bárbara; Ortiz-Conchi, Narciso; Rodríguez-Cruz, Luis; Cervantes-Sánchez, Axel; Gudiño-Enríquez, Tomasa; Cinta-Severo, Carmen; Sifuentes-Osornio, José; Ponce de León, Alfredo

2015-09-01

Mycobacterium tuberculosis causes the majority of tuberculosis (TB) cases in humans; however, in developing countries, human TB caused by M. bovis may be frequent but undetected. Human TB caused by M. bovis is considered a zoonosis; transmission is mainly through consumption of unpasteurized dairy products, and it is less frequently attributed to animal-to-human or human-to-human contact. We describe the trends of M. bovis isolation from human samples and first-line drug susceptibility during a 15-year period in a referral laboratory located in a tertiary care hospital in Mexico City. Data on mycobacterial isolates from human clinical samples were retrieved from the laboratory's database for the 2000-2014 period. Susceptibility to first-line drugs: rifampin, isoniazid, streptomycin (STR) and ethambutol was determined. We identified 1,165 isolates, 73.7% were M. tuberculosis and 26.2%, M. bovis. Among pulmonary samples, 16.6% were M. bovis. The proportion of M. bovis isolates significantly increased from 7.8% in 2000 to 28.4% in 2014 (X(2)trend, ptuberculosis isolates (10.9% vs.3.4%, ptuberculosis, respectively (p = 0.637). A rising trend of primary STR monoresistance was observed for both species (3.4% in 2000-2004 vs. 7.6% in 2010-2014; p = 0.02). There is a high prevalence and a rising trend of M. bovis isolates in our region. The proportion of pulmonary M. bovis isolates is higher than in previous reports. Additionally, we report high rates of primary anti-tuberculosis resistance and secondary MDR in both M. tuberculosis and M. bovis. This is one of the largest reports on drug susceptibility of M. bovis from human samples and shows a significant proportion of first-line anti-tuberculosis drug resistance.

Trends of Mycobacterium bovis Isolation and First-Line Anti-tuberculosis Drug Susceptibility Profile: A Fifteen-Year Laboratory-Based Surveillance

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Bobadilla-del Valle, Miriam; Torres-González, Pedro; Cervera-Hernández, Miguel Enrique; Martínez-Gamboa, Areli; Crabtree-Ramirez, Brenda; Chávez-Mazari, Bárbara; Ortiz-Conchi, Narciso; Rodríguez-Cruz, Luis; Cervantes-Sánchez, Axel; Gudiño-Enríquez, Tomasa; Cinta-Severo, Carmen; Sifuentes-Osornio, José; Ponce de León, Alfredo

2015-01-01

Background Mycobacterium tuberculosis causes the majority of tuberculosis (TB) cases in humans; however, in developing countries, human TB caused by M. bovis may be frequent but undetected. Human TB caused by M. bovis is considered a zoonosis; transmission is mainly through consumption of unpasteurized dairy products, and it is less frequently attributed to animal-to-human or human-to-human contact. We describe the trends of M. bovis isolation from human samples and first-line drug susceptibility during a 15-year period in a referral laboratory located in a tertiary care hospital in Mexico City. Methodology/Principal Findings Data on mycobacterial isolates from human clinical samples were retrieved from the laboratory’s database for the 2000–2014 period. Susceptibility to first-line drugs: rifampin, isoniazid, streptomycin (STR) and ethambutol was determined. We identified 1,165 isolates, 73.7% were M. tuberculosis and 26.2%, M. bovis. Among pulmonary samples, 16.6% were M. bovis. The proportion of M. bovis isolates significantly increased from 7.8% in 2000 to 28.4% in 2014 (X 2 trend, ptuberculosis isolates (10.9% vs.3.4%, ptuberculosis, respectively (p = 0.637). A rising trend of primary STR monoresistance was observed for both species (3.4% in 2000–2004 vs. 7.6% in 2010–2014; p = 0.02). Conclusions/Significance There is a high prevalence and a rising trend of M. bovis isolates in our region. The proportion of pulmonary M. bovis isolates is higher than in previous reports. Additionally, we report high rates of primary anti-tuberculosis resistance and secondary MDR in both M. tuberculosis and M. bovis. This is one of the largest reports on drug susceptibility of M. bovis from human samples and shows a significant proportion of first-line anti-tuberculosis drug resistance. PMID:26421930

Renal endothelial function and blood flow predict the individual susceptibility to adriamycin-induced renal damage

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Ochodnicky, Peter; Henning, Robert H.; Buikema, Hendrik; Kluppel, Alex C. A.; van Wattum, Marjolein; de Zeeuw, Dick; van Dokkum, Richard P. E.

2009-01-01

Susceptibility to renal injury varies among individuals. Previously, we found that individual endothelial function of healthy renal arteries in vitro predicted severity of renal damage after 5/6 nephrectomy. Here we hypothesized that individual differences in endothelial function in vitro and renal

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

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Yuanyuan Li

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

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

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Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

2016-02-01

The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

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Saro Lee

2016-02-01

Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

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

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Cami, Aurel; Reis, Ben Y

2014-08-22

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

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

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Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

2018-01-01

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

Analysis of clinical drug-drug interaction data to predict uncharacterized interaction magnitudes between antiretroviral drugs and co-medications.

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Stader, Felix; Kinvig, Hannah; Battegay, Manuel; Khoo, Saye; Owen, Andrew; Siccardi, Marco; Marzolini, Catia

2018-04-23

Despite their high potential for drug-drug-interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required.We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes such as CYP3A and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers and inhibitors of CYP3A, to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs.The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and co-medications were successfully quantified meaning 53%, 85% and 98% of the predictions were within 1.25-fold (0.80 - 1.25), 1.5-fold (0.66 - 1.48) and 2-fold (0.66 - 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or conversely minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs.The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs being particularly relevant in a HIV-setting giving the treatments complexity, high DDI risk and limited guidance on the management of DDIs. Copyright © 2018 American Society for Microbiology.

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

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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.

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.

Ergothioneine Maintains Redox and Bioenergetic Homeostasis Essential for Drug Susceptibility and Virulence of Mycobacterium tuberculosis

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Vikram Saini

2016-01-01

Full Text Available The mechanisms by which Mycobacterium tuberculosis (Mtb maintains metabolic equilibrium to survive during infection and upon exposure to antimycobacterial drugs are poorly characterized. Ergothioneine (EGT and mycothiol (MSH are the major redox buffers present in Mtb, but the contribution of EGT to Mtb redox homeostasis and virulence remains unknown. We report that Mtb WhiB3, a 4Fe-4S redox sensor protein, regulates EGT production and maintains bioenergetic homeostasis. We show that central carbon metabolism and lipid precursors regulate EGT production and that EGT modulates drug sensitivity. Notably, EGT and MSH are both essential for redox and bioenergetic homeostasis. Transcriptomic analyses of EGT and MSH mutants indicate overlapping but distinct functions of EGT and MSH. Last, we show that EGT is critical for Mtb survival in both macrophages and mice. This study has uncovered a dynamic balance between Mtb redox and bioenergetic homeostasis, which critically influences Mtb drug susceptibility and pathogenicity.

Antimicrobial drug resistance of Salmonella enterica serovar typhi in asia and molecular mechanism of reduced susceptibility to the fluoroquinolones

NARCIS (Netherlands)

Chau, Tran Thuy; Campbell, James Ian; Galindo, Claudia M.; van Minh Hoang, Nguyen; Diep, To Song; Nga, Tran Thu Thi; van Vinh Chau, Nguyen; Tuan, Phung Quoc; Page, Anne Laure; Ochiai, R. Leon; Schultsz, Constance; Wain, John; Bhutta, Zulfiqar A.; Parry, Christopher M.; Bhattacharya, Sujit K.; Dutta, Shanta; Agtini, Magdarina; Dong, Baiqing; Honghui, Yang; Anh, Dang Duc; Canh, Do Gia; Naheed, Aliya; Albert, M. John; Phetsouvanh, Rattanaphone; Newton, Paul N.; Basnyat, Buddha; Arjyal, Amit; La, Tran Thi Phi; Rang, Nguyen Ngoc; Phuong, Le Thi; van Be Bay, Phan; von Seidlein, Lorenz; Dougan, Gordon; Clemens, John D.; Vinh, Ha; Hien, Tran Tinh; Chinh, Nguyen Tran; Acosta, Camilo J.; Farrar, Jeremy; Dolecek, Christiane

2007-01-01

This study describes the pattern and extent of drug resistance in 1,774 strains of Salmonella enterica serovar Typhi isolated across Asia between 1993 and 2005 and characterizes the molecular mechanisms underlying the reduced susceptibilities to fluoroquinolones of these strains. For 1,393 serovar

Predicting Drug-Target Interactions Based on Small Positive Samples.

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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

Drug response prediction in high-risk multiple myeloma

DEFF Research Database (Denmark)

Vangsted, A J; Helm-Petersen, S; Cowland, J B

2018-01-01

from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

Susceptibility Testing of Medically Important Parasites.

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Genetu Bayih, Abebe; Debnath, Anjan; Mitre, Edward; Huston, Christopher D; Laleu, Benoît; Leroy, Didier; Blasco, Benjamin; Campo, Brice; Wells, Timothy N C; Willis, Paul A; Sjö, Peter; Van Voorhis, Wesley C; Pillai, Dylan R

2017-07-01

In the last 2 decades, renewed attention to neglected tropical diseases (NTDs) has spurred the development of antiparasitic agents, especially in light of emerging drug resistance. The need for new drugs has required in vitro screening methods using parasite culture. Furthermore, clinical laboratories sought to correlate in vitro susceptibility methods with treatment outcomes, most notably with malaria. Parasites with their various life cycles present greater complexity than bacteria, for which standardized susceptibility methods exist. This review catalogs the state-of-the-art methodologies used to evaluate the effects of drugs on key human parasites from the point of view of drug discovery as well as the need for laboratory methods that correlate with clinical outcomes. Copyright © 2017 American Society for Microbiology.

Fast prediction of cytochrome P450 mediated drug metabolism

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

2009-01-01

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

Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine

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Lu, Yin; Liu, Lili; Lu, Dong; Cai, Yudong; Zheng, Mingyue; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2017-11-20

Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites. 64 hepatotoxic drug metabolites and 3,339 non-hepatotoxic drug metabolites were gathered from MDL Metabolite Database. Considering the imbalance of the dataset, we randomly split the negative samples and combined each portion with all the positive samples to construct individually balanced datasets for constructing independent classifiers. Then, we adopted an ensemble approach to make prediction based on the results of all individual classifiers and applied the minimum Redundancy Maximum Relevance (mRMR) feature selection method to select the molecular descriptors. Eventually, for the drugs in the external test set, a Bayesian inference method was used to predict the hepatotoxicity of a drug based on its metabolites. The model showed the average balanced accuracy=78.47%, sensitivity =74.17%, and specificity=82.77%. Five molecular descriptors characterizing molecular polarity, intramolecular bonding strength, and molecular frontier orbital energy were obtained. When predicting the hepatotoxicity of a drug based on all its metabolites, the sensitivity, specificity and balanced accuracy were 60.38%, 70.00%, and 65.19%, respectively, indicating that this method is useful for identifying the hepatotoxicity of drugs. We developed an in silico model to predict hepatotoxicity of drug metabolites. Moreover, Bayesian inference was applied to predict the hepatotoxicity of a drug based on its metabolites which brought out valuable high sensitivity and specificity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

Energy Technology Data Exchange (ETDEWEB)

Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

2005-07-26

A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

Mitochondrial abnormalities-A link to idiosyncratic drug hepatotoxicity?

International Nuclear Information System (INIS)

Boelsterli, Urs A.; Lim, Priscilla L.K.

2007-01-01

Idiosyncratic drug-induced liver injury (DILI) is a major clinical problem and poses a considerable challenge for drug development as an increasing number of successfully launched drugs or new potential drugs have been implicated in causing DILI in susceptible patient subsets. Although the incidence for a particular drug is very low (yet grossly underestimated), the outcome of DILI can be serious. Unfortunately, prediction has remained poor (both for patients at risk and for new chemical entities). The underlying mechanisms and the determinants of susceptibility have largely remained ill-defined. The aim of this review is to provide both clinical and experimental evidence for a major role of mitochondria both as a target of drugs causing idiosyncratic DILI and as mediators of delayed liver injury. We develop a unifying hypothesis that involves underlying genetic or acquired mitochondrial abnormalities as a major determinant of susceptibility for a number of drugs that target mitochondria and cause DILI. The mitochondrial hypothesis, implying gradually accumulating and initially silent mitochondrial injury in heteroplasmic cells which reaches a critical threshold and abruptly triggers liver injury, is consistent with the findings that typically idiosyncratic DILI is delayed (by weeks or months), that increasing age and female gender are risk factors and that these drugs are targeted to the liver and clearly exhibit a mitochondrial hazard in vitro and in vivo. New animal models (e.g., the Sod2 +/- mouse) provide supporting evidence for this concept. However, genetic analyses of DILI patient samples are needed to ultimately provide the proof-of-concept

Cardiovascular Disease Biomarkers Predict Susceptibility or Resistance to Lung Injury in World Trade Center Dust Exposed Firefighters

Science.gov (United States)

Weiden, Michael D.; Naveed, Bushra; Kwon, Sophia; Cho, Soo Jung; Comfort, Ashley L.; Prezant, David J.; Rom, William N.; Nolan, Anna

2013-01-01

Pulmonary vascular loss is an early feature of chronic obstructive pulmonary disease. Biomarkers of inflammation and of metabolic syndrome, predicts loss of lung function in World Trade Center Lung Injury (WTC-LI). We investigated if other cardiovascular disease (CVD) biomarkers also predicted WTC-LI. This nested case-cohort study used 801 never smoker, WTC exposed firefighters with normal pre-9/11 lung function presenting for subspecialty pulmonary evaluation (SPE) before March, 2008. A representative sub-cohort of 124/801 with serum drawn within six months of 9/11 defined CVD biomarker distribution. Post-9/11/01 FEV1 at subspecialty exam defined cases: susceptible WTC-LI cases with FEV1≤77% predicted (66/801) and resistant WTC-LI cases with FEV1≥107% (68/801). All models were adjusted for WTC exposure intensity, BMI at SPE, age at 9/11, and pre-9/11 FEV1. Susceptible WTC-LI cases had higher levels of Apo-AII, CRP, and MIP-4 with significant RRs of 3.85, 3.93, and 0.26 respectively with an area under the curve (AUC) of 0.858. Resistant WTC-LI cases had significantly higher sVCAM and lower MPO with RRs of 2.24, and 2.89 respectively; AUC 0.830. Biomarkers of CVD in serum six-month post-9/11 predicted either susceptibility or resistance to WTC-LI. These biomarkers may define pathways producing or protecting subjects from pulmonary vascular disease and associated loss of lung function after an irritant exposure. PMID:22903969

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

Science.gov (United States)

Zhang, Shao-Wu; Yan, Xiao-Ying

2017-01-01

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

A community effort to assess and improve drug sensitivity prediction algorithms.

Science.gov (United States)

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2014-12-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Second line drug susceptibility testing to inform the treatment of rifampin-resistant tuberculosis: a quantitative perspective

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Emily A. Kendall

2017-03-01

Full Text Available Treatment failure and resistance amplification are common among patients with rifampin-resistant tuberculosis (TB. Drug susceptibility testing (DST for second-line drugs is recommended for these patients, but logistical difficulties have impeded widespread implementation of second-line DST in many settings. To provide a quantitative perspective on the decision to scale up second-line DST, we synthesize literature on the prevalence of second-line drug resistance, the expected clinical and epidemiologic benefits of using second-line DST to ensure that patients with rifampin-resistant TB receive effective regimens, and the costs of implementing (or not implementing second-line DST for all individuals diagnosed with rifampin-resistant TB. We conclude that, in most settings, second-line DST could substantially improve treatment outcomes for patients with rifampin-resistant TB, reduce transmission of drug-resistant TB, prevent amplification of drug resistance, and be affordable or even cost-saving. Given the large investment made in each patient treated for rifampin-resistant TB, these payoffs would come at relatively small incremental cost. These anticipated benefits likely justify addressing the real challenges faced in implementing second-line DST in most high-burden settings.

DISIS: prediction of drug response through an iterative sure independence screening.

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Yun Fang

Full Text Available Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.

Predictive tools for the evaluation of microbial effects on drugs during gastrointestinal passage.

Science.gov (United States)

Pieper, Ines A; Bertau, Martin

2010-06-01

Predicting drug metabolism after oral administration is highly complex, yet indispensable. Hitherto, drug metabolism mainly focuses on hepatic processes. In the intestine, drug molecules encounter the metabolic activity of microorganisms prior to absorption through the gut wall. Drug biotransformation through the gastrointestinal microflora has the potential to evoke serious problems because the metabolites formed may cause unexpected and undesired side effects in patients. Hence, in the course of drug development, the question has to be addressed if microbially formed metabolites are physiologically active, pharmaceutically active or even toxic. In order to provide answers to these questions and to keep the number of laboratory tests needed low, predictive tools - in vivo as well as in silico - are invaluable. This review gives an outline of the current state of the art in the field of predicting the drug biotransformation through the gastrointestinal microflora on several levels of modelling. A comprehensive review of the literature with a thorough discussion on assets and drawbacks of the different modelling approaches. The impact of the gastrointestinal drug biotransformation on patients' health will grow with increasing complexity of drug entities. Predicting metabolic fates of drugs by combining in vitro and in silico models provides invaluable information which will be suitable to particularly reduce in vivo studies.

Trends of Mycobacterium bovis Isolation and First-Line Anti-tuberculosis Drug Susceptibility Profile: A Fifteen-Year Laboratory-Based Surveillance.

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Miriam Bobadilla-del Valle

2015-09-01

Full Text Available Mycobacterium tuberculosis causes the majority of tuberculosis (TB cases in humans; however, in developing countries, human TB caused by M. bovis may be frequent but undetected. Human TB caused by M. bovis is considered a zoonosis; transmission is mainly through consumption of unpasteurized dairy products, and it is less frequently attributed to animal-to-human or human-to-human contact. We describe the trends of M. bovis isolation from human samples and first-line drug susceptibility during a 15-year period in a referral laboratory located in a tertiary care hospital in Mexico City.Data on mycobacterial isolates from human clinical samples were retrieved from the laboratory's database for the 2000-2014 period. Susceptibility to first-line drugs: rifampin, isoniazid, streptomycin (STR and ethambutol was determined. We identified 1,165 isolates, 73.7% were M. tuberculosis and 26.2%, M. bovis. Among pulmonary samples, 16.6% were M. bovis. The proportion of M. bovis isolates significantly increased from 7.8% in 2000 to 28.4% in 2014 (X(2trend, p<0.001. Primary STR resistance was higher among M. bovis compared with M. tuberculosis isolates (10.9% vs.3.4%, p<0.001. Secondary multidrug resistance (MDR rates were 38.5% and 34.4% for M. bovis and M. tuberculosis, respectively (p = 0.637. A rising trend of primary STR monoresistance was observed for both species (3.4% in 2000-2004 vs. 7.6% in 2010-2014; p = 0.02.There is a high prevalence and a rising trend of M. bovis isolates in our region. The proportion of pulmonary M. bovis isolates is higher than in previous reports. Additionally, we report high rates of primary anti-tuberculosis resistance and secondary MDR in both M. tuberculosis and M. bovis. This is one of the largest reports on drug susceptibility of M. bovis from human samples and shows a significant proportion of first-line anti-tuberculosis drug resistance.

M. tuberculosis genotypic diversity and drug susceptibility pattern in HIV- infected and non-HIV-infected patients in northern Tanzania

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van Soolingen Dick

2007-05-01

Full Text Available Abstract Background Tuberculosis (TB is a major health problem and HIV is the major cause of the increase in TB. Sub-Saharan Africa is endemic for both TB and HIV infection. Determination of the prevalence of M. tuberculosis strains and their drug susceptibility is important for TB control. TB positive culture, BAL fluid or sputum samples from 130 patients were collected and genotyped. The spoligotypes were correlated with anti-tuberculous drug susceptibility in HIV-infected and non-HIV patients from Tanzania. Results One-third of patients were TB/HIV co-infected. Forty-seven spoligotypes were identified. Fourteen isolates (10.8% had new and unique spoligotypes while 116 isolates (89.2% belonged to 33 known spoligotypes. The major spoligotypes contained nine clusters: CAS1-Kili 30.0%, LAM11- ZWE 14.6%, ND 9.2%, EAI 6.2%, Beijing 5.4%, T-undefined 4.6%, CAS1-Delhi 3.8%, T1 3.8% and LAM9 3.8%. Twelve (10.8% of the 111 phenotypically tested strains were resistant to anti-TB drugs. Eight (7.2% were monoresistant strains: 7 to isoniazid (INH and one to streptomycin. Four strains (3.5% were resistant to multiple drugs: one (0.9% was resistant to INH and streptomycin and the other three (2.7% were MDR strains: one was resistant to INH, rifampicin and ethambutol and two were resistant to all four anti-TB drugs. Mutation in the katG gene codon 315 and the rpoB hotspot region showed a low and high sensitivity, respectively, as predictor of phenotypic drug resistance. Conclusion CAS1-Kili and LAM11-ZWE were the most common families. Strains of the Beijing family and CAS1-Kili were not or least often associated with resistance, respectively. HIV status was not associated with spoligotypes, resistance or previous TB treatment.

Species used for drug testing reveal different inhibition susceptibility for 17beta-hydroxysteroid dehydrogenase type 1.

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Gabriele Möller

Full Text Available Steroid-related cancers can be treated by inhibitors of steroid metabolism. In searching for new inhibitors of human 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD 1 for the treatment of breast cancer or endometriosis, novel substances based on 15-substituted estrone were validated. We checked the specificity for different 17beta-HSD types and species. Compounds were tested for specificity in vitro not only towards recombinant human 17beta-HSD types 1, 2, 4, 5 and 7 but also against 17beta-HSD 1 of several other species including marmoset, pig, mouse, and rat. The latter are used in the processes of pharmacophore screening. We present the quantification of inhibitor preferences between human and animal models. Profound differences in the susceptibility to inhibition of steroid conversion among all 17beta-HSDs analyzed were observed. Especially, the rodent 17beta-HSDs 1 were significantly less sensitive to inhibition compared to the human ortholog, while the most similar inhibition pattern to the human 17beta-HSD 1 was obtained with the marmoset enzyme. Molecular docking experiments predicted estrone as the most potent inhibitor. The best performing compound in enzymatic assays was also highly ranked by docking scoring for the human enzyme. However, species-specific prediction of inhibitor performance by molecular docking was not possible. We show that experiments with good candidate compounds would out-select them in the rodent model during preclinical optimization steps. Potentially active human-relevant drugs, therefore, would no longer be further developed. Activity and efficacy screens in heterologous species systems must be evaluated with caution.

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.

Investigation of Susceptibility of Mycobacterium tuberculosis Complex Strains Isolated from Clinical Samples Against the First and Second-Line Anti-tuberculosis Drugs by the Sensititre MycoTB Plate Method

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Figen KAYSERİLİ ORHAN

2018-03-01

Full Text Available Introduction: Phenotypic methods for drug susceptibility testing of Mycobacterium tuberculosis complex (MTC to second-line drugs are not yet standardized. The Sensititre MycoTB Plate is a microtiter plate containing lyophilized antibiotics and configured for determination of MIC to first and second-line anti-tuberculosis drugs. The purpose of this study is to detect the susceptibility rates of MTC strains isolated from patients’ specimens for first and second-line anti-tuberculosis drugs. Materials and Methods: This study included 50 MTC strains isolated from various clinical specimens. Out of the 50 strains, 38 were isolated from sputum, three from cerebrospinal fluid, three from bronchoalveolar lavage, and six from other samples in this study. The susceptibility of strains to anti-tuberculosis drugs were determined by the Sensititre MycoTB Plate Method. Thawed isolates were subcultured, and dilutions were inoculated into MycoTB wells. The results were read at days 7, 14 and 21. Results: At the end of study, out of 50 MTC isolates, 7 (14% showed resistance to Isoniazid (INH, 5 (10% to streptomycin (SM, 4 (8% to ethambutol (EMB, 4 (8% to ethionamide (ETH, 3 (6% to rifampicin (RIF, 3 (6% to rifabutin (RFB, 2 (4% to kanamycin (KAN, 2 (4% to ofloxacin (OFL, 2 (4% to P-aminosalicyclic acid (PAS, 1 (2% to moxiflocacin (MOX, and 1 (2% to cycloserine (CYC. All strains were found sensitive to amikacin while 2 strains (4% were identified as multidrug-resistant tuberculosis (MDR-TB. Thirty-five strains (70% were sensitive to all drugs. Extensively drug resistant tuberculosis (XDR-TB was not determined in this study. Conclusion: This is the first study that tests second line anti-tuberculosis drugs in our location and provides us valuable data regarding MDR-TB and XDR-TB rates. The Sensititre MycoTB Plate Method is a fast, reliable and practical method and can be used to determine the susceptibility of first and second-line anti-tuberculosis drugs.

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...

Flow cytometry susceptibility testing for conventional antifungal drugs and Comparison with the NCCLS Broth Macrodilution Test

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M.J. Najafzadeh

2009-08-01

Full Text Available Introduction: During the last decade, the incidence of fungal infection has been increased in many countries. Because of the advent of resistant to antifungal agents, determination of an efficient strategic plan for treatment of fungal disease is an important issue in clinical mycology. Many methods have been introduced and developed for determination of invitro susceptibility tests. During the recent years, flow cytometry has developed to solving the problem and many papers have documented the usefulness of this technique. Materials and methods: As the first step, the invitro susceptibility of standard PTCC (Persian Type of Culture Collection strain and some clinical isolates of Candida consisting of Candida albicans, C. dubliniensis, C. glabrata, C. kefyer and C. parapsilosis were evaluated by macrodilution broth method according to NCCLS (National Committee for Clinical Laboratory Standards guidelines and flow cytometry susceptibility test. Results:  The data indicated that macro dilution broth methods and flow cytometry have the same results in determination of MIC (Minimum Inhibitory Concentration for amphotericin B, clotrimazole, fluconazole, ketoconazole and miconazole in C. albicans PTCC 5027 as well as clinical Candida isolates, such as C.albicans, C.dubliniensis, C.glabrata C.kefyr, and C.parapsilosis. Discussion: Comparing the results obtained by macrodilution broth and flow cytometry methods revealed that flow cytometry was faster. It is suggested that flow cytometry susceptibility test can be used as a powerful tool for determination of MIC and administration of the best antifungal drug in treatment of patients with Candida infections.

Environmental isolation, biochemical identification, and antifungal drug susceptibility of Cryptococcus species

Directory of Open Access Journals (Sweden)

Valter Luis Iost Teodoro

2013-12-01

Full Text Available Introduction The incidence of opportunistic fungal infections has increased in recent years and is considered an important public health problem. Among systemic and opportunistic mycoses, cryptococcosis is distinguished by its clinical importance due to the increased risk of infection in individuals infected by human immunodeficiency virus. Methods To determine the occurrence of pathogenic Cryptococcus in pigeon excrement in the City of Araraquara, samples were collected from nine environments, including state and municipal schools, abandoned buildings, parks, and a hospital. The isolates were identified using classical tests, and susceptibility testing for the antifungal drugs (fluconazole, itraconazole, voriconazole, and amphotericin B independently was also performed. After collection, the excrement samples were plated on Niger agar and incubated at room temperature. Results A total of 87 bird dropping samples were collected, and 66.6% were positive for the genus Cryptococcus. The following species were identified: Cryptococcus neoformans (17.2%, Cryptococcus gattii (5.2%, Cryptococcus ater (3.5%, Cryptococcus laurentti (1.7%, and Cryptococcus luteolus (1.7%. A total of 70.7% of the isolates were not identified to the species level and are referred to as Cryptococcus spp. throughout the manuscript. Conclusions Although none of the isolates demonstrated resistance to antifungal drugs, the identification of infested areas, the proper control of birds, and the disinfection of these environments are essential for the epidemiological control of cryptococcosis.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Science.gov (United States)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

A dual reporter cell assay for identifying serotype and drug susceptibility of herpes simplex virus.

Science.gov (United States)

Lu, Wen-Wen; Sun, Jun-Ren; Wu, Szu-Sian; Lin, Wan-Hsuan; Kung, Szu-Hao

2011-08-15

A dual reporter cell assay (DRCA) that allows real-time detection of herpes simplex virus (HSV) infection was developed. This was achieved by stable transfection of cells with an expression cassette that contains the dual reporter genes, secreted alkaline phosphatase (SEAP) and enhanced green fluorescent protein (EGFP), under the control of an HSV early gene promoter. Baby hamster kidney (BHK) and Chinese hamster ovary (CHO) cell lines were used as parental cell lines because the former is permissive for both HSV serotypes, HSV-1 and HSV-2, whereas the latter is susceptible to infection only by HSV-2. The DRCA permitted differential detection of HSV-1 and HSV-2 by observation of EGFP-positive cells, as substantiated by screening a total of 35 samples. The BHK-based cell line is sensitive to a viral titer as low as a single plaque-forming unit with a robust assay window as measured by a chemiluminescent assay. Evaluations of the DRCA with representative acyclovir-sensitive and acyclovir-resistant HSV strains demonstrated that their drug susceptibilities were accurately determined by a 48-h format. In summary, this novel DRCA is a useful means for serotyping of HSV in real time as well as a rapid screening method for determining anti-HSV susceptibilities. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

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

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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.

Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

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Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

2017-01-01

The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

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

Science.gov (United States)

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

2018-02-01

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

Strategy for the Prediction of Steady-State Exposure of Digoxin to Determine Drug-Drug Interaction Potential of Digoxin With Other Drugs in Digitalization Therapy.

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Srinivas, Nuggehally R

2016-01-20

Digoxin, a narrow therapeutic index drug, is widely used in congestive heart failure. However, the digitalization therapy involves dose titration and can exhibit drug-drug interaction. Ctrough versus area under the plasma concentration versus time curve in a dosing interval of 24 hours (AUC0-24h) and Cmax versus AUC0-24h for digoxin were established by linear regression. The predictions of digoxin AUC0-24h values were performed using published Ctrough or Cmax with appropriate regression lines. The fold difference, defined as the quotient of the observed/predicted AUC0-24h values, was evaluated. The mean square error and root mean square error, correlation coefficient (r), and goodness of the fold prediction were used to evaluate the models. Both Ctrough versus AUC0-24h (r = 0.9215) and Cmax versus AUC0-24h models for digoxin (r = 0.7781) showed strong correlations. Approximately 93.8% of the predicted digoxin AUC0-24h values were within 0.76-fold to 1.25-fold difference for Ctrough model. In sharp contrast, the Cmax model showed larger variability with only 51.6% of AUC0-24h predictions within 0.76-1.25-fold difference. The r value for observed versus predicted AUC0-24h for Ctrough (r = 0.9551; n = 177; P < 0.001) was superior to the Cmax (r = 0.6134; n = 275; P < 0.001) model. The mean square error and root mean square error (%) for the Ctrough model were 11.95% and 16.2% as compared to 67.17% and 42.3% obtained for the Cmax model. Simple linear regression models for Ctrough/Cmax versus AUC0-24h were derived for digoxin. On the basis of statistical evaluation, Ctrough was superior to Cmax model for the prediction of digoxin AUC0-24h and can be potentially used in a prospective setting for predicting drug-drug interaction or lack of it.

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

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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.

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

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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.

Bacteriuria and antimicrobial susceptibility pattern of bacterial ...

African Journals Online (AJOL)

Bacterial isolates and drug susceptibility patterns of urinary tract infection among ... Key words: Urinary tract infection, pregnant women, antimicrobial drug ..... and premature labour as well as adverse outcome for the unborn child (Raz, 2003).

Rapid, automated, nonradiometric susceptibility testing of Mycobacterium tuberculosis complex to four first-line antituberculous drugs used in standard short-course chemotherapy

DEFF Research Database (Denmark)

Johansen, Isik Somuncu; Thomsen, Vibeke Østergaard; Marjamäki, Merja

2004-01-01

The increasing prevalence of drug-resistant tuberculosis necessitates rapid and accurate susceptibility testing. The nonradiometric BACTEC Mycobacteria Growth Indicator Tube 960 (MGIT) system for susceptibility testing was evaluated on 222 clinical Mycobacterium tuberculosis complex isolates...... for isoniazid, rifampin, and ethambutol. Fifty-seven of the isolates were tested for pyrazinamide. Results were compared to those of radiometric BACTEC 460 system and discrepancies were resolved by the agar proportion method. We found an overall agreement of 99.0% for isoniazid, 99.5% for rifampin, 98.......2% for ethambutol, and 100% for pyrazinamide. After resolution of discrepancies, MGIT yielded no false susceptibility for rifampin and isoniazid. Although turnaround times were comparable, MGIT provides an advantage as inoculation can be done on any weekday as the growth is monitored automatically. The automated...

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

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Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e

Distribution and antibiotic susceptibility pattern of methicillin ...

African Journals Online (AJOL)

Distribution and antibiotic susceptibility pattern of methicillin-resistant Staphylococcus aureus isolates in a university Teaching hospital in Nigeria. ... Amoxycillin clavunanic acid and ciprofloxacin were most active with MRSA isolates showing 97% and 93.9% susceptibility to the two drugs respectively. Eighteen (54.5%) ...

Extended spectrum of antibiotic susceptibility for tuberculosis, Djibouti.

Science.gov (United States)

Bouzid, Fériel; Astier, Hélène; Osman, Djaltou Aboubaker; Javelle, Emilie; Hassan, Mohamed Osman; Simon, Fabrice; Garnotel, Eric; Drancourt, Michel

2018-02-01

In the Horn of Africa, there is a high prevalence of tuberculosis that is reported to be partly driven by multidrug-resistant (MDR) Mycobacterium tuberculosis strictu sensu strains. We conducted a prospective study to investigate M. tuberculosis complex species causing tuberculosis in Djibouti, and their in vitro susceptibility to standard anti-tuberculous antibiotics in addition to clofazimine, minocycline, chloramphenicol and sulfadiazine. Among the 118 mycobacteria isolates from 118 successive patients with suspected pulmonary tuberculosis, 111 strains of M. tuberculosis, five Mycobacterium canettii, one 'Mycobacterium simulans' and one Mycobacterium kansasii were identified. Drug-susceptibility tests performed on the first 78 isolates yielded nine MDR M. tuberculosis isolates. All isolates were fully susceptible to clofazimine, minocycline and chloramphenicol, and 75 of 78 isolates were susceptible to sulfadiazine. In the Horn of Africa, patients with confirmed pulmonary tuberculosis caused by an in vitro susceptible strain may benefit from anti-leprosy drugs, sulfamides and phenicol antibiotics. Copyright © 2017 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.

Cocrystal solubilization in biorelevant media and its prediction from drug solubilization

Science.gov (United States)

Lipert, Maya P.; Roy, Lilly; Childs, Scott L.

2015-01-01

This work examines cocrystal solubility in biorelevant media, (FeSSIF, fed state simulated intestinal fluid), and develops a theoretical framework that allows for the simple and quantitative prediction of cocrystal solubilization from drug solubilization. The solubilities of four hydrophobic drugs and seven cocrystals containing these drugs were measured in FeSSIF and in acetate buffer at pH 5.00. In all cases, the cocrystal solubility (Scocrystal) was higher than the drug solubility (Sdrug) in both buffer and FeSSIF; however, the solubilization ratio of drug, SRdrug = (SFeSSIF/Sbuffer)drug, was not the same as the solubilization ratio of cocrystal, SRcocrystal = (SFeSSIF/Sbuffer)cocrystal, meaning drug and cocrystal were not solubilized to the same extent in FeSSIF. This highlights the potential risk of anticipating cocrystal behavior in biorelevant media based on solubility studies in water. Predictions of SRcocrystal from simple equations based only on SRdrug were in excellent agreement with measured values. For 1:1 cocrystals, the cocrystal solubilization ratio can be obtained from the square root of the drug solubilization ratio. For 2:1 cocrystals, SRcocrystal is found from (SRdrug)2/3. The findings in FeSSIF can be generalized to describe cocrystal behavior in other systems involving preferential solubilization of a drug such as surfactants, lipids, and other drug solubilizing media. PMID:26390213

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

Genetic architecture of intrinsic antibiotic susceptibility.

Directory of Open Access Journals (Sweden)

Hany S Girgis

2009-05-01

Full Text Available Antibiotic exposure rapidly selects for more resistant bacterial strains, and both a drug's chemical structure and a bacterium's cellular network affect the types of mutations acquired.To better characterize the genetic determinants of antibiotic susceptibility, we exposed a transposon-mutagenized library of Escherichia coli to each of 17 antibiotics that encompass a wide range of drug classes and mechanisms of action. Propagating the library for multiple generations with drug concentrations that moderately inhibited the growth of the isogenic parental strain caused the abundance of strains with even minor fitness advantages or disadvantages to change measurably and reproducibly. Using a microarray-based genetic footprinting strategy, we then determined the quantitative contribution of each gene to E. coli's intrinsic antibiotic susceptibility. We found both loci whose removal increased general antibiotic tolerance as well as pathways whose down-regulation increased tolerance to specific drugs and drug classes. The beneficial mutations identified span multiple pathways, and we identified pairs of mutations that individually provide only minor decreases in antibiotic susceptibility but that combine to provide higher tolerance.Our results illustrate that a wide-range of mutations can modulate the activity of many cellular resistance processes and demonstrate that E. coli has a large mutational target size for increasing antibiotic tolerance. Furthermore, the work suggests that clinical levels of antibiotic resistance might develop through the sequential accumulation of chromosomal mutations of small individual effect.

Drug susceptibility of influenza A/H3N2 strains co-circulating during 2009 influenza pandemic: first report from Mumbai.

Science.gov (United States)

Gohil, Devanshi J; Kothari, Sweta T; Shinde, Pramod S; Chintakrindi, Anand S; Meharunkar, Rhuta; Warke, Rajas V; Kanyalkar, Meena A; Chowdhary, Abhay S; Deshmukh, Ranjana A

2015-01-01

From its first instance in 1977, resistance to amantadine, a matrix (M2) inhibitor has been increasing among influenza A/H3N2, thus propelling the use of oseltamivir, a neuraminidase (NA) inhibitor as a next line drug. Information on drug susceptibility to amantadine and neuraminidase inhibitors for influenza A/H3N2 viruses in India is limited with no published data from Mumbai. This study aimed at examining the sensitivity to M2 and NA inhibitors of influenza A/H3N2 strains isolated from 2009 to 2011 in Mumbai. Nasopharyngeal swabs positive for influenza A/H3N2 virus were inoculated on Madin-Darby canine kidney (MDCK) cell line for virus isolation. Molecular analysis of NA and M2 genes was used to detect known mutations contributing to resistance. Resistance to neuraminidase was assayed using a commercially available chemiluminescence based NA-Star assay kit. Genotypically, all isolates were observed to harbor mutations known to confer resistance to amantadine. However, no know mutations conferring resistance to NA inhibitors were detected. The mean IC50 value for oseltamivir was 0.25 nM. One strain with reduced susceptibility to the neuraminidase inhibitor (IC₅₀=4.08 nM) was isolated from a patient who had received oseltamivir treatment. Phylogenetic analysis postulate the emergence of amantadine resistance in Mumbai may be due to genetic reassortment with the strains circulating in Asia and North America. Surveillance of drug susceptibility helped us to identify an isolate with reduced sensitivity to oseltamivir. Therefore, we infer that such surveillance would help in understanding possible trends underlying the emergence of resistant variants in humans. Copyright © 2014 Elsevier B.V. All rights reserved.

Towards Point-of-Care Diagnosis of Pulmonary Tuberculosis and Drug Susceptibility Testing by Whole Genome Sequencing of DNA Isolated from Sputum

Directory of Open Access Journals (Sweden)

Kayzad S. Nilgiriwala

2017-12-01

Full Text Available Preliminary screening of pulmonary tuberculosis (TB in India still relies on sputum microscopy, which has low sensitivity leading to high rate of false negatives. Moreover, conventional phenotypic drug susceptibility testing (DST is conducted over a period of weeks leading to delays in correct treatment. Next generation sequencing technologies (Illumina and Oxford Nanopore have made it possible to sequence miniscule amount of DNA and generate enough data within a day for detecting specific microbes and their DST profile. Sputum samples from two pulmonary TB patients were processed by decontamination and DNA was isolated from the decontaminated sputum sediments. The isolated DNA was used for sequencing by Illumina and by MinION (Oxford Nanopore Technologies. The sequence data was used to diagnose TB and to determine the DST profiles for the first- and second-line drugs by Mykrobe Predictor. Validation was conducted by sequencing DNA (by Illumina isolated from pure growth culture from both the samples individually. DNA sequencing data (for both, Ilumina and MinION from one of the sputum samples indicated the presence of Mycobacterium tuberculosis (M. tb resistant to streptomycin, isoniazid, rifampicin and ethambutol and its lineage was predicted to be Beijing East Asia. The second sample indicated the presence of M. tb sensitive to the first- and second-line drugs by MinION and showed minor resistance call only to rifampicin by Illumina. Lineage of the second sample was predicted to be East Africa Indian Ocean, whereas Illumina data indicated it to be Delhi Central Asia. The two samples were correctly diagnosed for the presence of M. tb in the sputum DNA. Their DST profiles and lineage were also successfully determined from both the sequencing platforms (with minor discrepancies paving the way towards diagnosis and DST of TB from DNA isolated from sputum samples at point-of-care. Nanopore sequencing currently requires skilled personnel for DNA

Outcomes of treatment of drug-susceptible tuberculosis at public sector primary healthcare clinics in Johannesburg, South Africa: A retrospective cohort study.

Science.gov (United States)

Budgell, E P; Evans, D; Schnippel, K; Ive, P; Long, L; Rosen, S

2016-09-05

Despite the large number of tuberculosis (TB) patients treated in South Africa (SA), there are few descriptions in the published literature of drug-susceptible TB patient characteristics, mode of diagnosis or treatment outcomes in routine public sector treatment programmes. To enhance the evidence base for public sector TB treatment service delivery, we reported the characteristics of and outcomes for a retrospective cohort of adult TB patients at public sector clinics in the Johannesburg Metropolitan Municipality (JHB), SA. We collected medical record data for a retrospective cohort of adult (≥18 years) TB patients registered between 1 April 2011 and 31 March 2012 at three public sector clinics in JHB. Data were abstracted from National TB Programme clinic cards and the TB case registers routinely maintained at study sites. We report patient characteristics, mode of diagnosis, mode of treatment supervision, treatment characteristics, HIV status and treatment outcomes for this cohort. A total of 544 patients were enrolled in the cohort. Most (86%) were new TB cases, 81% had pulmonary TB, 58% were smear-positive at treatment initiation and 71% were HIV co-infected. Among 495 patients with treatment outcomes reported, 80% (n=394) had successful outcomes, 11% (n=55) were lost to follow-up, 8% (n=40) died and 1% (n=6) failed treatment. Primary healthcare clinics in JHB are achieving relatively high rates of success in treating drug-susceptible TB. Missing laboratory results were common, including follow-up smears, cultures and drug susceptibility tests, making it difficult to assess adherence to guidelines and leaving scope for substantial improvements in record-keeping at the clinics involved.

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.

Antifungal susceptibility and phylogeny of opportunistic members of the order mucorales.

Science.gov (United States)

Vitale, Roxana G; de Hoog, G Sybren; Schwarz, Patrick; Dannaoui, Eric; Deng, Shuwen; Machouart, Marie; Voigt, Kerstin; van de Sande, Wendy W J; Dolatabadi, Somayeh; Meis, Jacques F; Walther, Grit

2012-01-01

The in vitro susceptibilities of 66 molecularly identified strains of the Mucorales to eight antifungals (amphotericin B, terbinafine, itraconazole, posaconazole, voriconazole, caspofungin, micafungin, and 5-fluorocytosine) were tested. Molecular phylogeny was reconstructed based on the nuclear ribosomal large subunit to reveal taxon-specific susceptibility profiles. The impressive phylogenetic diversity of the Mucorales was reflected in susceptibilities differing at family, genus, and species levels. Amphotericin B was the most active drug, though somewhat less against Rhizopus and Cunninghamella species. Posaconazole was the second most effective antifungal agent but showed reduced activity in Mucor and Cunninghamella strains, while voriconazole lacked in vitro activity for most strains. Genera attributed to the Mucoraceae exhibited a wide range of MICs for posaconazole, itraconazole, and terbinafine and included resistant strains. Cunninghamella also comprised strains resistant to all azoles tested but was fully susceptible to terbinafine. In contrast, the Lichtheimiaceae completely lacked strains with reduced susceptibility for these antifungals. Syncephalastrum species exhibited susceptibility profiles similar to those of the Lichtheimiaceae. Mucor species were more resistant to azoles than Rhizopus species. Species-specific responses were obtained for terbinafine where only Rhizopus arrhizus and Mucor circinelloides were resistant. Complete or vast resistance was observed for 5-fluorocytosine, caspofungin, and micafungin. Intraspecific variability of in vitro susceptibility was found in all genera tested but was especially high in Mucor and Rhizopus for azoles and terbinafine. Accurate molecular identification of etiologic agents is compulsory to predict therapy outcome. For species of critical genera such as Mucor and Rhizopus, exhibiting high intraspecific variation, susceptibility testing before the onset of therapy is recommended.

Psychophysiological prediction of choice: relevance to insight and drug addiction

Science.gov (United States)

Moeller, Scott J.; Hajcak, Greg; Parvaz, Muhammad A.; Dunning, Jonathan P.; Volkow, Nora D.

2012-01-01

An important goal of addiction research and treatment is to predict behavioural responses to drug-related stimuli. This goal is especially important for patients with impaired insight, which can interfere with therapeutic interventions and potentially invalidate self-report questionnaires. This research tested (i) whether event-related potentials, specifically the late positive potential, predict choice to view cocaine images in cocaine addiction; and (ii) whether such behaviour prediction differs by insight (operationalized in this study as self-awareness of image choice). Fifty-nine cocaine abusers and 32 healthy controls provided data for the following laboratory components that were completed in a fixed-sequence (to establish prediction): (i) event-related potential recordings while passively viewing pleasant, unpleasant, neutral and cocaine images, during which early (400–1000 ms) and late (1000–2000 ms) window late positive potentials were collected; (ii) self-reported arousal ratings for each picture; and (iii) two previously validated tasks: one to assess choice for viewing these same images, and the other to group cocaine abusers by insight. Results showed that pleasant-related late positive potentials and arousal ratings predicted pleasant choice (the choice to view pleasant pictures) in all subjects, validating the method. In the cocaine abusers, the predictive ability of the late positive potentials and arousal ratings depended on insight. Cocaine-related late positive potentials better predicted cocaine image choice in cocaine abusers with impaired insight. Another emotion-relevant event-related potential component (the early posterior negativity) did not show these results, indicating specificity of the late positive potential. In contrast, arousal ratings better predicted respective cocaine image choice (and actual cocaine use severity) in cocaine abusers with intact insight. Taken together, the late positive potential could serve as a biomarker

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

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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

21 CFR 866.1620 - Antimicrobial susceptibility test disc.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Antimicrobial susceptibility test disc. 866.1620 Section 866.1620 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Diagnostic Devices § 866.1620 Antimicrobial...

Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

Science.gov (United States)

Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

2017-11-01

The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

Role of spontaneous physical activity in prediction of susceptibility to activity based anorexia in male and female rats.

Science.gov (United States)

Perez-Leighton, Claudio E; Grace, Martha; Billington, Charles J; Kotz, Catherine M

2014-08-01

Anorexia nervosa (AN) is a chronic eating disorder affecting females and males, defined by body weight loss, higher physical activity levels and restricted food intake. Currently, the commonalities and differences between genders in etiology of AN are not well understood. Animal models of AN, such as activity-based anorexia (ABA), can be helpful in identifying factors determining individual susceptibility to AN. In ABA, rodents are given an access to a running wheel while food restricted, resulting in paradoxical increased physical activity levels and weight loss. Recent studies suggest that different behavioral traits, including voluntary exercise, can predict individual weight loss in ABA. A higher inherent drive for movement may promote development and severity of AN, but this hypothesis remains untested. In rodents and humans, drive for movement is defined as spontaneous physical activity (SPA), which is time spent in low-intensity, non-volitional movements. In this paper, we show that a profile of body weight history and behavioral traits, including SPA, can predict individual weight loss caused by ABA in male and female rats with high accuracy. Analysis of the influence of SPA on ABA susceptibility in males and females rats suggests that either high or low levels of SPA increase the probability of high weight loss in ABA, but with larger effects in males compared to females. These results suggest that the same behavioral profile can identify individuals at-risk of AN for both male and female populations and that SPA has predictive value for susceptibility to AN. Copyright © 2014 Elsevier Inc. All rights reserved.

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

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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.

Neural responses to exclusion predict susceptibility to social influence.

Science.gov (United States)

Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

2014-05-01

Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

Acute effects of sex steroid hormones on susceptibility to cardiac arrhythmias: a simulation study.

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Pei-Chi Yang

2010-01-01

Full Text Available Acute effects of sex steroid hormones likely contribute to the observation that post-pubescent males have shorter QT intervals than females. However, the specific role for hormones in modulating cardiac electrophysiological parameters and arrhythmia vulnerability is unclear. Here we use a computational modeling approach to incorporate experimentally measured effects of physiological concentrations of testosterone, estrogen and progesterone on cardiac ion channel targets. We then study the hormone effects on ventricular cell and tissue dynamics comprised of Faber-Rudy computational models. The "female" model predicts changes in action potential duration (APD at different stages of the menstrual cycle that are consistent with clinically observed QT interval fluctuations. The "male" model predicts shortening of APD and QT interval at physiological testosterone concentrations. The model suggests increased susceptibility to drug-induced arrhythmia when estradiol levels are high, while testosterone and progesterone are apparently protective. Simulations predict the effects of sex steroid hormones on clinically observed QT intervals and reveal mechanisms of estrogen-mediated susceptibility to prolongation of QT interval. The simulations also indicate that acute effects of estrogen are not alone sufficient to cause arrhythmia triggers and explain the increased risk of females to Torsades de Pointes. Our results suggest that acute effects of sex steroid hormones on cardiac ion channels are sufficient to account for some aspects of gender specific susceptibility to long-QT linked arrhythmias.

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

Science.gov (United States)

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

2016-05-01

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

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

Science.gov (United States)

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

2016-05-01

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

Statistical Analysis of a Method to Predict Drug-Polymer Miscibility

DEFF Research Database (Denmark)

Knopp, Matthias Manne; Olesen, Niels Erik; Huang, Yanbin

2016-01-01

In this study, a method proposed to predict drug-polymer miscibility from differential scanning calorimetry measurements was subjected to statistical analysis. The method is relatively fast and inexpensive and has gained popularity as a result of the increasing interest in the formulation of drug...... as provided in this study. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci....

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

Science.gov (United States)

Melchiorre, C.; Castellanos Abella, E. A.; van Westen, C. J.; Matteucci, M.

2011-04-01

This paper describes a procedure for landslide susceptibility assessment based on artificial neural networks, and focuses on the estimation of the prediction capability, robustness, and sensitivity of susceptibility models. The study is carried out in the Guantanamo Province of Cuba, where 186 landslides were mapped using photo-interpretation. Twelve conditioning factors were mapped including geomorphology, geology, soils, landuse, slope angle, slope direction, internal relief, drainage density, distance from roads and faults, rainfall intensity, and ground peak acceleration. A methodology was used that subdivided the database in 3 subsets. A training set was used for updating the weights. A validation set was used to stop the training procedure when the network started losing generalization capability, and a test set was used to calculate the performance of the network. A 10-fold cross-validation was performed in order to show that the results are repeatable. The prediction capability, the robustness analysis, and the sensitivity analysis were tested on 10 mutually exclusive datasets. The results show that by means of artificial neural networks it is possible to obtain models with high prediction capability and high robustness, and that an exploration of the effect of the individual variables is possible, even if they are considered as a black-box model.

Direct nitrate reductase assay versus microscopic observation drug susceptibility test for rapid detection of MDR-TB in Uganda.

Directory of Open Access Journals (Sweden)

Freddie Bwanga

Full Text Available The most common method for detection of drug resistant (DR TB in resource-limited settings (RLSs is indirect susceptibility testing on Lowenstein-Jensen medium (LJ which is very time consuming with results available only after 2-3 months. Effective therapy of DR TB is therefore markedly delayed and patients can transmit resistant strains. Rapid and accurate tests suitable for RLSs in the diagnosis of DR TB are thus highly needed. In this study we compared two direct techniques--Nitrate Reductase Assay (NRA and Microscopic Observation Drug Susceptibility (MODS for rapid detection of MDR-TB in a high burden RLS. The sensitivity, specificity, and proportion of interpretable results were studied. Smear positive sputum was collected from 245 consecutive re-treatment TB patients attending a TB clinic in Kampala, Uganda. Samples were processed at the national reference laboratory and tested for susceptibility to rifampicin and isoniazid with direct NRA, direct MODS and the indirect LJ proportion method as reference. A total of 229 specimens were confirmed as M. tuberculosis, of these interpretable results were obtained in 217 (95% with either the NRA or MODS. Sensitivity, specificity and kappa agreement for MDR-TB diagnosis was 97%, 98% and 0.93 with the NRA; and 87%, 95% and 0.78 with the MODS, respectively. The median time to results was 10, 7 and 64 days with NRA, MODS and the reference technique, respectively. The cost of laboratory supplies per sample was low, around 5 USD, for the rapid tests. The direct NRA and MODS offered rapid detection of resistance almost eight weeks earlier than with the reference method. In the study settings, the direct NRA was highly sensitive and specific. We consider it to have a strong potential for timely detection of MDR-TB in RLS.

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

Science.gov (United States)

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

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Cai Huang

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

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.

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.

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.

Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion

Science.gov (United States)

Rahmati, Omid; Tahmasebipour, Nasser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

2017-12-01

Gully erosion constitutes a serious problem for land degradation in a wide range of environments. The main objective of this research was to compare the performance of seven state-of-the-art machine learning models (SVM with four kernel types, BP-ANN, RF, and BRT) to model the occurrence of gully erosion in the Kashkan-Poldokhtar Watershed, Iran. In the first step, a gully inventory map consisting of 65 gully polygons was prepared through field surveys. Three different sample data sets (S1, S2, and S3), including both positive and negative cells (70% for training and 30% for validation), were randomly prepared to evaluate the robustness of the models. To model the gully erosion susceptibility, 12 geo-environmental factors were selected as predictors. Finally, the goodness-of-fit and prediction skill of the models were evaluated by different criteria, including efficiency percent, kappa coefficient, and the area under the ROC curves (AUC). In terms of accuracy, the RF, RBF-SVM, BRT, and P-SVM models performed excellently both in the degree of fitting and in predictive performance (AUC values well above 0.9), which resulted in accurate predictions. Therefore, these models can be used in other gully erosion studies, as they are capable of rapidly producing accurate and robust gully erosion susceptibility maps (GESMs) for decision-making and soil and water management practices. Furthermore, it was found that performance of RF and RBF-SVM for modelling gully erosion occurrence is quite stable when the learning and validation samples are changed.

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

Science.gov (United States)

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Prospective Study of Tuberculosis Drug Susceptibility in Sabah, Malaysia, and an Algorithm for Management of Isoniazid Resistance

Science.gov (United States)

Rashid Ali, Muhammad Redzwan S.; Parameswaran, Uma; William, Timothy; Bird, Elspeth; Wilkes, Christopher S.; Lee, Wai Khew; Yeo, Tsin Wen; Anstey, Nicholas M.; Ralph, Anna P.

2015-01-01

Introduction. The burden of tuberculosis is high in eastern Malaysia, and rates of Mycobacterium tuberculosis drug resistance are poorly defined. Our objectives were to determine M. tuberculosis susceptibility and document management after receipt of susceptibility results. Methods. Prospective study of adult outpatients with smear-positive pulmonary tuberculosis (PTB) in Sabah, Malaysia. Additionally, hospital clinicians accessed the reference laboratory for clinical purposes during the study. Results. 176 outpatients were enrolled; 173 provided sputum samples. Mycobacterial culture yielded M. tuberculosis in 159 (91.9%) and nontuberculous Mycobacterium (NTM) in three (1.7%). Among outpatients there were no instances of multidrug resistant M. tuberculosis (MDR-TB). Seven people (4.5%) had isoniazid resistance (INH-R); all were switched to an appropriate second-line regimen for varying durations (4.5–9 months). Median delay to commencement of the second-line regimen was 13 weeks. Among 15 inpatients with suspected TB, 2 had multidrug resistant TB (one extensively drug resistant), 2 had INH-R, and 4 had NTM. Conclusions. Current community rates of MDR-TB in Sabah are low. However, INH-resistance poses challenges, and NTM is an important differential diagnosis in this setting, where smear microscopy is the usual diagnostic modality. To address INH-R management issues in our setting, we propose an algorithm for the treatment of isoniazid-resistant PTB. PMID:25838829

Predictive typing of drug-induced neurological sufferings from studies of the distribution of labelled drugs

International Nuclear Information System (INIS)

Takasu, T.

1980-01-01

A drug given to an animal becomes widely distributed throughout the body, acting on the living mechanisms or structures, and is gradually excreted. Some drugs can remain in some parts of the body for a long period. For example, 14 C-chloramphenical was found to remain preferentially in the salivary gland, liver and bone marrow of mice 24 hours after its oral administration. If such a drug is given repeatedly, it could possibly accumulate gradually in these organs. Thus, when its accumulation in a particular part of the body exceeds a certain level, the living mechanism or structure may possibly be injured. The harmful effects of a drug in repeated administration are called its chronic toxicity. The author discusses whether it is possible to predict the toxicity of a drug by studying its distribution in relation to time, and, if possible, the points in time. This problem is studied especially in relation to the nervous system. (Auth.)

Antimicrobial Drug Resistance of Salmonella enterica Serovar Typhi in Asia and Molecular Mechanism of Reduced Susceptibility to the Fluoroquinolones▿

OpenAIRE

Chau, Tran Thuy; Campbell, James Ian; Galindo, Claudia M.; Van Minh Hoang, Nguyen; Diep, To Song; Nga, Tran Thu Thi; Van Vinh Chau, Nguyen; Tuan, Phung Quoc; Page, Anne Laure; Ochiai, R. Leon; Schultsz, Constance; Wain, John; Bhutta, Zulfiqar A.; Parry, Christopher M.; Bhattacharya, Sujit K.

2007-01-01

This study describes the pattern and extent of drug resistance in 1,774 strains of Salmonella enterica serovar Typhi isolated across Asia between 1993 and 2005 and characterizes the molecular mechanisms underlying the reduced susceptibilities to fluoroquinolones of these strains. For 1,393 serovar Typhi strains collected in southern Vietnam, the proportion of multidrug resistance has remained high since 1993 (50% in 2004) and there was a dramatic increase in nalidixic acid resistance between ...

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

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Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Hospital costs of nosocomial multi-drug resistant Pseudomonas aeruginosa acquisition.

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Morales, Eva; Cots, Francesc; Sala, Maria; Comas, Mercè; Belvis, Francesc; Riu, Marta; Salvadó, Margarita; Grau, Santiago; Horcajada, Juan P; Montero, Maria Milagro; Castells, Xavier

2012-05-23

We aimed to assess the hospital economic costs of nosocomial multi-drug resistant Pseudomonas aeruginosa acquisition. A retrospective study of all hospital admissions between January 1, 2005, and December 31, 2006 was carried out in a 420-bed, urban, tertiary-care teaching hospital in Barcelona (Spain). All patients with a first positive clinical culture for P. aeruginosa more than 48 h after admission were included. Patient and hospitalization characteristics were collected from hospital and microbiology laboratory computerized records. According to antibiotic susceptibility, isolates were classified as non-resistant, resistant and multi-drug resistant. Cost estimation was based on a full-costing cost accounting system and on the criteria of clinical Activity-Based Costing methods. Multivariate analyses were performed using generalized linear models of log-transformed costs. Cost estimations were available for 402 nosocomial incident P. aeruginosa positive cultures. Their distribution by antibiotic susceptibility pattern was 37.1% non-resistant, 29.6% resistant and 33.3% multi-drug resistant. The total mean economic cost per admission of patients with multi-drug resistant P. aeruginosa strains was higher than that for non-resistant strains (15,265 vs. 4,933 Euros). In multivariate analysis, resistant and multi-drug resistant strains were independently predictive of an increased hospital total cost in compared with non-resistant strains (the incremental increase in total hospital cost was more than 1.37-fold and 1.77-fold that for non-resistant strains, respectively). P. aeruginosa multi-drug resistance independently predicted higher hospital costs with a more than 70% increase per admission compared with non-resistant strains. Prevention of the nosocomial emergence and spread of antimicrobial resistant microorganisms is essential to limit the strong economic impact.

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

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

2015-01-01

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

Observation of reversible, rapid changes in drug susceptibility of hypoxic tumor cells in a microfluidic device

Energy Technology Data Exchange (ETDEWEB)

Germain, Todd; Ansari, Megan; Pappas, Dimitri, E-mail: d.pappas@ttu.edu

2016-09-14

Hypoxia is a major stimulus for increased drug resistance and for survival of tumor cells. Work from our group and others has shown that hypoxia increases resistance to anti-cancer compounds, radiation, and other damage-pathway cytotoxic agents. In this work we utilize a microfluidic culture system capable of rapid switching of local oxygen concentrations to determine changes in drug resistance in prostate cancer cells. We observed rapid adaptation to hypoxia, with drug resistance to 2 μM staurosporine established within 30 min of hypoxia. Annexin-V/Sytox Green apoptosis assays over 9 h showed 78.0% viability, compared to 84.5% viability in control cells (normoxic cells with no staurosporine). Normoxic cells exposed to the same staurosporine concentration had a viability of 48.6% after 9 h. Hypoxia adaptation was rapid and reversible, with Hypoxic cells treated with 20% oxygen for 30 min responding to staurosporine with 51.6% viability after drug treatment for 9 h. Induction of apoptosis through the receptor-mediated pathway, which bypasses anti-apoptosis mechanisms induced by hypoxia, resulted in 39.4 ± 7% cell viability. The rapid reversibility indicates co-treatment of oxygen with anti-cancer compounds may be a potential therapeutic target. - Highlights: • Microfluidic system switches rapidly between normoxia and hypoxia (5 min). • Observation of rapid adaptation of PC3 cells to hypoxia and normoxia (30 min). • Drug susceptibility in tumor cells restored after chip switched to normoxia for 30 min.

Microbiological quality of water from the rivers of Curitiba, Paraná State, Brazil, and the susceptibility to antimicrobial drugs and pathogenicity of Escherichia coli.

Science.gov (United States)

Giowanella, Melissa; Bozza, Angela; do Rocio Dalzoto, Patricia; Dionísio, Jair Alves; Andraus, Sumaia; Guimarães, Edson Luiz Gomes; Pimentel, Ida Chapaval

2015-11-01

Water safety is determined by several markers, and Escherichia coli is one of the most important indicators of water quality. The objective of this study was to evaluate the microbiological parameters in environmental samples of fresh water from rivers of Curitiba and its metropolitan area in Paraná State, Brazil. In addition, we evaluated the pathogenicity and susceptibility to antimicrobial drugs in E. coli. These evaluations were performed by quantitative and qualitative methods employing selective media for isolating thermotolerant coliforms and biochemical tests for identifying E. coli. Pathogenic strains of E. coli were detected by PCR multiplex using specific primers. From the water samples, 494 thermotolerant coliforms were obtained, of which 96 (19.43%) isolates were characterized as E. coli. Three isolates were identified as enteroaggregative E. coli, one as enterotoxigenic E. coli, one as enteropathogenic E. coli, and two carried the Eae virulence gene. E. coli susceptibility to commonly employed antimicrobial drugs was analyzed by the disc diffusion method. The results showed 49 (51.04%) isolates resistant to all the drugs assayed, 16 (16.67%) with an intermediate resistance to all drugs, and 31 (32.29%) intermediately or fully resistant to one or more drugs tested. The highest rate of resistance was observed for tetracycline 30 μg, streptomycin 10 μg, and ceftazidime 30 μg. Detection of E. coli is associated with water contamination by fecal material from humans and warm-blooded animals. The occurrence of resistant strains can be the result of the indiscriminate use of antimicrobial drugs and poor sanitation in the areas assayed.

Predictive modeling of structured electronic health records for adverse drug event detection.

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Zhao, Jing; Henriksson, Aron; Asker, Lars; Boström, Henrik

2015-01-01

The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and

Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

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Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

2017-11-15

The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among

qPCR-High resolution melt analysis for drug susceptibility testing of Mycobacterium leprae directly from clinical specimens of leprosy patients.

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Araujo, Sergio; Goulart, Luiz Ricardo; Truman, Richard W; Goulart, Isabela Maria B; Vissa, Varalakshmi; Li, Wei; Matsuoka, Masanori; Suffys, Philip; Fontes, Amanda B; Rosa, Patricia S; Scollard, David M; Williams, Diana L

2017-06-01

Real-Time PCR-High Resolution Melting (qPCR-HRM) analysis has been recently described for rapid drug susceptibility testing (DST) of Mycobacterium leprae. The purpose of the current study was to further evaluate the validity, reliability, and accuracy of this assay for M. leprae DST in clinical specimens. The specificity and sensitivity for determining the presence and susceptibility of M. leprae to dapsone based on the folP1 drug resistance determining region (DRDR), rifampin (rpoB DRDR) and ofloxacin (gyrA DRDR) was evaluated using 211 clinical specimens from leprosy patients, including 156 multibacillary (MB) and 55 paucibacillary (PB) cases. When comparing the results of qPCR-HRM DST and PCR/direct DNA sequencing, 100% concordance was obtained. The effects of in-house phenol/chloroform extraction versus column-based DNA purification protocols, and that of storage and fixation protocols of specimens for qPCR-HRM DST, were also evaluated. qPCR-HRM results for all DRDR gene assays (folP1, rpoB, and gyrA) were obtained from both MB (154/156; 98.7%) and PB (35/55; 63.3%) patients. All PCR negative specimens were from patients with low numbers of bacilli enumerated by an M. leprae-specific qPCR. We observed that frozen and formalin-fixed paraffin embedded (FFPE) tissues or archival Fite's stained slides were suitable for HRM analysis. Among 20 mycobacterial and other skin bacterial species tested, only M. lepromatosis, highly related to M. leprae, generated amplicons in the qPCR-HRM DST assay for folP1 and rpoB DRDR targets. Both DNA purification protocols tested were efficient in recovering DNA suitable for HRM analysis. However, 3% of clinical specimens purified using the phenol/chloroform DNA purification protocol gave false drug resistant data. DNA obtained from freshly frozen (n = 172), formalin-fixed paraffin embedded (FFPE) tissues (n = 36) or archival Fite's stained slides (n = 3) were suitable for qPCR-HRM DST analysis. The HRM-based assay was also able to

DR2DI: a powerful computational tool for predicting novel drug-disease associations

Science.gov (United States)

Lu, Lu; Yu, Hua

2018-05-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

DR2DI: a powerful computational tool for predicting novel drug-disease associations

Science.gov (United States)

Lu, Lu; Yu, Hua

2018-04-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

Predicting abuse potential of stimulants and other dopaminergic drugs: overview and recommendations.

Science.gov (United States)

Huskinson, Sally L; Naylor, Jennifer E; Rowlett, James K; Freeman, Kevin B

2014-12-01

Examination of a drug's abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. This article is part of the Special Issue entitled 'CNS Stimulants'. Copyright © 2014 Elsevier Ltd. All rights reserved.

Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

Science.gov (United States)

Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

2014-01-01

Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set

A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

International Nuclear Information System (INIS)

Poulin, Patrick; Ekins, Sean; Theil, Frank-Peter

2011-01-01

A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V ss ) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V ss for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V ss of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.

Prediction of Central Nervous System Side Effects Through Drug Permeability to Blood-Brain Barrier and Recommendation Algorithm.

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Fan, Jun; Yang, Jing; Jiang, Zhenran

2018-04-01

Drug side effects are one of the public health concerns. Using powerful machine-learning methods to predict potential side effects before the drugs reach the clinical stages is of great importance to reduce time consumption and protect the security of patients. Recently, researchers have proved that the central nervous system (CNS) side effects of a drug are closely related to its permeability to the blood-brain barrier (BBB). Inspired by this, we proposed an extended neighborhood-based recommendation method to predict CNS side effects using drug permeability to the BBB and other known features of drug. To the best of our knowledge, this is the first attempt to predict CNS side effects considering drug permeability to the BBB. Computational experiments demonstrated that drug permeability to the BBB is an important factor in CNS side effects prediction. Moreover, we built an ensemble recommendation model and obtained higher AUC score (area under the receiver operating characteristic curve) and AUPR score (area under the precision-recall curve) on the data set of CNS side effects by integrating various features of drug.

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.

Further characterization of porcine Brachyspira hyodysenteriae isolates with decreased susceptibility to tiamulin.

Science.gov (United States)

Karlsson, M; Aspán, A; Landén, A; Franklin, A

2004-04-01

Brachyspira hyodysenteriae is the causative agent of swine dysentery, a severe diarrhoeal disease in pigs. Few drugs are available to treat the disease, owing to both antimicrobial resistance and withdrawal of drugs authorized for use in pigs. Tiamulin is the drug of choice in many countries, but isolates with decreased susceptibility have recently been reported. The mechanism of tiamulin resistance in B. hyodysenteriae is not known and this facet is essential to understand the dissemination of the trait. To study the resistance epidemiology of B. hyodysenteriae, further characterization of a set of isolates from Germany (n = 16) and the UK (n = 6) with decreased susceptibility to tiamulin was performed. The relatedness between the isolates was studied by comparing PFGE patterns, and the in vitro susceptibility to five other antimicrobials (aivlosin, doxycycline, salinomycin, chloramphenicol and avilamycin) was also determined. For comparison of the antimicrobial-susceptibility pattern, Swedish (n = 20) and British (n = 4) tiamulin-susceptible isolates were tested. The German isolates represented several different PFGE patterns, indicating that tiamulin usage has been sufficient to select clones with decreased tiamulin susceptibility at different farms in Germany. The PFGE pattern for the six British isolates with decreased tiamulin susceptibility was identical to that of the German isolates, and they had a similar antimicrobial-susceptibility pattern, except for resistance to aivlosin, which was only found in a few German isolates. No other co-resistance with tiamulin was found.

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

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Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

2018-05-10

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

Evaluation of semisolid agar method for antifungal susceptibility test of T. rubrum

Directory of Open Access Journals (Sweden)

Sultana Razia

2016-08-01

Full Text Available Background: With increasing fungal disease many newer antifungal drugs are available with different spectrum of activ­ity. Antifungal susceptibility test will help clinicians for selection of effective drug and thereby treatment of patient. Objective: The study was undertaken to perform a simple screening drug susceptibility test of T. rnbrum by Semi Solid Agar Antifungal Susceptibility (SAAS Method: Perfonnance of susceptibility method was assessed by comparing the MICs of three commonly prescribed antifungal agents namely- tluconazole (FCZ, itraconazole (ITZ and terbinafine (TER to the CLSI (Clinical and Laboratory Standard Institute recommended M-38, a broth microdilution method. Results: In SAAS method, among twenty nine T. rubrum, twenty five (86.2% were susceptible (MIC range 0.5-64 µg/ml to Fluconazole (FCZ and four (13.7% were resistant (MIC value >64 µg/ml. In broth microdilution method, among twenty nine T. rubrum, twenty six (89.6% were susceptible (MIC range 0.3-64 µg/ml to FCZ and three (10.3% were resistant (MIC value >64 µg/ml. In case of both ITZ and TER, all were susceptible (MIC range 0.3-64 µg/ml to both methods. The SAAS method demonstrated the susceptibility pattern of T. rubrum against FCZ, ITZ and TER usually within 72 to 96 hours after organism isolation and results were concordance with the results of CLSI broth microdilution method. Conclusion: Though it is a newer method with proper standardization of the test method, SAAS method is simple and easily applicable screening method for susceptibility testing of antifungal agents against dermatophytes in any microbiology laboratories.

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.

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

Energy Technology Data Exchange (ETDEWEB)

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov [Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993–0002 (United States); Cross, Kevin P. [Leadscope, Inc., 1393 Dublin Road, Columbus, OH, 43215–1084 (United States)

2012-05-01

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

International Nuclear Information System (INIS)

Valerio, Luis G.; Cross, Kevin P.

2012-01-01

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.

Scientific Prediction and Prophetic Patenting in Drug Discovery.

Science.gov (United States)

Curry, Stephen H; Schneiderman, Anne M

2015-01-01

Pharmaceutical patenting involves writing claims based on both discoveries already made, and on prophesy of future developments in an ongoing project. This is necessitated by the very different timelines involved in the drug discovery and product development process on the one hand, and successful patenting on the other. If patents are sought too early there is a risk that patent examiners will disallow claims because of lack of enablement. If patenting is delayed, claims are at risk of being denied on the basis of existence of prior art, because the body of relevant known science will have developed significantly while the project was being pursued. This review examines the role of prophetic patenting in relation to the essential predictability of many aspects of drug discovery science, promoting the concepts of discipline-related and project-related prediction. This is especially directed towards patenting activities supporting commercialization of academia-based discoveries, where long project timelines occur, and where experience, and resources to pay for patenting, are limited. The need for improved collaborative understanding among project scientists, technology transfer professionals in, for example, universities, patent attorneys, and patent examiners is emphasized.

Shifts in Mycobacterial Populations and Emerging Drug-Resistance in West and Central Africa.

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Florian Gehre

Full Text Available In this study, we retrospectively analysed a total of 605 clinical isolates from six West or Central African countries (Benin, Cameroon, Central African Republic, Guinea-Conakry, Niger and Senegal. Besides spoligotyping to assign isolates to ancient and modern mycobacterial lineages, we conducted phenotypic drug-susceptibility-testing for each isolate for the four first-line drugs. We showed that phylogenetically modern Mycobacterium tuberculosis strains are more likely associated with drug resistance than ancient strains and predict that the currently ongoing replacement of the endemic ancient by a modern mycobacterial population in West/Central Africa might result in increased drug resistance in the sub-region.

A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

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Cherkaoui-Rbati, Mohammed H; Paine, Stuart W; Littlewood, Peter; Rauch, Cyril

2017-01-01

All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

Directory of Open Access Journals (Sweden)

Mohammed H Cherkaoui-Rbati

Full Text Available All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs of new chemical entities (NCEs and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit, located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level. A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences

KAUST Repository

Coll, Francesc

2015-05-27

Mycobacterium tuberculosis drug resistance (DR) challenges effective tuberculosis disease control. Current molecular tests examine limited numbers of mutations, and although whole genome sequencing approaches could fully characterise DR, data complexity has restricted their clinical application. A library (1,325 mutations) predictive of DR for 15 anti-tuberculosis drugs was compiled and validated for 11 of them using genomic-phenotypic data from 792 strains. A rapid online ‘TB-Profiler’ tool was developed to report DR and strain-type profiles directly from raw sequences. Using our DR mutation library, in silico diagnostic accuracy was superior to some commercial diagnostics and alternative databases. The library will facilitate sequence-based drug-susceptibility testing.

Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker.

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Heewon Park

Full Text Available The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc. and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.

Herb-drug interactions: challenges and opportunities for improved predictions.

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Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

2014-03-01

Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

Susceptibility of Selected Multi-Drug Resistant Clinical Isolates to ...

African Journals Online (AJOL)

2018-03-01

Mar 1, 2018 ... under the terms of the Creative Commons. Attribution ... relies on traditional medicine for their primary ... Collection and identification of plant materials: ..... versus carbapenem- susceptible Gram-negative microorganisms: Risk.

Hospital costs of nosocomial multi-drug resistant Pseudomonas aeruginosa acquisition

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Morales Eva

2012-05-01

Full Text Available Abstract Background We aimed to assess the hospital economic costs of nosocomial multi-drug resistant Pseudomonas aeruginosa acquisition. Methods A retrospective study of all hospital admissions between January 1, 2005, and December 31, 2006 was carried out in a 420-bed, urban, tertiary-care teaching hospital in Barcelona (Spain. All patients with a first positive clinical culture for P. aeruginosa more than 48 h after admission were included. Patient and hospitalization characteristics were collected from hospital and microbiology laboratory computerized records. According to antibiotic susceptibility, isolates were classified as non-resistant, resistant and multi-drug resistant. Cost estimation was based on a full-costing cost accounting system and on the criteria of clinical Activity-Based Costing methods. Multivariate analyses were performed using generalized linear models of log-transformed costs. Results Cost estimations were available for 402 nosocomial incident P. aeruginosa positive cultures. Their distribution by antibiotic susceptibility pattern was 37.1% non-resistant, 29.6% resistant and 33.3% multi-drug resistant. The total mean economic cost per admission of patients with multi-drug resistant P. aeruginosa strains was higher than that for non-resistant strains (15,265 vs. 4,933 Euros. In multivariate analysis, resistant and multi-drug resistant strains were independently predictive of an increased hospital total cost in compared with non-resistant strains (the incremental increase in total hospital cost was more than 1.37-fold and 1.77-fold that for non-resistant strains, respectively. Conclusions P. aeruginosa multi-drug resistance independently predicted higher hospital costs with a more than 70% increase per admission compared with non-resistant strains. Prevention of the nosocomial emergence and spread of antimicrobial resistant microorganisms is essential to limit the strong economic impact.

Polymyxins: Antimicrobial susceptibility concerns and therapeutic options

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V Balaji

2011-01-01

Full Text Available The increasing prevalence of multidrug-resistant nosocomial pathogens such as Acinetobacter baumannii, Pseudomonas aeruginosa and Klebsiella pneumoniae poses a great challenge to the treating physicians. The paucity of newer effective antimicrobials has led to renewed interest in the polymyxin group of drugs, as a last resort for treatment of gram-negative bacterial infections. There is a dearth of information on the pharmacological properties of colistin, leading to difficulties in selecting the right dose, dosing interval, and route of administration for treatment, especially in critically-ill patients. The increasing use of colistin over the last few years necessitates the need for accurate and reliable in vitro susceptibility testing methods. Development of heteroresistant strains as a result of colistin monotherapy is also a growing concern. There is a compelling need from the clinicians to provide options for probable and possible colistin combination therapy for multidrug-resistant bacterial infections in the ICU setting. Newer combination drug synergy determination tests are being developed and reported. There are no standardized recommendations from antimicrobial susceptibility testing reference agencies for the testing and interpretation of these drug combinations. Comparison and analysis of these reported methodologies may help to understand and assist the microbiologist to choose the best method that produces accurate results at the earliest. This will help clinicians to select the appropriate combination therapy. In this era of multidrug resistance it is important for the microbiology laboratory to be prepared, by default, to provide timely synergistic susceptibility results in addition to routine susceptibility, if warranted. Not as a favour or at request, but as a responsibility.

Drugs and Young People

Science.gov (United States)

Drug abuse is a serious public health problem. It affects almost every community and family in some way. Drug abuse in children and teenagers may pose a ... of young people may be more susceptible to drug abuse and addiction than adult brains. Abused drugs ...

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 Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

Science.gov (United States)

Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati

2018-05-07

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals

International Nuclear Information System (INIS)

Peyret, Thomas; Poulin, Patrick; Krishnan, Kannan

2010-01-01

The algorithms in the literature focusing to predict tissue:blood PC (P tb ) for environmental chemicals and tissue:plasma PC based on total (K p ) or unbound concentration (K pu ) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P tb , K p and K pu for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P tb , K p or K pu of muscle (n = 174), liver (n = 139) and adipose tissue (n = 141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.

Antiretroviral neuropenetration scores better correlate with cognitive performance of HIV-infected patients after accounting for drug susceptibility.

Science.gov (United States)

Fabbiani, Massimiliano; Grima, Pierfrancesco; Milanini, Benedetta; Mondi, Annalisa; Baldonero, Eleonora; Ciccarelli, Nicoletta; Cauda, Roberto; Silveri, Maria C; De Luca, Andrea; Di Giambenedetto, Simona

2015-01-01

The aim of the study was to explore how viral resistance and antiretroviral central nervous system (CNS) penetration could impact on cognitive performance of HIV-infected patients. We performed a multicentre cross-sectional study enrolling HIV-infected patients undergoing neuropsychological testing, with a previous genotypic resistance test on plasma samples. CNS penetration-effectiveness (CPE) scores and genotypic susceptibility scores (GSS) were calculated for each regimen. A composite score (CPE-GSS) was then constructed. Factors associated with cognitive impairment were investigated by logistic regression analysis. A total of 215 patients were included. Mean CPE was 7.1 (95% CI 6.9, 7.3) with 206 (95.8%) patients showing a CPE≥6. GSS correction decreased the CPE value in 21.4% (mean 6.5, 95% CI 6.3, 6.7), 26.5% (mean 6.4, 95% CI 6.1, 6.6) and 24.2% (mean 6.4, 95% CI 6.2, 6.6) of subjects using ANRS, HIVDB and REGA rules, respectively. Overall, 66 (30.7%) patients were considered cognitively impaired. No significant association could be demonstrated between CPE and cognitive impairment. However, higher GSS-CPE was associated with a lower risk of cognitive impairment (CPE-GSSANRS odds ratio 0.75, P=0.022; CPE-GSSHIVDB odds ratio 0.77, P=0.038; CPE-GSSREGA odds ratio 0.78, P=0.038). Overall, a cutoff of CPE-GSS≥5 seemed the most discriminatory according to each different interpretation system. GSS-corrected CPE score showed a better correlation with neurocognitive performance than the standard CPE score. These results suggest that antiretroviral drug susceptibility, besides drug CNS penetration, can play a role in the control of HIV-associated neurocognitive disorders.

Species distribution and drug susceptibility of candida in clinical isolates from a tertiary care centre at Indore

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N Pahwa

2014-01-01

Full Text Available Background: The incidence of fungal infections has increased significantly, contributing to morbidity and mortality. This is caused by an alarming increase in infections with multi-drug resistant bacteria leading to overuse of broad-spectrum antimicrobials, which lead to overgrowth of Candida, thus enhancing its opportunity to cause disease. Candida are major human fungal pathogens that cause both mucosal and deep tissue infections. Objective : The aim of our study was to identify the distribution of Candida species among clinical isolates and their sensitivity pattern for common antifungal drugs. Materials and Methods : Two hundred and thirty-seven different clinical isolates of Candida were collected from patients visiting to a tertiary care centre of Indore from 2010 to 2012. Identification of Candida species as well as antifungal sensitivity testing was performed with Vitek2 Compact (Biomerieux France using vitek 2 cards for identification of yeast and yeast like organisms (ID-YST cards. Antifungal susceptibility testing was performed with Vitek2 "Fungal Susceptibility Card (AST YS01 kits respectively. Results : We found that the non-albicans Candida were more prevalent than Candida albicans in paediatric (60 year patients than other age group (4-18, 19-60 years patients and also in intensive care unit (ICU patients as compared to out patient department (OPD patients. Resistance rates for amphotericin B, fluconazole, flucytosine, itraconazole, and voriconazole were 2.9%, 5.9%, 0.0%, 4.2% and 2.5%%, respectively. All the strains of C. krusei were found resistant to fluconazole with intermediate sensitivity to flucytosine. Conclusion: Species-level identification of Candida and their antifungal sensitivity testing should be performed to achieve better clinical results.

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

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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

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

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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.

Predicting Adolescent Drug Abuse: A Review of Issues, Methods and Correlates. Research Issues 11.

Science.gov (United States)

Lettieri, Dan J., Ed.

Presented are 18 papers on predicting adolescent drug abuse. The papers have the following titles: "Current Issues in the Epidemiology of Drug Abuse as Related to Psychosocial Studies of Adolescent Drug Use"; "The Quest for Interpersonal Predictors of Marihuana Abuse in Adolescents"; "Assessing the Interpersonal Determinants of Adolescent Drug…

Inducing optimal substitution between antibiotics under open access to the resource of antibiotic susceptibility.

Science.gov (United States)

Herrmann, Markus; Nkuiya, Bruno

2017-06-01

This paper designs a bio-economic model to examine the use of substitute antibiotic drugs (analogs) sold by an industry that has open access to the resource of the antibiotic class's susceptibility (treatment effectiveness). Antibiotics are characterized by different expected recovery rates and production costs, which in conjunction with the class's treatment susceptibility determines their relative effectiveness. Our analysis reveals that the high-quality antibiotic drug loses its comparative advantage over time making the low-quality drug the treatment of last resort in the market equilibrium and the social optimum when antibiotic susceptibility cannot replenish. However, when antibiotic susceptibility is renewable, both antibiotics may be used in the long run, and the comparative advantage of the high-quality drug may be restored in the social optimum that allows lowering infection in the long run. We develop the optimal tax/subsidy scheme that would induce antibiotic producers under open access to behave optimally and account for the social cost of infection and value of antibiotic susceptibility. We show that the welfare loss associated with the uncorrected open-access allocation is highest; when the resource of antibiotic susceptibility is non-renewable, high morbidity costs are incurred by individuals, and low social discount rates apply. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Distribution, detection of enterotoxigenic strains and antimicrobial drug susceptibility patterns of Bacteroides fragilis group in diarrheic and non-diarrheic feces from Brazilian infants

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Débora Paula Ferreira

2010-10-01

Full Text Available Despite the importance of gastrointestinal diseases and their global distribution, affecting millions of individuals around the world, the role and antimicrobial susceptibility patterns of anaerobic bacteria such as those in the Bacteroides fragilis group (BFG are still unclear in young children. This study investigated the occurrence and distribution of species in the BFG and enterotoxigenic strains in the fecal microbiota of children and their antimicrobial susceptibility patterns. Diarrheic (n=110 and non-diarrheic (n=65 fecal samples from children aged 0-5 years old were evaluated. BFG strains were isolated and identified by conventional biochemical, physiological and molecular approaches. Alternatively, bacteria and enterotoxigenic strains were detected directly from feces by molecular biology. Antimicrobial drug susceptibility patterns were determined by the agar dilution method according to the guidelines for isolated bacteria. BFG was detected in 64.3% of the fecal samples (55% diarrheic and 80.4% non-diarrheic, and 4.6% were enterotoxigenic. Antimicrobial resistance was observed against ampicillin, ampicillin/sulbactam, piperacillin/tazobactam, meropenem, ceftriaxone, clindamycin and chloramphenicol. The data show that these bacteria are prevalent in fecal microbiota at higher levels in healthy children. The molecular methodology was more effective in identifying the B. fragilis group when compared to the biochemical and physiological techniques. The observation of high resistance levels stimulates thoughts about the indiscriminate use of antimicrobial drugs in early infancy. Further quantitative studies are needed to gain a better understanding of the role of these bacteria in acute diarrhea in children.

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

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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.

Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

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Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

2014-01-01

Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

The Role of Abcb5 Alleles in Susceptibility to Haloperidol-Induced Toxicity in Mice and Humans

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Zheng, Ming; Zhang, Haili; Dill, David L.; Clark, J. David; Tu, Susan; Yablonovitch, Arielle L.; Tan, Meng How; Zhang, Rui; Rujescu, Dan; Wu, Manhong; Tessarollo, Lino; Vieira, Wilfred; Gottesman, Michael M.; Deng, Suhua; Eberlin, Livia S.; Zare, Richard N.; Billard, Jean-Martin; Gillet, Jean-Pierre; Li, Jin Billy; Peltz, Gary

2015-01-01

Background We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study. Methods and Findings A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered

The role of Abcb5 alleles in susceptibility to haloperidol-induced toxicity in mice and humans.

KAUST Repository

Zheng, Ming

2015-02-03

We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study.A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered.ABCB5 alleles alter susceptibility to

Predicting and detecting adverse drug reactions in old age: challenges and opportunities.

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Mangoni, Arduino A

2012-05-01

Increased, often inappropriate, drug exposure, pharmacokinetic and pharmacodynamic changes, reduced homeostatic reserve and frailty increase the risk of adverse drug reactions (ADRs) in the older population, thereby imposing a significant public health burden. Predicting and diagnosing ADRs in old age presents significant challenges for the clinician, even when specific risk scoring systems are available. The picture is further compounded by the potential adverse impact of several drugs on more 'global' health indicators, for example, physical function and independence, and the fragmentation of care (e.g., increased number of treating doctors and care transitions) experienced by older patients during their clinical journey. The current knowledge of drug safety in old age is also curtailed by the lack of efficacy and safety data from pre-marketing studies. Moreover, little consideration is given to individual patients' experiences and reporting of specific ADRs, particularly in the presence of cognitive impairment. Pending additional data on these issues, the close review and monitoring of individual patients' drug prescribing, clinical status and biochemical parameters remain essential to predict and detect ADRs in old age. Recently developed strategies, for example, medication reconciliation and trigger tool methodology, have the potential for ADRs risk mitigation in this population. However, more information is required on their efficacy and applicability in different healthcare settings.

New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

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Frank Christian Kischkel

2017-03-01

Full Text Available Background To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods. Methods We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements. Results The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor. Conclusion Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model.

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Xue, Caifu; Zhang, Xunjie; Cai, Weimin

2017-12-21

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites-hydroxybupropion, threohydrobupropion and erythrohydrobupropion-based on a mixed "bottom-up" and "top-down" approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C max ) area under the concentration-time curve (AUC) values and C max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory

Antimicrobial susceptibility among clinical Nocardia species identified by multilocus sequence analysis.

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McTaggart, Lisa R; Doucet, Jennifer; Witkowska, Maria; Richardson, Susan E

2015-01-01

Antimicrobial susceptibility patterns of 112 clinical isolates, 28 type strains, and 9 reference strains of Nocardia were determined using the Sensititre Rapmyco microdilution panel (Thermo Fisher, Inc.). Isolates were identified by highly discriminatory multilocus sequence analysis and were chosen to represent the diversity of species recovered from clinical specimens in Ontario, Canada. Susceptibility to the most commonly used drug, trimethoprim-sulfamethoxazole, was observed in 97% of isolates. Linezolid and amikacin were also highly effective; 100% and 99% of all isolates demonstrated a susceptible phenotype. For the remaining antimicrobials, resistance was species specific with isolates of Nocardia otitidiscaviarum, N. brasiliensis, N. abscessus complex, N. nova complex, N. transvalensis complex, N. farcinica, and N. cyriacigeorgica displaying the traditional characteristic drug pattern types. In addition, the antimicrobial susceptibility profiles of a variety of rarely encountered species isolated from clinical specimens are reported for the first time and were categorized into four additional drug pattern types. Finally, MICs for the control strains N. nova ATCC BAA-2227, N. asteroides ATCC 19247(T), and N. farcinica ATCC 23826 were robustly determined to demonstrate method reproducibility and suitability of the commercial Sensititre Rapmyco panel for antimicrobial susceptibility testing of Nocardia spp. isolated from clinical specimens. The reported values will facilitate quality control and standardization among laboratories. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Software-aided approach to investigate peptide structure and metabolic susceptibility of amide bonds in peptide drugs based on high resolution mass spectrometry.

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Tatiana Radchenko

Full Text Available Interest in using peptide molecules as therapeutic agents due to high selectivity and efficacy is increasing within the pharmaceutical industry. However, most peptide-derived drugs cannot be administered orally because of low bioavailability and instability in the gastrointestinal tract due to protease activity. Therefore, structural modifications peptides are required to improve their stability. For this purpose, several in-silico software tools have been developed such as PeptideCutter or PoPS, which aim to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored from public sources such as MEROPS and ExPASy ENZYME databases. These tools can help design a peptide drug with increased stability against proteolysis, though they are limited to natural amino acids or cannot process cyclic peptides, for example. We worked to develop a new methodology to analyze peptide structure and amide bond metabolic stability based on the peptide structure (linear/cyclic, natural/unnatural amino acids. This approach used liquid chromatography / high resolution, mass spectrometry to obtain the analytical data from in vitro incubations. We collected experimental data for a set (linear/cyclic, natural/unnatural amino acids of fourteen peptide drugs and four substrate peptides incubated with different proteolytic media: trypsin, chymotrypsin, pepsin, pancreatic elastase, dipeptidyl peptidase-4 and neprilysin. Mass spectrometry data was analyzed to find metabolites and determine their structures, then all the results were stored in a chemically aware manner, which allows us to compute the peptide bond susceptibility by using a frequency analysis of the metabolic-liable bonds. In total 132 metabolites were found from the various in vitro conditions tested resulting in 77 distinct cleavage sites. The most frequent observed cleavage sites agreed with those reported in the literature. The

Carcinogen susceptibility is regulated by genome architecture and predicts cancer mutagenesis.

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García-Nieto, Pablo E; Schwartz, Erin K; King, Devin A; Paulsen, Jonas; Collas, Philippe; Herrera, Rafael E; Morrison, Ashby J

2017-10-02

The development of many sporadic cancers is directly initiated by carcinogen exposure. Carcinogens induce malignancies by creating DNA lesions (i.e., adducts) that can result in mutations if left unrepaired. Despite this knowledge, there has been remarkably little investigation into the regulation of susceptibility to acquire DNA lesions. In this study, we present the first quantitative human genome-wide map of DNA lesions induced by ultraviolet (UV) radiation, the ubiquitous carcinogen in sunlight that causes skin cancer. Remarkably, the pattern of carcinogen susceptibility across the genome of primary cells significantly reflects mutation frequency in malignant melanoma. Surprisingly, DNase-accessible euchromatin is protected from UV, while lamina-associated heterochromatin at the nuclear periphery is vulnerable. Many cancer driver genes have an intrinsic increase in carcinogen susceptibility, including the BRAF oncogene that has the highest mutation frequency in melanoma. These findings provide a genome-wide snapshot of DNA injuries at the earliest stage of carcinogenesis. Furthermore, they identify carcinogen susceptibility as an origin of genome instability that is regulated by nuclear architecture and mirrors mutagenesis in cancer. © 2017 The Authors.

Etest Cannot Be Recommended for In Vitro Susceptibility Testing of Mucorales

OpenAIRE

Caramalho, Rita; Maurer, Elisabeth; Binder, Ulrike; Araújo, Ricardo; Dolatabadi, Somayeh; Lass-Flörl, Cornelia; Lackner, Michaela

2015-01-01

Amphotericin B and posaconazole susceptibility patterns were determined for the most prevalent Mucorales, following EUCAST (European Committee on Antimicrobial Susceptibility Testing) broth microdilution guidelines. In parallel, Etest was performed and evaluated against EUCAST. The overall agreement of MICs gained with Etest and EUCAST was 75.1%; therefore, Etest cannot be recommended for antifungal susceptibility testing of Mucorales. Amphotericin B was the most active drug against Mucorales...

[Predictive factors of clinically significant drug-drug interactions among regimens based on protease inhibitors, non-nucleoside reverse transcriptase inhibitors and raltegravir].

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Cervero, Miguel; Torres, Rafael; Jusdado, Juan José; Pastor, Susana; Agud, Jose Luis

2016-04-15

To determine the prevalence and types of clinically significant drug-drug interactions (CSDI) in the drug regimens of HIV-infected patients receiving antiretroviral treatment. retrospective review of database. Centre: Hospital Universitario Severo Ochoa, Infectious Unit. one hundred and forty-two participants followed by one of the authors were selected from January 1985 to December 2014. from their outpatient medical records we reviewed information from the last available visit of the participants, in relation to HIV infection, comorbidities, demographics and the drugs that they were receiving; both antiretroviral drugs and drugs not related to HIV infection. We defined CSDI from the information sheet and/or database on antiretroviral drug interactions of the University of Liverpool (http://www.hiv-druginteractions.org) and we developed a diagnostic tool to predict the possibility of CSDI. By multivariate logistic regression analysis and by estimating the diagnostic performance curve obtained, we identified a quick tool to predict the existence of drug interactions. Of 142 patients, 39 (29.11%) had some type of CSDI and in 11.2% 2 or more interactions were detected. In only one patient the combination of drugs was contraindicated (this patient was receiving darunavir/r and quetiapine). In multivariate analyses, predictors of CSDI were regimen type (PI or NNRTI) and the use of 3 or more non-antiretroviral drugs (AUC 0.886, 95% CI 0.828 to 0.944; P=.0001). The risk was 18.55 times in those receiving NNRTI and 27,95 times in those receiving IP compared to those taking raltegravir. Drug interactions, including those defined as clinically significant, are common in HIV-infected patients treated with antiretroviral drugs, and the risk is greater in IP-based regimens. Raltegravir-based prescribing, especially in patients who receive at least 3 non-HIV drugs could avoid interactions. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

Global Phenotypic Characterization of Effects of Fluoroquinolone Resistance Selection on the Metabolic Activities and Drug Susceptibilities of Clostridium perfringens Strains

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Miseon Park

2014-01-01

Full Text Available Fluoroquinolone resistance affects toxin production of Clostridium perfringens strains differently. To investigate the effect of fluoroquinolone resistance selection on global changes in metabolic activities and drug susceptibilities, four C. perfringens strains and their norfloxacin-, ciprofloxacin-, and gatifloxacin-resistant mutants were compared in nearly 2000 assays, using phenotype microarray plates. Variations among mutant strains resulting from resistance selection were observed in all aspects of metabolism. Carbon utilization, pH range, osmotic tolerance, and chemical sensitivity of resistant strains were affected differently in the resistant mutants depending on both the bacterial genotype and the fluoroquinolone to which the bacterium was resistant. The susceptibilities to gentamicin and erythromycin of all resistant mutants except one increased, but some resistant strains were less susceptible to amoxicillin, cefoxitin, ceftriaxone, chloramphenicol, and metronidazole than their wild types. Sensitivity to ethidium bromide decreased in some resistant mutants and increased in others. Microarray analysis of two gatifloxacin-resistant mutants showed changes in metabolic activities that were correlated with altered expression of various genes. Both the chemical structures of fluoroquinolones and the genomic makeup of the wild types influenced the changes found in resistant mutants, which may explain some inconsistent reports of the effects of therapeutic use of fluoroquinolones on clinical isolates of bacteria.

A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

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Francesco Iorio

Full Text Available We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound. This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells-consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

Culture and drug susceptibility testing among previously treated tuberculosis patients in the Dominican Republic, 2014

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Katia J. Romero Mercado

Full Text Available ABSTRACT Multidrug-resistant tuberculosis (MDR-TB is a major public health concern that threatens global progress toward effective TB control. The risk of MDR-TB is increased in patients who have received previous TB treatment. This article describes the performance of culture and drug susceptibility testing (DST in patients registered as previously treated TB patients in the Dominican Republic in 2014, based on operational research that followed a retrospective cohort design and used routine program data. Under the current system of TB culturing and DST, the majority of patients with previously treated TB do not undergo DST, and those who do often experience considerable delay in obtaining their results. The lack of DST and delay in receiving DST results leads to underestimation of the number of MDR-TB cases and hinders the timely initiation of MDR-TB treatment.

Drugs + HIV, Learn the Link

Medline Plus

Full Text Available ... risk for getting HIV. Drug and alcohol intoxication affect judgment and can lead to unsafe sexual practices, ... effects of drugs. Drug misuse and addiction can affect a person's overall health, thereby altering susceptibility to ...

21 CFR 866.1700 - Culture medium for antimicrobial susceptibility tests.

Science.gov (United States)

2010-04-01

... tests. 866.1700 Section 866.1700 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Diagnostic Devices § 866.1700 Culture medium for antimicrobial susceptibility tests. (a) Identification. A culture medium for...

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

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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.

Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy.

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Honeyborne, Isobella; McHugh, Timothy D; Kuittinen, Iitu; Cichonska, Anna; Evangelopoulos, Dimitrios; Ronacher, Katharina; van Helden, Paul D; Gillespie, Stephen H; Fernandez-Reyes, Delmiro; Walzl, Gerhard; Rousu, Juho; Butcher, Philip D; Waddell, Simon J

2016-04-07

New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understanding of Mycobacterium tuberculosis (M.tb) bacilli that persist through drug therapy will aid drug development programs. Predictive biomarkers for treatment efficacy are also a research priority. Genome-wide transcriptional profiling was used to map the mRNA signatures of M.tb from the sputa of 15 patients before and 3, 7 and 14 days after the start of standard regimen drug treatment. The mRNA profiles of bacilli through the first 2 weeks of therapy reflected drug activity at 3 days with transcriptional signatures at days 7 and 14 consistent with reduced M.tb metabolic activity similar to the profile of pre-chemotherapy bacilli. These results suggest that a pre-existing drug-tolerant M.tb population dominates sputum before and after early drug treatment, and that the mRNA signature at day 3 marks the killing of a drug-sensitive sub-population of bacilli. Modelling patient indices of disease severity with bacterial gene expression patterns demonstrated that both microbiological and clinical parameters were reflected in the divergent M.tb responses and provided evidence that factors such as bacterial load and disease pathology influence the host-pathogen interplay and the phenotypic state of bacilli. Transcriptional signatures were also defined that predicted measures of early treatment success (rate of decline in bacterial load over 3 days, TB test positivity at 2 months, and bacterial load at 2 months). This study defines the transcriptional signature of M.tb bacilli that have been expectorated in sputum after two weeks of drug therapy, characterizing the phenotypic state of bacilli that persist through treatment. We demonstrate that variability in clinical manifestations of disease are detectable in bacterial sputa signatures, and that the changing M.tb m

Predicting Drug Court Treatment Completion Using the MMPI-2-RF

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Mattson, Curtis; Powers, Bradley; Halfaker, Dale; Akeson, Steven; Ben-Porath, Yossef

2012-01-01

We examined the ability of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) substantive scales to predict Drug Court treatment completion in a sample of individuals identified as being at risk for failure to complete the program. Higher scores on MMPI-2-RF scales…

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

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

2017-09-01

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

Preliminary soil-slip susceptibility maps, southwestern California

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Morton, Douglas M.; Alvarez, Rachel M.; Campbell, Russell H.; Digital preparation by Bovard, Kelly R.; Brown, D.T.; Corriea, K.M.; Lesser, J.N.

2003-01-01

This group of maps shows relative susceptibility of hill slopes to the initiation sites of rainfall-triggered soil slip-debris flows in southwestern California. As such, the maps offer a partial answer to one part of the three parts necessary to predict the soil-slip/debris-flow process. A complete prediction of the process would include assessments of “where”, “when”, and “how big”. These maps empirically show part of the “where” of prediction (i.e., relative susceptibility to sites of initiation of the soil slips) but do not attempt to show the extent of run out of the resultant debris flows. Some information pertinent to “when” the process might begin is developed. “When” is determined mostly by dynamic factors such as rainfall rate and duration, for which local variations are not amenable to long-term prediction. “When” information is not provided on the maps but is described later in this narrative. The prediction of “how big” is addressed indirectly by restricting the maps to a single type of landslide process—soil slip-debris flows. The susceptibility maps were created through an iterative process from two kinds of information. First, locations of sites of past soil slips were obtained from inventory maps of past events. Aerial photographs, taken during six rainy seasons that produced abundant soil slips, were used as the basis for soil slip-debris flow inventory. Second, digital elevation models (DEM) of the areas that were inventoried were used to analyze the spatial characteristics of soil slip locations. These data were supplemented by observations made on the ground. Certain physical attributes of the locations of the soil-slip debris flows were found to be important and others were not. The most important attribute was the mapped bedrock formation at the site of initiation of the soil slip. However, because the soil slips occur in surficial materials overlying the bedrocks units, the bedrock formation can only serve as

PBPK Modeling - A Predictive, Eco-Friendly, Bio-Waiver Tool for Drug Research.

Science.gov (United States)

De, Baishakhi; Bhandari, Koushik; Mukherjee, Ranjan; Katakam, Prakash; Adiki, Shanta K; Gundamaraju, Rohit; Mitra, Analava

2017-01-01

The world has witnessed growing complexities in disease scenario influenced by the drastic changes in host-pathogen- environment triadic relation. Pharmaceutical R&Ds are in constant search of novel therapeutic entities to hasten transition of drug molecules from lab bench to patient bedside. Extensive animal studies and human pharmacokinetics are still the "gold standard" in investigational new drug research and bio-equivalency studies. Apart from cost, time and ethical issues on animal experimentation, burning questions arise relating to ecological disturbances, environmental hazards and biodiversity issues. Grave concerns arises when the adverse outcomes of continued studies on one particular disease on environment gives rise to several other pathogenic agents finally complicating the total scenario. Thus Pharma R&Ds face a challenge to develop bio-waiver protocols. Lead optimization, drug candidate selection with favorable pharmacokinetics and pharmacodynamics, toxicity assessment are vital steps in drug development. Simulation tools like Gastro Plus™, PK Sim®, SimCyp find applications for the purpose. Advanced technologies like organ-on-a chip or human-on-a chip where a 3D representation of human organs and systems can mimic the related processes and activities, thereby linking them to major features of human biology can be successfully incorporated in the drug development tool box. PBPK provides the State of Art to serve as an optional of animal experimentation. PBPK models can successfully bypass bio-equivalency studies, predict bioavailability, drug interactions and on hyphenation with in vitro-in vivo correlation can be extrapolated to humans thus serving as bio-waiver. PBPK can serve as an eco-friendly bio-waiver predictive tool in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Rapid antibiotic susceptibility testing of Mycobacterium tuberculosis : Its utility in resource poor settings

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Poojary A

2006-01-01

Full Text Available Purpose: To compare the rapid colorimetric nitrate reductase based antibiotic susceptibility (CONRAS test performed on Mycobacterium tuberculosis isolates with the conventional method i.e., the proportion method. Methods: One hundred clinical isolates of M. tuberculosis were tested for susceptibility to isoniazid (INH and rifampicin (RIF by the conventional proportion method and CONRAS in Middlebrook 7H9 liquid medium enriched with growth supplements (MB7H9S. Results: The performance of the CONRAS test was evaluated using proportion method as the gold standard. The sensitivity (ability to detect true drug resistance and specificity (ability to detect true drug susceptibility of the CONRAS test to INH was 93.75 and 98.52% and for RIF it was 96.10 and 100% respectively. The mean time for reporting was 6.3 days and the test showed excellent reproducibility. The kappa (k value for INH was 0.92 and for RIF was 0.99, indicating excellent agreement between the two methods. Conclusions: CONRAS test is a rapid and reliable method of drug susceptibility for M. tuberculosis.

Agar dilution method for susceptibility testing of Neisseria gonorrhoeae

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Marta C de Castillo

1996-12-01

Full Text Available The antibiotic susceptibilities of Neisseria gonorrhoeae isolates obtained from patients attending a clinic for sexually transmitted diseases in Tucumán, Argentina, were determined by the agar dilution method (MIC. 3.5% of the isolates produced ²-lactamase. A total of 96.5% of ²-lactamase negative isolates tested were susceptible to penicillin (MIC < 2 µgml-1; 14.03% of the tested isolates were resistant to tetracycline (MIC < 2 µgml-1, and 98% of the tested isolates were susceptible to spectinomycin (MIC < 64 µgml-1. The MICs for 95% of the isolates, tested for other drugs were: < 2 µgml-1 for cefoxitin, < 0.06 µgml-1 for cefotaxime, < 0.25 µgml-1 for norfloxacin, < 10 µgml-1 for cephaloridine, < 10 µgml-1 for cephalexin, and < 50 µgml-1 for kanamycin. Antibiotic resistance among N. gonorrhoeae isolates from Tucumán, Argentina, appeared to be primarily limited to penicillin and tetracycline, which has been a general use against gonorrhoeae in Tucumán since 1960. Periodic monitoring of the underlying susceptibility profiles of the N. gonorrhoeae strains prevalent in areas of frequent transmission may provide clues regarding treatment options and emerging of drug resistance.

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.

Antifungal susceptibility profiles of 1698 yeast reference strains revealing potential emerging human pathogens.

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Marie Desnos-Ollivier

Full Text Available New molecular identification techniques and the increased number of patients with various immune defects or underlying conditions lead to the emergence and/or the description of novel species of human and animal fungal opportunistic pathogens. Antifungal susceptibility provides important information for ecological, epidemiological and therapeutic issues. The aim of this study was to assess the potential risk of the various species based on their antifungal drug resistance, keeping in mind the methodological limitations. Antifungal susceptibility profiles to the five classes of antifungal drugs (polyens, azoles, echinocandins, allylamines and antimetabolites were determined for 1698 yeast reference strains belonging to 992 species (634 Ascomycetes and 358 Basidiomycetes. Interestingly, geometric mean minimum inhibitory concentrations (MICs of all antifungal drugs tested were significantly higher for Basidiomycetes compared to Ascomycetes (p<0.001. Twenty four strains belonging to 23 species of which 19 were Basidiomycetes seem to be intrinsically "resistant" to all drugs. Comparison of the antifungal susceptibility profiles of the 4240 clinical isolates and the 315 reference strains belonging to 53 shared species showed similar results. Even in the absence of demonstrated in vitro/in vivo correlation, knowing the in vitro susceptibility to systemic antifungal agents and the putative intrinsic resistance of yeast species present in the environment is important because they could become opportunistic pathogens.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

Science.gov (United States)

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Microfluidics for Antibiotic Susceptibility and Toxicity Testing

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Jing Dai

2016-10-01

Full Text Available The recent emergence of antimicrobial resistance has become a major concern for worldwide policy makers as very few new antibiotics have been developed in the last twenty-five years. To prevent the death of millions of people worldwide, there is an urgent need for a cheap, fast and accurate set of tools and techniques that can help to discover and develop new antimicrobial drugs. In the past decade, microfluidic platforms have emerged as potential systems for conducting pharmacological studies. Recent studies have demonstrated that microfluidic platforms can perform rapid antibiotic susceptibility tests to evaluate antimicrobial drugs’ efficacy. In addition, the development of cell-on-a-chip and organ-on-a-chip platforms have enabled the early drug testing, providing more accurate insights into conventional cell cultures on the drug pharmacokinetics and toxicity, at the early and cheaper stage of drug development, i.e., prior to animal and human testing. In this review, we focus on the recent developments of microfluidic platforms for rapid antibiotics susceptibility testing, investigating bacterial persistence and non-growing but metabolically active (NGMA bacteria, evaluating antibiotic effectiveness on biofilms and combinatorial effect of antibiotics, as well as microfluidic platforms that can be used for in vitro antibiotic toxicity testing.

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

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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.

SEIIrR: Drug abuse model with rehabilitation

Science.gov (United States)

Sutanto, Azizah, Afina; Widyaningsih, Purnami; Saputro, Dewi Retno Sari

2017-05-01

Drug abuse in the world quite astonish and tend to increase. The increase and decrease on the number of drug abusers showed a pattern of spread that had the same characteristics with patterns of spread of infectious disease. The susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) epidemic models for infectious disease was developed to study social epidemic. In this paper, SEIR model for disease epidemic was developed to study drug abuse epidemic with rehabilitation treatment. The aims of this paper were to analogize susceptible exposed infected isolated recovered (SEIIrR) model on the drug abusers, to determine solutions of the model, to determine equilibrium point, and to do simulation on β. The solutions of SEIIrR model was determined by using fourth order of Runge-Kutta algorithm, equilibrium point obtained was free-drug equilibrium point. Solutions of SEIIrR showed that the model was able to suppress the spread of drug abuse. The increasing value of contact rate was not affect the number of infected individuals due to rehabilitation treatment.

In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models.

Science.gov (United States)

Sjögren, Erik; Thörn, Helena; Tannergren, Christer

2016-06-06

Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and

[Susceptibilities of Escherichia coli, Salmonella and Staphylococcus aureus isolated from animals to ofloxacin and commonly used antimicrobial agents].

Science.gov (United States)

Takahashi, I; Yoshida, T; Higashide, Y; Sakano, T

1990-01-01

Susceptibilities of Escherichia coli, Salmonella and Staphylococcus aureus isolated from chickens, pigs and cattle to ofloxacin (OFLX) and commonly used antimicrobial agents were investigated. 1. E. coli (28 isolates) demonstrated the highest level of susceptibility of OFLX (MIC 0.10-0.39 micrograms/ml for all the isolates) among all the test drugs. Commonly used antimicrobial agents to which these isolates responded with relatively high susceptibilities (MIC50 0.78-6.25 micrograms/ml) included oxolinic acid (OXA), ampicillin (ABPC), kanamycin (KM) and chloramphenicol (CP) with their MIC50 values in the increasing order as above. Drugs to which these isolates responded with moderate to weak susceptibilities (MIC50 25 approximately greater than 800 micrograms/ml) were doxycycline (DOXY), streptomycin (SM), spectinomycin (SPCM) and sulfadimethoxine (SDMX) in the increasing order of MIC50. E. coli isolates with resistances to all the test drugs other than OFLX and OXA amounted to 7.1-57.1% of the isolates examined and 20 isolates (71.4%) in total. 2. Susceptibilities to OFLX and 4 existing pyridonecarboxylic acid derivatives of E. coli (48 samples) isolated recently from diarrheal pigs were compared. When evaluated in terms of MIC50, the values of OFLX and norfloxacin were both 0.10 micrograms/ml. The values increased by differences of 0.39-3.13 micrograms/ml in an order of OXA, pipemidic acid and nalidixic acid. 3. Salmonella (28 isolates) demonstrated the highest level of susceptibility to OFLX (MIC 0.20-0.39 micrograms/ml for all the isolates) among all the test drugs. The drugs to which these isolates responded with relatively high to moderate susceptibilities (MIC50 0.78-12.5 micrograms/ml) included ABPC, OXA, DOXY, KM, CP and SM with their MIC50 values increasing in this order. The drugs to which the isolates responded with low susceptibilities (MIC50 above 100 micrograms/ml) were SPCM and SDMX. Of all the 28 Salmonella isolates tested, 7.1-32.1% were resistant

PknB remains an essential and a conserved target for drug development in susceptible and MDR strains of M. Tuberculosis.

Science.gov (United States)

Gupta, Anamika; Pal, Sudhir K; Pandey, Divya; Fakir, Najneen A; Rathod, Sunita; Sinha, Dhiraj; SivaKumar, S; Sinha, Pallavi; Periera, Mycal; Balgam, Shilpa; Sekar, Gomathi; UmaDevi, K R; Anupurba, Shampa; Nema, Vijay

2017-08-18

The Mycobacterium tuberculosis (M.tb) protein kinase B (PknB) which is now proved to be essential for the growth and survival of M.tb, is a transmembrane protein with a potential to be a good drug target. However it is not known if this target remains conserved in otherwise resistant isolates from clinical origin. The present study describes the conservation analysis of sequences covering the inhibitor binding domain of PknB to assess if it remains conserved in susceptible and resistant clinical strains of mycobacteria picked from three different geographical areas of India. A total of 116 isolates from North, South and West India were used in the study with a variable profile of their susceptibilities towards streptomycin, isoniazid, rifampicin, ethambutol and ofloxacin. Isolates were also spoligotyped in order to find if the conservation pattern of pknB gene remain consistent or differ with different spoligotypes. The impact of variation as found in the study was analyzed using Molecular dynamics simulations. The sequencing results with 115/116 isolates revealed the conserved nature of pknB sequences irrespective of their susceptibility status and spoligotypes. The only variation found was in one strains wherein pnkB sequence had G to A mutation at 664 position translating into a change of amino acid, Valine to Isoleucine. After analyzing the impact of this sequence variation using Molecular dynamics simulations, it was observed that the variation is causing no significant change in protein structure or the inhibitor binding. Hence, the study endorses that PknB is an ideal target for drug development and there is no pre-existing or induced resistance with respect to the sequences involved in inhibitor binding. Also if the mutation that we are reporting for the first time is found again in subsequent work, it should be checked with phenotypic profile before drawing the conclusion that it would affect the activity in any way. Bioinformatics analysis in our study

Co-trimoxazole susceptibility tests improved with separate trimethoprim and sulfamethoxazole disks.

OpenAIRE

Amyes, S G

1981-01-01

It is impossible to test accurately bacterial susceptibility to the trimethoprim-sulfamethoxazole combination co-trimoxazole with a single combined susceptibility disk. However, a variety of factors still affect the result even when separate trimethoprim and sulfamethoxazole disks are used. Experiments with separate disks showed that the optimum conditions for testing the susceptibilities of enterobacteria to these drugs were to flood-seed an agar plate with an inoculum of 10(4) to 10(5) orga...

Drug susceptibility of Mycobacterium tuberculosis in a rural area of Bangladesh and its relevance to the national treatment regimens.

Science.gov (United States)

Van Deun, A; Aung, K J; Chowdhury, S; Saha, S; Pankaj, A; Ashraf, A; Rigouts, L; Fissette, K; Portaels, F

1999-02-01

Greater Mymensingh District, a rural area of Bangladesh, at the start of the National Tuberculosis Programme (NTP). To determine the prevalence of initial and acquired drug resistance of Mycobacterium tuberculosis, and to assess the appropriateness of the NTP's standard regimens. Sampling of pre-treatment sputum from all newly registered smear-positive cases in five centres covering the area. Culture and susceptibility testing in a supra-national reference laboratory. Initial resistance to isoniazid (H) was 5.4%, and to rifampicin (R) 0.5%. Acquired H and R resistance were 25.9% and 7.4%, respectively. Multidrug resistance (MDR) was observed in one new case only and in 5.6% of previously treated patients. Changing the present NTP indication for retreatment regimen to one month of previous H intake would increase coverage of H-resistant cases from 52% to 89%, adding 6% to drug costs. The prevalence of drug resistance is surprisingly low in Bangladesh, but could rise with improving economic conditions. The NTP regimens for smear-positive cases are appropriate, all the more so since the human immunodeficiency virus is virtually absent. Indications for the retreatment regimen should be extended to include all patients treated for at least one month with any drug. The NTP regimen for smear-negative cases runs the risk of leading to MDR under present field conditions.

Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

Science.gov (United States)

Wagner, Christian; Pan, Yuzhuo; Hsu, Vicky; Grillo, Joseph A; Zhang, Lei; Reynolds, Kellie S; Sinha, Vikram; Zhao, Ping

2015-01-01

The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI. This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors' PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration-time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]). In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range. These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

Prediction of Smoking, Alcohol, Drugs, and Psychoactive Drugs Abuse Based on Emotional Dysregulation and Child Abuse Experience in People with Borderline Personality Traits

Directory of Open Access Journals (Sweden)

M GannadiFarnood

2014-12-01

Full Text Available Objective: This research was an attempt to predict the tendency of people having borderline personality traits to smoking, drinking alcohol, and taking psychoactive drugs based on emotional dysregulation and child abuse. Method: This study employed a correlation method which is categorized in descriptive category. A sample including 600 male and female bachelor students of Tabriz University was selected by cluster sampling. Then, high risk behaviors scale, Emotional dysregulation Scale, Child abuse scale, and borderline personality scale (STB were distributed among this group. Findings: Stepwise multiple regression analysis suggested that emotional dysregulation and child abuse significantly predicted varying degrees of smoking, drug, and alcohol usage. Conclusion: The research findings suggest the basic role of initial biological vulnerability in terms of emotional regulation (dysregulation and invalidating family environment (child abuse in the prediction of catching the disorder of borderline personality traits and producing high riskbehaviorssuch as alcohol drink and drug usage.

An analysis of the gastro-intestinal side-effects of non-steroidal anti-inflammatory drugs, with particular reference to comparative studies in man and laboratory species.

Science.gov (United States)

Rainsford, K D

1982-01-01

A critical analysis has been performed of reports published on the incidence of gastro-intestinal (GI) side-effects found in arthritic patients being treated with non-steroid anti-inflammatory (NSAI) drugs. The results show the following: 1. The incidence of GI ulceration (as revealed by gastroscopy) and haemorrhage in arthritic patients taking NSAI drugs may be higher than suspected from clinical trial data. 2. Incidence of all GI side-effects (including ulceration and haemorrhage) may be lower with some of the new NSAI drugs than with traditional drugs (e.g. aspirin, indomethacin and phenylbutazone). 3. Arthritic patients may be more susceptible to the ulcerogenic actions of NSAI drugs. Experiments with animals, together with evidence from clinical studies, indicate that stress factors and the presence of decreased mucosal resistance in the diseased state may contribute to the enhanced susceptibility of the GI tract towards the ulcerogenicity of NSAI drugs. 4. Comparison of data on gastroscopic observations in man with the author's data on the effects of NSAI drugs in stress-sensitized rats shows the latter technique appears to be a useful means of predicting the ulcerogenic potential of NSAI drugs in man. The comparison has also been used to predict the ulcerogenicity of drug - alcohol combinations; alcohol being a common ulcerogen consumed by many patients. Some NSAI drugs with low ulcerogenic activity (i.e. azapropazone, benoxaprofen and fenclofenac) in the stressed-rat assay show little or no interaction with alcohol. These studies using laboratory animals show the importance of employing conditions to mimic environmental factors (e.g. stress and alcohol consumption) which might predispose individuals to ulcerogenic or other side-effects of NSAI drugs. From these studies it appears possible to construct 'predictive profiles' of the relative ulcerogenicity of NSAI drugs which may be applicable to the clinical situation in man.

Drugs + HIV, Learn the Link

Medline Plus

Full Text Available ... effects of drugs. Drug misuse and addiction can affect a person's overall health, thereby altering susceptibility to HIV and progression of ... drugs and alcohol even once can have serious health consequences. We also work to reach parents and teachers—influential figures in ...

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.

Drugs + HIV, Learn the Link

Medline Plus

Full Text Available ... HIV or transmitting it to someone else. Biological effects of drugs. Drug misuse and addiction can affect a person's overall health, thereby altering susceptibility to HIV and progression of ...

Temporal and seasonal changes of genetic polymorphisms associated with altered drug susceptibility to chloroquine, lumefantrine and quinine in Guinea-Bissau between 2003 and 2012

DEFF Research Database (Denmark)

Jovel, Irina Tatiana; Kofoed, Poul-Erik; Rombo, Lars

2015-01-01

BACKGROUND: Guinea-Bissau, West-Africa introduced artemether-lumefantrine in 2008 but quinine has also been commonly prescribed for treatment of uncomplicated Plasmodium falciparum malaria. An efficacious high-dose chloroquine treatment regimen was used previously. Temporal and seasonal changes...... of genetic polymorphisms associated with altered drug susceptibility to chloroquine, lumefantrine and quinine are described. METHODS: Pfcrt K76T, pfmdr1 gene copy numbers, N86Y, Y184F and 1034-1246 sequences were determined using PCR-based methods. Blood samples came from virtually all (n=1806) children aged.......001). CONCLUSIONS: Following the discontinuation of an effective chloroquine regimen highly artemether-lumefantrine susceptible P. falciparum (with pfcrt 76T) accumulated possibly due to suboptimal use of quinine and despite a fitness cost linked to 76T....

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

Science.gov (United States)

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-02-01

For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

Directory of Open Access Journals (Sweden)

Hongbin Yang

2018-02-01

Full Text Available During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

Science.gov (United States)

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-01-01

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

Prediction of the overall renal tubular secretion and hepatic clearance of anionic drugs and a renal drug-drug interaction involving organic anion transporter 3 in humans by in vitro uptake experiments.

Science.gov (United States)

Watanabe, Takao; Kusuhara, Hiroyuki; Watanabe, Tomoko; Debori, Yasuyuki; Maeda, Kazuya; Kondo, Tsunenori; Nakayama, Hideki; Horita, Shigeru; Ogilvie, Brian W; Parkinson, Andrew; Hu, Zhuohan; Sugiyama, Yuichi

2011-06-01

The present study investigated prediction of the overall renal tubular secretion and hepatic clearances of anionic drugs based on in vitro transport studies. The saturable uptake of eight drugs, most of which were OAT3 substrates (rosuvastatin, pravastatin, pitavastatin, valsartan, olmesartan, trichlormethiazide, p-aminohippurate, and benzylpenicillin) by freshly prepared human kidney slices underestimated the overall intrinsic clearance of the tubular secretion; therefore, a scaling factor of 10 was required for in vitro-in vivo extrapolation. We examined the effect of gemfibrozil and its metabolites, gemfibrozil glucuronide and the carboxylic metabolite, gemfibrozil M3, on pravastatin uptake by human kidney slices. The inhibition study using human kidney slices suggests that OAT3 plays a predominant role in the renal uptake of pravastatin. Comparison of unbound concentrations and K(i) values (1.5, 9.1, and 4.0 μM, for gemfibrozil, gemfibrozil glucuronide, and gemfibrozil M3, respectively) suggests that the mechanism of the interaction is due mainly to inhibition by gemfibrozil and gemfibrozil glucuronide. Furthermore, extrapolation of saturable uptake by cryopreserved human hepatocytes predicts clearance comparable with the observed hepatic clearance although fluvastatin and rosuvastatin required a scaling factor of 11 and 6.9, respectively. This study suggests that in vitro uptake assays using human kidney slices and hepatocytes provide a good prediction of the overall tubular secretion and hepatic clearances of anionic drugs and renal drug-drug interactions. It is also recommended that in vitro-in vivo extrapolation be performed in animals to obtain more reliable prediction.

Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction.

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Iga, Katsumi; Kiriyama, Akiko

2017-12-01

Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF h ). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA i, CYP s) and victim-specific fractional CYP-isoform contributions to the clearance (vf m, CYP s)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness

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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

Prediction of phospholipidosis-inducing potential of drugs by in vitro biochemical and physicochemical assays followed by multivariate analysis.

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Kuroda, Yukihiro; Saito, Madoka

2010-03-01

An in vitro method to predict phospholipidosis-inducing potential of cationic amphiphilic drugs (CADs) was developed using biochemical and physicochemical assays. The following parameters were applied to principal component analysis, as well as physicochemical parameters: pK(a) and clogP; dissociation constant of CADs from phospholipid, inhibition of enzymatic phospholipid degradation, and metabolic stability of CADs. In the score plot, phospholipidosis-inducing drugs (amiodarone, propranolol, imipramine, chloroquine) were plotted locally forming the subspace for positive CADs; while non-inducing drugs (chlorpromazine, chloramphenicol, disopyramide, lidocaine) were placed scattering out of the subspace, allowing a clear discrimination between both classes of CADs. CADs that often produce false results by conventional physicochemical or cell-based assay methods were accurately determined by our method. Basic and lipophilic disopyramide could be accurately predicted as a nonphospholipidogenic drug. Moreover, chlorpromazine, which is often falsely predicted as a phospholipidosis-inducing drug by in vitro methods, could be accurately determined. Because this method uses the pharmacokinetic parameters pK(a), clogP, and metabolic stability, which are usually obtained in the early stages of drug development, the method newly requires only the two parameters, binding to phospholipid, and inhibition of lipid degradation enzyme. Therefore, this method provides a cost-effective approach to predict phospholipidosis-inducing potential of a drug. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Improving the prediction of the brain disposition for orally administered drugs using BDDCS

DEFF Research Database (Denmark)

Broccatelli, Fabio; Larregieu, Caroline A.; Cruciani, Gabriele

2012-01-01

outcome. Passive permeability and P-glycoprotein (Pgp, ABCB1) efflux have been successfully recognized to impact xenobiotic extrusion from the brain, as Pgp is known to play a role in limiting the BBB penetration of oral drugs in humans. However, these two properties alone fail to explain the BBB...... penetration for a significant number of marketed central nervous system (CNS) agents. The Biopharmaceutics Drug Disposition Classification System (BDDCS) has proved useful in predicting drug disposition in the human body, particularly in the liver and intestine. Here we discuss the value of using BDDCS...

Evaluation of GenoType® MTBDRplus assay for rapid detection of drug susceptibility testing of multi-drug resistance tuberculosis in Northern India

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Anand Kumar Maurya

2013-01-01

Full Text Available Background: The problem of multi-drug resistance tuberculosis (MDR-TB is growing in several hotspots throughout the world. Rapid and accurate diagnosis of MDR-TB is crucial to facilitate early treatment and to reduce its spread in the community. The aim of the present study was to evaluate the new, novel GenoType® MTBDRplus assay for rapid detection of drug susceptibility testing (DST of MDR-TB cases in Northern India. Materials and Methods: A total of 550 specimens were collected from highly suspected drug resistant from pulmonary and extra-pulmonary TB cases. All the specimens were processed by Ziehl- Neelsen staining, culture, differentiation by the GenoType® CM assay, first line DST using BacT/ALERT 3D system and GenoType® MTBDRplus assay. The concordance of the GenoType® MTBDRplus assay was calculated in comparison with conventional DST results. Results: Overall the sensitivity for detection of rifampicin, isoniazid and MDR-TB resistance by GenoType® MTBDRplus assay was 98.0%, 98.4% and 98.2% respectively. Out of 55 MDR-TB strains, 45 (81.8%, 52 (94.5% and 17 (30.9% strains showed mutation in rpoB, katG and inhA genes respectively (P < 0.05. The most prominent mutations in rpoB, katG and inhA genes were; 37 (67.3% in S531L, 52 (94.5% in S315T1 and 11 (20% in C15T regions respectively (P < 0.05. Conclusions: Our study demonstrated a high concordance between the GenoType® MTBDRplus assay resistance patterns and those were observed by conventional DST with good sensitivity, specificity with short turnaround times and to control new cases of MDR-TB in countries with a high prevalence of MDR-TB.

Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities.

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Frid, Anna A; Matthews, Edwin J

2010-04-01

This report describes the use of three quantitative structure-activity relationship (QSAR) programs to predict drug-related cardiac adverse effects (AEs), BioEpisteme, MC4PC, and Leadscope Predictive Data Miner. QSAR models were constructed for 9 cardiac AE clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes) and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). The models were based on a database of post-marketing AEs linked to 1632 chemical structures, and identical training data sets were configured for three QSAR programs. Model performance was optimized and shown to be affected by the ratio of the number of active to inactive drugs. Results revealed that the three programs were complementary and predictive performances using any single positive, consensus two positives, or consensus three positives were as follows, respectively: 70.7%, 91.7%, and 98.0% specificity; 74.7%, 47.2%, and 21.0% sensitivity; and 138.2, 206.3, and 144.2 chi(2). In addition, a prospective study using AE data from the U.S. Food and Drug Administration's (FDA's) MedWatch Program showed 82.4% specificity and 94.3% sensitivity. Furthermore, an external validation study of 18 drugs with serious cardiotoxicity not considered in the models had 88.9% sensitivity. Published by Elsevier Inc.

Generation of pH responsive fluorescent nano capsules through simple steps for the oral delivery of low pH susceptible drugs

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Radhakumary, Changerath; Sreenivasan, Kunnatheeri

2016-11-01

pH responsive nano capsules are promising as it can encapsulate low pH susceptible drugs like insulin and guard them from the hostile environments in the intestinal tract. The strong acidity of the gastro-intestinal tract and the presence of proteolytic enzymes are the tumbling blocks for the design of drug delivery vehicles through oral route for drugs like insulin. Nano capsules are normally built over templates which are subsequently removed by further steps. Such processes are complex and often lead into deformed and collapsed capsules. In this study, we choose calcium carbonate (CaCO3) nano particles to serve as template. Over CaCO3 nanoparticles, silica layers were built followed by polymethacrylic acid chains to acquire pH responsiveness. During the polymerization process of the methacrylic acid, the calcium carbonate core particles were dissolved leading to the formation of nano hollow capsules having a size that ranges from 225 to 246 nm and thickness from 19 to 58 nm. The methodology is simple and devoid of additional steps. The nano shells exhibited 80% release of the loaded model drug, insulin at pH 7.4 while at pH 2.0 the capsules nearly stopped the release of the drug. Polymethacrylic acid shows pH responsive swelling behavior that it swells at intestinal pH (7.0-7.5) and shrinks at gastric pH (˜2.0) thus enabling the safe unloading of the drug from the nano capsules.

Biomarkers of adverse drug reactions.

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Carr, Daniel F; Pirmohamed, Munir

2018-02-01

Adverse drug reactions can be caused by a wide range of therapeutics. Adverse drug reactions affect many bodily organ systems and vary widely in severity. Milder adverse drug reactions often resolve quickly following withdrawal of the casual drug or sometimes after dose reduction. Some adverse drug reactions are severe and lead to significant organ/tissue injury which can be fatal. Adverse drug reactions also represent a financial burden to both healthcare providers and the pharmaceutical industry. Thus, a number of stakeholders would benefit from development of new, robust biomarkers for the prediction, diagnosis, and prognostication of adverse drug reactions. There has been significant recent progress in identifying predictive genomic biomarkers with the potential to be used in clinical settings to reduce the burden of adverse drug reactions. These have included biomarkers that can be used to alter drug dose (for example, Thiopurine methyltransferase (TPMT) and azathioprine dose) and drug choice. The latter have in particular included human leukocyte antigen (HLA) biomarkers which identify susceptibility to immune-mediated injuries to major organs such as skin, liver, and bone marrow from a variety of drugs. This review covers both the current state of the art with regard to genomic adverse drug reaction biomarkers. We also review circulating biomarkers that have the potential to be used for both diagnosis and prognosis, and have the added advantage of providing mechanistic information. In the future, we will not be relying on single biomarkers (genomic/non-genomic), but on multiple biomarker panels, integrated through the application of different omics technologies, which will provide information on predisposition, early diagnosis, prognosis, and mechanisms. Impact statement • Genetic and circulating biomarkers present significant opportunities to personalize patient therapy to minimize the risk of adverse drug reactions. ADRs are a significant heath issue

Experimental and computational prediction of glass transition temperature of drugs.

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Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

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

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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.

Using multicriteria decision analysis during drug development to predict reimbursement decisions.

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Williams, Paul; Mauskopf, Josephine; Lebiecki, Jake; Kilburg, Anne

2014-01-01

Pharmaceutical companies design clinical development programs to generate the data that they believe will support reimbursement for the experimental compound. The objective of the study was to present a process for using multicriteria decision analysis (MCDA) by a pharmaceutical company to estimate the probability of a positive recommendation for reimbursement for a new drug given drug and environmental attributes. The MCDA process included 1) selection of decisions makers who were representative of those making reimbursement decisions in a specific country; 2) two pre-workshop questionnaires to identify the most important attributes and their relative importance for a positive recommendation for a new drug; 3) a 1-day workshop during which participants undertook three tasks: i) they agreed on a final list of decision attributes and their importance weights, ii) they developed level descriptions for these attributes and mapped each attribute level to a value function, and iii) they developed profiles for hypothetical products 'just likely to be reimbursed'; and 4) use of the data from the workshop to develop a prediction algorithm based on a logistic regression analysis. The MCDA process is illustrated using case studies for three countries, the United Kingdom, Germany, and Spain. The extent to which the prediction algorithms for each country captured the decision processes for the workshop participants in our case studies was tested using a post-meeting questionnaire that asked the participants to make recommendations for a set of hypothetical products. The data collected in the case study workshops resulted in a prediction algorithm: 1) for the United Kingdom, the probability of a positive recommendation for different ranges of cost-effectiveness ratios; 2) for Spain, the probability of a positive recommendation at the national and regional levels; and 3) for Germany, the probability of a determination of clinical benefit. The results from the post

Identification and Antimicrobial Susceptibility Testing of Anaerobic Bacteria: Rubik's Cube of Clinical Microbiology?

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Gajdács, Márió; Spengler, Gabriella; Urbán, Edit

2017-11-07

Anaerobic bacteria have pivotal roles in the microbiota of humans and they are significant infectious agents involved in many pathological processes, both in immunocompetent and immunocompromised individuals. Their isolation, cultivation and correct identification differs significantly from the workup of aerobic species, although the use of new technologies (e.g., matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, whole genome sequencing) changed anaerobic diagnostics dramatically. In the past, antimicrobial susceptibility of these microorganisms showed predictable patterns and empirical therapy could be safely administered but recently a steady and clear increase in the resistance for several important drugs (β-lactams, clindamycin) has been observed worldwide. For this reason, antimicrobial susceptibility testing of anaerobic isolates for surveillance purposes or otherwise is of paramount importance but the availability of these testing methods is usually limited. In this present review, our aim was to give an overview of the methods currently available for the identification (using phenotypic characteristics, biochemical testing, gas-liquid chromatography, MALDI-TOF MS and WGS) and antimicrobial susceptibility testing (agar dilution, broth microdilution, disk diffusion, gradient tests, automated systems, phenotypic and molecular resistance detection techniques) of anaerobes, when should these methods be used and what are the recent developments in resistance patterns of anaerobic bacteria.

Antifungal susceptibilities of Candida species isolated from the patients with vaginal candidiasis.

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Nagashima, Masahito; Yamagishi, Yuka; Mikamo, Hiroshige

2016-02-01

There have been the current Japanese data on susceptibility testing for Candida isolates from vaginal candidiasis. The in vitro activities of therapeutic antifungal drugs for vulvovaginal candidiasis (VVC); miconazole (MCZ), itraconazole (ITCZ), fluconazole (FLCZ), clotrimazole (CTZ), oxiconazole (OCZ), isoconazole (ICZ) and bifonazole (BFZ) against vaginal isolates. Fifty-four strains Candida albicans and 19 strains of Candida glabrata were evaluated using a broth microdilution method specified by Clinical Laboratories Standard Institute (CLSI) document M27-A3. The MIC90 of each drug, MCZ, ITCZ, FLCZ, CTZ, OCZ, ICZ and BFZ, against C. albicans and C. glabrata isolates were 0.25, 0.12, 1, 0.06, 0.12, 0.12 and 1 μg/ml and 1, 1, 8, 0.5, 0.25, 0.5 and 1 μg/ml respectively. The activities of these drugs, except for BFZ, against C. glabrata were lower than that of C. albicans. There was one azole-resistant isolate in C. glabrata of which MIC of FLCZ is > 64 μg/ml and this isolate had cross resistance to other antifungal drugs tested. These results suggest that antifungal drugs for treatment of VVC continues to have potent antifungal activities against C. albicans and C. glabrata isolates from vaginitis. CTZ, OCZ and ICZ susceptibility of FLCZ low susceptibility C. glabrata are relatively higher than MCZ, ITCZ and FLCZ. Copyright © 2015. Published by Elsevier Ltd.

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

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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.

Bacterial profile and drug susceptibility pattern of urinary tract infection in pregnant women at Tikur Anbessa Specialized Hospital Addis Ababa, Ethiopia.

Science.gov (United States)

Assefa, Addisu; Asrat, Daniel; Woldeamanuel, Yimtubezinash; G/Hiwot, Yirgu; Abdella, Ahmed; Melesse, Tadele

2008-07-01

Urinary tract infection (UTI) is a common complication of pregnancy. It may be symptomatic or asymptomatic. The aim of this cross sectional study was to identify bacterial agents and their antibiotic susceptibility pattern isolated from pregnant women with UTI attending antenatal clinic of Tikur Anbessa Specialized Hospital (TASH). Four hundred and fourteen pregnant women with asymptomatic UTI (n = 369) and symptomatic UTI (n = 45) were investigated for urinary tract infection from January to March 2005. The age range of both groups was 18 to 44 years. Bacteriological screening of mid-stream urine specimens revealed that 39/369 (10.6%) and 9/45 (20%) had significant bacteriuria in asymptomatic and symptomatic group, respectively (p = 0.10). The overall prevalence of urinary tract infection was 48/414 (11.6%). The bacterial pathogens isolated were predominantly E. coil (44%), followed by S. aureus (20%), coagulase-negative staphylococci (16%), and K. pneumoniae (8%). Others found in small in number included P. mirabilis, P. aeruginosa, Enterococcus spp. and non-Group A-beta hemolytic Streptococcus, this accounted 2% for each. The gram positive and negative bacteria accounted 40% and 60% respectively. The susceptibility pattern for gram-negative bacteria showed that most of the isolates (> 65% of the strains) were sensitive to amoxicillin-clavulanic acid (70%), chloramphenicol (83.3%), gentamicin (93.3%), kanamycin (93.3%), nitrofurantoin (87.7%) and trimethoprim-sulphamethoxazole (73.3%). Among the gram-positives, more than 60% of the isolates were sensitive to amoxicillin-clavulanic acid (100%), cephalothin (95%), chloramphenicol (70%), erythromycin (80%), gentamicin (85%), methicillin (83.3%), nitrofurantoin (100%) and trimethoprim-sulphamethoxazole (65%). Generally, amoxicillin-clavulanic acid, chloramphenicol, gentamicin, nitrofurantoin and trimethoprim-sulphamethoxazole were effective at least in 70% of the isolates. Multiple drug resistance (resistance two or

A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

Science.gov (United States)

Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

Drugs + HIV, Learn the Link

Medline Plus

Full Text Available ... HIV or transmitting it to someone else. Biological effects of drugs. Drug misuse and addiction can affect a person's overall health, thereby altering susceptibility to HIV and progression of AIDS. Drugs of abuse and HIV both affect the brain. Research has shown that HIV causes greater injury ...

A Specific Autistic Trait that Modulates Visuospatial Illusion Susceptibility

Science.gov (United States)

Walter, Elizabeth; Dassonville, Paul; Bochsler, Tiana M.

2009-01-01

Although several accounts of autism have predicted that the disorder should be associated with a decreased susceptibility to visual illusions, previous experimental results have been mixed. This study examined whether a link between autism and illusion susceptibility can be more convincingly demonstrated by assessing the relationships between…

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

Science.gov (United States)

Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A; Kim, Sungjoon; Wilson, Christopher J; Lehár, Joseph; Kryukov, Gregory V; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F; Monahan, John E; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H; Cheng, Jill; Yu, Guoying K; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P; Gabriel, Stacey B; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E; Weber, Barbara L; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L; Meyerson, Matthew; Golub, Todd R; Morrissey, Michael P; Sellers, William R; Schlegel, Robert; Garraway, Levi A

2012-03-28

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

Science.gov (United States)

Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A.; Kim, Sungjoon; Wilson, Christopher J.; Lehár, Joseph; Kryukov, Gregory V.; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F.; Monahan, John E.; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A.; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H.; Cheng, Jill; Yu, Guoying K.; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D.; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C.; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P.; Gabriel, Stacey B.; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E.; Weber, Barbara L.; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L.; Meyerson, Matthew; Golub, Todd R.; Morrissey, Michael P.; Sellers, William R.; Schlegel, Robert; Garraway, Levi A.

2012-01-01

The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens2. PMID:22460905

Individual behavioral characteristics of wild-type rats predict susceptibility to experimental autoimmune encephalomyelitis

NARCIS (Netherlands)

Kavelaars, A; Heijnen, CJ; Tennekes, R; Bruggink, JE; Koolhaas, JM

1999-01-01

Neuroendocrine-immune interactions are thought to be important in determining susceptibility to autoimmune disease. Animal studies have revealed that differences in susceptibility to experimental autoimmune encephalomyelitis (EAE) are related to:reactivity in the hypothalamo-pituitary-adrenal axis.

The Combination of GIS and Biphasic to Better Predict In Vivo Dissolution of BCS Class IIb Drugs, Ketoconazole and Raloxifene.

Science.gov (United States)

Tsume, Yasuhiro; Igawa, Naoto; Drelich, Adam J; Amidon, Gregory E; Amidon, Gordon L

2018-01-01

The formulation developments and the in vivo assessment of Biopharmaceutical Classification System (BCS) class II drugs are challenging due to their low solubility and high permeability in the human gastrointestinal (GI) tract. Since the GI environment influences the drug dissolution of BCS class II drugs, the human GI characteristics should be incorporated into the in vitro dissolution system to predict bioperformance of BCS class II drugs. An absorptive compartment may be important in dissolution apparatus for BCS class II drugs, especially for bases (BCS IIb) because of high permeability, precipitation, and supersaturation. Thus, the in vitro dissolution system with an absorptive compartment may help predicting the in vivo phenomena of BCS class II drugs better than compendial dissolution apparatuses. In this study, an absorptive compartment (a biphasic device) was introduced to a gastrointestinal simulator. This addition was evaluated if this in vitro system could improve the prediction of in vivo dissolution for BCS class IIb drugs, ketoconazole and raloxifene, and subsequent absorption. The gastrointestinal simulator is a practical in vivo predictive tool and exhibited an improved in vivo prediction utilizing the biphasic format and thus a better tool for evaluating the bioperformance of BCS class IIb drugs than compendial apparatuses. Copyright © 2018. Published by Elsevier Inc.

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

Directory of Open Access Journals (Sweden)

Tsafnat Guy

2011-04-01

Full Text Available Abstract Background The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP to help experts screen drugs that may have important clinical characteristics of interest. Results BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH and the PharmacoKinetic Interaction Screening (PKIS database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100% and 157 (of 197 minor drug classes (80% with areas under the receiver operating characteristic curve (AUC > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238 adverse events (73%, up to 12 (of 15 groups of clinically significant cytochrome P450 enzyme (CYP inducers or inhibitors (80%, and up to 11 (of 14 groups of narrow therapeutic index drugs (79%. Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task. Conclusions BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.

Rapid susceptibility testing of Mycobacterium tuberculosis by bioluminescence assay of mycobacterial ATP

International Nuclear Information System (INIS)

Nilsson, L.E.; Hoffner, S.E.; Ansehn, S.

1988-01-01

Mycobacterial growth was monitored by bioluminescence assay of mycobacterial ATP. Cultures of Mycobacterium tuberculosis H37Rv and of 25 clinical isolates of the same species were exposed to serial dilutions of ethambutol, isoniazid, rifampin, and streptomycin. A suppression of ATP, indicating growth inhibition, occurred for susceptible but not resistant strains within 5 to 7 days of incubation. Breakpoint concentrations between susceptibility and resistance were determined by comparing these results with those obtained by reference techniques. Full agreement was found in 99% of the assays with the resistance ratio method on Lowenstein-Jensen medium, and 98% of the assays were in full agreement with the radiometric system (BACTEC). A main advantage of the bioluminescence method is its rapidity, with results available as fast as with the radiometric system but at a lower cost and without the need for radioactive culture medium. The method provides kinetic data concerning drug effects within available in vivo drug concentrations and has great potential for both rapid routine susceptibility testing and research applications in studies of drug effects on mycobacteria

Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes

Directory of Open Access Journals (Sweden)

Trine Krogh-Madsen

2017-12-01

Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.

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

In vitro dissolution methodology, mini-Gastrointestinal Simulator (mGIS), predicts better in vivo dissolution of a weak base drug, dasatinib.

Science.gov (United States)

Tsume, Yasuhiro; Takeuchi, Susumu; Matsui, Kazuki; Amidon, Gregory E; Amidon, Gordon L

2015-08-30

USP apparatus I and II are gold standard methodologies for determining the in vitro dissolution profiles of test drugs. However, it is difficult to use in vitro dissolution results to predict in vivo dissolution, particularly the pH-dependent solubility of weak acid and base drugs, because the USP apparatus contains one vessel with a fixed pH for the test drug, limiting insight into in vivo drug dissolution of weak acid and weak base drugs. This discrepancy underscores the need to develop new in vitro dissolution methodology that better predicts in vivo response to assure the therapeutic efficacy and safety of oral drug products. Thus, the development of the in vivo predictive dissolution (IPD) methodology is necessitated. The major goals of in vitro dissolution are to ensure the performance of oral drug products and the support of drug formulation design, including bioequivalence (BE). Orally administered anticancer drugs, such as dasatinib and erlotinib (tyrosine kinase inhibitors), are used to treat various types of cancer. These drugs are weak bases that exhibit pH-dependent and high solubility in the acidic stomach and low solubility in the small intestine (>pH 6.0). Therefore, these drugs supersaturate and/or precipitate when they move from the stomach to the small intestine. Also of importance, gastric acidity for cancer patients may be altered with aging (reduction of gastric fluid secretion) and/or co-administration of acid-reducing agents. These may result in changes to the dissolution profiles of weak base and the reduction of drug absorption and efficacy. In vitro dissolution methodologies that assess the impact of these physiological changes in the GI condition are expected to better predict in vivo dissolution of oral medications for patients and, hence, better assess efficacy, toxicity and safety concerns. The objective of this present study is to determine the initial conditions for a mini-Gastrointestinal Simulator (mGIS) to assess in vivo

Etest cannot be recommended for in vitro susceptibility testing of mucorales.

Science.gov (United States)

Caramalho, Rita; Maurer, Elisabeth; Binder, Ulrike; Araújo, Ricardo; Dolatabadi, Somayeh; Lass-Flörl, Cornelia; Lackner, Michaela

2015-01-01

Amphotericin B and posaconazole susceptibility patterns were determined for the most prevalent Mucorales, following EUCAST (European Committee on Antimicrobial Susceptibility Testing) broth microdilution guidelines. In parallel, Etest was performed and evaluated against EUCAST. The overall agreement of MICs gained with Etest and EUCAST was 75.1%; therefore, Etest cannot be recommended for antifungal susceptibility testing of Mucorales. Amphotericin B was the most active drug against Mucorales species in vitro, while the activities of posaconazole were more restricted. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Spatial prediction of ground subsidence susceptibility using an artificial neural network.

Science.gov (United States)

Lee, Saro; Park, Inhye; Choi, Jong-Kuk

2012-02-01

Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.

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.

Prevalence and drug susceptibility pattern of group B Streptococci (GBS) among pregnant women attending antenatal care (ANC) in Nekemte Referral Hospital (NRH), Nekemte, Ethiopia.

Science.gov (United States)

Mengist, Hylemariam Mihiretie; Zewdie, Olifan; Belew, Adugna; Dabsu, Regea

2017-08-10

The main objective of this study was to determine the prevalence and drug susceptibility pattern of group B Streptococci (GBS) among pregnant women. The specific objectives include; (1) To determine the prevalence of GBS colonization among pregnant women (2) To determine the drug susceptibility pattern of GBS among pregnant women and (3) To identify associated risk factors with GBS colonization among pregnant women. The median age of the participants was 24.5 years (range 16-38) and 86% participants were urban residents. The total prevalence of maternal GBS colonization from vaginal swab culture was 12.2% (22/180). The prevalence of GBS colonization rate was significantly higher in those pregnant women above 37 weeks of gestation [AOR, 95% CI 2.1 (1.2, 11.6), P = 0.03] and married ones [AOR, 95% CI 3.2 (1.8, 11.6), P < 0.021]. Twenty (91%) of GBS isolates were sensitive to vancomycin and the highest resistance was observed against penicillin G (77.3%). The prevalence of GBS colonization in this study was significantly high and differed by gestational age and marital status. None of the GBS isolates were resistant to vancomycin but higher resistance was shown against Penicillin G.

Health Psychological Constructs as Predictors of Doping Susceptibility in Adolescent Athletes

Science.gov (United States)

Blank, Cornelia; Schobersberger, Wolfgang; Leichtfried, Veronika; Duschek, Stefan

2016-01-01

Background Doping is a highly relevant problem in sport, even in adolescent athletes. Knowledge of the psychological factors that influence doping susceptibility in young elite athletes remains sparse. Objectives This study investigated the predictive potential of different health-psychological constructs and well-being on doping susceptibility. The main hypotheses to be tested were positive associations of fear of failure, external locus of control, and ego-oriented goal orientation as well as negative associations of confidence of success, task orientation, internal locus of control, and performance motivation with doping susceptibility. Low levels of well-being are furthermore expected to be associated with doping susceptibility. Methods Within this cross-sectional study, 1,265 Austrian junior athletes aged between 14 and 19 years responded to a paper-pencil questionnaire. Results Performance motivation was a negative, while depressive mood, self-esteem, fear of failure and ego-oriented goal orientation were positive predictors of doping susceptibility. In addition, participants who were offered performance enhancing substances in the past were particularly susceptible to doping. Conclusions The study corroborates the predictive value of classical psychological constructs in doping research, initially analyzed in view of adult athletes, also for adolescents’ doping susceptibility. PMID:28144408

Health Psychological Constructs as Predictors of Doping Susceptibility in Adolescent Athletes.

Science.gov (United States)

Blank, Cornelia; Schobersberger, Wolfgang; Leichtfried, Veronika; Duschek, Stefan

2016-12-01

Doping is a highly relevant problem in sport, even in adolescent athletes. Knowledge of the psychological factors that influence doping susceptibility in young elite athletes remains sparse. This study investigated the predictive potential of different health-psychological constructs and well-being on doping susceptibility. The main hypotheses to be tested were positive associations of fear of failure, external locus of control, and ego-oriented goal orientation as well as negative associations of confidence of success, task orientation, internal locus of control, and performance motivation with doping susceptibility. Low levels of well-being are furthermore expected to be associated with doping susceptibility. Within this cross-sectional study, 1,265 Austrian junior athletes aged between 14 and 19 years responded to a paper-pencil questionnaire. Performance motivation was a negative, while depressive mood, self-esteem, fear of failure and ego-oriented goal orientation were positive predictors of doping susceptibility. In addition, participants who were offered performance enhancing substances in the past were particularly susceptible to doping. The study corroborates the predictive value of classical psychological constructs in doping research, initially analyzed in view of adult athletes, also for adolescents' doping susceptibility.

The HIV-1 protease resistance mutation I50L is associated with resistance to atazanavir and susceptibility to other protease inhibitors in multiple mutational contexts.

Science.gov (United States)

Sista, P; Wasikowski, B; Lecocq, P; Pattery, T; Bacheler, L

2008-08-01

The HIV-1 protease mutation I50 L causes atazanavir resistance but increases susceptibility to other PIs. Predicted phenotypic FC values were obtained from viral genotypes, using the virtual Phenotype-LM bioinformatics tool (powering vircoTYPE). To evaluate I50 L's effect on susceptibility to 8 PIs, in a large genotype database. I50 L containing routine clinical isolate samples in Virco's genotype database were paired with samples having like patterns (or profiles) of IAS-USA-defined primary PI mutations, but lacking I50 L. Using vircoTYPE (version 4.1), the median predicted FC for each mutational profile was determined. I50 L-associated shifts in FC were evaluated using drug-specific CCOs. We selected 307 and 37098 samples with and without I50 L. These corresponded to 31 mutation patterns of > or =3 samples each. I50 L caused resistance to atazanavir in all 31 mutation contexts, but was associated with higher susceptibility for other PIs. The largest I50 L-associated shifts in median predicted FC were: 1.2 to 42.4 (atazanavir), 10.2 to 3.2 (amprenavir), 3.3 to 0.5 (darunavir), 13 to 0.5 (indinavir), 34.9 to 1.3 (lopinavir), 22.3 to 1.3 (nelfinavir), 5.2 to 0.3 (saquinavir) and 29.9 to 5.2 (tipranavir). The PI mutation I50 L causes clinically relevant resistance and increased susceptibility to atazanavir and other PIs respectively.

Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

Science.gov (United States)

Fukunishi, Yoshifumi

2010-01-01

For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

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.

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

Science.gov (United States)

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

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

The biowaiver extension for BCS class III drugs: the effect of dissolution rate on the bioequivalence of BCS class III immediate-release drugs predicted by computer simulation.

Science.gov (United States)

Tsume, Yasuhiro; Amidon, Gordon L

2010-08-02

The Biopharmaceutical Classification System (BCS) guidance issued by the FDA allows waivers for in vivo bioavailability and bioequivalence studies for immediate-release (IR) solid oral dosage forms only for BCS class I drugs. However, a number of drugs within BCS class III have been proposed to be eligible for biowaivers. The World Health Organization (WHO) has shortened the requisite dissolution time of BCS class III drugs on their Essential Medicine List (EML) from 30 to 15 min for extended biowaivers; however, the impact of the shorter dissolution time on AUC(0-inf) and C(max) is unknown. The objectives of this investigation were to assess the ability of gastrointestinal simulation software to predict the oral absorption of the BCS class I drugs propranolol and metoprolol and the BCS class III drugs cimetidine, atenolol, and amoxicillin, and to perform in silico bioequivalence studies to assess the feasibility of extending biowaivers to BCS class III drugs. The drug absorption from the gastrointestinal tract was predicted using physicochemical and pharmacokinetic properties of test drugs provided by GastroPlus (version 6.0). Virtual trials with a 200 mL dose volume at different drug release rates (T(85%) = 15 to 180 min) were performed to predict the oral absorption (C(max) and AUC(0-inf)) of the above drugs. Both BCS class I drugs satisfied bioequivalence with regard to the release rates up to 120 min. The results with BCS class III drugs demonstrated bioequivalence using the prolonged release rate, T(85%) = 45 or 60 min, indicating that the dissolution standard for bioequivalence is dependent on the intestinal membrane permeability and permeability profile throughout the gastrointestinal tract. The results of GastroPlus simulations indicate that the dissolution rate of BCS class III drugs could be prolonged to the point where dissolution, rather than permeability, would control the overall absorption. For BCS class III drugs with intestinal absorption patterns

Drug resistance in Mexico: results from the National Survey on Drug-Resistant Tuberculosis.

Science.gov (United States)

Bojorquez-Chapela, I; Bäcker, C E; Orejel, I; López, A; Díaz-Quiñonez, A; Hernández-Serrato, M I; Balandrano, S; Romero, M; Téllez-Rojo Solís, M M; Castellanos, M; Alpuche, C; Hernández-Ávila, M; López-Gatell, H

2013-04-01

To present estimations obtained from a population-level survey conducted in Mexico of prevalence rates of mono-, poly- and multidrug-resistant strains among newly diagnosed cases of pulmonary tuberculosis (TB), as well as the main factors associated with multidrug resistance (combined resistance to isoniazid and rifampicin). Study data came from the National Survey on TB Drug Resistance (ENTB-2008), a nationally representative survey conducted during 2008-2009 in nine states with a stratified cluster sampling design. Samples were obtained for all newly diagnosed cases of pulmonary TB in selected sites. Drug susceptibility testing (DST) was performed for anti-tuberculosis drugs. DST results were obtained for 75% of the cases. Of these, 82.2% (95%CI 79.5-84.7) were susceptible to all drugs. The prevalence of multidrug-resistant TB (MDR-TB) was estimated at 2.8% (95%CI 1.9-4.0). MDR-TB was associated with previous treatment (OR 3.3, 95%CI 1.1-9.4). The prevalence of drug resistance is relatively low in Mexico. ENTB-2008 can be used as a baseline for future follow-up of drug resistance.

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

LENUS (Irish Health Repository)

O'Connor, Marie N

2012-11-01

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

Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

Science.gov (United States)

Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

2018-03-01

Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

Behavioral characteristics and neurobiological substrates shared by Pavlovian sign-tracking and drug abuse.

Science.gov (United States)

Tomie, Arthur; Grimes, Kathryn L; Pohorecky, Larissa A

2008-06-01

Drug abuse researchers have noted striking similarities between behaviors elicited by Pavlovian sign-tracking procedures and prominent symptoms of drug abuse. In Pavlovian sign-tracking procedures, repeated paired presentations of a small object (conditioned stimulus, CS) with a reward (unconditioned stimulus, US) elicits a conditioned response (CR) that typically consists of approaching the CS, contacting the CS, and expressing consummatory responses at the CS. Sign-tracking CR performance is poorly controlled and exhibits spontaneous recovery and long-term retention, effects that resemble relapse. Sign-tracking resembles psychomotor activation, a syndrome of behavioral responses evoked by addictive drugs, and the effects of sign-tracking on corticosterone levels and activation of dopamine pathways resemble the neurobiological effects of abused drugs. Finally, the neurobiological profile of individuals susceptible to sign-tracking resembles the pathophysiological profile of vulnerability to drug abuse, and vulnerability to sign-tracking predicts vulnerability to impulsive responding and alcohol self-administration. Implications of sign-tracking for models of drug addiction are considered.

Social and clinical predictors of drug-resistant tuberculosis in a public hospital, Monterrey, Mexico.

Science.gov (United States)

Young, Bonnie N; Burgos, Marcos; Handal, Alexis J; Baker, Jack; Rendón, Adrian; Rosas-Taraco, Adrian; Long, Jeffrey; Hunley, Keith

2014-10-01

Drug-resistant tuberculosis (DRTB) is steadily increasing in Mexico, but little is known of patient risk factors in the Mexico-United States border region. This preliminary case-control study included 95 patients with active pulmonary TB with drug susceptibility results attending the José E. González University Hospital in the urban hub of Nuevo León-the Monterrey Metropolitan Area. We report potential social and clinical risk factors of DRTB among this hospital-based sample. We collected data through face-to-face interviews and medical record reviews from 25 cases with DRTB and 70 drug-sensitive controls. DNA was collected to assess an effect of genetic ancestry on DRTB by using a panel of 291,917 genomic markers. We calculated crude and multivariate logistic regression. After adjusting for potential confounding factors, we found that prior TB treatment (odds ratio, 4.5; 95% confidence interval, 0.9-21.1) and use of crack cocaine (odds ratio, 4.6; 95% confidence interval, 1.1-18.7) were associated with DRTB. No other variables, including genetic ancestry and comorbidities, were predictive. Health care providers may benefit from recognizing predictors of DRTB in regions where routine drug susceptibility testing is limited. Prior TB treatment and illicit drug use, specifically crack cocaine, may be important risk factors for DRTB in this region. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

International Nuclear Information System (INIS)

Morton, M.J.; Armstrong, D.; Abi Gerges, N.; Bridgland-Taylor, M.; Pollard, C.E.; Bowes, J.; Valentin, J.-P.

2014-01-01

Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility

Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

Energy Technology Data Exchange (ETDEWEB)

Morton, M.J., E-mail: michael.morton@astrazeneca.com [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Armstrong, D.; Abi Gerges, N. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Bridgland-Taylor, M. [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Pollard, C.E.; Bowes, J.; Valentin, J.-P. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom)

2014-09-01

Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility.

Identification and Antimicrobial Susceptibility Testing of Anaerobic Bacteria: Rubik’s Cube of Clinical Microbiology?

Science.gov (United States)

Gajdács, Márió; Spengler, Gabriella; Urbán, Edit

2017-01-01

Anaerobic bacteria have pivotal roles in the microbiota of humans and they are significant infectious agents involved in many pathological processes, both in immunocompetent and immunocompromised individuals. Their isolation, cultivation and correct identification differs significantly from the workup of aerobic species, although the use of new technologies (e.g., matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, whole genome sequencing) changed anaerobic diagnostics dramatically. In the past, antimicrobial susceptibility of these microorganisms showed predictable patterns and empirical therapy could be safely administered but recently a steady and clear increase in the resistance for several important drugs (β-lactams, clindamycin) has been observed worldwide. For this reason, antimicrobial susceptibility testing of anaerobic isolates for surveillance purposes or otherwise is of paramount importance but the availability of these testing methods is usually limited. In this present review, our aim was to give an overview of the methods currently available for the identification (using phenotypic characteristics, biochemical testing, gas-liquid chromatography, MALDI-TOF MS and WGS) and antimicrobial susceptibility testing (agar dilution, broth microdilution, disk diffusion, gradient tests, automated systems, phenotypic and molecular resistance detection techniques) of anaerobes, when should these methods be used and what are the recent developments in resistance patterns of anaerobic bacteria. PMID:29112122

Use of Pinus sylvestris L. (Pinaceae), Origanum vulgare L. (Lamiaceae), and Thymus vulgaris L. (Lamiaceae) essential oils and their main components to enhance itraconazole activity against azole susceptible/not-susceptible Cryptococcus neoformans strains.

Science.gov (United States)

Scalas, Daniela; Mandras, Narcisa; Roana, Janira; Tardugno, Roberta; Cuffini, Anna Maria; Ghisetti, Valeria; Benvenuti, Stefania; Tullio, Vivian

2018-05-03

Cryptococcal infections, besides being a problem for immunocompromised patients, are occasionally being a problem for immunocompetent patients. In addition, the lower susceptibility of this yeast to azoles is a growing problem in health care. To date, there are very few molecules with any activity towards Cryptococcus neoformans, leading to heightened interest in finding new alternatives or adjuvants to conventional drugs for the treatment of mycosis caused by this yeast. Since the essential oils (EOs) are considered as a potential rich source of bioactive antimicrobial compounds, we evaluated the antifungal activity of Origanum vulgare (oregano), Pinus sylvestris (pine), and Thymus vulgaris (thyme red) EOs, and their components (α-pinene, carvacrol, thymol) compared with fluconazole, itraconazole, and voriconazole, against C.neoformans clinical strains. Then, we investigated the effect of EOs and components in combination with itraconazole. EO composition was analysed by Gas chromatography-mass spectrometry (GC-MS). A broth microdilution method was used to evaluate the susceptibility of C.neoformans to azoles, EOs and components. Checkerboard tests, isobolograms and time-kill assays were carried out for combination studies. Six C.neoformans isolates were susceptible to azoles, while one C.neoformans exhibited a reduced susceptibility to all tested azole drugs. All EOs exerted a good inhibitory activity against all C.neoformans strains. Pine EO was the most effective. Among components, thymol exerted the most remarkable activity. By checkerboard testing and isobolographic analysis, combinations of itraconazole with oregano, pine, or thyme EOs, and carvacrol were found to be synergistic (FICI≤0.5) against azole susceptible C.neoformans. Regarding the azole not susceptible C.neoformans strain, the synergistic effect with itraconazole was observed with thyme EO (chemotype: thymol 26.52%; carvacrol 7.85%), and carvacrol. Time-kill assays confirmed the synergistic

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.

In Vitro Susceptibility Test of Different Clinical Isolates against Ceftriaxone

Directory of Open Access Journals (Sweden)

Syed Hakim Masood

2010-06-01

Full Text Available Objectives: Because of the prevailing penicillin resistance in microorganisms, broad spectrum cephalosporins are used empirically specially in developing countries. The aim of this study is to determine the susceptibility pattern of different gram positive and gram negative pathogens against third generation cephalosporin-ceftriaxone to explore the existing effectiveness of this antibiotic.Methods: 180 clinical isolates of different gram positive and gram negative pathogens including P.mirabilis, S. typhi P.aeruginosa, E. coli, S. aureus and Klebsiella were collected from blood and urine samples of in-patients. 30 isolates of all species were tested against each of six brands of ceftriaxone using in vitro sensitivity tests by disc diffusion method (NCCLS criteria. The susceptibility limit was ≥21 mm zone of inhibition, while moderately susceptible was considered at 20-14 mm, and those isolates which showed >13 mm or no zone of inhibition were resistant to this antibacterial drug.Results: Ceftriaxone was found most effective against S. aureus. While 96.1% of the isolates showed susceptibility towards ceftriaxone, followed by E. coli (95%, P. aeruginosa (92.7%, K. pneumonia (89.4% and S. typhi (87.2%. P. mirabilis showed lowest susceptibility amongst all the test organisms (83.8%.Conclusion: Ceftriaxone can be used as a drug of choice in infections caused by S. aureus, E. coli, P. aurigenosa, K. pneumonia and S. typhi. However, it should be used with other antimicrobial agents in order to increase its effectiveness against P. mirabilis.

Development of a Unified Dissolution and Precipitation Model and Its Use for the Prediction of Oral Drug Absorption.

Science.gov (United States)

Jakubiak, Paulina; Wagner, Björn; Grimm, Hans Peter; Petrig-Schaffland, Jeannine; Schuler, Franz; Alvarez-Sánchez, Rubén

2016-02-01

Drug absorption is a complex process involving dissolution and precipitation, along with other kinetic processes. The purpose of this work was to (1) establish an in vitro methodology to study dissolution and precipitation in early stages of drug development where low compound consumption and high throughput are necessary, (2) develop a mathematical model for a mechanistic explanation of generated in vitro dissolution and precipitation data, and (3) extrapolate in vitro data to in vivo situations using physiologically based models to predict oral drug absorption. Small-scale pH-shift studies were performed in biorelevant media to monitor the precipitation of a set of poorly soluble weak bases. After developing a dissolution-precipitation model from this data, it was integrated into a simplified, physiologically based absorption model to predict clinical pharmacokinetic profiles. The model helped explain the consequences of supersaturation behavior of compounds. The predicted human pharmacokinetic profiles closely aligned with the observed clinical data. In summary, we describe a novel approach combining experimental dissolution/precipitation methodology with a mechanistic model for the prediction of human drug absorption kinetics. The approach unifies the dissolution and precipitation theories and enables accurate predictions of in vivo oral absorption by means of physiologically based modeling.

No support for HLA-DQ encoded susceptibility in rheumatoid arthritis

NARCIS (Netherlands)

de Vries, N. [=Niek; van Elderen, C.; Tijssen, H.; van Riel, P. L.; van de Putte, L. B.

1999-01-01

To test predictions based on data from immunogenetic and peptide-binding studies of collagen-induced arthritis in mice, in which it has been suggested that susceptibility to rheumatoid arthritis (RA) might be determined by the interaction between susceptibility alleles at the HLA-DQ locus and

Rapid bacterial antibiotic susceptibility test based on simple surface-enhanced Raman spectroscopic biomarkers

Science.gov (United States)

Liu, Chia-Ying; Han, Yin-Yi; Shih, Po-Han; Lian, Wei-Nan; Wang, Huai-Hsien; Lin, Chi-Hung; Hsueh, Po-Ren; Wang, Juen-Kai; Wang, Yuh-Lin

2016-03-01

Rapid bacterial antibiotic susceptibility test (AST) and minimum inhibitory concentration (MIC) measurement are important to help reduce the widespread misuse of antibiotics and alleviate the growing drug-resistance problem. We discovered that, when a susceptible strain of Staphylococcus aureus or Escherichia coli is exposed to an antibiotic, the intensity of specific biomarkers in its surface-enhanced Raman scattering (SERS) spectra drops evidently in two hours. The discovery has been exploited for rapid AST and MIC determination of methicillin-susceptible S. aureus and wild-type E. coli as well as clinical isolates. The results obtained by this SERS-AST method were consistent with that by the standard incubation-based method, indicating its high potential to supplement or replace existing time-consuming methods and help mitigate the challenge of drug resistance in clinical microbiology.

Predictive modeling of freezing and thawing of frost-susceptible soils.

Science.gov (United States)

2015-09-01

Frost depth is an essential factor in design of various transportation infrastructures. In frost : susceptible soils, as soils freezes, water migrates through the soil voids below the freezing line : towards the freezing front and causes excessive he...

High-throughput behavioral phenotyping of drug and alcohol susceptibility traits in the expanded panel of BXD recombinant inbred strains

Energy Technology Data Exchange (ETDEWEB)

Philip, Vivek M [ORNL; Ansah, T [University of Tennessee Health Science Center, Memphis; Blaha, C, [University of Tennessee Health Science Center, Memphis; Cook, Melloni N. [University of Memphis; Hamre, Kristin M. [University of Tennessee Health Science Center, Memphis; Lariviere, William R [University of Pittsburgh; Matthews, Douglas B [Baylor University; Goldowitz, Daniel [University of British Columbia, Vancouver; Chesler, Elissa J [ORNL

2010-01-01

Genetic reference populations, particularly the BXD recombinant inbred strains, are a valuable resource for the discovery of the bio-molecular substrates and genetic drivers responsible for trait variation and co- ariation. This approach can be profitably applied in the analysis of susceptibility and mechanisms of drug and alcohol use disorders for which many predisposing behaviors may predict occurrence and manifestation of increased preference for these substances. Many of these traits are modeled by common mouse behavioral assays, facilitating the detection of patterns and sources of genetic co-regulation of predisposing phenotypes and substance consumption. Members of the Tennessee Mouse Genome Consortium have obtained behavioral phenotype data from 260 measures related to multiple behavioral assays across several domains: self-administration, response to, and withdrawal from cocaine, MDMA, morphine and alcohol; novelty seeking; behavioral despair and related neurological phenomena; pain sensitivity; stress sensitivity; anxiety; hyperactivity; and sleep/wake cycles. All traits have been measured in both sexes and the recently expanded panel of 69 additional BXD recombinant inbred strains (N=69). Sex differences and heritability estimates were obtained for each trait, and a comparison of early (N = 32) and recent BXD RI lines was performed. Primary data is publicly available for heritability, sex difference and genetic analyses using www.GeneNetwork.org. These analyses include QTL detection and genetic analysis of gene expression. Stored results from these analyses are available at http://ontologicaldiscovery.org for comparison to other genomic analysis results. Together with the results of related studies, these data form a public resource for integrative systems genetic analysis of neurobehavioral traits.

Predictive toxicology in drug safety

National Research Council Canada - National Science Library

Xu, Jinghai J; Urban, Laszlo

2011-01-01

.... It provides information on the present knowledge of drug side effects and their mitigation strategy during drug discovery, gives guidance for risk assessment, and promotes evidence-based toxicology...

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

Science.gov (United States)

Ye, Min; Nagar, Swati; Korzekwa, Ken

2016-04-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Motivated malleability: Frontal cortical asymmetry predicts the susceptibility to social influence.

Science.gov (United States)

Schnuerch, Robert; Pfattheicher, Stefan

2017-07-16

Humans, just as many other animals, regulate their behavior in terms of approaching stimuli associated with pleasure and avoiding stimuli linked to harm. A person's current and chronic motivational direction - that is, approach versus avoidance orientation - is reliably reflected in the asymmetry of frontal cortical low-frequency oscillations. Using resting electroencephalography (EEG), we show that frontal asymmetry is predictive of the tendency to yield to social influence: Stronger right- than left-side frontolateral activation during a resting-state session prior to the experiment was robustly associated with a stronger inclination to adopt a peer group's judgments during perceptual decision-making (Study 1). We posit that this reflects the role of a person's chronic avoidance orientation in socially adjusted behavior. This claim was strongly supported by additional survey investigations (Studies 2a, 2b, 2c), all of which consistently revealed that trait avoidance was positively linked to the susceptibility to social influence. The present contribution thus stresses the relevance of chronic avoidance orientation in social conformity, refining (yet not contradicting) the longstanding view that socially influenced behavior is motivated by approach-related goals. Moreover, our findings valuably underscore and extend our knowledge on the association between frontal cortical asymmetry and a variety of psychological variables.

Secondary metabolite profiles and antifungal drug susceptibility of Aspergillus fumigatus and closely related species, Aspergillus lentulus, Aspergillus udagawae, and Aspergillus viridinutans.

Science.gov (United States)

Tamiya, Hiroyuki; Ochiai, Eri; Kikuchi, Kazuyo; Yahiro, Maki; Toyotome, Takahito; Watanabe, Akira; Yaguchi, Takashi; Kamei, Katsuhiko

2015-05-01

The incidence of Aspergillus infection has been increasing in the past few years. Also, new Aspergillus fumigatus-related species, namely Aspergillus lentulus, Aspergillus udagawae, and Aspergillus viridinutans, were shown to infect humans. These fungi exhibit marked morphological similarities to A. fumigatus, albeit with different clinical courses and antifungal drug susceptibilities. The present study used liquid chromatography/time-of-flight mass spectrometry to identify the secondary metabolites secreted as virulence factors by these Aspergillus species and compared their antifungal susceptibility. The metabolite profiles varied widely among A. fumigatus, A. lentulus, A. udagawae, and A. viridinutans, producing 27, 13, 8, and 11 substances, respectively. Among the mycotoxins, fumifungin, fumiquinazoline A/B and D, fumitremorgin B, gliotoxin, sphingofungins, pseurotins, and verruculogen were only found in A. fumigatus, whereas auranthine was only found in A. lentulus. The amount of gliotoxin, one of the most abundant mycotoxins in A. fumigatus, was negligible in these related species. In addition, they had decreased susceptibility to antifungal agents such as itraconazole and voriconazole, even though metabolites that were shared in the isolates showing higher minimum inhibitory concentrations than epidemiological cutoff values were not detected. These strikingly different secondary metabolite profiles may lead to the development of more discriminative identification protocols for such closely related Aspergillus species as well as improved treatment outcomes. Copyright © 2015 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Extensively and Pre-Extensively Drug Resistant Tuberculosis in Clinical Isolates of Multi-Drug Resistant Tuberculosis Using Classical Second Line Drugs (Levofloxacin and Amikacin)

International Nuclear Information System (INIS)

Mirza, I. A.; Khan, F. A.; Khan, K. A.; Satti, L.; Ghafoor, T.; Fayyaz, M.

2015-01-01

Objective:To find out the frequency of Extensively Drug Resistant (XDR) and pre-XDR tuberculosis in clinical isolates of Multi-Drug Resistant (MDR) Tuberculosis (TB) by determining the susceptibilities against Levofloxacin and Amikacin (classical second line antituberculosis drugs). Study Design: A descriptive cross-sectional study. Place and Duration of Study: Microbiology Department, Armed Forces Institute of Pathology (AFIP), Rawalpindi, from September 2011 to August 2013. Methodology: Amikacin (AK) and Levofloxacin (LEVO) were obtained in chemically pure form from Sigma (Taufkirchen, Germany). The breakpoint concentration used for AK was 1.0 micro g/ml and for LEVO 2.0 micro g/ml. Mycobacterial Growth Indicator Tube (MGIT) 960 system was used to carry out drug susceptibility testing as per recommended protocol. Results: A total of 3 MDR-TB isolates (3 percentage) turned out to be XDR-TB based upon simultaneous resistance to injectable second line antituberculosis drug AK and one of the fluoro-quinolones (LEVO). A total of 24 MDR-TB isolates (24 percentage) were found to be pre-XDR based upon resistance to LEVO alone. Treatment status record of patients with XDR and pre-XDRTB isolates revealed that majority of patients had received fluoroquinolones (FQs) during the course of treatment. Conclusion: XDR-TB has started to emerge in MDR-TB isolates in our set up. The worrying sign is the high frequency of pre-XDR tuberculosis. Urgent steps need to be taken to stem the tide of pre-XDR-TB in our population. It is thus recommended to develop facilities to carry out drug susceptibility testing to monitor the status of pre-XDR and XDR-TB in our population. (author)

Initial magnetic susceptibility of the diluted magnetopolymer elastic composites

International Nuclear Information System (INIS)

Borin, D.Yu.; Odenbach, S.

2017-01-01

In this work diluted magnetopolymer elastic composites based on magnetic microparticles are experimentally studied. Considered samples have varied concentration of the magnetic powder and different structural anisotropy. Experimental data on magnetic properties are accomplished by microstructural observations performed using X-Ray tomography. Influence of the particles amount and structuring effects on the initial magnetic susceptibility of the composites as well as the applicability of the Maxwell-Garnett approximation, which is widely used in considerations of magnetopolymer elastic composites, are evaluated. It is demonstrated that the approximation works well for diluted samples containing randomly distributed magnetic particles and for the diluted samples with chain-like structures oriented perpendicular to an externally applied field, while it fails to predict the susceptibility of the samples with structures oriented parallel to the field. Moreover, it is shown, that variation of the chains morphology does not significantly change the composite initial magnetic susceptibility. - Highlights: • The Maxwell-Garnet prediction works well for the diluted isotropic composites. • The Maxwell-Garnet prediction can be used for composites with structures oriented perpendicular to an applied field. • Chains oriented parallel to an applied field significantly increase the composite initial magnetic susceptibility. • The number and thickness of chains is not of the highest importance for the diluted composites. • The crucial reason of the observed effect is expected to be the demagnetisation factor of the chains.

Intersite interactions and susceptibility in mixed valence systems

International Nuclear Information System (INIS)

Xiaoqian Wang; Gao Lin; Bingjian Ni; Fusui Liu.

1985-10-01

This paper considers the effect of intersite processes on the susceptibility in mixed valence system. The method of thermodynamical perturbation used in this paper can also be generalized to study other properties of mixed valence system. The general formula of partition function of two-site interactions for the mixed valence system is given. The numerical calculations show that the intersite interaction is large enough to explain the minimum of susceptibility discovered in experiments. The different types of our theoretical curves predict that the susceptibility should exhibit a rich variety of behaviour at low temperature for various materials. (author)

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

Some uses of predictive probability of success in clinical drug development

Directory of Open Access Journals (Sweden)

Mauro Gasparini

2013-03-01

Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

Antianaerobic Antimicrobials: Spectrum and Susceptibility Testing

Science.gov (United States)

Wexler, Hannah M.; Goldstein, Ellie J. C.

2013-01-01

SUMMARY Susceptibility testing of anaerobic bacteria recovered from selected cases can influence the choice of antimicrobial therapy. The Clinical and Laboratory Standards Institute (CLSI) has standardized many laboratory procedures, including anaerobic susceptibility testing (AST), and has published documents for AST. The standardization of testing methods by the CLSI allows comparisons of resistance trends among various laboratories. Susceptibility testing should be performed on organisms recovered from sterile body sites, those that are isolated in pure culture, or those that are clinically important and have variable or unique susceptibility patterns. Organisms that should be considered for individual isolate testing include highly virulent pathogens for which susceptibility cannot be predicted, such as Bacteroides, Prevotella, Fusobacterium, and Clostridium spp.; Bilophila wadsworthia; and Sutterella wadsworthensis. This review describes the current methods for AST in research and reference laboratories. These methods include the use of agar dilution, broth microdilution, Etest, and the spiral gradient endpoint system. The antimicrobials potentially effective against anaerobic bacteria include beta-lactams, combinations of beta-lactams and beta-lactamase inhibitors, metronidazole, chloramphenicol, clindamycin, macrolides, tetracyclines, and fluoroquinolones. The spectrum of efficacy, antimicrobial resistance mechanisms, and resistance patterns against these agents are described. PMID:23824372

Molecular chess? Hallmarks of anti-cancer drug resistance.

Science.gov (United States)

Cree, Ian A; Charlton, Peter

2017-01-05

The development of resistance is a problem shared by both classical chemotherapy and targeted therapy. Patients may respond well at first, but relapse is inevitable for many cancer patients, despite many improvements in drugs and their use over the last 40 years. Resistance to anti-cancer drugs can be acquired by several mechanisms within neoplastic cells, defined as (1) alteration of drug targets, (2) expression of drug pumps, (3) expression of detoxification mechanisms, (4) reduced susceptibility to apoptosis, (5) increased ability to repair DNA damage, and (6) altered proliferation. It is clear, however, that changes in stroma and tumour microenvironment, and local immunity can also contribute to the development of resistance. Cancer cells can and do use several of these mechanisms at one time, and there is considerable heterogeneity between tumours, necessitating an individualised approach to cancer treatment. As tumours are heterogeneous, positive selection of a drug-resistant population could help drive resistance, although acquired resistance cannot simply be viewed as overgrowth of a resistant cancer cell population. The development of such resistance mechanisms can be predicted from pre-existing genomic and proteomic profiles, and there are increasingly sophisticated methods to measure and then tackle these mechanisms in patients. The oncologist is now required to be at least one step ahead of the cancer, a process that can be likened to 'molecular chess'. Thus, as well as an increasing role for predictive biomarkers to clinically stratify patients, it is becoming clear that personalised strategies are required to obtain best results.

Predictive performance of three practical approaches for grapefruit juice-induced 2-fold or greater increases in AUC of concomitantly administered drugs.

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Takahashi, M; Onozawa, S; Ogawa, R; Uesawa, Y; Echizen, H

2015-02-01

Clinical pharmacists have a challenging task when answering patients' question about whether they can take specific drugs with grapefruit juice (GFJ) without risk of drug interaction. To identify the most practicable method for predicting clinically relevant changes in plasma concentrations of orally administered drugs caused by the ingestion of GFJ, we compared the predictive performance of three methods using data obtained from the literature. We undertook a systematic search of drug interactions associated with GFJ using MEDLINE and the Metabolism & Transport Drug Interaction Database (DIDB version 4.0). We considered an elevation of the area under the plasma concentration-time curve (AUC) of 2 or greater relative to the control value [AUC ratio (AUCR) ≥ 2.0] as a clinically significant interaction. The data from 74 drugs (194 data sets) were analysed. When the reported information of CYP3A involvement in the metabolism of a drug of interest was adopted as a predictive criterion for GFJ-drug interaction, the performance assessed by positive predictive value (PPV) was low (0.26), but that assessed by negative predictive value (NPV) and sensitivity was high (1.00 for both). When the reported oral bioavailability of ≤ 0.1 was used as a criterion, the PPV improved to 0.50 with an acceptable NPV of 0.81, but sensitivity was reduced to 0.21. When the reported AUCR was ≥ 10 after co-administration of a typical CYP3A inhibitor, the corresponding values were 0.64, 0.79 and 0.19, respectively. We consider that an oral bioavailability of ≤ 0.1 or an AUCR of ≥ 10 caused by a CYP3A inhibitor of a drug of interest may be a practical prediction criterion for avoiding significant interactions with GFJ. Information about the involvement of CYP3A in their metabolism should also be taken into account for drugs with narrow therapeutic ranges. © 2014 John Wiley & Sons Ltd.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

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Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Has introduction of rapid drug susceptibility testing at diagnosis impacted treatment outcomes among previously treated tuberculosis patients in Gujarat, India?

Directory of Open Access Journals (Sweden)

Paresh Dave

Full Text Available Revised National TB Control Programme (RNTCP in India recommends that all previously-treated TB (PT patients are offered drug susceptibility testing (DST at diagnosis, using rapid diagnostics and screened out for rifampicin resistance before being treated with standardized, eight-month, retreatment regimen. This is intended to improve the early diagnosis of rifampicin resistance and its appropriate management and improve the treatment outcomes among the rest of the patients. In this state-wide study from Gujarat, India, we assess proportion of PT patients underwent rapid DST at diagnosis and the impact of this intervention on their treatment outcomes.This is a retrospective cohort study involving review of electronic patient-records maintained routinely under RNTCP. All PT patients registered for treatment in Gujarat during January-June 2013 were included. Information on DST and treatment outcomes were extracted from 'presumptive DR-TB patient register' and TB treatment register respectively. We performed a multivariate analysis to assess if getting tested is independently associated with unfavourable outcomes (death, loss-to-follow-up, failure, transfer out.Of 5,829 PT patients, 5306(91% were tested for drug susceptibility with rapid diagnostics. Overall, 71% (4,113 TB patients were successfully treated - 72% among tested versus 60% among non-tested. Patients who did not get tested at diagnosis had a 34% higher risk of unsuccessful outcomes as compared to those who got tested (aRR - 1.34; 95% CI 1.20-1.50 after adjusting for age, sex, HIV status and type of TB. Unfavourable outcomes (particularly failure and switched to category IV were higher among INH-resistant patients (39% as compared to INH-sensitive (29%.Offering DST at diagnosis improved the treatment outcomes among PT patients. However, even among tested, treatment outcomes remained suboptimal and were related to INH resistance and high loss-to-follow-up. These need to be addressed

Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

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Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E; Turnbaugh, Peter J; Bright, Roselie A; Altman, Russ B

2018-01-01

Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design.

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Grebner, Christoph; Iegre, Jessica; Ulander, Johan; Edman, Karl; Hogner, Anders; Tyrchan, Christian

2016-04-25

Computer-aided drug design plays an important role in medicinal chemistry to obtain insights into molecular mechanisms and to prioritize design strategies. Although significant improvement has been made in structure based design, it still remains a key challenge to accurately model and predict induced fit mechanisms. Most of the current available techniques either do not provide sufficient protein conformational sampling or are too computationally demanding to fit an industrial setting. The current study presents a systematic and exhaustive investigation of predicting binding modes for a range of systems using PELE (Protein Energy Landscape Exploration), an efficient and fast protein-ligand sampling algorithm. The systems analyzed (cytochrome P, kinase, protease, and nuclear hormone receptor) exhibit different complexities of ligand induced fit mechanisms and protein dynamics. The results are compared with results from classical molecular dynamics simulations and (induced fit) docking. This study shows that ligand induced side chain rearrangements and smaller to medium backbone movements are captured well in PELE. Large secondary structure rearrangements, however, remain challenging for all employed techniques. Relevant binding modes (ligand heavy atom RMSD PELE method within a few hours of simulation, positioning PELE as a tool applicable for rapid drug design cycles.

Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery

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Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

2014-01-01

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138

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

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