Dataset Information

Predictive Power of In Silico Approach to Evaluate Chemicals against M. tuberculosis: A Systematic Review.

ABSTRACT: Mycobacterium tuberculosis (Mtb) is an endemic bacterium worldwide that causes tuberculosis (TB) and involves long-term treatment that is not always effective. In this context, several studies are trying to develop and evaluate new substances active against Mtb. In silico techniques are often used to predict the effects on some known target. We used a systematic approach to find and evaluate manuscripts that applied an in silico technique to find antimycobacterial molecules and tried to prove its predictive potential by testing them in vitro or in vivo. After searching three different databases and applying exclusion criteria, we were able to retrieve 46 documents. We found that they all follow a similar screening procedure, but few studies exploited equal targets, exploring the interaction of multiple ligands to 29 distinct enzymes. The following in vitro/vivo analysis showed that, although the virtual assays were able to decrease the number of molecules tested, saving time and money, virtual screening procedures still need to develop the correlation to more favorable in vitro outcomes. We find that the in silico approach has a good predictive power for in vitro results, but call for more studies to evaluate its clinical predictive possibilities.

SUBMITTER: Timo GO

PROVIDER: S-EPMC6789803 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Predictive Power of In Silico Approach to Evaluate Chemicals against <i>M. tuberculosis</i>: A Systematic Review.

Timo Giulia Oliveira GO Reis Rodrigo Souza Silva Valle Dos RSSVD Melo Adriana Françozo de AF Costa Thales Viana Labourdette TVL Magalhães Pérola de Oliveira PO Homem-de-Mello Mauricio M

Pharmaceuticals (Basel, Switzerland) 20190916 3

<i>Mycobacterium tuberculosis</i> (Mtb) is an endemic bacterium worldwide that causes tuberculosis (TB) and involves long-term treatment that is not always effective. In this context, several studies are trying to develop and evaluate new substances active against Mtb. In silico techniques are often used to predict the effects on some known target. We used a systematic approach to find and evaluate manuscripts that applied an in silico technique to find antimycobacterial molecules and tried to p ...[more]

PMID: 31527425

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Project description:BackgroundResearch suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing.ObjectivesWe sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals.MethodsUsing a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells.ResultsThe literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model.DiscussionUsing the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779.

Project description:INTRODUCTION:Tuberculosis (TB) is a disease caused by Mycobacterium tuberculosis (M.TB) and other species of the Mycobacterium tuberculosis complex. Globally, TB is ranked as the ninth leading cause of death and the leading cause of death from a single infectious agent. The bacille Calmette-Guerin (BCG) vaccine has been used globally since 1921 for the prevention of TB in humans, and was derived from an attenuated strain of Mycobacterium bovis. Evidence from previous randomised trials show that the efficacy of primary BCG vaccination against pulmonary TB ranged from no protection to very high protection. In addition, some studies suggest a benefit of BCG revaccination. For example, a recent trial conducted in South Africa showed that BCG revaccination of adolescents could reduce the risk of TB infection by half. However, we are not aware of any recent systematic reviews of the effects of BCG revaccination. Thus, the need for this systematic review of the effects of BCG revaccination on protection against TB infection and disease. METHOD AND ANALYSIS:We will search PubMed, the Cochrane Central Register of Controlled Trials, EMBASE, WHO International Clinical Trials Registry Platform and reference lists of relevant publications for potentially eligible studies. We will screen search outputs, select eligible studies, extract data and assess risk of bias in duplicate. Discrepancies will be resolved by discussion and consensus or arbitration. We will use the Grading of Recommendations Assessment, Development and Evaluation method to assess the certainty of the evidence. The planned systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) in August 2018. ETHICS AND DISSEMINATION:Publicly available data will be used, hence no formal ethical approval will be required for this review. The findings of the review will be disseminated through conference presentations and publication in an open-access peer-reviewed journal. PROSPERO REGISTRATION NUMBER:CRD42018105916.

Dataset Information

Predictive Power of In Silico Approach to Evaluate Chemicals against M. tuberculosis: A Systematic Review.

Publications

Predictive Power of In Silico Approach to Evaluate Chemicals against <i>M. tuberculosis</i>: A Systematic Review.

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