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Predictive Power of In Silico Approach to Evaluate Chemicals Against : A Systematic Review

Overview
Publisher MDPI
Specialty Chemistry
Date 2019 Sep 19
PMID 31527425
Citations 6
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Abstract

(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.

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References
1.
Hoagland D, Liu J, Lee R, Lee R . New agents for the treatment of drug-resistant Mycobacterium tuberculosis. Adv Drug Deliv Rev. 2016; 102:55-72. PMC: 4903924. DOI: 10.1016/j.addr.2016.04.026. View

2.
Tiwari R, Mahasenan K, Pavlovicz R, Li C, Tjarks W . Carborane clusters in computational drug design: a comparative docking evaluation using AutoDock, FlexX, Glide, and Surflex. J Chem Inf Model. 2009; 49(6):1581-9. PMC: 2702476. DOI: 10.1021/ci900031y. View

3.
He X, Alian A, Stroud R, de Montellano P . Pyrrolidine carboxamides as a novel class of inhibitors of enoyl acyl carrier protein reductase from Mycobacterium tuberculosis. J Med Chem. 2006; 49(21):6308-23. PMC: 2517584. DOI: 10.1021/jm060715y. View

4.
Dkhar H, Gopalsamy A, Loharch S, Kaur A, Bhutani I, Saminathan K . Discovery of Mycobacterium tuberculosis α-1,4-glucan branching enzyme (GlgB) inhibitors by structure- and ligand-based virtual screening. J Biol Chem. 2014; 290(1):76-89. PMC: 4281769. DOI: 10.1074/jbc.M114.589200. View

5.
Swain S, Paidesetty S, Padhy R . Development of antibacterial conjugates using sulfamethoxazole with monocyclic terpenes: A systematic medicinal chemistry based computational approach. Comput Methods Programs Biomed. 2017; 140:185-194. DOI: 10.1016/j.cmpb.2016.12.013. View