Combined Bioinformatics and Combinatorial Chemistry Tools to Locate Drug-Able Anti-TB Phytochemicals: A Cost-Effective Platform for Natural Product-Based Drug Discovery.

Abstract

Based on extensive experimental studies, a huge number of phytochemicals showed potential activity against tuberculosis (TB) at a lower minimum inhibitory concentration (MIC) and fewer toxicity profiles. However, these promising drugs have not been able to convert from 'lead' to 'mainstream' due to inadequate drug-ability profiles. Thus, early drug-prospective analyses are required at the primary stage to accelerate natural-product-based drug discovery with limited resources and time. In the present study, we have selected seventy-three potential anti-TB phytochemicals (MIC value ≤10 μg/mL) and assessed the drug-ability profiles using bioinformatics and combinatorial chemistry tools, systematically. Primarily, the molecular docking study was done against two putative drug targets, catalase-peroxidase enzyme (katG) and RNA polymerase subunit-β (rpoB) of Mycobacterium tuberculosis (Mtb) using AutoDock 4.2 software. Further, assessed the drug-ability score from Molsoft, toxicity profiles from ProTox, pharmacokinetics from SwisADME, hierarchical cluster analysis (HCA) by ChemMine tools and frontier molecular orbitals (FMOs) with Avogadro and structural activity relationships (SAR) analysis with ChemDraw 18.0 software. Above analyses indicated that, lower MIC exhibited anti-TB phytochemicals, abietane, 12-demethylmulticaulin exhibited poor docking and drug-ability scores, while tiliacorinine, 2-nortiliacorinine showed higher binding energy and drug-ability profiles. Overall, tiliacorinine, 2-nortiliacorinine, 7α-acetoxy-6β-hydroxyroyleanone (AHR), (2S)-naringenin and isovachhalcone were found as the most active and drug-able anti-TB candidates from 73 candidates. Phytochemicals are always a vital source of mainstream drugs, but the MIC value of a phytochemical is not sufficient for it to be promoted. An ideal drug-ability profile is therefore essential for achieving clinical success, where advanced bioinformatics tools help to assess and analyse that profile. Additionally, several natural pharmacophores found in existing anti-TB drugs in SAR analyses also provide crucial information for developing potential anti-TB drug. As a conclusion, combined bioinformatics and combinatorial chemistry are the most effective strategies to locate potent-cum-drug-able candidates in the current drug-development module.