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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Computer-aided Drug Design Investigations for Benzothiazinone Derivatives Against Tuberculosis

Author(s): Jéssika O. Viana, Marcus T. Scotti and Luciana Scotti*

Volume 23, Issue 1, 2020

Page: [66 - 82] Pages: 17

DOI: 10.2174/1386207323666200117102316

Price: $65

Abstract

Background: Tuberculosis (Mycobacterium tuberculosis) is an infectious bacterial disease with the highest levels of mortality worldwide, presenting numerous cases of resistance. In silico studies, which elaborate chemical and biological models in computational tools and make it possible to interpret molecular characteristics, are among the methods used in the search for new drugs.

Objective: In this perspective, our aim was to use QSAR and molecular modeling to propose possible pharmacophores from benzothiazinone derivatives.

Methods: In this study, a set of 69 benzothiazinone derivatives, together with computational tools such as molecular descriptor analysis in chemometrics, metabolic prediction, and molecular coupling to 4 proteins: DprE1, InhA, PS, and DHFR important for the bacillus were investigated.

Results: The chemometric model computed in the Volsurf+ program presented good predictive values for both amphiphilicity and molecular volume. These are essential for biological activity. Metabolites from the cytochrome isoforms CYP3A4 and 2D6 interactions revealed coupling divergences which, noting that the metabolites did not present changes to the QSAR proposed pharmacophore structures, may be due to the reaction medium and existing differences in the benzothiazinone structures. Similarly, molecular docking with the four TB enzymes presented good interactions for the more active compounds. The fragments found using QSAR (being essential for biological activity) also presented as being essential for ligand-protein site interactions.

Conclusion: From the benzothiazinone derivative series evaluated, compound 11026134 presented the best profile in all study analyses, noting that the trifluoromethyl, nitro group, and piperazine fragment with aliphatic hydrocarbon groups are likely pharmacophores for the benzothiazinones studied.

Keywords: Antitubercular drugs, benzothiazinones, QSAR, molecular docking, metabolic prediction, pharmacophore.

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