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Design, synthesis, and computational studies of benzimidazole derivatives as new antitubercular agents

18

Citations

46

References

2022

Year

Abstract

The increase in the drug-resistant strains of <i>Mycobacterium tuberculosis</i> has led researchers to new drug targets. The development of new compounds that have effective inhibitory properties with the selective vital structure of <i>Mycobacterium tuberculosis</i> is required in new scientific approaches. The most important of these approaches is the development of inhibitor molecules for Mycobacterium cell wall targets. In this study, first of all, the antitubercular activity of 23 benzimidazole derivatives was experimentally determined. And then molecular docking studies were carried out with 4 different targets: Arabinosyltransferase C (EmbC), Filamentous Temperature Sensitive Mutant Z (FtsZ), Protein Tyrosine Phosphatase B (PtpB), and Decaprenylphosphoryl-β-D-ribose-2'-oxidase (DprE1). It has been determined that benzimidazole derivatives show activity through the DprE1 enzyme. It is known that DprE1, which has an important role in the synthesis of the cell envelope from Arabinogalactan, is also effective in the formation of drug resistance. Due to this feature, the DprE1 enzyme has become an important target for drug development studies. Also, it was chosen as a target for this study. This study aims to identify molecules that inhibit DprE1 for the development of more potent and selective antitubercular drugs. For this purpose, molecular docking studies by AutoDock Vina, and CDOCKER and molecular dynamics (MD) simulations and <i>in silico</i> ADME/Tox analysis were implemented for 23 molecules. The molecules exhibited binding affinity values of less than -8.0 kcal/mol. After determining the compound's anti-TB activities by a screening test, the best-docked results were detected using compounds <b>20</b>, <b>21</b>, and <b>30</b>. It was found that <b>21</b>, was the best molecule with its binding affinity value, which was supported by MD simulations and <i>in silico</i> ADME modeling results.Communicated by Ramaswamy H. Sarma.

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