Computer-Aided Drug Design and Development of Anti-Tubercular Agents as a Multi-Target Inhibitors

Abstract

Tuberculosis or Mycobacterium tuberculosis is one of the most infectious disease around the world. Inspite of large number of development in the field of science and research today has reached at a pandemic stage. It kills about 1.3 million people per in the world with a high prevalence ratio. Therefore with this high sensitivity has made researchers to develop the new drugs or scaffolds in the field of drug discovery and development. In the current study the computational studies were carried for the development of new lead compound. Here, the computational technique was carried out i.e. Pharmacophore mapping, Virtual screening, Molecular Docking and 3D QSAR studies. The studies were carried out by targeting three inhibitors i.e. FtsZ(filamentous temperature sensitive mutant Z gene), DNA Gyrase and Mycolic acid biosynthesis acting against the mycobacterium tuberculosis. Out of the multiple inhibitors that were chosen the two targets FtsZ and DNA Gyrase were acting extracellularly on the cell wall while the mycolic acid biosynthesis (InhA) enzyme acts intracellularly on the cell wall. The three of the targets acts on the cell wall synthesis. The set of diverse compounds were taken respectively for multiple inhibitors and the Pharmacophore models were developed. The best model was generated by using the GALAHAD module of SYBYL X with the good pharmacophoric features. Further, validation of the model and using the ZINC database the virtual screening was carried out. After the results of virtual screening the model with good Qfit value was taken for the SURFLEX dock. The common amino acid residues were obtained and was compared with standard drug in order to check the number of hydrogen bond interactions. Lastly, the series of benzimidazole, quinolones and quinolines were taken for the QSAR studies. The Comparative molecular field analysis (CoMFA) and Comparative molecular similarity indices analysis (CoMSIA) studies were performed. Using the statistical analysis and regression analysis parameters the best models was produced having the good correlation and coefficient. The models were validated by the means of partial least square analysis (PLS). Hence, based on the computational studies benzimidazole moiety was obtained. It is known that benzimidazole acting both on gram positive as well as gram negative bacteria and has the bactericidal effect. It acts on the cell wall bio-synthesis in both ways and therefore can be considered as the promising lead molecule for the mycobacterium tuberculosis.