Abstract
Introduction: Mycobacterium tuberculosis has instigated a serious challenge toward the effective treatment of tuberculosis. The reoccurrence of the resistant strains of the disease to accessible drugs/medications has mandate for the development of more effective anti-tubercular agents with efficient activities. Time expended and costs in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multidrug resistance strain M. tuberculosis (TB). Meanwhile, to solve the stated problem, a new approach i.e. QSAR which establish connection between novel drugs with a better biological against M. tuberculosis is adopted.
Methods: The anti-tubercular model established in this study to forecast the biological activities of some anti-tubercular compounds selected and to design new hypothetical drugs is subjective to the molecular descriptors; MATS7s, SM1_DzZ, SpMin4_Bhv, TDB3v and RDF70v. Ligand-receptor interactions between quinoline derivatives and the receptor (DNA gyrase) was carried out using molecular docking technique by employing the PyRx virtual screening software and discovery studio visualizer software. Furthermore, docking study indicates that compound 20 of the derivatives with promising biological activity has the utmost binding energy of -17.79 kcal/mol.
Results: Meanwhile, the interaction of the standard drug; isoniazid with the target enzyme was observed with the binding energy -14.6 kcal/mol which was significantly lesser than the binding energy of the ligand (compound 20). Therefore, compound 20 served as a template structure to design compounds with more efficient activities. Among the compounds designed; compounds 20p was observed with better anti-tubercular activities with more prominent binding affinities of - 24.3kcal/mol.
Conclusion: The presumption of this research aid the medicinal chemists and pharmacist to design and synthesize a novel drug candidate against tuberculosis. Moreover, in-vitro and in-vivo test could be carried out to validate the computational results.
Keywords: Model, Triazole QSAR, Tuberculosis, DNA gyrase, SpMin4_Bhv, TDB3v and RDF70v
Graphical Abstract
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