Identification of novel selective <i>Mtb</i>‐DHFR inhibitors as antitubercular agents through structure‐based computational techniques

Abstract

Inhibition of dihydrofolate reductase from Mycobacterium tuberculosis-dihydrofolate reductase (Mtb-DHFR) has emerged as a promising approach for the treatment of tuberculosis. To identify novel Mtb-DHFR inhibitors, structure-based virtual screening (SBVS) of the Molecular Diversity Preservation International (MolMall) database was performed using Glide against the Mtb-DHFR and h-DHFR enzymes. On the basis of SBVS, receptor fit, drug-like filters, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, 16 hits were selected and tested for their antitubercular activity against the H₃₇ R_V strain of M. tuberculosis. Five compounds showed promising activity with compounds 11436 and 15275 as the most potent hits with IC₅₀ values of 0.65 and 12.51 μM, respectively, against the H₃₇ R_V strain of M. tuberculosis. The two compounds were further tested in the Mtb-DHFR and h-DHFR enzymatic assay for selectivity and were found to be three- to eight-fold selective towards Mtb-DHFR over h-DHFR with minimum inhibitory concentration values of 5.50, 73.89 µM and 42.00, 263.00 µM, respectively. In silico simulation studies also supported the stability of the protein-ligand complex formation. The present study demonstrates the successful utilization of in silico SBVS tools for the identification of novel and potential Mtb-DHFR inhibitors and compound 11436 ((2,4-dihydroxyphenyl)(3,4,5-trihydroxyphenyl)methanone) as a potential lead for the development of novel Mtb-DHFR inhibitors.