Identification of IrtAB inhibitors as novel therapeutic agents against drug-resistant Mycobacterium tuberculosis.

The global emergence of multidrug-resistant Mycobacterium tuberculosis necessitates innovative therapeutic strategies targeting essential metabolic pathways. This study employs an integrated computational approach to identify inhibitors of IrtAB, a critical iron transporter in Mtb, leveraging structure-based drug design to overcome existing resistance mechanisms. Virtual screening of 201,544 compounds from the ZINC20 database was performed using defined drug-likeness criteria (MW 300-350 Da, LogP 1-3.5, neutral charge at pH 7.4), followed by molecular docking. Three lead compounds demonstrated high-affinity binding to IrtAB's histidine-rich substrate pocket (His356/402/407): ZINC000225478236 (DPTU, -8.55 kcal/mol), featuring a triazole-urea scaffold; ZINC000038021820 (DMPA-HCl, -8.18 kcal/mol), with optimized morpholine-propylamino acid pharmacophores; and ZINC000004689680 (TMSPI, -8.16 kcal/mol), a spirocyclic indole derivative. Principal Component Analysis (PCA) of top candidates revealed significant correlations between binding affinity (S) and molecular descriptors (R²=0.819 for PCA1). Molecular dynamics simulations (50 ns, OPLS3e force field) confirmed complex stability, with DMPA-HCl showing superior rigidity (RMSD <0.8 Å). Comprehensive ADMET profiling using SwissADME and ProTox-3.0 predicted favorable pharmacokinetics (Lipinski compliance, high GI absorption) and low toxicity risks for DMPA-HCl (hepatotoxicity probability=0.61). While these computational results identify promising IrtAB inhibitors, key limitations include the relatively short duration of MD simulations (50 ns), which may not fully capture long-term protein-ligand interaction dynamics, and the lack of experimental validation to confirm the biological efficacy and safety of the candidate compounds. This work establishes a rational foundation for developing novel antitubercular agents targeting iron acquisition, with DMPA-HCl emerging as the prime candidate for further optimization.