In silico design of novel quinazoline-based compounds as potential Mycobacterium tuberculosis PknB inhibitors through 2D and 3D-QSAR, molecular dynamics simulations combined with pharmacokinetic predictions

Abstract

Serine/threonine protein kinase B (PknB) is essential to Mycobacterium tuberculosis (M. tuberculosis) cell division and metabolism and a potential anti-tuberculosis drug target. Here we apply Hologram Quantitative Structure Activity Relationship (HQSAR) and three-dimensional QSAR (Comparative Molecular Similarity Indices Analysis (CoMSIA)) methods to investigate structural requirements for PknB inhibition by a series of previously described quinazoline derivatives. PknB binding of quinazolines was evaluated by molecular dynamics (MD) simulations of the catalytic domain and binding energies calculated by Molecular Mechanics/Poisson Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) methods. Evaluation of a training set against experimental data showed both HQSAR and CoMSIA models to reliably predict quinazoline binding to PknB, and identified the quinazoline core and overall hydrophobicity as the major contributors to affinity. Calculated binding energies also agreed with experiment, and MD simulations identified hydrogen bonds to Glu93 and Val95, and hydrophobic interactions with Gly18, Phe19, Gly20, Val25, Thr99 and Met155, as crucial to PknB binding. Based on these results, additional quinazolines were designed and evaluated in silico, with HQSAR and CoMSIA models identifying sixteen compounds, with predicted PknB binding superior to the template, whose activity spectra and physicochemical, pharmacokinetic, and anti-M. tuberculosis properties were assessed. Compound, D060, bearing additional ortho- and meta-methyl groups on its R₂ substituent, was superior to template regarding PknB inhibition and % caseum fraction unbound, and equivalent in other aspects, although predictions identified hepatotoxicity as a likely issue with the quinazoline series. These data provide a structural basis for rational design of quinazoline derivatives with more potent PknB inhibitory activity as candidate anti-tuberculosis agents.

Keywords: 3D-QSAR CoMSIA; Binding energy; HQSAR; MD simulations; PknB inhibitors; Tuberculosis.