Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents.

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (rext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC50 values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

Molecular docking-based interactions in QSAR studies on Mycobacterium tuberculosis ATP synthase inhibitors.

Tuberculosis (TB) is a global threat with a large burden across the continents in terms of mortality, morbidity, and financial losses. The disease has evolved into multi-drug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) tuberculosis owing to numerous factors ranging from patients' non-compliance to demographical implications. There have been very few new drugs for resistant TB. Resistance has already been reported even for the newly introduced drug bedaquiline. An attempt has been made to integrate both structure-based and QSAR drug design techniques (QSAR-SBDD) for the identification of novel leads. The docking scores normally do not correlate with the activity. Hence, the docking results have been analysed in terms of the number of interactions rather than docking scores. The parameters derived from interactions have been used in developing the QSAR models. The best model shows a good correlation (r = 0.908) between the activity and interaction parameter 'C' describing the sum of all the interactions with each amino acid residue. This model also predicts external dataset with a good correlation (rext = 0.851) and can be used for the identification of novel chemical entities (NCEs) and repurposed drugs for TB therapeutics.