QSAR based therapeutic management of M-tuberculosis

Mycobacterium tuberculosis is responsible for severe mortality and morbidity worldwide but, under-developed and developing countries are more prone to infection. In search of effective and wide-spectrum anti-tubercular agents, interdisciplinary approaches are being explored. Of the several approaches used, computer based quantitative structure activity relationship (QSAR) have gained momentum. Structure-based drug design and discovery implies a combined knowledge of accurate prediction of ligand poses with the good prediction and interpretation of statistically validated models derived from the 3D-QSAR approach. The validated models are generally used to screen a small combinatorial library of potential synthetic candidates to identify hits which further subjected to docking to filter out compounds as novel potential emerging drug molecules to address multidrug-resistant tuberculosis. Several newer models are integrated to QSAR methods which include different types of chemical and biological data, and simultaneous prediction of pharmacological activities including toxicities and/or other safety profiles to get new compounds with desired activity. In the process, several newer molecules have been identified which are now being assessed for their clinical efficacy. Present review deals with the advances made in the field highlighting overall future prospects of the development of anti-tuberculosis drugs.