Three-Dimensional Quantitative Structure−Activity Relationship of 1,4-Dihydropyridines As Antitubercular Agents

• Published in: Journal of medicinal chemistry 2002-10, Vol.45 (22), p.4858-4867
• Main Authors: Kharkar, Prashant S, Desai, Bhavik, Gaveria, Harsukh, Varu, Bharat, Loriya, Rajesh, Naliapara, Yogesh, Shah, Anamik, Kulkarni, Vithal M
• Format: Article
• Language: English
• Subjects: Antibacterial agents; Antibiotics. Antiinfectious agents. Antiparasitic agents; Antitubercular Agents - chemistry; Biological and medical sciences; Dihydropyridines - chemistry; Medical sciences; Models, Molecular; Mycobacterium tuberculosis; Pharmacology. Drug treatments; Quantitative Structure-Activity Relationship
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm020217z

Three-dimensional quantitative structure−activity relationship (3D QSAR) methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were applied on a series of 1,4-dihydropyridines possessing antitubercular activity. The study was performed using 33 compounds, in which 22 molecules were used for the derivation of the 3D QSAR models (training set) and 11 molecules were used to evaluate the predictive ability of the derived models (test set). Superimpositions were performed using three alignment rules:  atom-based fitting, SYBYL QSAR rigid body field fit of the steric and electrostatic fields of the molecules, and flexible fitting (multifit). Both methods were analyzed in terms of their predictive abilities and produced comparable results with high internal as well as external predictivities. Steric and electrostatic fields of the inhibitors were found to be relevant descriptors for SAR. Use of lowest unoccupied molecular orbital energies or ClogP as additional descriptors in the QSAR table did not improve the significance of the 3D QSAR models. Both CoMFA and CoMSIA models based on multifit alignment showed better correlative and predictive properties than other models. A QSAR study using genetic function approximation was also performed for the same set of molecules using different types of physicochemical descriptors to deal with cell-based activity data. The QSAR models revealed the importance of spatial properties and conformational flexibility of side chains for antitubercular activity. Inclusion of fractional polar solvent accessible surface area as a descriptor in the model generation resulted in models with significant internal and external predictivities for the same test set molecules, which may support the possible mode of action of these compounds.