Iman, M., Davood, A., Khamesipour, A. (2015). Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR. Iranian Journal of Pharmaceutical Research, 14(3), 775-784.
Maryam Iman; Asghar Davood; Ali Khamesipour. "Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR". Iranian Journal of Pharmaceutical Research, 14, 3, 2015, 775-784.
Iman, M., Davood, A., Khamesipour, A. (2015). 'Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR', Iranian Journal of Pharmaceutical Research, 14(3), pp. 775-784.
Iman, M., Davood, A., Khamesipour, A. Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR. Iranian Journal of Pharmaceutical Research, 2015; 14(3): 775-784.
Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR
1Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
2Department of Medicinal Chemistry, Pharmaceutical Sciences branch, Islamic Azad University, Tehran, Iran
3Center for Research and Training in Skin Diseases and Leprosy, Tehran University of Medical Sciences, Tehran, Iran.
Abstract
Malaria is a parasitic disease with limited chemotherapy options. Chemotherapy options are limited; moreover, drug resistant frequently occurs. The speed of drug development should be faster to overcome the emerging drug resistance. In the current study, a series of quinolone derivatives were subjected to quantitative structure activity relationship to identify the ideal physicochemical characteristics of potential anti-malaria activities. Quinolone with desirable properties was built using HyperChem program, and conformational studies were performed through the semi-empirical method followed by the PM3 force field. Multi linear regression (MLR) was used as a chemo metric tool for quantitative structure activity relationship modeling and the developed models were shown to be statistically significant according to the validation parameters. The obtained QSAR model reveals that the descriptors PJI2, Mv, PCR, nBM, and VAR mainly affect the anti-malaria activity and descriptors MSD, MAXDP, and X1sol affect the cytotoxicity of the series of ligands.
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