Shirin Mollazadeh - Department of Medicinal Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
Jamal Shamsara - Pharmaceutical Research Center, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
Maryam Iman - Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Farzin Hadizadeh - Biotechnology Research Center, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran

Introduction: Multidrug resistance (MDR) of cancer cells has become a great barrier to the success of chemotherapy. In this study a series of symmetrical and unsymmetrical 1,4-Dihydropyridines (DHPs) were investigated as multidrug resistance inhibitors. A QSAR study using multiple linear regression (MLR) was carried out to find the important factors on the MDR reversing ability of data compounds in cancer. Materials and methods: Structures (46 DHPs) were optimized by the sybyl software. Descriptor generation was done by DRAGON. SPSS and MATLAB programs have been used for performing MLR analyses and theoretical model validation. Kennard-stone strategy was employed for splitting data set into train and test sets. Results: Four descriptors (CIC4, MATS7e, RDF060m, RDF080m) significantly correlated with -log(Cc50) values according to stepwise regression. The MLR model for MDR inhibitory activity indicated a correlation between experimental and predicted activity for training set was suitable (R2=0.73) and external validation metric for the test set was favorable (R2pred=0.7) and cross validation was good (q2loo= 0.64). Conclusion: The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of 1,4-Dihydropyridines as potent MDR inhibitors.

Quantitative structure activity relationship, Multidrug resistance, P-glycoprotein, Dihydropyridine, Multiple linear regression