Quantitative Structure–Activity Relationship and Quantitative Structure–Pharmacokinetics Relationship of 1,4-Dihydropyridines and Pyridines as Multidrug Resistance Modulators

Purpose

The aim of this study was to develop quantitative structure–activity/pharmacokinetic relationships (QSAR/QSPKR) for a series of synthesized 1,4-dihydropyridines (DHPs) and pyridines as P-glycoprotein (P-gp) inhibitors.

Methods

Molecular descriptors of test compounds were generated by 3D molecular modeling using SYBYL and KowWin programs. Forward inclusion coupled with multiple linear regression (MLR) was used to derive a QSAR equation for Ca²⁺ channel binding. A multivariate statistical technique, partial least square (PLS) regression, was applied to derive a QSAR model for P-gp inhibition and QSPKR models. Cross-validation using the “leave-one-out” method was performed to evaluate the predictive performance of models.

Results

For Ca²⁺ channel binding, the MLR equation indicated a good correlation between observed and predicted values (R² = 0.90), and cross-validation confirmed the predictive ability of the model (Q² = 0.67). For P-gp reversal, the model obtained by PLS could account for most of the variation in P-gp inhibition (R² = 0.76) with fair predictive performance (Q² = 0.62). Nine structurally related 1,4-DHP drugs were used for QSPKR analysis. The models could explain the majority of the variation in clearance (R² = 0.90), and cross-validation confirmed the prediction ability (Q² = 0.69).

Conclusion

QSAR/QSPKR models were developed, and the QSAR models were capable of identifying synthesized 1,4-DHPs and pyridines with potent P-gp inhibition and reduced Ca²⁺ channel binding. The QSPKR models provide insight into the contribution of electronic, steric, and lipophilic factors to the clearance of DHPs.