Quantitative structure–retention relationship studies with immobilized artificial membrane chromatography: II: Partial least squares regression

We aimed to establish quantitative structure–retention relationship (QSRR) with immobilized artificial membrane (IAM) chromatography using easily understood and obtained physicochemical molecular descriptors and to elucidate which descriptors are critical to affect the interaction process between solutes and immobilized phospholipid membranes. The retention indices (log kIAM) of 55 structurally diverse drugs were determined on an immobilized artificial membrane column (IAM.PC.DD2) directly or obtained by extrapolation method for highly hydrophobic compounds. Ten simple physicochemical property descriptors (clog P, rings, rotatory bond, hydro-bond counting, etc.) of these drugs were collected and used to establish QSRR and predict the retention data by partial least squares regression (PLSR). Five descriptors, clog P, rotatory bond (RotB), rings, molecular weight (MW) and total surface area (TSA), were reserved by using the Variable Importance for Projection (VIP) values as criterion to build the final PLSR model. An external test set was employed to verify the QSRR based on the training set with the five variables, and QSRR by PLSR exhibited a satisfying predictive ability with Rp = 0.902 and RMSEp = 0.400. Comparison of coefficients of centered and scaled variables by PLSR demonstrated that, for the descriptors studied, clog P and TSA have the most significant positive effect but the rotatable bond has significant negative effect on drug IAM chromatographic retention.