Prediction of immobilized artificial membrane-liquid chromatography retention of some drugs from their molecular...

In this work multiple linear regression (MLR) was carried out for the prediction of immobilized artificial membrane (IAM) retention factors of 40 basic and neutral drugs in two mobile phase compositions. We developed some MLR models by using linear free energy relationships (LFER) parameters and also theoretically derived molecular descriptor. Root mean square error of MLR model in prediction of log k(wPBS)(IAM) and k(wMOPS)(IAM) are 0.332 and 0.351, respectively, while these values are 0.371 and 0.500 for LFER models. Inspections to these values indicate that the statistical parameters of MLR models are better than LFER models. The credibility of MLR models was evaluated by using leave-many-out cross-validation and y-scrambling procedures. The results of these tests indicate the applicability of theoretically derived molecular descriptors and LFER parameters prediction of IAM retention of drugs.