Prediction Models of P-Glycoprotein Substrates Using Simple 2D and 3D Descriptors by a Recursive Partitioning Approach

Abstract

E-mail: kyn@youai.co.krReceived October 31, 2011, Accepted December 21, 2011P-gp (P-glycoprotein) is a member of the ATP binding cassette (ABC) family of transporters. It transportsmany kinds of anticancer drugs out of the cell. It plays a major role as a cause of multidrug resistance (MDR).MDR function may be a cause of the failure of chemotherapy in cancer and influence pharmacokineticproperties of many drugs. Hence classification of candidate drugs as substrates or nonsubstrate of the P-gp isimportant in drug development. Therefore to identify whether a compound is a P-gp substrate or not, in silicomethod is promising. Recursive Partitioning (RP) method was explored for prediction of P-gp substrate. A setof 261 compounds, including 146 substrates and 115 nonsubstrates of P-gp, was used to training and validation.Using molecular descriptors that we can interpret their own meaning, we have established two models forprediction of P-gp substrates. In the first model, we chose only 6 descriptors which have simple physicalmeaning. In the training set, the overall predictability of our model is 78.95%. In case of test set, overallpredictability is 69.23%. Second model with 2D and 3D descriptors shows a little better predictability (overallpredictability of training set is 79.29%, test set is 79.37%), the second model with 2D and 3D descriptors showsbetter discriminating power than first model with only 2D descriptors. This approach will be used to reduce thenumber of compounds required to be run in the P-gp efflux assay.Key Words : ADME prediction, P-Glycoprotein, Recursive partitioning, 2D Descriptors, 3D DescriptorsIntroductionAbsorption, distribution, metabolism, excretion, and toxi-city (ADMET) properties are very important in the drugdiscovery.