Ligandand Structure-Based Classification Models forPrediction of P-Glycoprotein Inhibitors

摘要

The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitoroninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external testset compounds. Classification based on the docking experiments usingthe scoring function ChemScore resulted in the correct predictionof 61% of the external test set. This demonstrates that ligand-basedmodels currently remain the methods of choice for accurately predictingP-gp inhibitors. However, structure-based classification offers informationabout possible drug/protein interactions, which helps in understandingthe molecular basis of ligand-transporter interaction and could thereforealso support lead optimization.