Predicting substrates of the human breast cancer resistance protein using a support vector machine method

Eszter Hazai,Sophie P Chung,Isabelle Ragueneau-Majlessi,Zsolt Bikadi,Istvan Hazai,Qingcheng Mao

doi:10.1186/1471-2105-14-130

Abstract

BackgroundHuman breast cancer resistance protein (BCRP) is an ATP-binding cassette (ABC) efflux transporter that confers multidrug resistance in cancers and also plays an important role in the absorption, distribution and elimination of drugs. Prediction as to if drugs or new molecular entities are BCRP substrates should afford a cost-effective means that can help evaluate the pharmacokinetic properties, efficacy, and safety of these drugs or drug candidates. At present, limited studies have been done to develop in silico prediction models for BCRP substrates.In this study, we developed support vector machine (SVM) models to predict wild-type BCRP substrates based on a total of 263 known BCRP substrates and non-substrates collected from literature. The final SVM model was integrated to a free web server.ResultsWe showed that the final SVM model had an overall prediction accuracy of ~73% for an independent external validation data set of 40 compounds. The prediction accuracy for wild-type BCRP substrates was ~76%, which is higher than that for non-substrates. The free web server (http://bcrp.althotas.com) allows the users to predict whether a query compound is a wild-type BCRP substrate and calculate its physicochemical properties such as molecular weight, logP value, and polarizability.ConclusionsWe have developed an SVM prediction model for wild-type BCRP substrates based on a relatively large number of known wild-type BCRP substrates and non-substrates. This model may prove valuable for screening substrates and non-substrates of BCRP, a clinically important ABC efflux drug transporter.

Highlights

Human breast cancer resistance protein (BCRP) is an ATP-binding cassette (ABC) efflux transporter that confers multidrug resistance in cancers and plays an important role in the absorption, distribution and elimination of drugs
It is important to develop in silico prediction models for BCRP substrates that could be used as cost-effective tools for screening of drug candidates in early drug discovery stage and for identification of BCRP substrates among existing drugs so that potential drug-drug interactions may be predicted
In the present study, using a carefully defined and relatively large data set with 263 known wild-type BCRP substrates and non-substrates, we have developed an support vector machine (SVM) model for prediction of wild-type BCRP substrates and non-substrates with an overall prediction accuracy of ~73% for an independent external validation data set of 40 compounds

Summary

Introduction

Human breast cancer resistance protein (BCRP) is an ATP-binding cassette (ABC) efflux transporter that confers multidrug resistance in cancers and plays an important role in the absorption, distribution and elimination of drugs. The substrates of BCRP have been rapidly expanding to include chemotherapeutics such as mitoxantrone, topotecan and imatinib, and nonchemotherapeutic drugs such as prazosin, glyburide, nitrofurantoin and statins as well as non-therapeutic predicted One of such methods would be the development of in silico models for prediction of BCRP substrates. In the recent years, in silico prediction models have emerged into the pipeline of drug discovery which allow initial screening and selection of promising compounds from chemical libraries and large databases. These models could provide information concerning the mechanism of protein-ligand interactions. Ligand-based methods based on structural similarity of ligands to known substrates generally yield much greater prediction accuracies than protein structure-based methods

Methods

Results

Conclusion