Development and validation of a computational model ensemble for the early detection of BCRP/ABCG2 substrates during the drug design stage

MELISA E. GANTNER; ROXANA PERONI; JUAN F. MORALES; MARÍA L. VILLALBA; MARÍA E. RUIZ; ALAN TALEVI

JOURNAL OF CHEMICAL INFORMATION AND MODELING

AMER CHEMICAL SOC

Lugar: Washington; Año: 2017 vol. 57

1549-9596

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer, and a potential source of drug interactions. For those reasons, the early identification of substrates and non-substrates of this transporter during the drug discovery stage is of great interest.We have developed a computational non-linear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified asnon-substrates for BRCP by the model ensemble were experimentally evaluated and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.