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Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during
Melisa E Gantner1, Roxana N Peroni2, Juan F Morales1
1Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Departamento de Ciencias Biológicas, Facultad de Ciencias Exactas, Universidad Nacional de La Plata (UNLP) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , La Plata, B1900AJI Buenos Aires, Argentina.
Abstract:
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 these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear 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 as nonsubstrates 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 an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.
Insights
A new computational model predicts Breast Cancer Resistance Protein (BCRP) substrates, aiding drug discovery. This model helps identify drug interactions and overcome multidrug resistance by filtering potential BCRP substrates early.
Area of Science:
- Pharmacology
- Computational Chemistry
- Drug Discovery
Background:
- Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter implicated in multidrug resistance and drug interactions.
- Early identification of BCRP substrates and nonsubstrates is crucial in drug discovery to mitigate resistance and adverse effects.
Purpose of the Study:
- To develop and validate a computational nonlinear model ensemble for predicting BCRP substrates and nonsubstrates.
- To provide an in silico tool for computer-aided drug discovery to address BCRP-mediated multidrug resistance and drug-drug interactions.
Main Methods:
- Development of a computational nonlinear model ensemble using conformational independent molecular descriptors.
- Combined strategy involving genetic algorithms, J48 decision tree classifiers, and data fusion.
- Experimental validation using the ex vivo everted rat intestinal sac model.
Main Results:
- The best model ensemble averaged the rankings of 12 decision trees, showing good performance on training and test sets.
- Five anticonvulsant drugs predicted as nonsubstrates by the model were experimentally confirmed as nonsubstrates.
- The model ensemble demonstrated significant predictive ability for BCRP substrate identification.
Conclusions:
- The developed model ensemble is a valuable in silico ADME filter for drug discovery.
- This tool can aid in overcoming BCRP-mediated multidrug resistance and preventing drug-drug interactions.
- The findings support the early screening of drug candidates for BCRP interaction in silico.
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