Identification of novel MRP3 inhibitors based on computational models and validation using anin vitromembrane vesicle assay

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Abstract

Introduction:

Multidrug resistance-associated protein 3 (MRP3), an efflux transporter on the hepatic basolateral membrane, may function as a compensatory mechanism to prevent the accumulation of anionic substrates (e.g., bile acids) in hepatocytes. Inhibition of MRP3 may disrupt bile acid homeostasis and is one hypothesized risk factor for the development of drug-induced liver injury (DILI). Therefore, identifying potential MRP3 inhibitors could help mitigate the occurrence of DILI.

Methods:

Bayesian models were developed using MRP3 transporter inhibition data for 86 structurally diverse drugs. The compounds were split into training and test sets of 57 and 29 compounds, respectively, and six models were generated based on distinct inhibition thresholds and molecular fingerprint methods. The six Bayesian models were validated against the test set and the model with the highest accuracy was utilized for a virtual screen of 1470 FDA-approved drugs from DrugBank. Compounds that were predicted to be inhibitors were selected for in vitro validation. The ability of these compounds to inhibit MRP3 transport at a concentration of 100 μM was measured in membrane vesicles derived from stably transfected MRP3-over-expressing HEK-293 cells with [3H]-estradiol-17β-d-glucuronide (E217G; 10 μM; 5 min uptake) as the probe substrate.

Results:

A predictive Bayesian model was developed with a sensitivity of 73% and specificity of 71% against the test set used to evaluate the six models. The area under the Receiver Operating Characteristic (ROC) curve was 0.710 against the test set. The final selected model was based on compounds that inhibited substrate transport by at least 50% compared to the negative control, and functional-class fingerprints (FCFP) with a circular diameter of six atoms, in addition to one-dimensional physicochemical properties. The in vitro screening of predicted inhibitors and non-inhibitors resulted in similar model performance with a sensitivity of 64% and specificity of 70%. The strongest inhibitors of MRP3-mediated E217G transport were fidaxomicin, suramin, and dronedarone. Kinetic assessment revealed that fidaxomicin was the most potent of these inhibitors (IC50 = 1.83 ± 0.46 μM). Suramin and dronedarone exhibited IC50 values of 3.33 ± 0.41 and 47.44 ± 4.41 μM, respectively.

Conclusion:

Bayesian models are a useful screening approach to identify potential inhibitors of transport proteins. Novel MRP3 inhibitors were identified by virtual screening using the selected Bayesian model, and MRP3 inhibition was confirmed by an in vitro transporter inhibition assay. Information generated using this modeling approach may be valuable in predicting the potential for DILI and/or MRP3-mediated drug-drug interactions.

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