Ali Izna, Welch Matthew A, Lu Yang, Swaan Peter W, Brouwer Kim L R
Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD 21201, USA.
Eur J Pharm Sci. 2017 May 30;103:52-59. doi: 10.1016/j.ejps.2017.02.011. Epub 2017 Feb 24.
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.
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 100μM was measured in membrane vesicles derived from stably transfected MRP3-over-expressing HEK-293 cells with [H]-estradiol-17β-d-glucuronide (E17G; 10μM; 5min uptake) as the probe substrate.
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 E17G transport were fidaxomicin, suramin, and dronedarone. Kinetic assessment revealed that fidaxomicin was the most potent of these inhibitors (IC=1.83±0.46μM). Suramin and dronedarone exhibited IC values of 3.33±0.41 and 47.44±4.41μM, respectively.
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.
多药耐药相关蛋白3（MRP3）是肝基底外侧膜上的一种外排转运蛋白，可能作为一种补偿机制，防止阴离子底物（如胆汁酸）在肝细胞中蓄积。抑制MRP3可能会破坏胆汁酸稳态，是药物性肝损伤（DILI）发生的一个假设风险因素。因此，识别潜在的MRP3抑制剂有助于减轻DILI的发生。
利用86种结构多样药物的MRP3转运蛋白抑制数据建立贝叶斯模型。这些化合物分别被分为57种和29种化合物的训练集和测试集，并基于不同的抑制阈值和分子指纹方法生成了六个模型。这六个贝叶斯模型针对测试集进行了验证，具有最高准确性的模型被用于对DrugBank中1470种FDA批准药物进行虚拟筛选。选择预测为抑制剂的化合物进行体外验证。在以[H]-雌二醇-17β-d-葡萄糖醛酸（E17G；10μM；5分钟摄取）为探针底物、源自稳定转染过表达MRP3的HEK-293细胞的膜囊泡中，测定这些化合物在100μM浓度下抑制MRP3转运的能力。
开发了一种预测性贝叶斯模型，针对用于评估六个模型的测试集，其灵敏度为73%，特异性为71%。针对测试集，受试者操作特征（ROC）曲线下面积为0.710。最终选择的模型基于与阴性对照相比至少抑制底物转运50%的化合物，以及除一维物理化学性质外直径为六个原子的功能类指纹（FCFP）。对预测的抑制剂和非抑制剂进行体外筛选，得到了类似的模型性能，灵敏度为64%，特异性为70%。MRP3介导的E17G转运的最强抑制剂是非达霉素、苏拉明和决奈达隆。动力学评估显示，非达霉素是这些抑制剂中最有效的（IC = 1.83±0.46μM）。苏拉明和决奈达隆的IC值分别为3.33±0.41和47.44±4.41μM。
贝叶斯模型是识别转运蛋白潜在抑制剂的一种有用筛选方法。通过使用选定的贝叶斯模型进行虚拟筛选，鉴定出了新型MRP3抑制剂，并通过体外转运蛋白抑制试验证实了MRP3抑制作用。使用这种建模方法生成的信息对于预测DILI和/或MRP3介导的药物相互作用的可能性可能具有重要价值。
