Martinez-Guerrero Lucy J, Vignaux Patricia A, Farrera Dominique O, Harris Joshua S, Raman Renuka, Lane Thomas R, Wright Stephen H, Ekins Sean, Cherrington Nathan J
College of Pharmacy, Department of Pharmacology & Toxicology, University of Arizona, Tucson, Arizona.
Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina.
J Pharmacol Exp Ther. 2025 Sep;392(9):103660. doi: 10.1016/j.jpet.2025.103660. Epub 2025 Jul 15.
Equilibrative nucleoside transporters (ENTs) facilitate the equilibrative movement of nucleosides and nucleobases across cell membranes in a sodium-independent manner. ENT1 (SLC29A1) and ENT2 (SLC29A2) also transport nucleoside analogs and can affect the pharmacokinetics and pharmacodynamics of drugs used in cancer, viral infections, and inflammatory disorders. ENT1 and ENT2 may be differentiated functionally by their sensitivity to inhibition by nitrobenzylthioinosine (NBMPR), and we used this difference in NBMPR sensitivity to create a HeLa-based ENT2 inhibition assay. We then screened a library of 1600 diverse compounds composed of drugs and natural products for inhibition against ENT1 and ENT2, selecting a subset of compounds for side-by-side comparison of dose-response studies. We used these screening data to build machine learning models for ENT1 and ENT2 inhibition, employing dataset balancing and conformal prediction to adjust for the asymmetrical nature of the data. A random forest model predicted a prospective test set of 44 additional molecules (from the MedChem Express Drug Repurposing Library [2700 compounds]) as potential ENT1 inhibitors with 59% accuracy. This resulted in the identification of the Food and Drug Administration-approved drugs isradipine, avanafil, and istradefylline as inhibitors of ENT1. These new experimental and computational methods and models for these clinically relevant transporters can be used to evaluate drug-transporter interactions early in drug discovery, before testing in vivo. SIGNIFICANCE STATEMENT: Recent regulatory guidance have suggest the inclusion of the equilibrative nucleoside transporters (eg, ENT1 and ENT2) as transporters with emerging clinical relevance for in vitro and in vivo assessment. We have screened over 1600 diverse molecules, allowing us to build machine learning models that in turn were further used to make predictions to validate the models. Our combined experimental and machine learning approach resulted in the identification of multiple Food and Drug Administration-approved medications as inhibitors of ENT1 or ENT2.
平衡核苷转运体（ENTs）以不依赖钠的方式促进核苷和核碱基跨细胞膜的平衡转运。ENT1（SLC29A1）和ENT2（SLC29A2）也转运核苷类似物，并可影响用于癌症、病毒感染和炎症性疾病的药物的药代动力学和药效学。ENT1和ENT2在功能上可能因其对硝基苄硫肌苷（NBMPR）抑制的敏感性而有所不同，我们利用NBMPR敏感性的这种差异创建了基于HeLa细胞的ENT2抑制试验。然后，我们筛选了一个由药物和天然产物组成的包含1600种不同化合物的文库，以寻找对ENT1和ENT2的抑制作用，选择了一组化合物进行剂量反应研究的并排比较。我们利用这些筛选数据构建了用于ENT1和ENT2抑制的机器学习模型，采用数据集平衡和共形预测来调整数据的不对称性质。一个随机森林模型预测了另外44个分子（来自MedChem Express药物重新利用文库[2700种化合物]）的前瞻性测试集为潜在的ENT1抑制剂，准确率为59%。这导致确定了美国食品药品监督管理局批准的药物伊拉地平、阿伐那非和异他林为ENT1的抑制剂。这些针对这些临床相关转运体的新实验和计算方法及模型可用于在体内测试之前的药物发现早期评估药物-转运体相互作用。意义声明：最近的监管指南建议将平衡核苷转运体（如ENT1和ENT2）纳入具有新兴临床相关性的用于体外和体内评估的转运体。我们筛选了1600多种不同的分子，从而构建了机器学习模型，这些模型进而被进一步用于进行预测以验证模型。我们结合实验和机器学习的方法导致确定了多种美国食品药品监督管理局批准的药物为ENT1或ENT2的抑制剂。
