Breast cancer resistance protein (ABCG2) is a human ATP-binding cassette (ABC) that plays a paramount role in multidrug resistance (MDR) in cancer therapy. The discovery of ABCG2 inhibitors could assist in designing unprecedented therapeutic strategies for cancer treatment. There is as yet no approved drug targeting ABCG2, although a large number of drug candidates have been clinically investigated. In this work, binding affinities of 181 drug candidates in clinical-trial or investigational stages as ABCG2 inhibitors were inspected using in silico techniques. Based on available experimental data, the performance of AutoDock4.2.6 software was first validated to predict the inhibitor-ABCG2 binding mode and affinity. Combined molecular docking calculations and molecular dynamics (MD) simulations, followed by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations, were then performed to filter out the studied drug candidates. From the estimated docking scores and MM-GBSA binding energies, six auspicious drug candidates—namely, pibrentasvir, venetoclax, ledipasvir, avatrombopag, cobicistat, and revefenacin—exhibited auspicious binding energies with value < −70.0 kcal/mol. Interestingly, pibrentasvir, venetoclax, and ledipasvir were observed to show even higher binding affinities with the ABCG2 transporter with binding energies of < −80.0 kcal/mol over long MD simulations of 100 ns. The stabilities of these three promising candidates in complex with ABCG2 transporter were demonstrated by their energetics and structural analyses throughout the 100 ns MD simulations. The current study throws new light on pibrentasvir, venetoclax, and ledipasvir as curative options for multidrug resistant cancers by inhibiting ABCG2 transporter.

乳腺癌耐药蛋白 (ABCG2) 是一种人类 ATP 结合盒 (ABC)，在癌症治疗中的多药耐药性 (MDR) 中起着至关重要的作用。ABCG2 抑制剂的发现有助于设计前所未有的癌症治疗策略。尽管已经对大量候选药物进行了临床研究，但目前还没有批准的靶向 ABCG2 的药物。在这项工作中，使用计算机技术检查了 181 种候选药物在临床试验或研究阶段作为 ABCG2 抑制剂的结合亲和力。基于现有的实验数据，AutoDock4.2.6 软件的性能首先得到验证，可以预测抑制剂-ABCG2 的结合模式和亲和力。结合分子对接计算和分子动力学（MD）模拟，然后进行分子力学广义玻恩表面积 (MM-GBSA) 结合能计算，以筛选出研究的候选药物。根据估计的对接分数和 MM-GBSA 结合能，六种吉祥候选药物——即pibrentasvir、venetoclax、ledipasvir、avatrombopag、cobicistat和revefenacin——表现出的吉祥结合能值<-70.0 kcal/mol。有趣的是，在 100 ns 的长 MD 模拟中，观察到 pibrentasvir、venetoclax 和 ledipasvir 与 ABCG2 转运蛋白显示出更高的结合亲和力，结合能 < -80.0 kcal/mol。在整个 100 ns MD 模拟中，这三个有希望的候选物与 ABCG2 转运蛋白复合物的稳定性通过它们的能量学和结构分析得到证明。