Luo Ding, Sha Zhou, Mao Junli, Liu Jialing, Zhou Yue, Wu Haibo, Xue Weiwei
School of Pharmaceutical Sciences, Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Chongqing University, Chongqing, 401331, China.
School of Life Sciences, Chongqing University, Chongqing, 401331, China.
J Pharm Anal. 2025 Aug;15(8):101368. doi: 10.1016/j.jpha.2025.101368. Epub 2025 Jun 14.
Computational approaches, encompassing both physics-based and machine learning (ML) methodologies, have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities. The human dopamine (DA) transporter (hDAT) is the primary therapeutic target of numerous psychiatric medications. However, traditional hDAT-targeting drugs, which interact with the primary binding site, encounter significant limitations, including addictive potential and stimulant effects. In this study, we propose an integrated workflow combining virtual screening based on weighted holistic atom localization and entity shape (WHALES) descriptors with experimental validation to repurpose novel hDAT-targeting drugs. Initially, WHALES descriptors facilitated a similarity search, employing four benztropine-like atypical inhibitors known to bind hDAT's allosteric site as templates. Consequently, from a compound library of 4,921 marketed and clinically tested drugs, we identified 27 candidate atypical inhibitors. Subsequently, ADMETlab was employed to predict the pharmacokinetic and toxicological properties of these candidates, while induced-fit docking (IFD) was performed to estimate their binding affinities. Six compounds were selected for assessments of neurotransmitter reuptake inhibitory activities. Among these, three exhibited significant inhibitory potency, with half maximal inhibitory concentration (IC) values of 0.753 μM, 0.542 μM, and 1.210 μM, respectively. Finally, molecular dynamics (MD) simulations and end-point binding free energy analyses were conducted to elucidate and confirm the inhibitory mechanisms of the repurposed drugs against hDAT in its inward-open conformation. In conclusion, our study not only identifies promising active compounds as potential atypical inhibitors for novel therapeutic drug development targeting hDAT but also validates the effectiveness of our integrated computational and experimental workflow for drug repurposing.
计算方法,包括基于物理的方法和机器学习(ML)方法,在针对特定治疗实体的药物再利用研究中已获得广泛关注。人类多巴胺(DA)转运体(hDAT)是众多精神科药物的主要治疗靶点。然而,传统的靶向hDAT的药物与主要结合位点相互作用,存在显著局限性,包括成瘾潜力和刺激作用。在本研究中,我们提出了一种综合工作流程,将基于加权整体原子定位和实体形状(WHALES)描述符的虚拟筛选与实验验证相结合,以寻找新的靶向hDAT的药物。首先,WHALES描述符促进了相似性搜索,使用四种已知可结合hDAT变构位点的苯海索样非典型抑制剂作为模板。因此,从一个包含4921种上市和临床测试药物的化合物库中,我们鉴定出27种候选非典型抑制剂。随后,使用ADMETlab预测这些候选物的药代动力学和毒理学性质,同时进行诱导契合对接(IFD)以估计它们的结合亲和力。选择了六种化合物进行神经递质再摄取抑制活性评估。其中,三种表现出显著的抑制效力,半数最大抑制浓度(IC)值分别为0.753μM、0.542μM和1.210μM。最后,进行分子动力学(MD)模拟和终点结合自由能分析,以阐明并确认再利用药物对处于向内开放构象的hDAT的抑制机制。总之,我们的研究不仅鉴定出有前景的活性化合物作为靶向hDAT的新型治疗药物开发的潜在非典型抑制剂,还验证了我们用于药物再利用的综合计算和实验工作流程的有效性。