Drug repurposing screening validated by experimental assays identifies two clinical drugs targeting SARS-CoV-2 main protease

Denis N Prada Gori, S. Ruatta, Martín Fló, L. Alberca, C. Bellera, Soonju Park, Jinyeong Heo, Honggun Lee, K. P. Park, O. Pritsch, D. Shum, M. Comini, A. Talevi
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Abstract

The COVID-19 pandemic prompted several drug repositioning initiatives with the aim to rapidly deliver pharmacological candidates able to reduce SARS-CoV-2 dissemination and mortality. A major issue shared by many of the in silico studies addressing the discovery of compounds or drugs targeting SARS-CoV-2 molecules is that they lacked experimental validation of the results. Here we present a computer-aided drug-repositioning campaign against the indispensable SARS-CoV-2 main protease (MPro or 3CLPro) that involved the development of ligand-based ensemble models and the experimental testing of a small subset of the identified hits. The search method explored random subspaces of molecular descriptors to obtain linear classifiers. The best models were then combined by selective ensemble learning to improve their predictive power. Both the individual models and the ensembles were validated by retrospective screening, and later used to screen the DrugBank, Drug Repurposing Hub and Sweetlead libraries for potential inhibitors of MPro. From the 4 in silico hits assayed, atpenin and tinostamustine inhibited MPro (IC50 1 µM and 4 μM, respectively) but not the papain-like protease of SARS-CoV-2 (drugs tested at 25 μM). Preliminary kinetic characterization suggests that tinostamustine and atpenin inhibit MPro by an irreversible and acompetitive mechanisms, respectively. Both drugs failed to inhibit the proliferation of SARS-CoV-2 in VERO cells. The virtual screening method reported here may be a powerful tool to further extent the identification of novel MPro inhibitors. Furthermore, the confirmed MPro hits may be subjected to optimization or retrospective search strategies to improve their molecular target and anti-viral potency.
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经实验验证的药物再利用筛选鉴定出两种靶向SARS-CoV-2主要蛋白酶的临床药物
COVID-19大流行促使了几项药物重新定位举措,目的是快速提供能够减少SARS-CoV-2传播和死亡率的候选药物。许多关于发现针对SARS-CoV-2分子的化合物或药物的计算机研究的一个主要问题是,它们缺乏对结果的实验验证。在这里,我们提出了一种针对必不可少的SARS-CoV-2主要蛋白酶(MPro或3CLPro)的计算机辅助药物重新定位运动,该运动涉及基于配体的集合模型的开发和对已确定的一小部分命中的实验测试。该搜索方法对分子描述符的随机子空间进行搜索,得到线性分类器。然后通过选择性集成学习将最佳模型组合起来以提高其预测能力。通过回顾性筛选验证了个体模型和集合,随后用于筛选DrugBank、Drug Repurposing Hub和Sweetlead库中潜在的MPro抑制剂。在4个实验中,atpenin和tinostamustine抑制MPro (IC50分别为1 μM和4 μM),但对SARS-CoV-2的木瓜蛋白酶没有抑制作用(药物测试在25 μM)。初步的动力学表征表明,丁司莫司汀和atpenin分别通过不可逆机制和竞争机制抑制MPro。这两种药物都未能抑制SARS-CoV-2在VERO细胞中的增殖。本文报道的虚拟筛选方法可能是进一步鉴定新型MPro抑制剂的有力工具。此外,已确认的MPro命中可以进行优化或回顾性搜索策略,以提高其分子靶点和抗病毒效力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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