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Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach
Drug Design, Development and Therapy ( IF 4.8 ) Pub Date : 2022-09-09 , DOI: 10.2147/dddt.s366423
Heba Abdel-Halim 1 , Malak Hajar 1 , Luma Hasouneh 1 , Suzanne M A Abdelmalek 2
Affiliation  

Purpose: The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs’ possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations.
Methods: Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands’ binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit.
Results: Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme.
Conclusion: In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.

Keywords: molecular dynamics simulations, ligand docking, multiple binding sites, drug synergy, SARS-CoV-2


中文翻译:

SARS-CoV-2 药物组合疗法的鉴定:分子动力学模拟方法

目的:由 SARS-Coronavirus-2 引起的冠状病毒传染病 19 (COVID-19) 的有效治疗方法的开发因大流行开始时有关该病毒的数据很少而受到阻碍。药物再利用为探索已批准药物可能的 SARS-CoV-2 抗病毒活性提供了一个良好的策略。此外,药物协同作用在抗病毒治疗中至关重要,因为它可以提高疗效并降低毒性。在这项工作中,我们研究了已批准和在研药物对 SARS-CoV-2 必需蛋白之一(主要蛋白酶 (M pro ))的影响,以寻找抗病毒治疗和/或药物组合。
方法:根据包含 4000 多种化合物的内部库来鉴定和筛选M pro的不同可能药物位点。进行分子动力学模拟以探索不同配体结合引起的构象变化。随后,使用 3CL 蛋白酶 (SARS-CoV-2) 检测试剂盒确定了已鉴定化合物和建议药物组合对 M pro的抑制作用。
结果:鉴定出三个不同结合位点的三种潜在抑制剂;法匹拉韦、头孢克肟和卡维地洛。分子动力学模拟预测了两种药物组合的协同作用:法匹拉韦/头孢克肟和法匹拉韦/卡维地洛。建立了预测的药物组合对该酶的体外抑制作用。
结论:在这项工作中,我们可以研究一种有前景的 SARS-CoV-2 病毒蛋白靶标,以寻找 COVID-19 的治疗方法。通过计算机和体外测定确定了几种药物对 M pro的抑制作用。分子动力学模拟在预测药物组合的协同效应方面显示出有希望的结果。

关键词:分子动力学模拟,配体对接,多结合位点,药物协同,SARS-CoV-2
更新日期:2022-09-09
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