The main protease of SARS-CoV-2 is one of the key targets to develop and design antiviral drugs. There is no general agreement on the use of non-steroidal anti-inflammatory drugs (NSAIDs) in COVID-19. In this study, we investigated NSAIDs as potential inhibitors for chymotrypsin-like protease (3CLpro) and the main protease of the SARS-CoV-2 to find out the best candidates, which can act as potent inhibitors against the main protease. We also predicted the effect of NSAIDs on the arachidonic pathway and evaluated the hepatotoxicity of the compounds using systems biology techniques. Molecular docking was conducted via AutoDock Vina to estimate the interactions and binding affinities between selected NSAIDs and the main protease. Molecular docking results showed the presence of 10 NSAIDs based on lower binding energy (kcal/mol) toward the 3CLpro inhibition site compared to the co-crystal native ligand Inhibitor N3 (−6.6 kcal/mol). To validate the docking results, molecular dynamic (MD) simulations on the top inhibitor, Talniflumate, were performed. To obtain differentially-expressed genes under the 27 NSAIDs perturbations, we utilized the L1000 final Z-scores from the NCBI GEO repository (GSE92742). The obtained dataset included gene expression profiling signatures for 27 NSAIDs. The hepatotoxicity of NSAIDs was studied by systems biology modeling of Disturbed Metabolic Pathways. This study highlights the new application of NSAIDs as anti-viral drugs used against COVID-19. NSAIDs may also attenuate the cytokine storm through the downregulation of inflammatory mediators in the arachidonic acid pathway.

SARS-CoV-2 的主要蛋白酶是开发和设计抗病毒药物的关键靶点之一。对于在 COVID-19 中使用非甾体抗炎药 (NSAID)，尚无普遍共识。在这项研究中，我们研究了 NSAIDs 作为胰凝乳蛋白酶样蛋白酶 (3CLpro) 和 SARS-CoV-2 主要蛋白酶的潜在抑制剂，以找出可以作为主要蛋白酶有效抑制剂的最佳候选药物。我们还预测了 NSAIDs 对花生四烯酸途径的影响，并使用系统生物学技术评估了化合物的肝毒性。通过 AutoDock Vina 进行分子对接，以估计所选 NSAID 与主要蛋白酶之间的相互作用和结合亲和力。分子对接结果显示，与共晶天然配体抑制剂 N3 (-6.6 kcal/mol) 相比，存在 10 种 NSAIDs，基于对 3CLpro 抑制位点的结合能 (kcal/mol) 较低。为了验证对接结果，对顶级抑制剂他尼氟酯进行了分子动力学 (MD) 模拟。为了获得 27 种 NSAIDs 扰动下的差异表达基因，我们利用了 NCBI GEO 存储库 (GSE92742) 中的 L1000 最终 Z 分数。获得的数据集包括 27 种非甾体抗炎药的基因表达谱特征。非甾体抗炎药的肝毒性通过代谢途径紊乱的系统生物学模型进行研究。这项研究强调了非甾体抗炎药作为抗 COVID-19 的抗病毒药物的新应用。