Djokovic Nemanja, Ruzic Dusan, Djikic Teodora, Cvijic Sandra, Ignjatovic Jelisaveta, Ibric Svetlana, Baralic Katarina, Buha Djordjevic Aleksandra, Curcic Marijana, Djukic-Cosic Danijela, Nikolic Katarina
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221, Belgrade, Serbia.
Department of Pharmaceutical Technology and Cosmetology, University of Belgrade, Faculty of Pharmacy, Vojvode Stepe 450, 11221, Belgrade, Serbia.
Mol Inform. 2021 May;40(5):e2000187. doi: 10.1002/minf.202000187. Epub 2021 Mar 30.
Considering the urgent need for novel therapeutics in ongoing COVID-19 pandemic, drug repurposing approach might offer rapid solutions comparing to de novo drug design. In this study, we designed an integrative in silico drug repurposing approach for rapid selection of potential candidates against SARS-CoV-2 Main Protease (M ). To screen FDA-approved drugs, we implemented structure-based molecular modelling techniques, physiologically-based pharmacokinetic (PBPK) modelling of drugs disposition and data mining analysis of drug-gene-COVID-19 association. Through presented approach, we selected the most promising FDA approved drugs for further COVID-19 drug development campaigns and analysed them in context of available experimental data. To the best of our knowledge, this is unique in silico study which integrates structure-based molecular modeling of M inhibitors with predictions of their tissue disposition, drug-gene-COVID-19 associations and prediction of pleiotropic effects of selected candidates.
考虑到在当前新冠疫情中对新型治疗方法的迫切需求，与从头设计药物相比，药物重新利用方法可能提供快速解决方案。在本研究中，我们设计了一种综合的计算机辅助药物重新利用方法，用于快速筛选针对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）主要蛋白酶（M）的潜在候选药物。为了筛选美国食品药品监督管理局（FDA）批准的药物，我们采用了基于结构的分子建模技术、药物处置的基于生理学的药代动力学（PBPK）建模以及药物-基因-新冠病毒关联的数据挖掘分析。通过所提出的方法，我们选择了最有前景的FDA批准药物用于进一步的新冠病毒药物研发活动，并结合现有实验数据对其进行分析。据我们所知，这是一项独特的计算机辅助研究，它将M抑制剂的基于结构的分子建模与它们的组织处置预测、药物-基因-新冠病毒关联以及所选候选药物的多效性预测相结合。
