Piplani Sakshi, Singh Puneet, Petrovsky Nikolai, Winkler David A
College of Medicine and Public Health, Flinders University, Bedford, SA, Australia.
Vaxine Pty Ltd., Warradale, SA, Australia.
Front Mol Biosci. 2022 Mar 14;9:781039. doi: 10.3389/fmolb.2022.781039. eCollection 2022.
We urgently need to identify drugs to treat patients suffering from COVID-19 infection. Drugs rarely act at single molecular targets. Off-target effects are responsible for undesirable side effects and beneficial synergy between targets for specific illnesses. They have provided blockbuster drugs, e.g., Viagra for erectile dysfunction and Minoxidil for male pattern baldness. Existing drugs, those in clinical trials, and approved natural products constitute a rich resource of therapeutic agents that can be quickly repurposed, as they have already been assessed for safety in man. A key question is how to screen such compounds rapidly and efficiently for activity against new pandemic pathogens such as SARS-CoV-2. Here, we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-CoV-2. We show that the shortlist of 84 candidates with the strongest predicted binding affinities is highly enriched (≥25%) in compounds validated or to have activity in SARS-CoV-2. The top candidates also include drugs and natural products not previously identified as having COVID-19 activity, thereby providing leads for experimental validation. This predictive screening pipeline will be valuable for repurposing existing drugs and discovering new drug candidates against other medically important pathogens relevant to future pandemics.
我们迫切需要确定治疗新冠病毒感染患者的药物。药物很少作用于单一分子靶点。脱靶效应会导致不良副作用以及特定疾病靶点之间的有益协同作用。它们催生了重磅药物，例如用于治疗勃起功能障碍的伟哥和用于治疗男性型脱发的米诺地尔。现有药物、处于临床试验阶段的药物以及已获批的天然产物构成了丰富的治疗药物资源，这些药物可迅速被重新利用，因为它们已经在人体中进行了安全性评估。一个关键问题是如何快速、高效地筛选这类化合物，以确定其对新型大流行病原体（如严重急性呼吸综合征冠状病毒2，即SARS-CoV-2）的活性。在此，我们展示了如何利用快速且可靠的计算流程来筛选大量药物和天然化合物库，以识别可能抑制SARS-CoV-2主要蛋白酶的化合物。我们发现，预测结合亲和力最强的84种候选药物的候选名单中，在已被验证或对SARS-CoV-2有活性的化合物中高度富集（≥25%）。排名靠前的候选药物还包括以前未被确定具有新冠病毒活性的药物和天然产物，从而为实验验证提供了线索。这种预测性筛选流程对于重新利用现有药物以及发现针对与未来大流行相关的其他重要医学病原体的新候选药物将具有重要价值。
