Galindez Gihanna, Matschinske Julian, Rose Tim Daniel, Sadegh Sepideh, Salgado-Albarrán Marisol, Späth Julian, Baumbach Jan, Pauling Josch Konstantin
Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.
LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.
Nat Comput Sci. 2021 Jan;1(1):33-41. doi: 10.1038/s43588-020-00007-6. Epub 2021 Jan 14.
Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.
迅速应对未知病原体对于阻止导致疫情的疾病（如新型冠状病毒）的无控制传播，并将保护措施维持在对社会和经济造成尽可能小的损害的水平至关重要。这可以通过显著加速药物发现的计算方法来实现。一种强大的方法是通过药物重新利用将搜索限制在现有药物上，这可以极大地加速通常漫长的审批过程。在本综述中，我们研究了一组具有代表性的当前使用的计算方法，以识别可用于治疗新冠肺炎的重新利用药物及其基础数据资源。此外，我们将计算方法预测的候选药物与正在临床试验中评估的药物进行比较。最后，我们讨论从所审查的研究工作中吸取的经验教训，包括如何成功地将计算方法与实验研究联系起来，并提出一种统一的药物重新利用策略，以便在未来疫情爆发时能更好地做好准备。
