Department of CSE, IIIT-Delhi, New Delhi, 110020, India.
Department of Community Medicine, IPGMER Kolkata, Kolkata, India.
Sci Rep. 2021 Apr 27;11(1):9047. doi: 10.1038/s41598-021-88153-3.
The year 2020 witnessed a heavy death toll due to COVID-19, calling for a global emergency. The continuous ongoing research and clinical trials paved the way for vaccines. But, the vaccine efficacy in the long run is still questionable due to the mutating coronavirus, which makes drug re-positioning a reasonable alternative. COVID-19 has hence fast-paced drug re-positioning for the treatment of COVID-19 and its symptoms. This work builds computational models using matrix completion techniques to predict drug-virus association for drug re-positioning. The aim is to assist clinicians with a tool for selecting prospective antiviral treatments. Since the virus is known to mutate fast, the tool is likely to help clinicians in selecting the right set of antivirals for the mutated isolate. The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals. The database gathers similarity information using the chemical structure of drugs and the genomic structure of viruses. Along with this database, we make available a set of state-of-the-art computational drug re-positioning tools based on matrix completion. The tools are first analysed on a standard set of experimental protocols for drug target interactions. The best performing ones are applied for the task of re-positioning antivirals for COVID-19. These tools select six drugs out of which four are currently under various stages of trial, namely Remdesivir (as a cure), Ribavarin (in combination with others for cure), Umifenovir (as a prophylactic and cure) and Sofosbuvir (as a cure). Another unanimous prediction is Tenofovir alafenamide, which is a novel Tenofovir prodrug developed in order to improve renal safety when compared to its original counterpart (older version) Tenofovir disoproxil. Both are under trail, the former as a cure and the latter as a prophylactic. These results establish that the computational methods are in sync with the state-of-practice. We also demonstrate how the drugs to be used against the virus would vary as SARS-Cov-2 mutates over time by predicting the drugs for the mutated strains, suggesting the importance of such a tool in drug prediction. We believe this work would open up possibilities for applying machine learning models to clinical research for drug-virus association prediction and other similar biological problems.
2020 年因 COVID-19 导致大量死亡，这是一场全球性的紧急事件。不断进行的研究和临床试验为疫苗铺平了道路。但是，由于冠状病毒不断变异，疫苗的长期效果仍存在疑问，这使得重新定位药物成为一种合理的选择。因此，针对 COVID-19 及其症状，COVID-19 进行了快速的药物重新定位。这项工作使用矩阵完成技术构建计算模型，以预测药物-病毒的关联，从而进行药物重新定位。其目的是为临床医生提供一种选择潜在抗病毒治疗方法的工具。由于众所周知病毒变异很快，因此该工具可能有助于临床医生为变异株选择合适的抗病毒药物组合。这项工作的主要贡献是公开共享一个经过人工整理的数据库，其中包含病毒及其相应抗病毒药物之间现有的关联。该数据库使用药物的化学结构和病毒的基因组结构来收集相似性信息。除了这个数据库，我们还提供了一套基于矩阵完成的最先进的计算药物重新定位工具。这些工具首先在药物靶标相互作用的标准实验方案上进行分析。应用表现最好的工具来重新定位 COVID-19 的抗病毒药物。这些工具从六种药物中选择了四种，其中四种目前处于不同的试验阶段，即瑞德西韦（作为一种治疗方法）、利巴韦林（与其他药物联合用于治疗）、乌米福韦（作为一种预防和治疗方法）和索非布韦（作为一种治疗方法）。另一个一致的预测是替诺福韦艾拉酚胺，它是一种新型替诺福韦前药，与原药（旧版本）替诺福韦二吡呋酯相比，旨在提高肾脏安全性。两者都在试验中，前者是一种治疗方法，后者是一种预防方法。这些结果表明，计算方法与实践相符。我们还通过预测用于突变株的药物来演示随着时间的推移 SARS-Cov-2 如何变异，对抗病毒的药物将如何变化，从而表明此类工具在药物预测方面的重要性。我们相信，这项工作将为将机器学习模型应用于药物-病毒关联预测和其他类似的临床研究开辟可能性，并为其他类似的生物学问题提供可能性。
