Institute of Informatics and Telematics (IIT), CNR, Pisa, 56124, Italy.
Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, 53100, Italy.
Sci Rep. 2021 Oct 6;11(1):19839. doi: 10.1038/s41598-021-99399-2.
Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.
计算药物重定位旨在对现有药物进行排名和选择，以用于治疗新出现的疾病或旧疾病的新用途。基于计算机的药物筛选有可能大大加快有希望的候选药物的筛选速度，以应对 COVID-19 等尚无满意治疗方法的疾病爆发。我们描述了 DrugMerge 方法，这是一种基于合并多个疾病活性子网的药物排名的临床前计算药物重定位方法。DrugMerge 使用药物和疾病的差异转录组数据，以及一个大型基因共表达网络。对四种基准疾病的实验表明，在所有四种情况下，我们的方法都能在第一位检测到用于特定疾病的临床药物。将 DrugMerge 应用于 COVID-19 发现，在排名中，许多目前正在临床试验中的 COVID-19 药物都排在前几位，这表明 DrugMerge 可以模拟人类专家的判断。
