Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China; Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
Food Chem Toxicol. 2020 Nov;145:111767. doi: 10.1016/j.fct.2020.111767. Epub 2020 Sep 21.
Currently, coronavirus disease 2019 (COVID-19), has posed an imminent threat to global public health. Although some current therapeutic agents have showed potential prevention or treatment, a growing number of associated adverse events have occurred on patients with COVID-19 in the course of medical treatment. Therefore, a comprehensive assessment of the safety profile of therapeutic agents against COVID-19 is urgently needed. In this study, we proposed a network-based framework to identify the potential side effects of current COVID-19 drugs in clinical trials. We established the associations between 116 COVID-19 drugs and 30 kinds of human tissues based on network proximity and gene-set enrichment analysis (GSEA) approaches. Additionally, we focused on four types of drug-induced toxicities targeting four tissues, including hepatotoxicity, renal toxicity, lung toxicity, and neurotoxicity, and validated our network-based predictions by preclinical and clinical evidence available. Finally, we further performed pharmacovigilance analysis to validate several drug-tissue toxicities via data mining adverse event reporting data, and we identified several new drug-induced side effects without labeling in Food and Drug Administration (FDA) drug instructions. Overall, this study provides forceful approaches to assess potential side effects on COVID-19 drugs, which will be helpful for their safe use in clinical practice and promoting the discovery of antiviral therapeutics against SARS-CoV-2.
目前,2019 年冠状病毒病(COVID-19)对全球公共卫生构成了迫在眉睫的威胁。尽管一些现有治疗药物已显示出潜在的预防或治疗作用,但 COVID-19 患者在治疗过程中发生的相关不良事件越来越多。因此,迫切需要对治疗 COVID-19 的药物的安全性进行全面评估。在这项研究中,我们提出了一种基于网络的框架,以识别临床试验中当前 COVID-19 药物的潜在副作用。我们根据网络接近度和基因集富集分析(GSEA)方法,建立了 116 种 COVID-19 药物与 30 种人类组织之间的关联。此外,我们还关注了针对四种组织(包括肝毒性、肾毒性、肺毒性和神经毒性)的四种药物诱导的毒性类型,并通过现有临床前和临床证据验证了我们基于网络的预测。最后,我们通过挖掘不良事件报告数据进行药物-组织毒性的药物警戒分析,并通过药物警戒分析在 FDA 药物说明书中未标记的情况下鉴定了几种新的药物诱导的副作用。总体而言,这项研究提供了评估 COVID-19 药物潜在副作用的有力方法,这将有助于其在临床实践中的安全使用,并促进针对 SARS-CoV-2 的抗病毒治疗的发现。