Du Ping, Wang Guoyong, Hu Ting, Li Han, An Zhuoling
Department of Pharmacy/Phase I Clinical Trial and Research Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Front Pharmacol. 2022 Jan 5;12:779135. doi: 10.3389/fphar.2021.779135. eCollection 2021.
Remdesivir has displayed pharmacological activity against SARS-CoV-2. However, no pharmacometabolomics (PM) or correlation analysis with pharmacokinetics (PK) was revealed. Rats were intravenously administered remdesivir, and a series of blood samples were collected before and after treatment. Comprehensive metabolomics profile and PK were investigated and quantitated simultaneously using our previous reliable HPLC-MS/MS method. Both longitudinal and transversal metabolic analyses were conducted, and the correlation between PM and PK parameters was evaluated using Pearson's correlation analysis and the PLS model. Multivariate statistical analysis was employed for discovering candidate biomarkers which predicted drug exposure or toxicity of remdesivir. The prominent metabolic profile variation was observed between pre- and posttreatment, and significant changes were found in 65 metabolites. A total of 15 metabolites-12 carnitines, one N-acetyl-D-glucosamine, one allantoin, and one corticosterone-were significantly correlated with the concentration of Nuc (active metabolite of remdesivir). Adenosine, spermine, guanosine, sn-glycero-3-phosphocholine, and l-homoserine may be considered potential biomarkers for predicting drug exposure or toxicity. This study is the first attempt to apply PM and PK to study remdesivir response/toxicity, and the identified candidate biomarkers might be used to predict the AUC and C, indicating capability of discriminating good or poor responders. Currently, this study originally offers considerable evidence to metabolite reprogramming of remdesivir and sheds light on precision therapy development in fighting COVID-19.
瑞德西韦已显示出对新型冠状病毒（SARS-CoV-2）的药理活性。然而，尚未有关于药物代谢组学（PM）或与药代动力学（PK）的相关性分析报道。对大鼠静脉注射瑞德西韦，并在治疗前后采集一系列血样。使用我们之前可靠的高效液相色谱-串联质谱（HPLC-MS/MS）方法同时研究并定量综合代谢组学图谱和PK。进行了纵向和横向代谢分析，并使用Pearson相关性分析和偏最小二乘（PLS）模型评估了PM与PK参数之间的相关性。采用多变量统计分析来发现预测瑞德西韦药物暴露或毒性的候选生物标志物。在治疗前后观察到显著的代谢图谱变化，在65种代谢物中发现了显著变化。共有15种代谢物——12种肉碱、1种N-乙酰-D-葡萄糖胺、1种尿囊素和1种皮质酮——与Nuc（瑞德西韦的活性代谢物）浓度显著相关。腺苷、精胺、鸟苷、sn-甘油-3-磷酸胆碱和L-高丝氨酸可能被视为预测药物暴露或毒性的潜在生物标志物。本研究首次尝试应用PM和PK来研究瑞德西韦的反应/毒性，所确定的候选生物标志物可能用于预测曲线下面积（AUC）和血药浓度（C），表明具有区分反应良好或不良者的能力。目前，本研究最初为瑞德西韦的代谢重编程提供了大量证据，并为抗击2019冠状病毒病（COVID-19）的精准治疗发展提供了线索。
