Integration Analysis of Pharmacokinetics and Metabolomics to Predict Metabolic Phenotype and Drug Exposure of Remdesivir

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(max), 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.