Pharmacokinetic simulations for remdesivir and its metabolites in healthy subjects and patients with renal impairment

Introduction Remdesivir (RDV) is used for treating COVID-19 patients. This study aims to utilize an in silico pharmacokinetics model to simulate the pharmacokinetics of RDV, its intermediate metabolites (IM), and nucleoside monophosphate (NUC) in both healthy individuals and patients with renal impairment. Methods A system of six ordinary differential equations (ODEs) was developed to describe the concentration profiles of RDV, IM and NUC in both central and peripheral compartments, with metabolism assumed to occur in both. Parameter fitting was conducted using the Monolix software, incorporating renal impairment as a covariant in the mixed-effects model. The pharmacokinetic data was sourced from a recently published clinical trial involving healthy controls and patients with varying degrees of renal impairment, as well as a prior clinical report on a kidney transplant patient. Goodness-of-fit was assessed by comparing the observed data with the prediction results. Results The simulations captured the key pharmacokinetic characteristics of RDV and its metabolites, including the rapid decline of RDV and IM during the first hour. The simulation results were in good agreement with the observed data, with most observations falling within the 90% confidence intervals. Conclusion A mathematical model has been developed that effectively captures the main pharmacokinetic features of RDV and its primary metabolites in both healthy subjects and patients with varying degrees of renal impairment.