Physiologically‐based pharmacokinetic modeling of remdesivir and its metabolites in pregnant women with COVID‐19

Pregnant individuals are at high risk for severe illness from COVID‐19, and there is an urgent need to identify safe and effective therapeutics for this population. Remdesivir (RDV) is a SARS‐CoV‐2 nucleotide analog RNA polymerase inhibitor. Limited RDV pharmacokinetic (PK) and safety data are available for pregnant women receiving RDV. The aims of this study were to translate a previously published nonpregnant adult physiologically based PK (PBPK) model for RDV to pregnancy and evaluate model performance with emerging clinical PK data in pregnant women with COVID‐19. The pregnancy model was built in the Open Systems Pharmacology software suite (Version 10) including PK‐Sim® and MoBi® with pregnancy‐related changes of relevant enzymes applied. PK were predicted in a virtual population of 1000 pregnant subjects, and prediction results were compared with in vivo PK data from the International Maternal, Pediatric, Adolescent AIDS Clinical Trials (IMPAACT) Network 2032 study. The developed PBPK model successfully captured RDV and its metabolites' plasma concentrations during pregnancy. The ratios of prediction versus observation for RDV area under the curve from time 0 to infinity (AUC(0–∞)) and maximum concentration (C(max)) were 1.61 and 1.17, respectively. For GS‐704277, the ratios of predicted versus observed were 0.94 for AUC(0–∞) and 1.20 for C(max). For GS‐441524, the ratios of predicted versus observed were 1.03 for AUC(0–24), 1.05 for C(max), and 1.07 for concentrations at 24 h. All predictions of AUC and C(max) for RDV and its metabolites were within a twofold error range, and about 60% of predictions were within a 10% error range. These findings demonstrate the feasibility of translating PBPK models to pregnant women to potentially guide trial design, clinical decision making, and drug development.