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 non-pregnant adult physiologically-based pharmacokinetic (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 Open Systems Pharmacology (OSP) software suite (version 10) including PK-Sim® and MoBi® with pregnancy-related changes of relevant enzymes applied. Pharmacokinetics were predicted in a virtual population of 1000 pregnant subjects and prediction results were compared with in vivo PK data from the IMPAACT 2032 study. The developed PBPK model successfully captured RDV and its metabolites' plasma concentrations during pregnancy. The ratio of prediction vs observation for RDV AUC(0-∞) , and C(max) were 1.61, and 1.17 respectively. For GS-704277, the ratio of predicted vs observed was 0.94 for AUC(0-∞) and 1.20 for C(max) . For GS-441524, the ratio of predicted vs observed was 1.03 for AUC(0-∞) , 1.05 for C(max) , and 1.07 for C(24h) . All predictions of AUC(0-∞) , and C(max) for RDV and its metabolites were within a 2-fold 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.