Factors Influencing COVID-19 Risk: Insights From Molnupiravir Exposure-Response Modeling of Clinical Outcomes.

Molnupiravir (MOV) is an oral antiviral for the treatment of coronavirus disease 2019 (COVID-19) in outpatient settings. This analysis investigated the relationship between ß-D-N4-hydroxycytidine (NHC) pharmacokinetics and clinical outcomes in patients with mild to moderate COVID-19 in the phase III part of the randomized, double-blind, placebo-controlled MOVe-OUT trial. Logistic regression models of the dependency of outcomes on exposures and covariates were constructed using a multistep process. Influential covariates were identified first using placebo arm data, followed by assessment of exposure-dependency in drug effect using data from both the placebo and MOV arms. The exposure-response (E-R) analysis included 1,313 participants; 630 received MOV and 683 received placebo. Baseline viral load, baseline disease severity, age, weight, viral clade, active cancer, and diabetes were identified as significant determinants of response using placebo data. Absolute measures of viral load on days 5 and 10 were strong on-treatment predictors of hospitalization. An additive area under the curve (AUC)-based maximum effect (Emax ) model with a fixed Hill coefficient of 1 best represented the exposure-dependency in drug effect and the AUC50 was estimated to be 19,900 nM hour. Patients at 800 mg achieved near maximal response, which was larger than for 200 or 400 mg. The final E-R model was externally validated and predicted that the relative reduction in hospitalization with MOV treatment would vary with patient characteristics and factors in the population. In conclusion, the E-R results support the MOV dose of 800 mg twice daily to treat COVID-19. Many patient characteristics and factors impacted outcomes beyond drug exposures.

Use of Immune-Viral Dynamics Modeling to Understand Molnupiravir Drug Effect for COVID-19

Background Molnupiravir (MOV) is an orally administered ribonucleoside prodrug of β-D-N4-hydroxycytidine (NHC) against SARS-CoV-2. Here we present viral dynamics analysis of Phase 2 clinical virology data to inform MOV Phase 3 study design and development strategy. Methods An Immune-Viral Dynamics Model (IVDM) was developed with mechanisms of SARS-CoV-2 infection, replication, and induced immunity, which together describe the dynamics of viral load (VL) during disease progression. Longitudinal virology data from ferret studies (Cox, et al. Nat. Microbiol 2021:6-11) were used to inform IVDM, which was further translated to human by adjusting parameter values to capture clinical data from MOVe-IN/MOVe-OUT studies. Different placements of drug effects (on viral infectivity vs. productivity) and representations of immune response were explored to identify the best ones to describe data. A simplified 95% drug effect was implemented to represent a highly effective dose of MOV. Results IVDM showed data were best described when MOV acts on viral infectivity, consistent with the error catastrophe mechanism of action. A cascade of innate and adaptive immune response and a basal level activation enabled durable immunity and continued viral decay after treatment end. IVDM reasonably describes VL and viral titer data from animals and humans. Influence of MOV start time was explored using simulations. Consistent with the ferret studies, simulations showed when treatment is started within the first week post infection, MOV reduces viral growth, resulting in a lower and shortened duration of detectable VL. When started later (e.g. >7 days since symptom onset), the magnitude of drug effect is substantially diminished in a typical patient with an effective immune response which reduces VL prior to treatment start. Further work is needed to model response in patients with longer term infection, where MOV drug effects may have more persistent utility. Conclusion A COVID-19 IVDM developed using multiscale MOV virology data supports drug action on viral infectivity and importance of interplay of treatment and immune response and can describe infection time course and drug effect. IVDM provided mechanistic interpretations for VL drug effect in clinical studies. Disclosures Youfang Cao, PhD, Merck & Co. (Employee) Wei Gao, PhD, Merck & Co., Inc. (Employee, Shareholder) Ruthie Birger, PhD, Merck (Employee) Julie Stone, PhD, Merck & Co., Inc. (Employee, Shareholder)

Immune-viral dynamics modeling for SARS-CoV-2 drug development.

Coronavirus disease 2019 (COVID-19) global pandemic is caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral infection, which can lead to pneumonia, lung injury, and death in susceptible populations. Understanding viral dynamics of SARS-CoV-2 is critical for development of effective treatments. An Immune-Viral Dynamics Model (IVDM) is developed to describe SARS-CoV-2 viral dynamics and COVID-19 disease progression. A dataset of 60 individual patients with COVID-19 with clinical viral load (VL) and reported disease severity were assembled from literature. Viral infection and replication mechanisms of SARS-CoV-2, viral-induced cell death, and time-dependent immune response are incorporated in the model to describe the dynamics of viruses and immune response. Disease severity are tested as a covariate to model parameters. The IVDM was fitted to the data and parameters were estimated using the nonlinear mixed-effect model. The model can adequately describe individual viral dynamics profiles, with disease severity identified as a covariate on infected cell death rate. The modeling suggested that it takes about 32.6 days to reach 50% of maximum cell-based immunity. Simulations based on virtual populations suggested a typical mild case reaches VL limit of detection (LOD) by 13 days with no treatment, a moderate case by 17 days, and a severe case by 41 days. Simulations were used to explore hypothetical treatments with different initiation time, disease severity, and drug effects to demonstrate the usefulness of such modeling in informing decisions. Overall, the IVDM modeling and simulation platform enables simulations for viral dynamics and treatment efficacy and can be used to aid in clinical pharmacokinetic/pharmacodynamic (PK/PD) and dose-efficacy response analysis for COVID-19 drug development.