Population pharmacokinetics of molnupiravir in adults with COVID-19: Lack of clinically important exposure variation across individuals.

Effective antiviral treatments for coronavirus disease 2019 (COVID-19) are needed to reduce the morbidity and mortality associated with severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection, particularly in patients with risk factors for severe disease. Molnupiravir (MK-4482, EIDD-2801) is an orally administered, ribonucleoside prodrug of ß-D-N4-hydroxycytidine (NHC) with submicromolar potency against SARS-CoV-2. A population pharmacokinetic (PopPK) analysis for molnupiravir exposure was conducted using 4202 NHC plasma concentrations collected in 1207 individuals from a phase I trial in healthy participants, a phase IIa trial in non-hospitalized participants with COVID-19, a phase II trial in hospitalized participants with COVID-19, and a phase II/III trial in non-hospitalized participants with COVID-19. Molnupiravir pharmacokinetics (PK) was best described by a two-compartment model with a transit-compartment absorption model and linear elimination. Molnupiravir apparent elimination clearance increased with body weight less-than-proportionally (power 0.412) and was estimated as 70.6 L/h in 80-kg individuals with a moderate interindividual variability (43.4% coefficient of variation). Additionally, effects of sex and body mass index on apparent central volume and food status and formulation on the absorption mean transit time were identified as statistically significant descriptors of variability in these PK parameters. However, none of the identified covariate effects caused clinically relevant changes in the area under the NHC concentration versus time curve between doses, the exposure metric most closely related to clinical response. Overall, the PopPK model indicates that molnupiravir can be administered in adults without dose adjustment based on age, sex, body size, food, and mild-to-moderate renal or mild hepatic impairment.

Population pharmacokinetic analysis of the P2X3-receptor antagonist gefapixant.

Gefapixant, a P2X3-receptor antagonist, demonstrated objective and subjective efficacy in individuals with refractory or unexplained chronic cough. We report a population pharmacokinetic (PopPK) analysis that characterizes gefapixant pharmacokinetics (PKs), quantifies between- and within-participant variability, and evaluates the impact of intrinsic and extrinsic factors on gefapixant exposure. The PopPK model was initially developed using PK data from six phase I studies. Stepwise covariate method was utilized to identify covariates impacting PK parameters; the model was re-estimated and covariate effects were re-assessed after integrating PK data from three phase II and III studies. Simulations were conducted to evaluate the magnitude of covariate effects on gefapixant exposure. Of 1677 participants included in this data set, 1618 had evaluable PK records. Age, body weight, and sex had statistically significant, but not clinically relevant, effects on exposure. Degree of renal impairment (RI) had statistically significant and clinically relevant effects on exposure; exposure was 17% to 89% higher in those with versus without RI. Simulation results indicated that gefapixant 45 mg administered once daily to patients with severe RI has similar exposure to gefapixant 45 mg administered twice daily to patients with normal renal function. There were no significant effects of proton pump inhibitors or food. Of evaluated intrinsic and extrinsic factors, only RI had a clinically relevant effect on gefapixant exposure. Patients with mild or moderate RI do not require dosage adjustments; however, for patients with severe RI who are not on dialysis, gefapixant 45 mg once daily is recommended.

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.

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

BACKGROUND: The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, we developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data. METHODS: A systematic literature review identified studies of vaccines against SARS-CoV-2 in rhesus macaques (RM) and humans. Summary-level data of 13 RM and 8 clinical trials were used in the analysis. A RM MBMA model was developed to quantify the relationship between serum neutralizing (SN) titres after vaccination and peak viral load (VL) post-challenge in RM. The translation of the RM MBMA model to a clinical protection model was then carried out to predict clinical efficacies based on RM data alone. Subsequently, clinical SN and efficacy data were integrated to develop three predictive models of efficacy - a calibrated RM MBMA, a joint (RM-Clinical) MBMA, and the clinical MBMA model. The three models were leveraged to predict efficacies of vaccine candidates not included in the model and efficacies against newer strains of SARS-CoV-2. FINDINGS: Clinical efficacies predicted based on RM data alone were in reasonable agreement with the reported data. The SN titre predicted to provide 50% efficacy was estimated to be about 21% of the mean human convalescent titre level, and that value was consistent across the three models. Clinical efficacies predicted from the MBMA models agreed with reported efficacies for two vaccine candidates (BBV152 and CoronaVac) not included in the modelling and for efficacies against delta variant. INTERPRETATION: The three MBMA models are predictive of protection against SARS-CoV-2 and provide a translational framework to enable early Go/No-Go and study design decisions using non-clinical and/or limited clinical immunogenicity data in the development of novel SARS-CoV-2 vaccines. FUNDING: This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

Operating characteristics of stepwise covariate selection in pharmacometric modeling.

Stepwise covariate modeling (SCM) is a widely used tool in pharmacometric analyses to identify covariates that explain between-subject variability (BSV) in exposure and exposure-response relationships. However, this approach has several potential weaknesses, including over-estimated covariate effect and incorrect selection of covariates due to collinearity. In this work, we investigated the operating characteristics (i.e., accuracy, precision, and power) of SCM in a controlled setting by simulating sixteen scenarios with up to four covariate relationships. The SCM analysis showed a decrease in the power to detect the true covariates as model complexity increased. Furthermore, false highly correlated covariates were frequently selected in place of or in addition to the true covariates. Relative root mean square errors (RMRSE) ranged from 1 to 51% for the fixed effects parameters, increased with the number of covariates included in the model, and were slightly higher than the RMRSE obtained with a simple re-estimation exercise with the true model (i.e., stochastic simulation and estimation). RMRSE for BSV increased with the number of covariates included in the model, with a covariance parameter RMRSE of almost 135% in the most complex scenario. Loose boundary conditions on the continuous covariate power relation appeared to have an impact on the covariate model selection in SCM. A stricter boundary condition helped achieve high power (> 90%), even in the most complex scenario. Finally, reducing the sample size in terms of number of subjects or number of samples proved to have an impact on the power to detect the correct model.

Cryptic individual scaling relationships and the evolution of morphological scaling.

Morphological scaling relationships between organ and body size-also known as allometries-describe the shape of a species, and the evolution of such scaling relationships is central to the generation of morphological diversity. Despite extensive modeling and empirical tests, however, the modes of selection that generate changes in scaling remain largely unknown. Here, we mathematically model the evolution of the group-level scaling as an emergent property of individual-level variation in the developmental mechanisms that regulate trait and body size. We show that these mechanisms generate a "cryptic individual scaling relationship" unique to each genotype in a population, which determines body and trait size expressed by each individual, depending on developmental nutrition. We find that populations may have identical population-level allometries but very different underlying patterns of cryptic individual scaling relationships. Consequently, two populations with apparently the same morphological scaling relationship may respond very differently to the same form of selection. By focusing on the developmental mechanisms that regulate trait size and the patterns of cryptic individual scaling relationships they produce, our approach reveals the forms of selection that should be most effective in altering morphological scaling, and directs researcher attention on the actual, hitherto overlooked, targets of selection.