Early administration of tecovirimat shortens the time to mpox clearance in a model of human infection.

Despite use of tecovirimat since the beginning of the 2022 outbreak, few data have been published on its antiviral effect in humans. We here predict tecovirimat efficacy using a unique set of data in nonhuman primates (NHPs) and humans. We analyzed tecovirimat antiviral activity on viral kinetics in NHP to characterize its concentration-effect relationship in vivo. Next, we used a pharmacological model developed in healthy volunteers to project its antiviral efficacy in humans. Finally, a viral dynamic model was applied to characterize mpox kinetics in skin lesions from 54 untreated patients, and we used this modeling framework to predict the impact of tecovirimat on viral clearance in skin lesions. At human-recommended doses, tecovirimat could inhibit viral replication from infected cells by more than 90% after 3 to 5 days of drug administration and achieved over 97% efficacy at drug steady state. With an estimated mpox within-host basic reproduction number, R0, equal to 5.6, tecovirimat could therefore shorten the time to viral clearance if given before viral peak. We predicted that initiating treatment at symptom onset, which on average occurred 2 days before viral peak, could reduce the time to viral clearance by about 6 days. Immediate postexposure prophylaxis could not only reduce time to clearance but also lower peak viral load by more than 1.0 log10 copies/mL and shorten the duration of positive viral culture by about 7 to 10 days. These findings support the early administration of tecovirimat against mpox infection, ideally starting from the infection day as a postexposure prophylaxis.

Cardiac Adverse Events and Remdesivir in Hospitalized Patients With COVID-19: A Post Hoc Safety Analysis of the Randomized DisCoVeRy Trial.

BACKGROUND: We aimed to evaluate the cardiac adverse events (AEs) in hospitalized patients with coronavirus disease 2019 (COVID-19) who received remdesivir plus standard of care (SoC) compared with SoC alone (control), as an association was noted in some cohort studies and disproportionality analyses of safety databases. METHODS: This post hoc safety analysis is based on data from the multicenter, randomized, open-label, controlled DisCoVeRy trial in hospitalized patients with COVID-19. Any first AE that occurred between randomization and day 29 in the modified intention-to-treat (mITT) population randomized to either remdesivir or control group was considered. Analysis was performed using Kaplan-Meier survival curves, and Kaplan-Meier estimates were calculated for event rates. RESULTS: Cardiac AEs were reported in 46 (11.2%) of 410 and 48 (11.3%) of 423 patients in the mITT population (n = 833) enrolled in the remdesivir and control groups, respectively. The difference between both groups was not significant (hazard ratio [HR], 1.0; 95% confidence interval [CI], .7-1.5; P = .98), even when serious and nonserious cardiac AEs were evaluated separately. The majority of reports in both groups were of arrhythmic nature (remdesivir, 84.8%; control, 83.3%) and were associated with a favorable outcome. There was no significant difference between the two groups in the occurrence of cardiac AE subclasses, including arrhythmic events (HR, 1.1; 95% CI, .7-1.7; P = .68). CONCLUSIONS: Remdesivir treatment was not associated with an increased risk of cardiac AEs compared with control in patients hospitalized with moderate or severe COVID-19. These results are consistent with other randomized, controlled trials and meta-analyses. Clinical Trials Registration. NCT04315948; EudraCT 2020-000936-23.

Model-based bioequivalence approach for sparse pharmacokinetic bioequivalence studies: Model selection or model averaging?

Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non-compartmental analysis (NCA) and the two one-sided test (TOST). Recently published regulatory guidance recommends alternative model-based (MB) approaches for BE assessment when NCA is challenging, as for long-acting injectables and products which require sparse PK sampling. However, our previous research on MB-TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two-way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB-TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB-TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB-TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.

Distributive randomization: a pragmatic fractional factorial design to screen or evaluate multiple simultaneous interventions in a clinical trial.

BACKGROUND: In some medical indications, numerous interventions have a weak presumption of efficacy, but a good track record or presumption of safety. This makes it feasible to evaluate them simultaneously. This study evaluates a pragmatic fractional factorial trial design that randomly allocates a pre-specified number of interventions to each participant, and statistically tests main intervention effects. We compare it to factorial trials, parallel-arm trials and multiple head-to-head trials, and derive some good practices for its design and analysis. METHODS: We simulated various scenarios involving 4 to 20 candidate interventions among which 2 to 8 could be simultaneously allocated. A binary outcome was assumed. One or two interventions were assumed effective, with various interactions (positive, negative, none). Efficient combinatorics algorithms were created. Sample sizes and power were obtained by simulations in which the statistical test was either difference of proportions or multivariate logistic regression Wald test with or without interaction terms for adjustment, with Bonferroni multiplicity-adjusted alpha risk for both. Native R code is provided without need for compiling or packages. RESULTS: Distributive trials reduce sample sizes 2- to sevenfold compared to parallel arm trials, and increase them 1- to twofold compared to factorial trials, mostly when fewer allocations than for the factorial design are possible. An unexpectedly effective intervention causes small decreases in power (< 10%) if its effect is additive, but large decreases (possibly down to 0) if not, as for factorial designs. These large decreases are prevented by using interaction terms to adjust the analysis, but these additional estimands have a sample size cost and are better pre-specified. The issue can also be managed by adding a true control arm without any intervention. CONCLUSION: Distributive randomization is a viable design for mass parallel evaluation of interventions in constrained trial populations. It should be introduced first in clinical settings where many undercharacterized interventions are potentially available, such as disease prevention strategies, digital behavioral interventions, dietary supplements for chronic conditions, or emerging diseases. Pre-trial simulations are recommended, for which tools are provided.

Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches.

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data.

BACKGROUND AND OBJECTIVE: Joint modeling of longitudinal and time-to-event data has gained attention over recent years with extensive developments including nonlinear models for longitudinal outcomes and flexible time-to-event models for survival outcomes, possibly involving competing risks. However, in popular software such as R, the function used to describe the biomarker dynamic is mainly linear in the parameters, and the survival submodel relies on pre-implemented functions (exponential, Weibull, ...). The objective of this work is to extend the code from the saemix package (version 3.1 on CRAN) to fit parametric joint models where longitudinal submodels are not necessary linear in their parameters, with full user control over the model function. METHODS: We used the saemix package, designed to fit nonlinear mixed-effects models (NLMEM) through the Stochastic Approximation Expectation Maximization (SAEM) algorithm, and extended the main functions to joint model estimation. To compute standard errors (SE) of parameter estimates, we implemented a recently developed stochastic algorithm. A simulation study was proposed to assess (i) the performances of parameter estimation, (ii) the SE computation and (iii) the type I error when testing independence between the two submodels. Four joint models were considered in the simulation study, combining a linear or nonlinear mixed-effects model for the longitudinal submodel, with a single terminal event or a competing risk model. RESULTS: For all simulation scenarios, parameters were precisely and accurately estimated with low bias and uncertainty. For complex joint models (with NLMEM), increasing the number of chains of the algorithm was necessary to reduce bias, but earlier censoring in the competing risk scenario still challenged the estimation. The empirical SE of parameters obtained over all simulations were very close to those computed with the stochastic algorithm. For more complex joint models (involving NLMEM), some estimates of random effects variances had higher uncertainty and their SE were moderately under-estimated. Finally, type I error was controlled for each joint model. CONCLUSIONS: saemix is a flexible open-source package and we adapted it to fit complex parametric joint models that may not be estimated using standard tools. Code and examples to help users get started are freely available on Github.

Prediction of Individual Disease Progression Including Parameter Uncertainty in Rare Neurodegenerative Diseases: The Example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS).

The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4-8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.

Perturbation and resilience of the gut microbiome up to 3 months after ß-lactams exposure in healthy volunteers suggest an important role of microbial ß-lactamases.

BACKGROUND: Antibiotics notoriously perturb the gut microbiota. We treated healthy volunteers either with cefotaxime or ceftriaxone for 3 days, and collected in each subject 12 faecal samples up to day 90. Using untargeted and targeted phenotypic and genotypic approaches, we studied the changes in the bacterial, phage and fungal components of the microbiota as well as the metabolome and the ß-lactamase activity of the stools. This allowed assessing their degrees of perturbation and resilience. RESULTS: While only two subjects had detectable concentrations of antibiotics in their faeces, suggesting important antibiotic degradation in the gut, the intravenous treatment perturbed very significantly the bacterial and phage microbiota, as well as the composition of the metabolome. In contrast, treatment impact was relatively low on the fungal microbiota. At the end of the surveillance period, we found evidence of resilience across the gut system since most components returned to a state like the initial one, even if the structure of the bacterial microbiota changed and the dynamics of the different components over time were rarely correlated. The observed richness of the antibiotic resistance genes repertoire was significantly reduced up to day 30, while a significant increase in the relative abundance of ß-lactamase encoding genes was observed up to day 10, consistent with a concomitant increase in the ß-lactamase activity of the microbiota. The level of ß-lactamase activity at baseline was positively associated with the resilience of the metabolome content of the stools. CONCLUSIONS: In healthy adults, antibiotics perturb many components of the microbiota, which return close to the baseline state within 30 days. These data suggest an important role of endogenous ß-lactamase-producing anaerobes in protecting the functions of the microbiota by de-activating the antibiotics reaching the colon. Video Abstract.

Preclinical in vivo assessment of the activity of AZD7442 anti-SARS-CoV-2 monoclonal antibodies against Omicron sublineages.

Therapeutic monoclonal antibodies have been successful in protecting vulnerable populations against SARS-CoV-2. However, their effectiveness has been hampered by the emergence of new variants. To adapt the therapeutic landscape, health authorities have based their recommendations mostly on in vitro neutralization tests. However, these do not provide a reliable understanding of the changes in the dose-effect relationship and how they may translate into clinical efficacy. Taking the example of EvusheldTM (AZD7442), we aimed to investigate how in vivo data can provide critical quantitative results and project clinical effectiveness. We used the Golden Syrian hamster model to estimate 90 % effective concentrations (EC90) of AZD7442 in vivo against SARS-CoV-2 Omicron BA.1, BA.2 and BA.5 variants. While our in vivo results confirmed the partial loss of AZD7442 activity for BA.1 and BA.2, they showed a much greater loss of efficacy against BA.5 than that obtained in vitro. We analyzed in vivo EC90s in perspective with antibody levels measured in a cohort of immunocompromised patients who received 300 mg of AZD7442. We found that a substantial proportion of patients had serum levels of anti-SARS-CoV-2 spike protein IgG above the estimated in vivo EC90 for BA.1 and BA.2 (21 % and 92 % after 1 month, respectively), but not for BA.5. These findings suggest that AZD7442 is likely to retain clinical efficacy against BA.2 and BA.1, but not against BA.5. Overall, the present study illustrates the importance of complementing in vitro investigations by preclinical studies in animal models to help predict the efficacy of monoclonal antibodies in humans.