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 Coronavirus Disease 2019 (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) receiving remdesivir plus standard of care (SoC) compared to 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 (NCT04315948). Any first AE occurring 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 (HR 1.0, 95% CI 0.7-1.5, p = 0.98), even when evaluating serious and non-serious cardiac AEs 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 remdesivir and control groups in the occurrence of different cardiac AE subclasses, including arrhythmic events (HR 1.1, 95% CI: 0.7-1.7, p = 0.68). CONCLUSIONS: Remdesivir treatment was not associated with an increased risk of cardiac AEs, whether serious or not, and regardless of AE severity, compared to control, in patients hospitalized with moderate or severe COVID-19. This is consistent with the results of other randomized controlled trials and meta-analyses.

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.

Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort.

The characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral kinetics in hospitalized patients and its association with mortality is unknown. We analyzed death and nasopharyngeal viral kinetics in 655 hospitalized patients from the prospective French COVID cohort. The model predicted a median peak viral load that coincided with symptom onset. Patients with age ≥65 y had a smaller loss rate of infected cells, leading to a delayed median time to viral clearance occurring 16 d after symptom onset as compared to 13 d in younger patients (P < 10-4). In multivariate analysis, the risk factors associated with mortality were age ≥65 y, male gender, and presence of chronic pulmonary disease (hazard ratio [HR] > 2.0). Using a joint model, viral dynamics after hospital admission was an independent predictor of mortality (HR = 1.31, P < 10-3). Finally, we used our model to simulate the effects of effective pharmacological interventions on time to viral clearance and mortality. A treatment able to reduce viral production by 90% upon hospital admission would shorten the time to viral clearance by 2.0 and 2.9 d in patients of age <65 y and ≥65 y, respectively. Assuming that the association between viral dynamics and mortality would remain similar to that observed in our population, this could translate into a reduction of mortality from 19 to 14% in patients of age ≥65 y with risk factors. Our results show that viral dynamics is associated with mortality in hospitalized patients. Strategies aiming to reduce viral load could have an effect on mortality rate in this population.

Multivariate joint model under competing risks to predict death of hospitalized patients for SARS-CoV-2 infection.

During the coronavirus disease 2019 (COVID-19) pandemic, several clinical prognostic scores have been proposed and evaluated in hospitalized patients, relying on variables available at admission. However, capturing data collected from the longitudinal follow-up of patients during hospitalization may improve prediction accuracy of a clinical outcome. To answer this question, 327 patients diagnosed with COVID-19 and hospitalized in an academic French hospital between January and July 2020 are included in the analysis. Up to 59 biomarkers were measured from the patient admission to the time to death or discharge from hospital. We consider a joint model with multiple linear or nonlinear mixed-effects models for biomarkers evolution, and a competing risks model involving subdistribution hazard functions for the risks of death and discharge. The links are modeled by shared random effects, and the selection of the biomarkers is mainly based on the significance of the link between the longitudinal and survival parts. Three biomarkers are retained: the blood neutrophil counts, the arterial pH, and the C-reactive protein. The predictive performances of the model are evaluated with the time-dependent area under the curve (AUC) for different landmark and horizon times, and compared with those obtained from a baseline model that considers only information available at admission. The joint modeling approach helps to improve predictions when sufficient information is available. For landmark 6 days and horizon of 30 days, we obtain AUC [95% CI] 0.73 [0.65, 0.81] and 0.81 [0.73, 0.89] for the baseline and joint model, respectively (p = 0.04). Statistical inference is validated through a simulation study.

Pharmacokinetic Model of Tenofovir and Emtricitabine and Their Intracellular Metabolites in Patients in the ANRS 134-COPHAR 3 Trial Using Dose Records.

Tenofovir (TFV) and emtricitabine (FTC) are part of the recommended highly active antiretroviral therapy (ART). Both molecules show a large interindividual pharmacokinetic (PK) variability. Here, we modeled the concentrations of plasma TFV and FTC and their intracellular metabolites (TFV diphosphate [TFV-DP] and FTC triphosphate [FTC-TP]) collected after 4 and 24 weeks of treatment in 34 patients from the ANRS 134-COPHAR 3 trial. These patients received daily (QD) atazanavir (300 mg), ritonavir (100 mg), and a fixed-dose combination of coformulated TFV disoproxil fumarate (300 mg) and FTC (200 mg). Dosing history was collected using a medication event monitoring system. A three-compartment model with absorption delay (Tlag) was selected to describe the PK of, respectively, TFV/TFV-DP and FTC/FTC-TP. TFV and FTC apparent clearances, 114 L/h (relative standard error [RSE] = 8%) and 18.1 L/h (RSE = 5%), respectively, were found to decrease with age. However, no significant association was found with the polymorphisms ABCC2 rs717620, ABCC4 rs1751034, and ABCB1 rs1045642. The model allows prediction of TFV-DP and FTC-TP concentrations at steady state with alternative regimens.

Efficient model-based bioequivalence testing.

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and $C_{\max}$ are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the $90\%$ confidence interval for these ratios falls between $0.8$ and $1.25 $. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of $\rm AUC$ and $C_{\max}$ using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of $0.05$, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

Implementation of a centralized pharmacovigilance system in academic pan-European clinical trials: Experience from EU-Response and conect4children consortia.

Setting-up a high quality, compliant and efficient pharmacovigilance (PV) system in multi-country clinical trials can be more challenging for academic sponsors than for companies. To ensure the safety of all participants in academic studies and that the PV system fulfils all regulations, we set up a centralized PV system that allows sponsors to delegate work on PV. This initiative was put in practice by our Inserm-ANRS MIE PV department in two distinct multinational European consortia with 19 participating countries: conect4children (c4c) for paediatrics research and EU-Response for Covid-19 platform trials. The centralized PV system consists of some key procedures to harmonize the complex safety processes, creation of a local safety officer (LSO) network and centralization of all safety activities. The key procedures described the safety management plan for each trial and how tasks were shared and delegated between all stakeholders. Processing of serious adverse events (SAEs) in a unique database guaranteed the full control of the safety data and continuous evaluation of the risk-benefit ratio. The LSO network participated in efficient regulatory compliance across multiple countries. In total, there were 1312 SAEs in EU-Response and 83 SAEs in c4c in the four trials. We present here the lessons learnt from our experience in four clinical trials. We managed heterogeneous European local requirements and implemented efficient communication with all trial teams. Our approach builds capacity for PV that can be used by multiple academic sponsors.

An open randomized multicentre Phase 2 trial to assess the safety of DAV132 and its efficacy to protect gut microbiota diversity in hospitalized patients treated with fluoroquinolones.

BACKGROUND: DAV132 (colon-targeted adsorbent) has prevented antibiotic-induced effects on microbiota in healthy volunteers. OBJECTIVES: To assess DAV132 safety and biological efficacy in patients. PATIENTS AND METHODS: An open-label, randomized [stratification: fluoroquinolone (FQ) indication] multicentre trial comparing DAV132 (7.5 g, 3 times a day, orally) with No-DAV132 in hospitalized patients requiring 5-21 day treatment with FQs and at risk of Clostridioides difficile infection (CDI). FQ and DAV132 were started simultaneously, DAV132 was administered for 48 h more, and patients were followed up for 51 days. The primary endpoint was the rate of adverse events (AEs) independently adjudicated as related to DAV132 and/or FQ. The planned sample size of 260 patients would provide a 95% CI of ±11.4%, assuming a 33% treatment-related AE rate. Plasma and faecal FQ concentrations, intestinal microbiota diversity, intestinal colonization with C. difficile, MDR bacteria and yeasts, and ex vivo resistance to C. difficile faecal colonization were assessed. RESULTS: Two hundred and forty-three patients (median age 71 years; 96% with chronic comorbidity) were included (No-DAV132, n = 120; DAV132, n = 123). DAV132- and/or FQ-related AEs did not differ significantly: 18 (14.8%) versus 13 (10.8%) in DAV132 versus No-DAV132 patients (difference 3.9%; 95% CI: -4.7 to 12.6). Day 4 FQ plasma levels were unaffected. DAV132 was associated with a >98% reduction in faecal FQ levels (Day 4 to end of treatment; P < 0.001), less impaired microbiota diversity (Shannon index; P = 0.003), increased ex vivo resistance to C. difficile colonization (P = 0.0003) and less frequent FQ-induced VRE acquisition (P = 0.01). CONCLUSIONS: In FQ-treated hospitalized patients, DAV132 was well tolerated, and FQ plasma concentrations unaffected. DAV132 preserved intestinal microbiota diversity and C. difficile colonization resistance.

Effect of remdesivir on viral dynamics in COVID-19 hospitalized patients: a modelling analysis of the randomized, controlled, open-label DisCoVeRy trial.

BACKGROUND: The antiviral efficacy of remdesivir in COVID-19 hospitalized patients remains controversial. OBJECTIVES: To estimate the effect of remdesivir in blocking viral replication. METHODS: We analysed nasopharyngeal normalized viral loads from 665 hospitalized patients included in the DisCoVeRy trial (NCT04315948; EudraCT 2020-000936-23), randomized to either standard of care (SoC) or SoC + remdesivir. We used a mathematical model to reconstruct viral kinetic profiles and estimate the antiviral efficacy of remdesivir in blocking viral replication. Additional analyses were conducted stratified on time of treatment initiation (≤7 or >7 days since symptom onset) or viral load at randomization (< or ≥3.5 log10 copies/104 cells). RESULTS: In our model, remdesivir reduced viral production by infected cells by 2-fold on average (95% CI: 1.5-3.2-fold). Model-based simulations predict that remdesivir reduced time to viral clearance by 0.7 days compared with SoC, with large inter-individual variabilities (IQR: 0.0-1.3 days). Remdesivir had a larger impact in patients with high viral load at randomization, reducing viral production by 5-fold on average (95% CI: 2.8-25-fold) and the median time to viral clearance by 2.4 days (IQR: 0.9-4.5 days). CONCLUSIONS: Remdesivir halved viral production, leading to a median reduction of 0.7 days in the time to viral clearance compared with SoC. The efficacy was larger in patients with high viral load at randomization.

SARS-CoV-2 viral dynamics in non-human primates.

Non-human primates infected with SARS-CoV-2 exhibit mild clinical signs. Here we used a mathematical model to characterize in detail the viral dynamics in 31 cynomolgus macaques for which nasopharyngeal and tracheal viral load were frequently assessed. We identified that infected cells had a large burst size (>104 virus) and a within-host reproductive basic number of approximately 6 and 4 in nasopharyngeal and tracheal compartment, respectively. After peak viral load, infected cells were rapidly lost with a half-life of 9 hours, with no significant association between cytokine elevation and clearance, leading to a median time to viral clearance of 10 days, consistent with observations in mild human infections. Given these parameter estimates, we predict that a prophylactic treatment blocking 90% of viral production or viral infection could prevent viral growth. In conclusion, our results provide estimates of SARS-CoV-2 viral kinetic parameters in an experimental model of mild infection and they provide means to assess the efficacy of future antiviral treatments.

Impact of model misspecification on model-based tests in PK studies with parallel design: real case and simulation studies.

This article evaluates the performance of pharmacokinetic (PK) equivalence testing between two formulations of a drug through the Two-One Sided Tests (TOST) by a model-based approach (MB-TOST), as an alternative to the classical non-compartmental approach (NCA-TOST), for a sparse design with a few time points per subject. We focused on the impact of model misspecification and the relevance of model selection for the reference data. We first analysed PK data from phase I studies of gantenerumab, a monoclonal antibody for the treatment of Alzheimer's disease. Using the original rich sample data, we compared MB-TOST to NCA-TOST for validation. Then, the analysis was repeated on a sparse subset of the original data with MB-TOST. This analysis inspired a simulation study with rich and sparse designs. With rich designs, we compared NCA-TOST and MB-TOST in terms of type I error and study power. With both designs, we explored the impact of misspecifying the model on the performance of MB-TOST and adding a model selection step. Using the observed data, the results of both approaches were in general concordance. MB-TOST results were robust with sparse designs when the underlying PK structural model was correctly specified. Using the simulated data with a rich design, the type I error of NCA-TOST was close to the nominal level. When using the simulated model, the type I error of MB-TOST was controlled on rich and sparse designs, but using a misspecified model led to inflated type I errors. Adding a model selection step on the reference data reduced the inflation. MB-TOST appears as a robust alternative to NCA-TOST, provided that the PK model is correctly specified and the test drug has the same PK structural model as the reference drug.

eOSCE stations live versus remote evaluation and scores variability.

BACKGROUND: Objective structured clinical examinations (OSCEs) are known to be a fair evaluation method. These recent years, the use of online OSCEs (eOSCEs) has spread. This study aimed to compare remote versus live evaluation and assess the factors associated with score variability during eOSCEs. METHODS: We conducted large-scale eOSCEs at the medical school of the Université de Paris Cité in June 2021 and recorded all the students' performances, allowing a second evaluation. To assess the agreement in our context of multiple raters and students, we fitted a linear mixed model with student and rater as random effects and the score as an explained variable. RESULTS: One hundred seventy observations were analyzed for the first station after quality control. We retained 192 and 110 observations for the statistical analysis of the two other stations. The median score and interquartile range were 60 out of 100 (IQR 50-70), 60 out of 100 (IQR 54-70), and 53 out of 100 (IQR 45-62) for the three stations. The score variance proportions explained by the rater (ICC rater) were 23.0, 16.8, and 32.8%, respectively. Of the 31 raters, 18 (58%) were male. Scores did not differ significantly according to the gender of the rater (p = 0.96, 0.10, and 0.26, respectively). The two evaluations showed no systematic difference in scores (p = 0.92, 0.053, and 0.38, respectively). CONCLUSION: Our study suggests that remote evaluation is as reliable as live evaluation for eOSCEs.

cLRT-Mod: An efficient methodology for pharmacometric model-based analysis of longitudinal phase II dose finding studies under model uncertainty.

Within the challenging context of phase II dose-finding trials, longitudinal analyses may increase drug effect detection power compared to an end-of-treatment analysis. This work proposes cLRT-Mod, a pharmacometric adaptation of the MCP-Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose-response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose-finding trial using different scenarios and comparing different methods such as MCP-Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre-specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under-estimation of the size of the confidence interval. Finally, for illustration purpose cLRT-Mod was applied to the analysis of a real phase II dose-finding trial.

Evaluation by simulation of clinical trial designs for evaluation of treatment during a viral haemorrhagic fever outbreak.

BACKGROUND: Viral haemorrhagic fevers are characterized by irregular outbreaks with high mortality rate. Difficulties arise when implementing therapeutic trials in this context. The outbreak duration is hard to predict and can be short compared to delays of trial launch and number of subject needed (NSN) recruitment. Our objectives were to compare, using clinical trial simulation, different trial designs for experimental treatment evaluation in various outbreak scenarios. METHODS: Four type of designs were compared: fixed or group-sequential, each being single- or two-arm. The primary outcome was 14-day survival rate. For single-arm designs, results were compared to a pre-trial historical survival rate pH. Treatments efficacy was evaluated by one-sided tests of proportion (fixed designs) and Whitehead triangular tests (group-sequential designs) with type-I-error = 0.025. Both survival rates in the control arm pC and survival rate differences Δ (including 0) varied. Three specific cases were considered: "standard" (fixed pC, reaching NSN for fixed designs and maximum sample size NMax for group-sequential designs); "changing with time" (increased pC over time); "stopping of recruitment" (epidemic ends). We calculated the proportion of simulated trials showing treatment efficacy, with K = 93,639 simulated trials to get a type-I-error PI95% of [0.024;0.026]. RESULTS: Under H0 (Δ = 0), for the "standard" case, the type-I-error was maintained regardless of trial designs. For "changing with time" case, when pC > pH, type-I-error was inflated, and when pC < pH it decreased. Wrong conclusions were more often observed for single-arm designs due to an increase of Δ over time. Under H1 (Δ = + 0.2), for the "standard" case, the power was similar between single- and two-arm designs when pC = pH. For "stopping of recruitment" case, single-arm performed better than two-arm designs, and fixed designs reported higher power than group-sequential designs. A web R-Shiny application was developed. CONCLUSIONS: At an outbreak beginning, group-sequential two-arm trials should be preferred, as the infected cases number increases allowing to conduct a strong randomized control trial. Group-sequential designs allow early termination of trials in cases of harmful experimental treatment. After the epidemic peak, fixed single-arm design should be preferred, as the cases number decreases but this assumes a high level of confidence on the pre-trial historical survival rate.

Rationale of a loading dose initiation for hydroxychloroquine treatment in COVID-19 infection in the DisCoVeRy trial.

Around the world, several dose regimens of hydroxychloroquine have been used for COVID-19 infection treatment, with the objective of identifying a short-term course. Hydroxychloroquine was found to decrease the viral replication in a concentration-dependent manner in vitro and to be more active when added prior to the viral challenge. A loading dose is used to rapidly attain a target drug concentration, which is usually considered as approximately the steady-state concentration. With a loading dose, the minimum effective concentration is reached much more rapidly than when using only the maintenance dose from the start. Thus, we propose a hydroxychloroquine sulphate dose regimen of 400 mg twice daily at Day 1 then 400 mg once daily from Day 2 to Day 10. We aim to evaluate this in the C-20-15 DisCoVeRy trial.

Pharmacovirological analyses of blood and male genital compartment in patients receiving dolutegravir + lamivudine dual therapy as a switch strategy (ANRS 167 LAMIDOL trial).

OBJECTIVES: To describe plasma residual HIV viraemia, cellular HIV reservoir size, blood plasma drug concentrations and their male genital tract penetration during the maintenance dual therapy dolutegravir + lamivudine. PATIENTS AND METHODS: ANRS167 LAMIDOL enrolled 104 virologically suppressed patients to switch to dolutegravir + lamivudine. In this pharmacovirological substudy, ultrasensitive plasma viral load (USpVL) and plasma drug concentrations were measured at Day 0 (D0), Week 24 (W24) and W48 of dolutegravir + lamivudine, and HIV-DNA was measured at W-8 and W48. Semen samples were collected at D0 and W24 from 18 participants. Total and unbound blood and seminal plasma drug concentrations were measured using UPLC-MS/MS. RESULTS: Median HIV-DNA was 2.5 log10 copies/106 PBMC (IQR = 2.2-3.0, n = 100) at W-8 and 2.4 log10 copies/106 PBMC (IQR = 2.1-2.9, n = 100) at W48 (P = 0.17). The proportion of patients with undetected USpVL was 38% (n = 98), 43% (n = 98) and 49% (n = 97) at D0, W24 and W48, respectively (P = 0.08). Total and unbound plasma dolutegravir concentrations were stable between timepoints (P = 0.13) and all total plasma dolutegravir concentrations except one were adequate. Median free fraction of dolutegravir in plasma was 0.21%. Median blood plasma and seminal plasma concentrations of total dolutegravir at 24 h were 1812 ng/mL and 206 ng/mL, respectively. Median seminal plasma/blood plasma total concentration ratios were 11.6% and 2478% for dolutegravir and lamivudine, respectively. HIV-RNA (365 to 475 copies/mL) was detected in seminal plasma of one patient at D0 (5.9%) and of two patients at W24 (11.8%). CONCLUSIONS: These findings add further important information regarding the effectiveness of dolutegravir + lamivudine maintenance dual therapy in terms of plasma residual viraemia, cellular reservoir size and drug penetration in the male genital tract.

Timing of ß-Blocker Reintroduction and the Occurrence of Postoperative Atrial Fibrillation after Cardiac Surgery: A Prospective Cohort Study.

BACKGROUND: For cardiac surgery patients under chronic ß-blocker therapy, guidelines recommend their early postoperative reintroduction to decrease the incidence of postoperative atrial fibrillation. The authors hypothesized that the timing of ß-blocker reintroduction affects their effectiveness on the incidence of postoperative atrial fibrillation. METHODS: This multicenter prospective French cohort study included patients on ß-blockers (more than 30 days before surgery) in sinus rhythm without a pacemaker. The primary outcome, time sequence of ß-blocker reintroduction, was analyzed for 192 h after surgery. The secondary outcome, relationship between the occurrence of postoperative atrial fibrillation and timing of ß-blocker reintroduction, was analyzed based on pre- and intraoperative predictors (full and selected sets) according to landmark times (patients in whom atrial fibrillation occurred before a given landmark time were not analyzed). RESULTS: Of 663 patients, ß-blockers were reintroduced for 532 (80%) but for only 261 (39%) patients in the first 48 h after surgery. Median duration before reintroduction was 49.5 h (95% CI, 48 to 51.5 h). Postoperative atrial fibrillation or death (N = 4) occurred in 290 (44%) patients. After performing a landmark analysis to take into account the timing of ß-blocker reintroduction, the adjusted odds ratios (95% CI) for predictor full and selected (increased age, history of paroxysmal atrial fibrillation, and duration of aortic cross clamping) sets for the occurrence of postoperative atrial fibrillation were: adjusted odds ratio (full) = 0.87 (0.58 to 1.32; P = 0.517) and adjusted odds ratio (selected) = 0.84 (0.58 to 1.21; P = 0.338) at 48 h; adjusted odds ratio (full) = 0.64 (0.39 to 1.05; P = 0.076) and adjusted odds ratio (selected) = 0.58 (0.38 to 0.89; P = 0.013) at 72 h; adjusted odds ratio (full) = 0.58 (0.31 to 1.07; P = 0.079) and adjusted odds ratio (selected) = 0.53 (0.31 to 0.91; P = 0.021) at 96 h. CONCLUSIONS: ß-Blockers were reintroduced early (after less than 48 h) in fewer than half of the cardiac surgery patients. Reintroduction decreased postoperative atrial fibrillation occurrence only at later time points and only in the predictor selected set model. These results are an incentive to optimize (timing, doses, or titration) ß-blocker reintroduction after cardiac surgery.