PLoS Negl Trop Dis

International audienc

Endpoints for randomized controlled clinical trials for COVID-19 treatments

Background: Endpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between “cure” and “death” represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of “recovered” versus “not recovered.” Methods: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Results: Power for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time. Discussion: Time-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses. © The Author(s) 2020

CPT Pharmacometrics Syst Pharmacol

We modeled the viral dynamics of 13 untreated patients infected with SARS-CoV-2 to infer viral growth parameters and predict the effects of antiviral treatments. In order to reduce peak viral load by more than 2 logs, drug efficacy needs to be greater than 90% if treatment is administered after symptom onset; an efficacy of 60% could be sufficient if treatment is initiated before symptom onset. Given their pharmacokinetic/pharmacodynamic properties, current investigated drugs may be in a range of 6-87% efficacy. They may help control virus if administered very early, but may not have a major effect in severe patients

The role of population PK-PD modelling in paediatric clinical research

Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK-PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK-PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK-PD parameters with the highest precision. Once a population PK-PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

Background: We previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15–20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in ~ 80% of cases. Methods: We report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded. Results: No gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5–528.7, P = 1.1 × 10−4) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR = 3.70[95%CI 1.3–8.2], P = 2.1 × 10−4). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR = 19.65[95%CI 2.1–2635.4], P = 3.4 × 10−3), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR = 4.40[9%CI 2.3–8.4], P = 7.7 × 10−8). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD] = 43.3 [20.3] years) than the other patients (56.0 [17.3] years; P = 1.68 × 10−5). Conclusions: Rare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old

Clinical applicability of current pharmacokinetic models: Splanchnic elimination of 5-fluorouracil in cancer patients

What can be inferred from limited clinical data by using current models of hepatic elimination? We examined this question by analyzing previously published data on the steady-state uptake of the anticancer agent 5-fluorouracil (5-FU) in seven cancer patients in terms of the venous equilibration model, the undistributed and distributed forms of the sinusoidal perfusion model, and the convection-dispersion model. Because of appreciable extrasplanchnic removal of 5-FU, the value of the steady infusion rate was not used in our analysis. When the data from all patients were pooled by plotting the measured hepatic venous concentration against the measured hepatic arterial concentration, the high concentration data fell on a limiting straight line of slope 1, indicating that at high dose rates elimination of 5-FU in both the liver and gastrointestinal tract was close to saturation. The intercept of this line gave a model-independent estimate of V max /Q= 48.0± 11.6 (SD) μM for the pooled data set, where V max is the maximum splanchnic elimination rate of 5-FU, and Q is the hepatic blood flow. The low concentration data points fell on a limiting straight line through the origin, from which model-dependent values of the Michaelis constant were determined. The venous equilibration model gave K m =9.4 μM , while the undistributed sinusoidal perfusion model gave K m * =26,5 μM. With these values of K m , both models fit the pooled data equally well. These methods were applied to analyses of the five individual data sets which contained sufficiently high concentration data points. The resulting mean values were V max /Q=41.0±5.1 (sem) μM, K m =8.4±1.3μM and K m * =23.2±3.2 μM. However, the splanchnic region is a highly heterogeneous organ system, for which an undistributed analysis provides no more than an upper bound on the Michaelis constant K m + ( K m + ⩽ K m * ). A perfusion model distributed to represent total splanchnic elimination is developed in the Appendix. Using previous estimates of the degree of functional heterogeneity in the liver alone, this model yields K m + values for individual patients which have a mean of 20.3±2.8 μM .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45038/1/10928_2005_Article_BF01062135.pd

Development and implementation of the population Fisher information matrix for the evaluation of population pharmacokinetic designs.

In population pharmacokinetic studies, the precision of parameter estimates is dependent on the population design. Methods based on the Fisher information matrix have been developed and extended to population studies to evaluate and optimize designs. In this paper we propose simple programming tools to evaluate population pharmacokinetic designs. This involved the development of an expression for the Fisher information matrix for nonlinear mixed-effects models, including estimation of the variance of the residual error. We implemented this expression as a generic function for two software applications: S-PLUS and MATLAB. The evaluation of population designs based on two pharmacokinetic examples from the literature is shown to illustrate the efficiency and the simplicity of this theoretic approach. Although no optimization method of the design is provided, these functions can be used to select and compare population designs among a large set of possible designs, avoiding a lot of simulations