Comparative assessment of viral dynamic models for SARS‐CoV‐2 for pharmacodynamic assessment in early treatment trials

Pharmacometric analyses of time series viral load data may detect drug effects with greater power than approaches using single time points. Because SARS-CoV-2 viral load rapidly rises and then falls, viral dynamic models have been used. We compared different modelling approaches when analysing Phase II-type viral dynamic data. Using two SARS-CoV-2 datasets of viral load starting within 7 days of symptoms, we fitted the slope-intercept exponential decay (SI), reduced target cell limited (rTCL), target cell limited (TCL) and TCL with eclipse phase (TCLE) models using nlmixr. Model performance was assessed via Bayesian information criterion (BIC), visual predictive checks (VPCs), goodness-of-fit plots, and parameter precision. The most complex (TCLE) model had the highest BIC for both datasets. The estimated viral decline rate was similar for all models except the TCL model for dataset A with a higher rate [median (range) day-1: dataset A; 0.63 (0.56 – 1.84); dataset B: 0.81 (0.74-0.85)]. Our findings suggest simple models should be considered during pharmacodynamic model development

Human infection with influenza H9N2

We report the clinical features of two cases of human infection with influenza A virus subtype H9N2 in Hong Kong, and show that serum samples from blood donors in Hong Hong had neutralising antibody suggestive of prior infection with influenza H9N2.link_to_subscribed_fulltex

Efficient Metropolis-Hastings sampling for nonlinear mixed effects models

International audienceThe ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely used to conduct such sampling, but such a method can converge slowly for high dimension problems, or when the joint structure of the distributions to sample is complex. We propose a Metropolis-Hastings (MH) algorithm based on a multidimensional Gaussian proposal that takes into account the joint conditional distribution of the random effects and does not require any tuning, in contrast with more sophisticated samplers such as the Metropolis Adjusted Langevin Algorithm or the No-U-Turn Sampler that involve costly tuning runs or intensive computation. Indeed, this distribution is automatically obtained thanks to a Laplace approximation of the original model. We show that such approximation is equivalent to linearizing the model in the case of continuous data. Numerical experiments based on real data highlight the very good performances of the proposed method for continuous data models