Comparative assessment of viral dynamic models for SARS-CoV-2 for pharmacodynamic assessment in early treatment trials.
Br J Clin Pharmacol
; 88(12): 5428-5433, 2022 12.
Article
en En
| MEDLINE
| ID: mdl-36040430
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
SARS-CoV-2
/
Tratamiento Farmacológico de COVID-19
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Br J Clin Pharmacol
Año:
2022
Tipo del documento:
Article
País de afiliación:
Reino Unido