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


Agyeman, Akosua A ORCID: 0000-0002-8866-3524, You, Tao ORCID: 0000-0003-2033-6188, Chan, Phylinda LS, Lonsdale, Dagan O ORCID: 0000-0003-0838-921X, Hadjichrysanthou, Christoforos, Mahungu, Tabitha, Wey, Emmanuel Q, Lowe, David M, Lipman, Marc CI ORCID: 0000-0001-7501-4448, Breuer, Judy ORCID: 0000-0001-8246-0534 et al (show 2 more authors) (2022) Comparative assessment of viral dynamic models for SARS-CoV-2 for pharmacodynamic assessment in early treatment trials. British journal of clinical pharmacology, 88 (12). pp. 5428-5433.

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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<sup>-1</sup> : 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.

Item Type: Article
Uncontrolled Keywords: Humans, Viral Load, Bayes Theorem, SARS-CoV-2, COVID-19 Drug Treatment
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 28 Mar 2023 09:24
Last Modified: 18 Mar 2024 03:29
DOI: 10.1111/bcp.15518
Open Access URL: https://doi.org/10.1111/bcp.15518
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3169288
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