Infection, Immunity and Inflammation Research and Teaching Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Beyond Consulting Ltd., Cheshire, UK.
Br J Clin Pharmacol. 2022 Dec;88(12):5428-5433. doi: 10.1111/bcp.15518. Epub 2022 Sep 15.
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 : 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.
对时间序列病毒载量数据进行药代动力学分析，可能比使用单点法具有更大的检测药物效果的能力。由于 SARS-CoV-2 病毒载量迅速上升然后下降，因此使用了病毒动力学模型。我们在分析 II 期病毒动力学数据时比较了不同的建模方法。使用两个在症状出现后 7 天内开始的 SARS-CoV-2 病毒载量数据集，我们使用 nlmixr 拟合了斜率截距指数衰减（SI）、简化靶细胞有限（rTCL）、靶细胞有限（TCL）和带有蚀斑阶段的 TCL（TCLE）模型。通过贝叶斯信息准则（BIC）、预测性检查（VPCs）、拟合优度图和参数精度来评估模型性能。对于两个数据集，最复杂的模型（TCLE）的 BIC 最高。除了 A 数据集的 TCL 模型，所有模型的估计病毒下降率都相似，A 数据集的下降率更高（中位数[范围]天：数据集 A：0.63[0.56-1.84]；数据集 B：0.81[0.74-0.85]）。我们的研究结果表明，在药效动力学模型开发过程中应考虑使用简单的模型。
