Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Kelchstraße 31, 12169, Berlin, Germany.
Graduate Research Training Program PharMetrX, Berlin/Potsdam, Germany.
Clin Pharmacokinet. 2023 Oct;62(10):1461-1477. doi: 10.1007/s40262-023-01274-y. Epub 2023 Aug 21.
Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics and therapeutic drug monitoring data. MIPD is indicated for compounds with narrow therapeutic range and complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal drug for prevention and treatment of invasive fungal infections. To provide guidance and recommendations for evidence-based application of MIPD for voriconazole, this work aimed to (i) externally evaluate and compare the predictive performance of a published so-called 'hybrid' model for MIPD (an aggregate model comprising features and prior information from six previously published NLME models) versus two 'standard' NLME models of voriconazole, and (ii) investigate strategies and illustrate the clinical impact of Bayesian forecasting for voriconazole.
A workflow for external evaluation and application of MIPD for voriconazole was implemented. Published voriconazole NLME models were externally evaluated using a comprehensive in-house clinical database comprising nine voriconazole studies and prediction-/simulation-based diagnostics. The NLME models were applied using different Bayesian forecasting strategies to assess the influence of prior observations on model predictivity.
The overall best predictive performance was obtained using the aggregate model. However, all NLME models showed only modest predictive performance, suggesting that (i) important PK processes were not sufficiently implemented in the structural submodels, (ii) sources of interindividual variability were not entirely captured, and (iii) interoccasion variability was not adequately accounted for. Predictive performance substantially improved by including the most recent voriconazole observations in MIPD.
Our results highlight the potential clinical impact of MIPD for voriconazole and indicate the need for a comprehensive (pre-)clinical database as basis for model development and careful external model evaluation for compounds with complex PK before their successful use in MIPD.
模型指导下的精准剂量(MIPD)常用于根据患者特征和治疗药物监测数据,利用非线性混合效应(NLME)模型预测和优化治疗效果。窄治疗窗且药代动力学(PK)复杂的药物(如伏立康唑,广谱抗真菌药物,用于预防和治疗侵袭性真菌感染)需要 MIPD。本研究旨在(i)对一种发表的、用于 MIPD 的所谓“混合”模型(一种汇总模型,包含来自 6 个先前发表的 NLME 模型的特征和先验信息)和两种伏立康唑的“标准”NLME 模型的预测性能进行外部评估和比较,(ii)探讨并举例说明伏立康唑贝叶斯预测的临床应用策略。
建立了伏立康唑 MIPD 的外部评估和应用工作流程。利用包含 9 项伏立康唑研究和预测/模拟诊断的内部综合临床数据库,对发表的伏立康唑 NLME 模型进行外部评估。应用不同的贝叶斯预测策略,以评估先验观测对模型预测能力的影响。
汇总模型的整体预测性能最佳。然而,所有 NLME 模型的预测性能均仅为中等,表明(i)结构子模型未充分体现重要的 PK 过程,(ii)个体间变异性的来源未完全捕捉,(iii)间变异性未充分考虑。在 MIPD 中纳入最新的伏立康唑观察结果,可显著提高预测性能。
本研究结果突出了 MIPD 用于伏立康唑的潜在临床影响,并表明在成功应用 MIPD 之前,需要一个全面的(临床前)数据库,作为模型开发的基础,并仔细进行外部模型评估,以了解复杂 PK 药物的情况。