Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data

• DOI: 10.1007/s40262-023-01274-y
• Publication Date: 2023-08-21T12:02:18Z
• Authors: Franziska Kluwe; Robin Michelet; Wilhelm Huisinga; Markus Zeitlinger; Gerd Mikus; Charlotte Kloft
• Keywords: model-informed precision dosing; Antifungal Agents; Nonlinear Dynamics; Nonlinear Dynamics [MeSH] ; Voriconazole/pharmacokinetics [MeSH] ; Humans [MeSH] ; Original Research Article ; Antifungal Agents/pharmacokinetics [MeSH] ; Bayes Theorem [MeSH] ; Models, Biological [MeSH]; 615; voriconazole; Humans; Bayes Theorem; Original Research Article; Voriconazole; 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::615 Pharmakologie, Therapeutik; Models, Biological; nonlinear mixed-effects models

Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics therapeutic drug monitoring data. MIPD is indicated for compounds with narrow range complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal prevention treatment of invasive fungal infections. To provide guidance recommendations evidence-based application this work aimed (i) externally evaluate compare the predictive performance published so-called ‘hybrid’ model (an aggregate comprising features prior information from six previously NLME models) versus two ‘standard’ (ii) investigate strategies illustrate clinical impact Bayesian forecasting voriconazole. A workflow external evaluation voriconazole was implemented. Published were evaluated using comprehensive in-house database nine studies prediction-/simulation-based diagnostics. The applied different assess influence observations predictivity. overall best obtained model. However, all showed only modest performance, suggesting that important PK processes not sufficiently implemented in structural submodels, sources interindividual variability entirely captured, (iii) interoccasion adequately accounted for. Predictive substantially improved by including most recent MIPD. Our results highlight potential indicate need (pre-)clinical basis development careful before their successful use