Determination of a suitable voriconazole pharmacokinetic model for personalised dosing

McDougall, David A.J., Martin, Jennifer, Playford, E. Geoffrey and Green, Bruce (2015) Determination of a suitable voriconazole pharmacokinetic model for personalised dosing. Journal of Pharmacokinetics and Pharmacodynamics, 43 2: 165-177. doi:10.1007/s10928-015-9462-9

Author McDougall, David A.J.
Martin, Jennifer
Playford, E. Geoffrey
Green, Bruce
Title Determination of a suitable voriconazole pharmacokinetic model for personalised dosing
Journal name Journal of Pharmacokinetics and Pharmacodynamics   Check publisher's open access policy
ISSN 1573-8744
Publication date 2015-12-16
Year available 2015
Sub-type Article (original research)
DOI 10.1007/s10928-015-9462-9
Open Access Status Not yet assessed
Volume 43
Issue 2
Start page 165
End page 177
Total pages 13
Place of publication New York, United States
Publisher Springer
Language eng
Subject 3004 Pharmacology
Abstract Model based personalised dosing (MBPD) is a sophisticated form of individualised therapy, where a population pharmacokinetic (PK) or pharmacodynamic model is utilised to estimate the dose required to reach a target exposure or effect. The choice of which model to implement in MBPD is a subjective decision. By choosing one model, information from the remaining models is ignored, as well as the rest of the literature base. This manuscript describes a methodology to develop a ‘hybrid’ model for voriconazole that incorporated information from prior models in a biologically plausible manner. Voriconazole is a triazole antifungal with difficult to predict PK, although it does have a defined exposure–response relationship. Nine population PK models of voriconazole were identified from the literature. The models differed significantly in structural components. The hybrid model contained a two-compartment disposition model with mixed linear and nonlinear time-dependent clearance. The parameters for the hybrid model were determined using simulation techniques. Validation of the hybrid model was assessed via visual predictive checks, which indicated the majority of the variability in the literature models was captured by the hybrid model. The predictive performance was assessed using four different sampling strategies of limited concentrations from ten richly PK sampled subjects to predict future concentrations. Overall, the hybrid model predicted future concentrations with good precision. Further prospective and retrospective validation of the hybrid model is required before it could be used in clinical practice.
Keyword Personalised medicine
Bayesian dose forecasting
Model based personalised dosing
Dose individualisation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Medicine Publications
School of Pharmacy Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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