title: Determination of a suitable voriconazole pharmacokinetic model for personalised dosing.
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.
authors.affiliation: School of Pharmacy, University of Queensland, Brisbane, QLD, Australia. david.mcdougall1@uqconnect.edu.au.|Model Answers Pty Limited, Brisbane, QLD, Australia. david.mcdougall1@uqconnect.edu.au.
authors.forename: David A J
authors.identifier: http://orcid.org/0000-0002-8677-3770
authors.initials: DA
authors.lastname: McDougall
authors.affiliation: School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Martin
authors.affiliation: Infection Management Services, Princess Alexandra Hospital, Brisbane, QLD, Australia.|School of Medicine, University of Queensland, Brisbane, QLD, Australia.
authors.forename: E Geoffrey
authors.initials: EG
authors.lastname: Playford
authors.affiliation: Model Answers Pty Limited, Brisbane, QLD, Australia.
authors.forename: Bruce
authors.initials: B
authors.lastname: Green
mesh_terms: D000935:Antifungal Agents / Q000493:pharmacokinetics*; D006801:Humans; D008962:Models, Theoretical; D057285:Precision Medicine / Q000379:methods*; D065819:Voriconazole / Q000493:pharmacokinetics*
chemical_list: D000935:Antifungal Agents; D065819:Voriconazole
keywords: Bayesian dose forecasting; Dose individualisation; Model based personalised dosing; Personalised medicine; Voriconazole