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.