Pharmacometric estimation methods for aggregate data, including data simulated from other pharmacometric models.

Lack of data is an obvious limitation to what can be modelled. However, aggregate data in the form of means and possibly (co)variances, as well as previously published pharmacometric models, are often available. Being able to use all available data is desirable, and therefore this paper will outline several methods for using aggregate data as the basis of parameter estimation. The presented methods can be used for estimation of parameters from aggregate data, and as a computationally efficient alternative for the stochastic simulation and estimation procedure. They also allow for population PK/PD optimal design in the case when the data-generating model is different from the data-analytic model, a scenario for which no solutions have previously been available. Mathematical analysis and computational results confirm that the aggregate-data FO algorithm converges to the same estimates as the individual-data FO and yields near-identical standard errors when used in optimal design. The aggregate-data MC algorithm will asymptotically converge to the exactly correct parameter estimates if the data-generating model is the same as the data-analytic model. The performance of the aggregate-data methods were also compared to stochastic simulations and estimations (SSEs) when the data-generating model is different from the data-analytic model. The aggregate-data FO optimal design correctly predicted the sampling distributions of 200 models fitted to simulated datasets with the individual-data FO method.

Population pharmacokinetics of dexmedetomidine in critically ill patients.

BACKGROUND AND OBJECTIVES: Although the pharmacokinetics of dexmedetomidine in healthy volunteers have been studied, there are limited data about the pharmacokinetics of long-term administration of dexmedetomidine in critically ill patients. METHODS: This population pharmacokinetic analysis was performed to quantify the pharmacokinetics of dexmedetomidine in critically ill patients following infusions up to 14 days in duration. The data consisted of three phase III studies (527 patients with sparse blood sampling, for a total of 2,144 samples). Covariates were included in a full random-effects covariate model and the most important covariate relationships were tested separately. The linearity of dexmedetomidine clearance was evaluated by observing steady-state plasma concentrations acquired at various infusion rates. RESULTS: The data were adequately described with a one-compartment model. The clearance of dexmedetomidine was 39 (95 % CI 37-41) L/h and volume of distribution 104 (95 % CI 93-115) L. Both clearance and volume of distribution were highly variable between patients (coefficients of variation of 62 and 57 %, respectively), which highlights the importance of dose titration by response. Covariate analysis showed a strong correlation between body weight and clearance of dexmedetomidine. The clearance of dexmedetomidine was constant in the dose range 0.2-1.4 µg/kg/h. CONCLUSIONS: The pharmacokinetics of dexmedetomidine are dose-proportional in prolonged infusions when dosing rates of 0.2-1.4 µg/kg/h, recommended by the Dexdor(®) summary of product characteristics, are used.