Purpose. To develop a pharmacokinetic model for tenidap and to identify important relationships between the pharmacokinetic parameters and available covariates. Methods. Plasma concentration data from several phase I and phase II studies were used to develop a pharmacokinetic model for tenidap, a novel anti-rheumatic drug. An appropriate pharmacokinetic model was selected on the basis of individual nonlinear regression analyses and an EM algorithm was used to perform a nonlinear mixed-effects analysis. Scatter plots of posterior individual pharmacokinetic parameters were used to identify possible covariate effects. Results. Predicted responses were in good agreement with the observed data. A bi-exponential model with zero order absorption was subsequently used to develop the mixed-effects model. Covariate relationships selected on the basis of differences in the objective function, although statistically significant, were not particularly strong. Conclusions. The pharmacokinetics of tenidap can be described by a bi- exponential model with zero order absorption. Based on differences in the log-likelihood, significant covariate-parameter relationships were identified between smoking and CL, and between gender and Vss and CL(d). Simulated sparse data analyses indicated that the model would be robust for the analysis of sparse data generated in observational studies.
- EM algorithm
- Nonlinear mixed-effects modelling