Pharmacokinetic variability of nimodipine disposition after single and multiple oral dosing to hypertensive renal failure patients: parametric and nonparametric population analysis

Objectives: To explore the contribution ofrenal failure to nimodipine overa ll pharmacokinetic variability after single and multiple oral dosing and to develop a population pharmacokinetic model by means of the nonparametric e xpectation maximization (NPEM2) algorithm based on sampled individual drug concentrations close to the estimated patients'C(av)(SS)s (NPEM2-C-av(SS)). Patients. materials and methods: 24 hypertensive patients with normal and reduced renal function, without clinical and laboratory data for hepatic dy sfunction, were enrolled in the study and their nimodipine plasma levels we re analyzed by means of a parametric and nonparametric population pharmacok inetic modeling using a maximum a posteriori Bayesian (MAPB) estimator in a n iterative two-stage Bayesian population modeling program and NPEM2-algori thm. Results: Comparison of parameter dispersion revealed higher variabilit y of nimodipine disposition after the first dose than at steady-state excep t for apparent volume of distribution at steady-state, V-ss/F, whose variab ility increased from 98% to 223%. The most variable was mean residence time , MRT, whose coefficient of variation (CV) was 288% after the first dose an d decreased by more than 2 times at steady-state, followed by terminal elim ination half-life, t(1/2el), With CV = 171% after the first dosing and decr easing by more than 3 times at steadystate. Concerning the impact of renal failure on disposition parameters variability, patients with slightly to mo derately reduced renal function, creatinine clearances between 51 to 80 and 25 to 50 ml/min, resp., stated higher variation than patients with more de finitively altered renal function. The validation of NPEM2-C-av(SS) populat ion model was performed by using a set of 272 individual plasma drug concen trations, including trough levels as well as concentrations belonging to mo noexponential elimination phases after single and multiple dosing. Bayesian forecasting, using 4 trough levels per patient as Bayesian priors, reveale d highly significant correlation between observed and population model pred icted drug concentrations (r = 0.526, p < 0.0001). The predictive performan ce of NPEM2-C-av(SS) population model was characterized by low bias (mean e rror = -0.48 mu g/l, 95% CI = -0.99 - 0.04 mu g/l), and good precision (roo t mean squared error = 4.32 mu g/l, 95% CI = -2.53 - 11.17 mu g/l). Conclus ions: As predicted for high hepatic clearance drugs [Rowland 1985], nimodip ine parameters variability decreased after reaching steady-state. NPEM2-C-a v(SS) population model demonstrated high accuracy and precision in predicti ng drug levels from terminal exponential phase including trough levels at s teady-state.