M.Tod, J.M. Rocchisani
Departement de Pharmacotoxicologie et Medecine Nucléaire, Hôpital Avicenne , Bobigny Cedex 93009 , France
The most common approach to optimize the sampling schedule in parameter estimation experiments is the D-optimality criterion, which consists in maximizing the determinant of the Fisher information matrix (max det F). In order to incorporate prior parameter uncertainty in the optimal design, other criteria have been proposed: The ED = max E(det F), EID = min E(l/det F) and API = max E(Log det F) criteria, where the expectation is with respect to the given prior distribution of the parameters. However, respective merits of these criteria and application to real data have not been evaluated. We implemented an algorithm based on adaptive random search ARS (1) followed by stochastic gradient SG (2) to determine optimal sampling times for parameter estimation in varied pharmacokinetic models. Prior distributions are allowed to be uniform, normal or lognormal. This algorithm combines the robustness of ARS and the speediness of SG (convergence is obtained in a few minutes on a microcomputer). The algorithm has been validated by comparison to the results described by D’Argenio (3) on a one compartment model with first-order absorption. Then, it has been applied to a five parameters stochastic model with zero-order absorption rate and Weibull-distributed residence times which was shown to describe adequately the kinetics of metacycline in humans (4). Population pharmacokinetic parameters of metacycline were estimated from a 6 subjects pilot study , by the iterative two-stage method, using ADAPT II repeatedly. Optimal sampling times were determined with each criterion (ED,EID,API) and either uniform or normal prior parameter distributions. Six to seven distinct sampling times could be estimated. Higher numbers of samples revealed coalescing of design points. Performances of each criteria (prediction error and RMSE) for parameter estimation by comparison to a non-optimized 19 samples design in 16 healthy volunteers will be presented.
[1] Pronzato et al., Math.Comput.Simulation 1984,26,412-422.
[2] Pronzato and Walter, Math. Biosci. 1985,75,103-120.
[3] D’Argenio, Math. Biosci. 1990,99,105-118.
[4] Tod et al., J. Pharmacokinet. Biopharm. 1994, in press.
Reference: PAGE 3 () Abstr 861 [www.page-meeting.org/?abstract=861]
Poster: poster