Pharmacology Research Today is a free monthly online journal that collates and summarizes the latest research about Pharmacology, including details on pharmacogenomics, drug development, new medications.

A new approach to modeling covariate effects and individualization in population pharmacokinetics-pharmacodynamics.

Lai TL, Shih MC, Wong SP

Department of Statistics, Stanford University, Stanford, CA 94305, USA. lait@stat.stanford.edu

By combining Laplace's approximation and Monte Carlo methods to evaluate multiple integrals, this paper develops a new approach to estimation in nonlinear mixed effects models that are widely used in population pharmacokinetics and pharmacodynamics. Estimation here involves not only estimating the model parameters from Phase I and II studies but also using the fitted model to estimate the concentration versus time curve or the drug effects of a subject who has covariate information but sparse measurements. Because of its computational tractability, the proposed approach can model the covariate effects nonparametrically by using (i) regression splines or neural networks as basis functions and (ii) AIC or BIC for model selection. Its computational and statistical advantages are illustrated in simulation studies and in Phase I trials.

Published 14 April 2006 in J Pharmacokinet Pharmacodyn, 33(1): 49-74.
Full-text of this article is available online (may require subscription).

© 2005-2008 Pharmacology Research Today. All Rights Reserved.