title: Building population pharmacokinetic--pharmacodynamic models. I. Models for covariate effects.
abstract: One major task in clinical pharmacology is to determine the pharmacokinetic-pharmacodynamic (PK-PD) parameters of a drug in a patient population. NONMEM is a program commonly used to build population PK-PD models, that is, models that characterize the relationship between a patient's PK-PD parameters and other patient specific covariates such as the patient's (patho) physiological condition, concomitant drug therapy, etc. This paper extends a previously described approach to efficiently find the relationships between the PK-PD parameters and covariates. In a first step, individual estimates of the PK-PD parameters are obtained as empirical Bayes estimates, based on a prior NONMEN fit using no covariates. In a second step, the individual PK-PD parameter estimates are regressed on the covariates using a generalized additive model. In a third and final step, NONMEM is used to optimize and finalize the population model. Four real-data examples are used to demonstrate the effectiveness of the approach. The examples show that the generalized additive model for the individual parameter estimates is a good initial guess for the NONMEM population model. In all four examples, the approach successfully selects the most important covariates and their functional representation. The great advantage of this approach is speed. The time required to derive a population model is markedly reduced because the number of necessary NONMEM runs is reduced. Furthermore, the approach provides a nice graphical representation of the relationships between the PK-PD parameters and covariates.
authors.affiliation: Department of Pharmacy, School of Pharmacy, University of California, San Francisco 94143.
authors.forename: J W
authors.initials: JW
authors.lastname: Mandema
authors.forename: D
authors.initials: D
authors.lastname: Verotta
authors.forename: L B
authors.initials: LB
authors.lastname: Sheiner
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007206:Individuality; D008297:Male; D008432:Mathematical Computing; D008722:Methods; D008875:Middle Aged; D008954:Models, Biological; D010599:Pharmacokinetics; D011153:Population; D044469:Racial Groups; D012737:Sex Factors; D012984:Software