Journal of Pharmacokinetics and Biopharmaceutics

, Volume 20, Issue 5, pp 511–528

Building population pharmacokineticpharmacodynamic models. I. Models for covariate effects

Authors

  • Jaap W. Mandema
    • Department of Pharmacy, School of PharmacyUniversity of California
  • Davide Verotta
    • Department of Pharmacy, School of PharmacyUniversity of California
  • Lewis B. Sheiner
    • Department of Pharmacy, School of PharmacyUniversity of California
    • Department of Laboratory Medicine, School of MedicineUniversity of California
Pharmacometrics

DOI: 10.1007/BF01061469

Cite this article as:
Mandema, J.W., Verotta, D. & Sheiner, L.B. Journal of Pharmacokinetics and Biopharmaceutics (1992) 20: 511. doi:10.1007/BF01061469

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 NONMEM 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.

Key words

pharmacokineticspopulation analysismodel buildinggeneralized additive modelsNONMEM

Copyright information

© Plenum Publishing Corporation 1992