Covariate pharmacokinetic model building in oncology and its potential clinical relevance.

ABSTRACT: When modeling pharmacokinetic (PK) data, identifying covariates is important in explaining interindividual variability, and thus increasing the predictive value of the model. Nonlinear mixed-effects modeling with stepwise covariate modeling is frequently used to build structural covariate models, and the most commonly used software-NONMEM-provides estimations for the fixed-effect parameters (e.g., drug clearance), interindividual and residual unidentified random effects. The aim of covariate modeling is not only to find covariates that significantly influence the population PK parameters, but also to provide dosing recommendations for a certain drug under different conditions, e.g., organ dysfunction, combination chemotherapy. A true covariate is usually seen as one that carries unique inf

SUBMITTER: Joerger M 

PROVIDER: S-EPMC3291194 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature