Can Traditional Pharmacokinetic Nonlinear Models Be Replaced with Random Effects Linear Models?

• Activity Number: 351
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Francisco J. Diaz*+
• Companies: University of Kansas Medical Center
• Keywords: Random effects ; Linear models ; Nonlinear models ; Drug-drug interactions ; Drug dosage individualization ; Pharmacokinetics

Abstract

Traditional pharmacokinetic analyses use nonlinear models of drug plasma (or blood) concentrations. However, published empirical findings show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. In addition, experienced statisticians know that linear models are much easier to build and fit than nonlinear models. Another good reason for using linear models in the analysis of steady-state pharmacokinetic data is the sparse sampling designs that usually characterize phase III and IV studies, which impede a clear determination of absorption parameters. In this paper, we describe successful applications of random effects linear models to pharmacokinetic research, particularly to drug-drug interaction studies. We also describe new, published developments that show that random effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring.