Su, Jing,Peng Sun, Xiaodong Li and Alan H. Hartford (all at Merck & Co., Inc, North Wales, PA). 2009. Fitting Compartmental Models to Multiple Dose Pharmacokinetic Data using SAS PROC NLMIXED
Abstract: Pharmacokinetic (PK) modeling uses systems of ordinary differential equations derived from biological considerations along with statistical models to model the time course of drug in the body. The statistical model requires algorithms for fitting nonlinear mixed effects models. While the NLMIXED procedure in SAS is available, it does not allow for individual subject data to affect the structural form of the model. In the case of a multiple dose study where subjects experience different dosing times, a superposition principle can be used to recursively account for each additional dose.
A SAS template program was written to manipulate data and then construct mean functions for fitting pharmacokinetic data from multiple dose studies. One-compartment models for oral dose administration are considered to illustrate the methodology and challenges for fitting multiple dose data using PROC NLMIXED. The template program contains sample SAS code for fitting two types of models: a general model and a simplified model. The general structural model can handle many situations such as when a subject has irregular dosing intervals, changes dosage during therapy or has non-ignorable differences between actual dosing time and scheduled dosing time, etc. The simplified model is for the most common scenario in which all subjects are constrained to have the same multiple dosing schedules with regular dosing intervals and constant doses. Under each model, we also discuss how to handle missed dose problems.
KEYWORDS: pharmacokinetics, compartmental models, multiple dose, superposition
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