Population analysis is the methodology used to quantify intersubject variability in kinetic studies. In this revised research project, the applicants propose to develop a general population analysis package and to interface this package with the SAAM-II software system. This system will typically be used for the population modeling of pharmacokinetic/pharmacodynamic systems and metabolic studies using tracer kinetics. Population analysis is widely used on pharmacokinetic studies since it is the key to understanding how drugs behave in human and animals. It provides the foundation for the intelligent design of dosage regiments to treat disease processes. In metabolic studies, it is used to identify which parameters in a model change when a population of normal subjects is compared to a population of subjects with a known pathological condition. There are three significant obstacles in such modeling efforts: (i) there is no software package that includes both parameteric and nonparameteric methods, (ii) certain methods currently in use have not had the rigorous statistical and numerical analyses that one could desire, and (iii) current software is either limited in modeling capabilities or is not user friendly. This project brings together a unique group of researchers to overcome these obstacles by developing and incorporating into the SAAM-II a general purpose population analysis module. More specifically, the following three aims are proposed: 1) Develop convergent numerical algorithms for parametric and nonparameteric methods. 2) Prove consistency of algorithm in Aim-1. Investigate efficiency and robustness of these methods via simulation studies and compare with other existing methods. 3) Interface the algorithms in Aim-1 with the general model building/graphical user interface of SAAM-II. The result will be a population analysis program with general model building capabilities and a graphical user interface that is powerful, flexible and easy to use. Such programs do not currently exists. Such a package will improve the analysis of clinical trials, and resulting drug therapy and patient care.
