Abstract
We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.
Original language | English (US) |
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Pages (from-to) | 97-111 |
Number of pages | 15 |
Journal | Annals of Biomedical Engineering |
Volume | 22 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1994 |
Externally published | Yes |
Keywords
- Benzene
- Monte Carlo simulations
- Parameter estimation
- Parametric analysis
- Pharmacokinetic models
ASJC Scopus subject areas
- Biomedical Engineering