Individual Prior Information in a Physiological Model of super(2)H sub(8)-Toluene Kinetics: An Empirical Bayes Estimation Strategy

Physiologically-based toxicokinetic (PBTK) models are widely used to quantify whole-body kinetics of various substances. However, since they attempt to reproduce anatomical structures and physiological events, they have a high number of parameters. Their identification from kinetic data alone is oft...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Risk analysis 1999-12, Vol.19 (6), p.1127-1134
Hauptverfasser: Vicini, P, Pierce, CH, Dills, R L, Morgan, MS, Kalman, DA
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Physiologically-based toxicokinetic (PBTK) models are widely used to quantify whole-body kinetics of various substances. However, since they attempt to reproduce anatomical structures and physiological events, they have a high number of parameters. Their identification from kinetic data alone is often impossible, and other information about the parameters is needed to render the model identifiable. The most commonly used approach consists of independently measuring, or taking fom literature sources, some of the parameters, fixing them in the kinetic model, and then performing model identification on a reduced number of less certain parameters. This results in a substantial reduction of the degrees of freedom of the model. In this study, we show that this method results in final estimates of the free parameters whose precision is overestimated. We then compared this approach with an empirical Bayes approach, which takes into account not only the mean value, but also the error associated with the independently determined parameters. Blood and breath super(2)H sub(8)-toluene washout curves, obtained in 17 subjects, were analyzed with a previously presented PBTK model suitable for person-specific dosimetry. Model parameters with the greatest effect on predicted levels were alveolar ventilation rate Q sub(PC), fat tissue fraction V sub(FC), blood-air partition coefficient K sub(b), fraction of cardiac output to fat Q sub(a/co) and rate of extrahepatic metabolism V sub(max-p). Differences in the measured and Bayesian-fitted values of Q sub(PC), V sub(FC) and K sub(b) were significant (p < 0.05), and the precision of the fitted values V sub(max-p) and Q sub(a/co) went from 11 plus or minus 5% to 75 plus or minus 170% (NS) and from 8 plus or minus 2% to 9 plus or minus 2% (p < 0.05) respectively. The empirical Bayes approach did not result in less reliable parameter estimates: rather, it pointed out that the precision of parameter estimates can be overly optimistic when other parameters in the model, either directly measured or taken from literature sources, are treated as known without error. In conclusion, an empirical Bayes approach to parameter estimation resulted in a better model fit, different final parameter estimates, and more realistic parameter precisions.
ISSN:0272-4332
DOI:10.1023/A:1007034712016