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Vicini Paolo Pierce Crispin H. Dills Russell L. Morgan Michael S. Kalman David A. 《Risk analysis》1999,19(6):1127-1134
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 from 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 2H8-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 QPC, fat tissue fraction VFC, blood-air partition coefficient Kb, fraction of cardiac output to fat Qa/co and rate of extrahepatic metabolism Vmax-p. Differences in the measured and Bayesian-fitted values of QPC, VFC and Kb were significant (p < 0.05), and the precision of the fitted values Vmax-p and Qa/co went from 11 ± 5% to 75 ± 170% (NS) and from 8 ± 2% to 9 ± 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. 相似文献
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Wei Zhao Paolo Vicini Steven Novick Judith Anderton Gareth Davies Gina DAngelo Terrance O'Day Binbing Yu Jay Harper Rajesh Narwal Lorin Roskos Harry Yang 《Pharmaceutical statistics》2019,18(6):688-699
Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug‐drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies. 相似文献
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