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In other words, no data or information from warfarin-treated children was used to develop the model for dose prediction in children.
For consistency with the existing U.S. EPA RfD approach for MeHg, in this analysis I focus on the one-compartment model rather than the PBPK model for dose reconstruction.
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Figure 1 shows a graphical depiction of a neural network model for dose-response data.
The concentration of the drugs was assessed by using a one-way analysis of variance model for dose-normalised and log-transformed maximum observed serum concentration considering the concentration-time curve (AUC).
Extension of the modeling framework to incorporate models for dose limiting toxicities (such as the hand foot syndrome) may be warranted to further investigate dosing strategies that may improve the benefit to risk ratio of this drug combination.
Together with predicting activity and potential adverse clinical signs, the inclusion of NHP testing bequeaths to efficacy models for dose titration and pharmacodynamic effects.
It is thus attractive to use physiologically based pharmacokinetic (PBPK) models for dose and species extrapolation.
Currently, uncertainty factors may be used to account for human variability, and "average" kinetic values are used in PBPK models for dose extrapolation.
It lists the six (of 32) models for dose response that had posterior probabilities (based on the BIC approximation) of > 1%, as well as those posterior probabilities.
As the ICRP model is the recommended model used for dose estimation, there naturally exists information on the distribution types of the parameters involved in the model together with confidence intervals [ 7].
These uncertainties lead to dose overestimation by greater than two orders of magnitude, depending on the model used for dose calculation (Kendall and Smith 2002).
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