Parameter Selection and Uncertainty Quantification Techniques for Pharmacokinetic Models

The quantification of uncertainties inherent to pharmacokinetic models, parameters, and experiments is critical to assess the accuracy of predictions. This presentation will focus on the use of parameter subset selection and uncertainty quantification to establish the predictive capabilities of models. The initial discussion will illustrate how parameter subset selection techniques can be used to isolate model parameters, which are informed by measured data. To demonstrate the role of parameter selection, we consider a minimal physiologically-based pharmacokinetic (mPBPK) model. This example will illustrate both the reduction in parameter complexity, which can be achieved, and the dependence of parameter sensitivity on model concentrations. The presentation will then focus on the use of Bayesian inference techniques to quantify parameter uncertainties in a manner that can be subsequently employed to construct prediction intervals for model responses. This inverse and forward uncertainty analysis will be illustrated in the context of the mPBPK model.