Evaluating in vivo-in vitro correlation using a bayesian approach

A Bayesian approach with frequentist validity has been developed to support inferences derived from a BLevel A^ in vivo-in vitro correlation (IVIVC). Irrespective of whether the in vivo data reflect in vivo dissolution or absorption, the IVIVC is typically assessed using a linear regression model. Confidence intervals are generally used to describe the uncertainty around the model. While the confidence intervals can describe population-level variability, it does not address the individual-level variability. Thus, there remains an inability to define a range of individual-level drug concentration-time profiles across a population based upon the BLevel A^ predictions. This individual-level prediction is distinct from what can be accomplished by a traditional linear regression approach where the focus of the statistical assessment is at a marginal rather than an individual level. The objective of this study is to develop a hierarchical Bayesian method for evaluation of IVIVC, incorporating both the individual- and population-level variability, and to use this method to derive Bayesian tolerance intervals with matching priors that have frequentist validity in evaluating an IVIVC.