title: Evaluating In Vivo-In Vitro Correlation Using a Bayesian Approach.
abstract: A Bayesian approach with frequentist validity has been developed to support inferences derived from a "Level 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 "Level 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. In so doing, we can now generate population profiles that incorporate not only variability in subject pharmacokinetics but also the variability in the in vivo product performance.
authors.affiliation: Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA. junshan.qiu@fda.hhs.gov.
authors.forename: Junshan
authors.initials: J
authors.lastname: Qiu
authors.affiliation: Office of New Animal Drug Evaluation, Center for Veterinary Medicine, Food and Drug Administration, Rockville, Maryland, USA.
authors.forename: Marilyn
authors.initials: M
authors.lastname: Martinez
authors.affiliation: Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
authors.forename: Ram
authors.initials: R
authors.lastname: Tiwari
mesh_terms: D001499:Bayes Theorem; D008962:Models, Theoretical
keywords: IVIVC; MCMC; Weibull distribution; probability matching prior; tolerance intervals