Bayesian knowledge integration for an in vitro–in vivo correlation model
Résumé
The primary goal of “in vitro–in vivo correlation” (IVIVC) is the reliable prediction
of the in vivo serum concentration-time course, based on the in vitro drug dissolution
or release profiles. IVIVC methods are particularly appropriate for formulations
that are released over an extended period of time or with a lag in absorption and
may support approving a change in formulation of a drug without additional bioequivalence
trials in human subjects. Most of the current IVIVC models are assessed
using frequentistmethods, such as linear regression, based on averaged data and entail
complex and potentially unstable mathematical deconvolution. The proposed IVIVC
approach includes (a) a nonlinear-mixed effects model for the in vitro release data;
(b) a population pharmacokinetic (PK) compartment model for the in vivo immediate
release (IR) data; and (c) a system of ordinal differential equations (ODEs), containing
the submodels (a) and (b), which approximates and predicts the in vivo controlled
release (CR) data. The innovation in this paper consists of splitting the parameter
space between submodels (a) and (b) versus (c). Subsequently, the uncertainty on
these parameters is accounted for using a Bayesian framework, that is estimates from
the first two submodels serve as priors for the Bayesian hierarchical third submodel.
As such, the Bayesian method explained ensures a natural integration and transfer of
knowledge between various sources of information, balancing possible differences in
sample size and parameter uncertainty of in vitro and in vivo studies. Consequently,
it is a very flexible approach yielding results for a broad range of data situations. The
application of the method is demonstrated for a transdermal patch (TD).