A semiparametric deconvolution model to establish in vivo-in vitro correlation applied to OROS oxybutynin

Abstract

In vitro-in vivo correlation (IVIVC) models may be used to predict in vivo drug concentration-time profiles given in vitro release characteristics of a drug. This prediction is accomplished by incorporating in vitro release characteristics as an input function (A(vitro)) to a pharmacokinetics model. This simple approach often results in biased predictions of observed in vivo drug concentrations, and it can result in rejecting IVIVC. To solve this problem we propose a population IVIVC model that incorporates the in vitro information and allows one to quantify possibly changed in vivo release characteristic. The model assumes linear kinetics and describes the in vivo release as a sum of A(vitro) and a nonparametric function (A(d), a spline) representing the difference in release due to in vivo conditions. The function A(vitro) and its variability enter the model as a prior distribution. The function A(d) is estimated together with its intersubject variability. The number of parameters associated with A(d) defines the model: no parameters indicates perfect IVIVC, a large number of parameters indicates poor IVIVC. The number of parameters is determined using statistical model selection criteria. We demonstrate the approach to solve the IVIVC problem of an oral extended release oxybutynin form (OROS), administered in three pharmacokinetic studies. These studies present a particular challenging case; that is, the relative bioavailability for the OROS administration is >100% compared with that of the immediate-release form. The result of our modeling shows that the apparent lack of IVIVC can be overcome: in vivo concentration can be predicted (within or across data sets) based on in vitro release rate together with a simple form of systematic deviation from the in vitro release.