Developing an In Vitro-In Vivo Correlation (IVIVC) model is becoming an important part of the drug development process. Traditional methods such as deconvolution and convolution make the assumption of linearity of the system being studied and are, therefore, unsuitable for use with compounds exhibiting nonlinear kinetics. This study proposes the use of a compartmental approach which may be based on systems of differential equations, a method which can comfortably accommodate nonlinearity. This technique can easily be implemented using existing NONMEM libraries and is an accurate, fast and straightforward method of developing an IVIVC model.

In vitro-in vivo correlation (IVIVC) models prove very useful during drug formulation development, the setting of dissolution specifications and bio-waiver applications following post approval changes. A convolution-based population approach for developing an IVIVC has recently been proposed as an alternative to traditional deconvolution based methods, which pose some statistical concerns. Our aim in this study was to use a time-scaling approach using a convolution-based technique to successfully develop an IVIVC model for a drug with quite different in vitro and in vivo time scales. The in vitro and the in vivo data were longitudinal in nature with considerable between subject variation in the in vivo data. The model was successfully developed and fitted to the data using the NONMEM package. Model utility was assessed by comparing model-predicted plasma concentration-time profiles with the observed in vivo profiles. This comparison met validation criteria for both internal and external predictability as set out by the regulatory authorities. This study demonstrates that a time-scaling approach may prove useful when attempting to develop an IVIVC for data with the aforementioned properties. It also demonstrates that the convolution-based population approach is quite versatile and that it is capable of producing an IVIVC model with a big difference between the in vitro and in vivo time scales.