In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step

The purpose of in vivo-in vitro correlation (IVIVC) modeling is described. These models are usually fitted to deconvoluted data rather than the raw pl asma drug concentration/time data. Such a two-stage analysis is undesirable because the deconvolution step is unstable and because the fitted model pr edicts the fraction of a dosage unit dissolved/absorbed in vivo which gener ally is not the primary focus of our attention. interest usually centers on the plasma drug concentration or some function of it (e.g.. AUG, C-max). I ncorporation of a convolution step into the model overcomes these difficult ies. Odds, hazards, and reversed hazards models which include a convolution step are described. The identity model (which states that average in vivo and in vitro dissolution/rime curves are coincident or directly superimposa ble) is a special case of these models. The odds model and the identity, mo del were fitted to darn sets for two different products using nonlinear mix ed effects model fitting software. Results show that the odds model describ es both data sets reasonably well and is a significantly better fit than th e identity, model in each case.