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 plasma drug concentration/time data. Such a two-stage analysis is undesirable because the deconvolution step is unstable and because the fitted model predicts the fraction of a dosage unit dissolved/absorbed in vivo which generally is not the primary focus of our attention. Interest usually centers on the plasma drug concentration or some function of it (e.g., AUC, Cmax). Incorporation of a convolution step into the model overcomes these difficulties. 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/time curves are coincident or directly superimposable) is a special case of these models. The odds model and the identity model were fitted to data sets for two different products using nonlinear mixed effects model fitting software. Results show that the odds model describes both data sets reasonably well and is a significantly better fit than the identity model in each case.