How-to: Empirical IVIVR without intravenous data

Abstract

IVIVR as an extension of IVIVC beyond the domain of linear modeling is a predictive model that binds the in vivo PK profile with the in vitro dissolution profile of a particular drug. Several computational intelligence-modeling tools for IVIVR were chosen and tested in this study: decision trees (randomForest), artificial neural networks (monmlp), genetic programming (rgp), and a recently published tool, RIVIVR. R statistical environment was used for numerical experiments. All of the above-mentioned tools succeeded in the creation of empirical relationships between in vivo and in vitro profiles without the need of the additional impulse curve (intravenous [iv] profile). The best results were found for genetic programming and decision trees. RIVIVR achieved a superior cost–effectiveness ratio, namely, short time of execution and high level of automation.