Development of In Vitro-In Vivo Correlation/Relationship Modeling Approaches for Immediate Release Formulations Using Compartmental Dynamic Dissolution Data from "Golem": A Novel Apparatus.

Different batches of atorvastatin, represented by two immediate release formulation designs, were studied using a novel dynamic dissolution apparatus, simulating stomach and small intestine. A universal dissolution method was employed which simulated the physiology of human gastrointestinal tract, including the precise chyme transit behavior and biorelevant conditions. The multicompartmental dissolution data allowed direct observation and qualitative discrimination of the differences resulting from highly pH dependent dissolution behavior of the tested batches. Further evaluation of results was performed using IVIVC/IVIVR development. While satisfactory correlation could not be achieved using a conventional deconvolution based-model, promising results were obtained through the use of a nonconventional approach exploiting the complex compartmental dissolution data.

Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks.

BACKGROUND: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR. METHODS: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. RESULTS: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. CONCLUSION: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2-4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.