Prediction of Dissolution Profiles From Process Parameters, Formulation, and Spectroscopic Measurements

Abstract

This study utilized multiple modeling approaches to predict immediate release tablet dissolution profiles of 2 model drugs: theophylline and carbamazepine. Two sets of designs of experiments were applied based on individual drug characteristics to build in adequate dissolution variability. The tablets were scanned using a near-infrared (NIR) spectrometer and then subjected to in vitro dissolution test at critical time points. Because of the inherent difference in dissolution profiles, a hierarchical modeling approach was applied for theophylline data, whereas global models were constructed from carbamazepine data. The partial least squares models were trained using 3 predictor sets including (1) formulation, material, and process variables, (2) NIR spectra, and (3) a combination of both. The dependent variables of the models were the dissolution profiles, which were presented either as parameters of Weibull fitting curves or raw data. The comparison among the predictive models revealed that the incorporation of NIR spectral information in calibration reduced prediction error in the carbamazepine case but undermined the performance of theophylline models. It suggests that the modeling strategy for dissolution prediction of pharmaceutical tablets should not be universal but on a case-by-case basis.

Keywords: dissolution; dissolution model; near-infrared spectroscopy (NIRS); partial least squares; process analytical technology (PAT).