Spectroscopy-Based Partial Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks

In pharmaceutical industry, dissolution testing is part of the target product quality that essentials are in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. RAMAN and Near Infrared (NIR) spectroscopy are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods to support the decision of how much of the dissolution profile should be measured and which methods to use, so that by estimating the remaining part, the accuracy requirement of the industry is met. Artificial neural network models were created, in which parts of the measured dissolution profiles, along with the spectroscopy data and the measured compression curves were used as an input to estimate the remaining part of the dissolution profiles. It was found that by measuring the dissolution profiles for 30 minutes, the remaining part was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy methods along with the measured parts of the dissolution profile significantly increased the prediction accuracy.