Comparing Spectroscopy Measurements in the Prediction of in Vitro Dissolution Profile using Artificial Neural Networks

TL;DR

This study compares Raman and NIR spectroscopic methods combined with neural networks to predict pharmaceutical dissolution profiles, aiming to identify the most effective measurement approach for industry accuracy standards.

Contribution

It evaluates the predictive power of different spectroscopic techniques combined with neural networks for dissolution profile estimation in pharmaceuticals.

Findings

NIR transmission with compression data predicts dissolution within industry standards.

Combining Raman and NIR reflection improves prediction accuracy.

Additional spectroscopy measurements enhance the model's performance.

Abstract

Dissolution testing is part of the target product quality that is essential in approving new products in the pharmaceutical industry. 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) spectroscopies are two fast and complementary methods that provide information on the tablets' physical and chemical properties and can help predict their dissolution profiles. This work aims to compare the information collected by these spectroscopy methods to support the decision of which measurements should be used so that the accuracy requirement of the industry is met. Artificial neural network models were created, in which the spectroscopy data and the measured compression curves were used as an input individually and in different combinations in order to estimate the dissolution profiles. Results showed that using only the NIR transmission method along with the compression force data or the Raman and NIR reflection methods, the dissolution profile was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy measurements increased the prediction accuracy.