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.

A multi-material 3D printing offers nearly endless possibilities for the spatial arrangement of individual materials within the object being printed. In the case of pharmaceutical tablets, the spatial arrangement of individual material domains containing the active pharmaceutical ingredients (APIs) and excipients uniquely defines the release profiles of the APIs. However, the inverse is not necessarily true – identical or very similar dissolution profiles can potentially be obtained from different tablet internal structures, implemented as a combination of domains containing excipients with different individual dissolution rates and different local API concentration. This work presents a computational method based on an Evolutionary Algorithm for the solution of the inverse problem, i.e. finding such tablet internal structure that results in a prescribed dissolution profile of each API contained in the tablet. After testing the algorithm on cases with a known solution, the methodology is applied to a problem of finding tablet structures that result in delayed release and step-wise release profiles, respectively.