Creation of a tablet database containing several active ingredients and prediction of their pharmaceutical characteristics based on ensemble artificial neural networks.

A tablet database containing several active ingredients for a standard tablet formulation was created. Tablet tensile strength (TS) and disintegration time (DT) were measured before and after storage for 30 days at 40 degrees C and 75% relative humidity. An ensemble artificial neural network (EANN) was used to predict responses to differences in quantities of excipients and physical-chemical properties of active ingredients in tablets. Most classical neural networks involve a tedious trial and error approach, but EANNs automatically determine basal key parameters, which ensure that an optimal structure is rapidly obtained. We compared the predictive abilities of EANNs in which the following kinds of training algorithms were used: linear, radial basis function, general regression (GR), and multilayer perceptron. The GR EANN predicted pharmaceutical responses such as TS and DT most accurately, as evidenced by high correlation coefficients in a leave-some-out cross-validation procedure. When used in conjunction with a tablet database, the GR EANN is capable of identifying acceptable candidate tablet formulations.