Relating formulation variables to in vitro dissolution using an artificial neural network.

Abstract

The purpose of this paper was to investigate the effect of several experimental variables on the ability of a neural network to predict in vitro dissolution rate as a function of product formulation changes. Neural network software was trained with sets of hypothetical and experimental data consisting of 4-15 formulations with known in vitro drug dissolution profiles and the ability of the trained model to recognize patterns was validated against similar formations not used to train the neural network. The effect of selected variables, e.g., number of hidden-layer nodes and iterations, as well as the use of replicate or mean data on the accuracy of the predictions was investigated. The importance of optimizing the number of hidden-layer nodes and iterations was demonstrated. The prediction error increased for validation data sets that were outside the range of the training data set. Accurate predictions were obtained with as few as four formulations in the training set, provided the formulations were carefully chosen, and the number of formulation variables were small. Also, limiting the validation set to one formulation was not sufficient to validate the neural network model. Increasing the size of the training set, or replication of the input and output data, also provided more accurate predictions. The neural network accurately predicted in vitro drug release provided the neural network variables were optimized, and the training and validation data sets were appropriately selected.