Title: Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques,
Abstract: Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be
dissolved on the tongue within 3min or less especially for geriatric and
pediatric patients. Current ODT formulation studies usually rely on the
personal experience of pharmaceutical experts and trial-and-error in the
laboratory, which is inefficient and time-consuming. The aim of current
research was to establish the prediction model of ODT formulations with direct
compression process by Artificial Neural Network (ANN) and Deep Neural Network
(DNN) techniques. 145 formulation data were extracted from Web of Science. All
data sets were divided into three parts: training set (105 data), validation
set (20) and testing set (20). ANN and DNN were compared for the prediction of
the disintegrating time. The accuracy of the ANN model has reached 85.60%,
80.00% and 75.00% on the training set, validation set and testing set
respectively, whereas that of the DNN model was 85.60%, 85.00% and 80.00%,
respectively. Compared with the ANN, DNN showed the better prediction for ODT
formulations. It is the first time that deep neural network with the improved
dataset selection algorithm is applied to formulation prediction on small data.
The proposed predictive approach could evaluate the critical parameters about
quality control of formulation, and guide research and process development. The
implementation of this prediction model could effectively reduce drug product
development timeline and material usage, and proactively facilitate the
development of a robust drug product.