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.

Deep learning for in vitro prediction of pharmaceutical formulations

“…Currently, only two deep learning approaches have been reported for formulation prediction 25. , 44.…”

“…Therefore, this research still needs significant improvement on methodology for formulation prediction. Recently deep learning with MD-FIS was applied for formulation prediction of oral fast disintegrating tablets 25 . The results showed that the prediction of deep learning was better than that of ANN because deep learning could extract the features automatically without designing feature extractors.…”

“…Therefore, the full-connected deep feed-forward network is a good choice for the prediction of pharmaceutical formulations. A recent study showed that deep neural networks (DNNs) outperform ANNs with one hidden layer in oral disintegrating tablet prediction 25 . The Maximum Dissimilarity algorithm with the small group filter and representative initial set selection (MD-FIS) selected the representative validation set from the small and imbalanced oral disintegrating tablet data.…”

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

“…14 Run et al established a prediction model for the disintegration time of oral disintegrating tablets, and the model achieved 80% accuracy. 15 Recently, applications of artificial intelligence engines, derived from the random forest algorithm, were demonstrated toward the analysis of internal tablet defects. 1 In this study, quantitative analyses of internal tablet crack severity were achieved based on the machine learning program.…”

An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction

“…49,50 In recent years, many successes have also been made by deep learning in drug discovery and formulation development. 51,52 For examples, in the year 2013, compared with other machine learning methods, deep learning yielded good performance in aqueous solubility prediction based on the undirected graphs. 53 In the year 2015, it was found that deep learning could reach or exceed the performance of the RF algorithms on a series of Merck's drug discovery datasets.…”