Han Run, Yang Yilong, Li Xiaoshan, Ouyang Defang
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau 999078, China.
Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China.
Asian J Pharm Sci. 2018 Jul;13(4):336-342. doi: 10.1016/j.ajps.2018.01.003. Epub 2018 Feb 2.
Oral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min 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 datasets 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 have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 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.
口腔崩解片（ODTs）是一种新型剂型，尤其适用于老年和儿科患者，可在3分钟或更短时间内在舌头上溶解。目前的口腔崩解片制剂研究通常依赖于制药专家的个人经验以及实验室的反复试验，效率低下且耗时。当前研究的目的是通过人工神经网络（ANN）和深度神经网络（DNN）技术建立直接压片法制备口腔崩解片制剂的预测模型。从Web of Science中提取了145个制剂数据。所有数据集分为三个部分：训练集（105个数据）、验证集（20个）和测试集（20个）。比较了ANN和DNN对崩解时间的预测。ANN模型在训练集、验证集和测试集上的准确率分别达到了85.60%、80.00%和75.00%，而DNN模型的准确率分别为85.60%、85.00%和80.00%。与ANN相比，DNN对口腔崩解片制剂的预测效果更好。这是首次将改进数据集选择算法的深度神经网络应用于小数据制剂预测。所提出的预测方法可以评估制剂质量控制的关键参数，并指导研究和工艺开发。该预测模型的实施可以有效缩短药品开发周期和减少材料使用，并积极促进稳健药品的开发。
