A Data-Driven Approach to Predicting Tablet Properties after Accelerated Test Using Raw Material Property Database and Machine Learning
The purpose of this study was to develop a model for predicting tablet properties after an accelerated test and to determine whether molecular descriptors affect tablet properties. Tablets were prepared using 81 types of active pharmaceutical ingredients, with the same formulation and three different levels of compression pressure. The tablet properties measured were the tensile strength and disintegration time of tablets after two weeks of accelerated test. The material properties measured were the change in tablet thickness before and after the accelerated test, maximum swelling force, swelling time, and swelling rate. The acquired data were added to our previously constructed database containing a total of 20 material properties and 3381 molecular descriptors.
The feature importance values of molecular descriptors, material properties and the compression pressure for each tablet property were calculated by random forest, which is one type of machine learning (ML) that uses ensemble learning and decision trees. The results showed that more than half of the top 25 most important features were molecular descriptors for both tablet properties, indicating that molecular descriptors are strongly related to tablet properties. A prediction model of tablet properties was constructed by eight ML types using 25 of the most important features. The results showed that the boosted neural network exhibited the best prediction accuracy and was able to predict tablet properties with high accuracy. A data-driven approach is useful for discovering intricate relationships hidden within complex and large data sets and predicting tablet properties after an accelerated test.
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Materials
Eighty-one types of model APIs were purchased from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan), Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan), or Yamamoto Corporation Co., Ltd. (Osaka, Japan). Several APIs were ground in a mortar and pestle to reduce their particle size (as they were too large for direct compression). Microcrystalline cellulose (MCC; Ceolus PH-101, Asahi Kasei Chemicals Co. Ltd., Tokyo, Japan) and magnesium stearate (Mg-St; FUJIFILM Wako Pure Chemical Corporation) were purchased from commercial suppliers.
Yoshihiro Hayashi, Yuri Nakano, Yuki Marumo, Shungo Kumada, Kotaro Okada and Yoshinori Onukib, A Data-Driven Approach to Predicting Tablet Properties after Accelerated Test Using Raw Material Property Database and Machine Learning, Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd., 205–1, Shimoumezawa, Namerikawa, Toyama 936–0857, Japan: and bDepartment of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama 930–0194, Japan. Received July 25, 2022; accepted October 3, 2022
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