A Data-Driven Approach to Predicting Tablet Properties after Accelerated Test Using Raw Material Property Database and Machine Learning
Author:
Affiliation:
1. Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd.
2. Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama
Publisher
Pharmaceutical Society of Japan
Subject
Drug Discovery,General Chemistry,General Medicine
Link
https://www.jstage.jst.go.jp/article/cpb/71/6/71_c22-00538/_pdf
Reference38 articles.
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3. 3) Yu L. X., Amidon G., Khan M. A., Hoag S. W., Polli J., Raju G. K., Woodcock J., AAPS J., 16, 771–783 (2014).
4. 4) Wang Z., Sun Z., Yin H., Liu X., Wang J., Zhao H., Pang C. H., Wu T., Li S., Yin Z., Yu X., Adv. Mater., 34, 2104113 (2022).
5. 5) Himanen L., Geurts A., Foster A. S., Rinke P., Adv. Sci., 6, 1900808 (2019).
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1. A data-driven approach to predict the in vitro dissolution time of sustained-release tablets using raw material databases and machine learning algorithms;Pharmacia;2024-08-26
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