| Project/Area Number |
15K08111
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Medical pharmacy
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| Research Institution | Meiji Pharmaceutical University |
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Principal Investigator |
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| Co-Investigator(Renkei-kenkyūsha) |
KAGAYA Hajime 明治薬科大学, 薬学部, 教授 (00642969)
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| Research Collaborator |
NAGAI Junko
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | オピオイド / 構造活性相関 / データベース / 機械学習 / 人工知能 / 緩和医療 / 副作用 / 化学構造 / ニューラルネットワーク / ランダムフォレスト |
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| Outline of Final Research Achievements |
Potent opioids that are widely used in palliative care, including morphine, oxycodone, and fentanyl, have been found to commonly induce adverse reactions, such as delirium, nausea, and vomiting. The incidence of such adverse drug reactions varies greatly depending on the type of drug and patient. Thus, there is no adequate, clinically useful method for predicting these adverse drug reactions. However, in 2012, the Pharmaceuticals and Medical Devices Agency (PMDA) made the largescale Japanese Adverse Drug Event Report (JADER) database available to the public. The analyses of this adverse reaction database will serve as a promising tool to understand the complex relationship between patients and adverse drug reactions. In the present study, we performed analyses of opioid-induced adverse reactions using the JADER database with chemical information, and constructed high-performance prediction model for drugs with delirium-inducing potency by machine learning methods.
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