| Project/Area Number |
18K06796
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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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| Review Section |
Basic Section 47060:Clinical pharmacy-related
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| Research Institution | Teikyo University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
平田 圭一 帝京大学, 薬学部, 助手 (80424852)
中谷 絵理子 (林絵理子) 帝京大学, 薬学部, 助教 (90803916)
赤下 学 帝京大学, 薬学部, 助教 (90781542)
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| Project Period (FY) |
2018-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 薬剤服用歴情報 / 受容体占有率 / ムスカリン受容体 / 過活動膀胱 / 抗コリン薬 / 過活動膀胱治療薬 / 認知症 / 認知機能障害 / ポリファーマシー / 薬剤性認知機能障害 / 薬歴データベース / 受容体占有 / 相互作用 |
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| Outline of Final Research Achievements |
In this study, we integrated clinical and basic research to avoid dementia caused by polypharmacy, which is an urgent issue in drug treatment for the elderly. In the clinical study, we analyzed the risk of dementia caused by taking anticholinergic agents for overactive bladder (OAB) using medication history. We showed an increased risk of dementia caused by taking OAB anticholinergic agents in Japan as well as in large-scale clinical studies in U.S. and Europe. In basic research, we established a pharmacokinetic/pharmacodynamic analysis system by measuring rat brain distribution and receptor occupancy, and a prediction method for human muscarinic receptor occupancy. We showed that muscarinic receptor occupancy of OAB anticholinergic agents is a useful indicator for predicting not only the onset of drug effects in the bladder but also the onset of central adverse effects including dementia.
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| Academic Significance and Societal Importance of the Research Achievements |
薬剤服用歴情報を用いた後ろ向き研究により過活動膀胱治療薬の認知症発症リスクの解析に本邦で初めて成功した。薬剤服用歴情報は、慢性期疾患患者を含めた幅広い患者における薬剤服用歴情報、患者主観情報および薬学的管理情報が含まれ、リアルワールドデータとして研究への利活用が期待できる。また基礎研究で確立した受容体占有解析法により、メカニズムに基づいた医薬品安全性評価が可能となり、副作用の新たな予測法の構築が期待できる。副作用を回避した安全な高齢者薬物治療の実現に向けて、本研究成果の貢献が期待できる。
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