Field of Study

Clinical Pharmacy

Keywords

Clinical Pharmacy, Pharmaceutical care, Pharmaceutical communication, Pharmacoepidemiology, Pharmacotherapy

Lab URL

https://www.gifu-pu.ac.jp/lab/byoyaku/

• 

  教授　吉村 知哲 Professor Yoshimura Tomoaki Ph.D. mail_outlineyoshimurat@
  ※@以降はgifu-pu.ac.jpとなります。

• 

  准教授　野口 義紘 Associate Professor Noguchi Yoshihiro Ph.D. mail_outlinenoguchiy@
  ※@以降はgifu-pu.ac.jpとなります。

研究テーマ Research Subjects

　病院薬学研究室では、臨床における実務経験を有する教員により、臨床現場に真に貢献できる高度な臨床能力と問題解決能力のある臨床薬剤師やファーマシスト－サイエンティストの育成、そして臨床現場での薬剤師実務を基盤とした医療薬学研究を実施している。
医療薬学研究として、医薬品適正使用の推進のために、臨床現場との連携により課題の抽出を行うとともに、様々な医療ビッグデータ（文献レジストリデータ、規制当局が保有する大規模副作用自発報告データベースや複数の医療機関から集積されたリアルワールドデータなど）を活用して新たなエビデンスの構築を目指している。さらに、これら医療ビッグデータから、より簡便に適切な医療情報を導出するために、新たな解析アルゴリズムの構築とその妥当性の検証も行っている。

　In the Laboratory of Clinical Pharmacy, faculty members with practical clinical experience train clinical pharmacists and pharmacist-scientists with advanced clinical skills and problem-solving abilities who can genuinely contribute to clinical practice and conduct clinical pharmacy studies based on pharmaceutical practices in medical sites. As part of clinical pharmacy studies, to promote the appropriate use of medicines, our group works to identify issues in collaboration with medical sites, and also aims to establish new evidence by utilizing various medical big data (e.g., Literature Registry Data, Spontaneous Reporting System owned by regulatory authorities and Real-World Data accumulated from medical institutions). Furthermore, our group works to construct and evaluate new medical big data analysis algorithms to more easily derive appropriate medical information from medical big data.

研究課題 Research Objectives

1. 病院薬剤師の業務に関連した課題の解決に関する研究
   Research on solving problems related to the work of clinical pharmacists
2. 薬剤疫学的手法による医療ビッグデータの解析と臨床への活用
   Analysis of medical big data by pharmacoepidemiologic approach and its practical use
3. 医療ビッグデータを用いた既存薬の新規薬効の探索研究
   Drug repositioning research using medical big data
4. 医療ビッグデータ解析アルゴリズムの構築と評価
   Construction and evaluation of medical big data analysis algorithms

最近の研究成果 Research Results

1. Noguchi Y, Asano H, Masuda R, Teshigawara Y, Go M, Kimura M, Usami E, Yoshimura T. Relationship between Anaplastic Lymphoma Kinase (ALK) Inhibitors and Epileptic Seizure Disorder: A Post-Marketing Surveillance Study. Oncology. 2024. doi: 10.1159/000539426.
2. Go M, Noguchi Y, Masuda R, Asano H, Kimura M, Usami E, Yoshimura T. Association between CDK4/6 inhibitors and drug-related osteonecrosis of the jaw: A pharmacoepidemiological study using the FDA Adverse Events Reporting System. Int J Cancer. 2024. doi: 10.1002/ijc.34962.
3. Fusaroli M, Salvo F, Begaud B, AlShammari TM, Bate A, Battini V, Brueckner A, Candore G, Carnovale C, Crisafulli S, Cutroneo PM, Dolladille C, Drici MD, Faillie JL, Goldman A, Hauben M, Herdeiro MT, Mahaux O, Manlik K, Montastruc F, Noguchi Y, Norén GN, Noseda R, Onakpoya IJ, Pariente A, Poluzzi E, Salem M, Sartori D, Trinh NTH, Tuccori M, van Hunsel F, van Puijenbroek E, Raschi E, Khouri C. The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration. Drug Saf. 2024; 47(6): 585-599. doi: 10.1007/s40264-024-01423-7.
4. Noguchi Y, Yoshimura T. Detection Algorithms for Simple Two-Group Comparisons Using Spontaneous Reporting Systems. Drug Saf. 2024; 47(6): 535-543. doi: 10.1007/s40264-024-01404-w.
5. Noguchi Y, Yan M, Yoshimura T. Questioning Bayesian Inference-Based Models for Estimating Culprit Drugs for Adverse Drug Reactions. Clin Pharmacol Ther. 2023; 114(2): 259. doi: 10.1002/cpt.2933.

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