Drug target finding based on the analysis of real world data

2023 Fiscal Year Final Research Report

• Project/Area Number: 20H00491
• Research Category: Grant-in-Aid for Scientific Research (A)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Medium-sized Section 47:Pharmaceutical sciences and related fields
• Research Institution: Kyoto University
• Principal Investigator: Kaneko Shuji 京都大学, 医学研究科, 研究員 (60177516)
• Co-Investigator(Kenkyū-buntansha): 永安 一樹 京都大学, 薬学研究科, 助教 (00717902) ; 浅井 聰 日本大学, 医学部, 教授 (80231108) ; 浅岡 希美 京都大学, 医学研究科, 助教 (90826091)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: リアルワールドデータ / レセプト / ビッグデータ / 電子カルテ / 薬理学 / 創薬 / 臨床検査値 / 臨床予測性

Outline of Final Research Achievements

Using a method to discover drug combinations that reduce adverse events from clinical data analysis, we explored its application to new therapeutic targets for naturally occurring diseases that exhibit symptoms similar to those of adverse events. We targeted (1) dyslipidemia caused by olanzapine, (2) interstitial pneumonia caused by amiodarone, and (3) obsessive-compulsive disorder OCD caused by pramipexole. For (1), we discovered the usefulness of combined use of vitamin D and found skeletal muscle INSIG2 as a target. Regarding (2), we discovered that dabigatran has an anti-pulmonary fibrosis effect and found a therapeutic pathway mediated by the platelet-derived growth factor α receptor. Regarding (3), we discovered the usefulness of proton pump inhibitors and clarified the mechanism of suppressing OCD by lowering the pH within neurons of the lateral orbitofrontal cortex by inhibiting ATP4A.

Free Research Field

医療情報学、薬理学

Academic Significance and Societal Importance of the Research Achievements

本研究では国内外で公開されている大規模診療記録や有害事象に関連するビッグデータに着目し、統計学的手法を用いてそれらを解析・有効活用するとともに実証実験を行い、有害事象と類似した自然発症疾患の分子機序の解明や治療薬の開発を目指した。従来にはない研究戦略と実証的薬理学を組み合わせた計画が綿密に構築されており、有害事象を軽減する戦略や新規治療薬の創出に繋がると期待できる。また新しい創薬の方法論を創成できる可能性も高い。