Nakagawa Chihiro, Yokoyama Satoshi, Hosomi Kouichi, Takada Mitsutaka
Division of Drug Informatics, School of Pharmacy, Kindai University, Higashiosaka City, Japan.
Division of Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City 577-8502, Osaka, Japan.
Ther Adv Musculoskelet Dis. 2021 Sep 23;13:1759720X211047057. doi: 10.1177/1759720X211047057. eCollection 2021.
Treatment of rheumatoid arthritis (RA) has advanced with the introduction of biological disease-modifying antirheumatic drugs. However, more than 20% of patients with RA still have moderate or severe disease activity. Hence, novel antirheumatic drugs are required. Recently, drug repurposing, a process of identifying new indications for existing drugs, has received great attention. Furthermore, a few reports have shown that antipsychotics are capable of affecting several cytokines that are also modulated by existing antirheumatic drugs. Therefore, we investigated the association between antipsychotics and RA by data mining using real-world data and bioinformatics databases.
Disproportionality and sequence symmetry analyses were employed to identify the associations between the investigational drugs and RA using the US Food and Drug Administration Adverse Event Reporting System (2004-2016) and JMDC administrative claims database (January 2005-April 2017; JMDC Inc., Tokyo, Japan), respectively. The reporting odds ratio (ROR) and information component (IC) were used in the disproportionality analysis to indicate a signal. The adjusted sequence ratio (SR) was used in the sequence symmetry analysis to indicate a signal. The bioinformatics analysis suite, BaseSpace Correlation Engine (Illumina, CA, USA) was employed to explore the molecular mechanisms associated with the potential candidates identified by the drug-repurposing approach.
A potential inverse association between the antipsychotic haloperidol and RA, which exhibited significant inverse signals with ROR, IC, and adjusted SR, was found. Furthermore, the results suggested that haloperidol may exert antirheumatic effects by modulating various signaling pathways, including cytokine and chemokine signaling, major histocompatibility complex class-II antigen presentation, and Toll-like receptor cascade pathways.
Our drug-repurposing approach using data mining techniques identified haloperidol as a potential antirheumatic drug candidate.
随着生物改善病情抗风湿药的引入，类风湿关节炎（RA）的治疗取得了进展。然而，超过20%的RA患者仍有中度或重度疾病活动。因此，需要新型抗风湿药物。最近，药物重新利用，即确定现有药物新适应症的过程，受到了极大关注。此外，一些报告表明，抗精神病药物能够影响几种细胞因子，而这些细胞因子也受到现有抗风湿药物的调节。因此，我们通过使用真实世界数据和生物信息学数据库进行数据挖掘，研究了抗精神病药物与RA之间的关联。
分别使用美国食品药品监督管理局不良事件报告系统（2004 - 2016年）和JMDC行政索赔数据库（2005年1月 - 2017年4月；日本东京JMDC公司），采用不成比例分析和序列对称性分析来确定研究药物与RA之间的关联。在不成比例分析中使用报告比值比（ROR）和信息成分（IC）来表明信号。在序列对称性分析中使用调整后的序列比（SR）来表明信号。利用生物信息学分析套件BaseSpace Correlation Engine（美国加利福尼亚州Illumina公司）来探索与药物重新利用方法确定的潜在候选药物相关的分子机制。
发现抗精神病药物氟哌啶醇与RA之间存在潜在的负相关，其在ROR、IC和调整后的SR方面表现出显著的负信号。此外，结果表明氟哌啶醇可能通过调节各种信号通路发挥抗风湿作用，包括细胞因子和趋化因子信号通路、主要组织相容性复合体II类抗原呈递以及Toll样受体级联通路。
我们使用数据挖掘技术的药物重新利用方法确定氟哌啶醇为潜在的抗风湿药物候选物。
