特雷姆2
重新调整用途
虚拟筛选
药物重新定位
计算生物学
阿尔茨海默病
疾病
计算机科学
药理学
医学
药物发现
生物信息学
生物
内科学
药品
受体
生态学
髓系细胞
作者
Rory A. Greer,Hunter B. Dean,Tom J. Brett,Erik D. Roberson,Yuhua Song
标识
DOI:10.1016/j.bpj.2023.11.2536
摘要
Bringing a drug to market can take over a decade and cost more than one billion dollars. Just to reach clinical trials can take several years and hundreds of millions of dollars. In this study, we investigate the repurposing of available drugs for Alzheimer's disease (AD) by targeting triggering receptor expressed on myeloid cells-2 (TREM2), an innate immune receptor expressed on microglia, using a combined unbiased virtual screening and experimental validation method. TREM2 variants have been associated with AD in GWAS studies, and the R47H variant remains one of the strongest genetic risk factors for developing AD. To identify drugs targeting TREM2's immunoglobulin domain, we developed an unbiased virtual screening approach to remove ligand size bias, program bias, and increase ligand diversity to screen a library of 6,798 drugs that are either approved or in clinical/preclinical trials from the Broad Institute Drug Repurposing Hub. Top drugs will be verified with bio-layer interferometry (BLI) and followed with cellular studies to identify the top drugs that can activate TREM2 signaling and mediate TREM2-dependent cellular functions. Using our unbiased virtual screening method, we identified drugs that could bind TREM2 and verified binding with BLI experiments. In cellular studies we were able to observe the identified drugs triggering TREM2 signaling and regulating TREM2-dependent cellular functions. These drugs identified from our combined computational and experimental validation approach have the potential to be repurposed to target TREM2 as therapeutic treatment options for AD. Additionally, our method could be used to investigate the repurposing of available drugs to target receptors associated with other diseases to offer potential treatment options for these other diseases in future studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI