Ambesi-Impiombato Alberto, Cox Kimberly, Ramboz Sylvie, Brunner Daniela, Bansal Mukesh, Leahy Emer
PsychoGenics, Paramus, NJ, United States.
Front Pharmacol. 2023 Jul 25;14:1128562. doi: 10.3389/fphar.2023.1128562. eCollection 2023.
Drug-induced Behavioral Signature Analysis (DBSA), is a machine learning (ML) method for screening of compounds, inspired by analytical methods quantifying gene enrichment in genomic analyses. When applied to behavioral data it can identify drugs that can potentially reverse behavioral symptoms in animal models of human disease and suggest new hypotheses for drug discovery and repurposing. We present a proof-of-concept study aiming to assess Drug-induced Behavioral Signature Analysis (DBSA) as a systematic approach for drug discovery for rare disorders. We applied Drug-induced Behavioral Signature Analysis to high-content behavioral data obtained with SmartCube, an automated phenotyping platform. The therapeutic potential of several dozen approved drugs was assessed for phenotypic reversal of the behavioral profile of a Huntington's Disease (HD) murine model, the Q175 heterozygous knock-in mice. The Drug-induced Behavioral Signature Analysis predictions were enriched for drugs known to be effective in the symptomatic treatment of Huntington's Disease, including bupropion, modafinil, methylphenidate, and several SSRIs, as well as the atypical antidepressant tianeptine. To validate the method, we tested acute and chronic effects of tianeptine (20 mg/kg) , using Q175 mice and wild type controls. In both experiments, tianeptine significantly rescued the behavioral phenotype assessed with the SmartCube platform. Our target-agnostic method thus showed promise for identification of symptomatic relief treatments for rare disorders, providing an alternative method for hypothesis generation and drug discovery for disorders with huge disease burden and unmet medical needs.
药物诱导行为特征分析（DBSA）是一种用于化合物筛选的机器学习（ML）方法，其灵感来源于基因组分析中量化基因富集的分析方法。当应用于行为数据时，它可以识别出有可能逆转人类疾病动物模型中行为症状的药物，并为药物发现和重新利用提出新的假设。我们开展了一项概念验证研究，旨在评估药物诱导行为特征分析（DBSA）作为一种针对罕见疾病的药物发现系统方法。我们将药物诱导行为特征分析应用于通过自动化表型分析平台SmartCube获得的高内涵行为数据。对几十种已批准药物治疗亨廷顿舞蹈病（HD）小鼠模型（Q175杂合敲入小鼠）行为特征表型逆转的治疗潜力进行了评估。药物诱导行为特征分析的预测结果富集了已知对亨廷顿舞蹈病症状治疗有效的药物，包括安非他酮、莫达非尼、哌甲酯和几种选择性5-羟色胺再摄取抑制剂（SSRI），以及非典型抗抑郁药噻奈普汀。为了验证该方法，我们使用Q175小鼠和野生型对照测试了噻奈普汀（20mg/kg）的急性和慢性效应。在两个实验中，噻奈普汀均显著挽救了通过SmartCube平台评估的行为表型。因此，我们这种与靶点无关的方法在识别罕见疾病症状缓解治疗方面显示出了前景，为疾病负担巨大且医疗需求未得到满足的疾病提供了一种生成假设和药物发现的替代方法。
