Biomarkers for Opioid Use Disorder (OUD)

专有：本提案包含Highland Instruments的商业机密和其他专有或机密信息，仅供美国国立卫生研究院（NIH）用于评估本SBIR提案。不授予其他权利。这一建议和 未经Highland Instrument的许可，不得在NIH之外披露本文中包含的商业秘密和其他专有或机密信息的全部或部分内容。这一限制并不限制NIH使用数据中包含的信息的权利，如果这些信息是从以下来源获得的： 另一个来源没有限制。本图例适用于本提案的摘要、具体目的、研究计划（所有组成部分）、商业化计划和人类受试者部分。 摘要 美国正在遭受全国性阿片类药物流行病，其特点是成本高昂，过量和死亡。 使用定性标准诊断OUD（即，临床量表和问卷（例如，DMSV标准）），以及 毒理学筛查具有不同程度的测试准确性。OUD的生物标志物， 预后价值代表了NIDA和FDA已经认识到的未满足的需求。常规OUD 治疗（即，药物和心理社会干预）的特点是有限或减少 疗效、上限效应和/或严重副作用。有效的OUD生物标志物的可用性将是关键 更好的治疗方法的开发和批准。最终，OUD生物标志物的稀缺代表了 NIDA和FDA认识到，这是对抗阿片类药物成瘾的一个重大未满足的需求， 支持医疗器械开发工具（MDDT）和OUD生物标志物测试的开发。进展 在神经成像技术，特别是最近的证据支持脑电图（EEG）作为一个非常 研究成瘾与大脑状态之间关系的候选人。基于EEG数据 在我们的OUD临床研究中收集了大量信息，旨在开发一种用于OUD治疗的新医疗器械，我们 建议探索使用高密度脑电图（EEG）来识别候选生物标志物， OUD用于诊断、疾病监测和预测OUD治疗反应。我们将招募50 OUD 不同治疗阶段的患者以及20名非OUD健康对照。对于这些主题，我们将收集 当前和历史药物使用数据，临床数据（包括关于渴望和情绪的问卷），以及 毒理学（毛发、尿液）。接下来，我们将记录休息时和渴望/线索暴露任务期间的高密度EEG。 EEG分析将侧重于功率分析、头皮图和源重建。我们会从你的身体里 基于EEG的措施，并评估他们是否能够区分的临床特征， 患者，如治疗的稳定性，线索暴露渴望反应，多种物质的使用，和持续时间， 疾病最后，我们将在接受脑刺激的OUD患者的历史数据中重复分析 以确定基于EEG的测量是否对脑刺激治疗有反应。最终，这个项目 将致力于开发候选EEG生物标志物，这些生物标志物可能用于OUD诊断，疾病 以及治疗监测和治疗反应的预测。

Proprietary: This proposal includes trade secrets and other proprietary or confidential information of Highland Instruments and is being provided for use by the National Institutes of Health (NIH) for the sole purpose of evaluating this SBIR proposal. No other rights are conferred. This proposal and the trade secrets and other proprietary or confidential information contained herein shal further not be disclosed in whole or in parts, outside of NIH without Highland Instrument's permission. This restriction does not limit the NIH's right to use information contained in the data if it is obtained from another source without restriction. This legend applies to the Abstract, Specific Aims, Research Plan (al components), Commercialization Plan, and Human Subject's Sections of this proposal. Abstract The U.S. is suffering from a national opioid epidemic characterized by significant costs, overdoses, and deaths. OUD is diagnosed using qualitative criteria (i.e., clinical scales and questionnaires (e.g., DMSV criteria)), and toxicology screening with various degrees of testing accuracy. Biomarkers for OUD with diagnostic and prognostic value represent an unmet need that has been recognized by NIDA and the FDA. Conventional OUD treatments (i.e., pharmacological and psychosocial interventions) are characterized by limited or diminishing efficacy, ceiling effects, and/or serious side effects. The availability of validated OUD biomarkers would be a key step in the development and approval of better treatments. Ultimately, the scarcity of OUD biomarkers represents a significant unmet need in the fight against opioid addiction as recognized by NIDA and the FDA with their support for development of Medical Device Development Tools (MDDT) and biomarker tests for OUD. Advances in neuroimaging techniques, and in particular recent evidence supports electroencephalography (EEG) as a very promising candidate to investigate the correlation between addiction and brain state. Building on EEG data gathered during our OUD clinical study aimed at developing a new medical device for OUD treatment, we propose to explore the use of high-density electroencephalography (EEG) to identify candidate biomarkers for OUD for diagnosis, disease monitoring and prediction of OUD treatment response. We will enroll 50 OUD patients at various stages of treatment as well as 20 non-OUD healthy controls. For these subjects, we will gather current and historical drug use data, clinical data (including questionnaires on cravings and mood), and toxicology (hair, urine). Next, we will record high-density EEG at rest and during a Craving/Cue Exposure task. EEG analysis will focus on power analysis, scalp maps, and source reconstruction. We will extract a battery of EEG-based measures and assess whether they are able to differentiate between clinical characteristics of the patients such as stability of treatment, cue exposure craving response, polysubstance use, and duration of disease. Finally, we will repeat the analysis in our historical data from OUD patients undergoing brain stimulation to determine if the EEG-based measures are responsive to brain stimulation treatment. Ultimately this project will aim at developing candidate EEG biomarkers which could potentially be used for OUD diagnosis, disease and treatment monitoring, and prediction of treatment response.