Biomarkers for Opioid Use Disorder (OUD)

专有：本建议书包括高地仪器的商业秘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建议书的目的。不授予任何其他权利。这项建议和 未经高地文书许可，不得在NIH以外的地方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。这一限制不限制NIH使用数据中包含的信息的权利，如果该信息是从 另一个不受限制的来源。本图例适用于本提案的摘要、具体目标、研究计划(所有组成部分)、商业化计划和人类主题部分。 摘要 美国正遭受着全国性的阿片类药物流行，其特点是成本高昂、服药过量和死亡。 使用定性标准(即临床量表和问卷(例如，DMSV标准))诊断OUD；以及 毒理筛选具有不同程度的检测准确度。OUD的生物标志物与诊断和 预后价值代表NIDA和FDA已经认识到的未得到满足的需求。传统OUD 治疗(即药理学和心理社会干预)的特点是有限或减少。 疗效、天花板效应和/或严重副作用。有效的OUD生物标记物的可用性将是一个关键 参与开发和批准更好的治疗方法。归根结底，OUD生物标志物的稀缺代表了 NIDA和FDA在与阿片成瘾作斗争中认识到的未得到满足的重大需求 支持开发医疗器械开发工具(MDDT)和OUD的生物标志物测试。预付款 在神经成像技术中，特别是最近的证据支持脑电(EEG)是一种非常 很有希望成为研究成瘾与大脑状态相关性的候选人。以脑电数据为基础 在我们的乌德临床研究期间收集的，旨在开发一种用于乌德治疗的新医疗设备，我们 建议探索使用高密度脑电(EEG)来识别候选生物标记物 用于诊断、疾病监测和预测OUD治疗反应。我们将招收50名学生 不同治疗阶段的患者及20例非OUD健康对照。对于这些主题，我们将收集 当前和历史的药物使用数据、临床数据(包括关于渴望和情绪的问卷调查)，以及 毒理学(头发、尿液)。接下来，我们将记录在休息和渴望/线索暴露任务中的高密度脑电。 脑电分析将重点放在功率分析、头皮图和源重建上。我们将提取一组 以脑电为基础的测量，并评估它们是否能够区分 患者的治疗稳定性、线索暴露渴求反应、多物质使用和持续时间 疾病。最后，我们将重复我们对接受脑刺激的患者的历史数据的分析。 以确定基于脑电的措施是否对脑刺激治疗有反应。最终，这个项目 将致力于开发可能用于疾病诊断的候选脑电生物标记物 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.