Investigating the Mu:Kappa Opioid Receptor Imbalance in Alcohol Use Disorder

研究酒精使用障碍中的 Mu:Kappa 阿片受体失衡

• 批准号: 10731950
• 负责人: Kelly P Cosgrove
• 金额: $ 71.18万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731950
• 关键词: Abstinence; Adult; Age; Alcohol consumption; Alcohols; Amygdaloid structure; Anhedonia; Animals; Behavior; Binding; Brain imaging; Classification; Classification Scheme; Clinical; Corpus striatum structure; DSM-V; Data; Data Set; Development; Diagnosis; Down-Regulation; Drug Combinations; Drug Metabolic Detoxication; Drug Targeting; Equilibrium; Euphoria; Future; Globus Pallidus; Goals; Human; Image; Individual; Inpatients; Joints; Knowledge; Linear Models; Literature; Machine Learning; Measures; Medical; Monitor; Moods; Naltrexone; Neurobiology; Opioid; Opioid Antagonist; Opioid Receptor; Outcome; Outpatients; Participant; Patients; Persons; Pharmacotherapy; Play; Positron-Emission Tomography; Protocols documentation; Recovery; Relapse; Role; Scanning; System; Techniques; Testing; Time; Tracer; Up-Regulation; Ventral Striatum; Withdrawal; addiction; alcohol abuse therapy; alcohol behavior; alcohol craving; alcohol measurement; alcohol use disorder; biomarker identification; carfentanil; clinically relevant; cohort; contingency management; craving; cue reactivity; demographics; drinking; dysphoria; imaging biomarker; improved; individual patient; individualized medicine; kappa opioid receptors; kappa receptors; kinetic model; mu opioid receptors; mu receptors; neuroimaging; new therapeutic target; novel; pre-clinical; predict clinical outcome; receptor; recruit; regional difference; response; sex

Abstract This project will use PET imaging and machine learning to relate Mu and Kappa receptor levels in Alcohol Use Disorder (AUD) to clinical outcomes during a quit attempt. Over 14 million adults in the US suffer from AUD and there are few effective pharmacotherapies. Rates of lapse and relapse are remarkably high and this remains the biggest hurdle to successful recovery. Development of new treatments must be based on an understanding of the relationship between neurobiology and behaviors involved in recovery from AUD. Existing evidence suggests that the opioid system, through a balance between euphoria (Mu-Opioid Receptors; MOR) and dysphoria (Kappa-Opioid Receptors; KOR), regulates key clinical outcomes (e.g., alcohol craving, anhedonia, and withdrawal) which play critical roles in alcohol lapse/relapse behaviors in AUD. The balance between these OR receptors in healthy animals is disrupted by the consumption of alcohol and the progression of addiction. There is limited data in humans to characterize this disruption. Developing an understanding of the imbalance between MOR and KOR in individuals with AUD, and the associations of this imbalance with craving, mood, withdrawal, and time to lapse (first drink) during a quit attempt, will facilitate the development of tailored therapies to target the imbalance and improve clinical outcomes. PET studies of people with AUD have examined MOR and KOR availability, separately. Previous studies measured MOR in ventral striatum (VS) with an MOR-selective tracer and found higher MOR in VS of people with AUD compared to healthy subjects (HS). We used a KOR-selective PET tracer and observed that people with AUD had significantly lower KOR availability in amygdala and pallidum vs. HS. KOR availability in key regions (e.g., whole striatum) also predicted a reduction in drinking in participants treated with the opioid antagonist naltrexone. It seems that upregulation of MOR and downregulation of KOR occur in the course of AUD and that both are related to clinical outcomes. No one has ever imaged both targets in the same people. We will use PET to image both MOR and KOR availability in HS and AUD. We will quantify the relationships between MOR and KOR of AUD patients, separately and jointly, to key clinical outcomes during their subsequent quit attempt. We will use linear models to relate regional values of binding of each tracer to clinical outcomes. We will also use advanced clustering techniques to identify distinct groups of participants according to their imaging data. Our goal is to understand the interactions of the two major opioid receptor systems and how they encode the behaviors of AUD sufferers during a quit attempt. The results could guide development of new targeted therapies. Machine learning (Spectral Clustering) will also help us identify features in the images that may be useful in the future for prediction of clinical outcomes.

抽象的 该项目将使用 PET 成像和机器学习来关联酒精使用中的 Mu 和 Kappa 受体水平 戒烟尝试期间临床结果的障碍（AUD）。 美国有超过 1400 万成年人患有 AUD，且有效的药物疗法很少。费率 复发率和复发率非常高，这仍然是成功康复的最大障碍。 新疗法的开发必须基于对神经生物学之间关系的理解 以及与澳元恢复相关的行为。现有证据表明阿片类药物系统通过 欣快感（Mu-阿片受体；MOR）和烦躁感（Kappa-阿片受体；KOR）之间的平衡， 调节关键的临床结果（例如，酒精渴望、快感缺乏和戒断），这些结果在 酗酒/酗酒行为（澳元）。健康动物中这些 OR 受体之间的平衡是 因饮酒和成瘾的进展而受到干扰。人类的数据有限 描述这种破坏的特征。加深对 MOR 和 KOR 之间不平衡的理解 患有 AUD 的个体，以及这种不平衡与渴望、情绪、戒断和失效时间的关系 （第一杯饮料）在尝试戒烟期间，将有助于开发针对不平衡的定制疗法 并改善临床结果。 对 AUD 患者的 PET 研究分别检查了 MOR 和 KOR 的可用性。之前的研究 使用 MOR 选择性示踪剂测量腹侧纹状体 (VS) 的 MOR，发现人的 VS 中的 MOR 较高 与健康受试者 (HS) 相比。我们使用 KOR 选择性 PET 示踪剂并观察到人们 与 HS 相比，使用 AUD 的杏仁核和苍白球中的 KOR 可用性显着降低。关键的 KOR 可用性 区域（例如整个纹状体）也预测接受阿片类药物治疗的参与者饮酒量会减少 拮抗剂纳曲酮。 MOR 的上调和 KOR 的下调似乎发生在 AUD 和两者都与临床结果相关。没有人想象过同一个人身上有这两个目标。 我们将使用 PET 来对 HS 和 AUD 中的 MOR 和 KOR 可用性进行成像。我们将量化这些关系 AUD 患者的 MOR 和 KOR 之间的研究（分别或联合）对治疗期间的关键临床结果 随后的戒烟尝试。我们将使用线性模型将每种示踪剂结合的区域值与临床联系起来 结果。我们还将使用先进的聚类技术来根据不同的参与者群体来识别不同的参与者群体。 他们的成像数据。我们的目标是了解两种主要阿片受体系统的相互作用 他们如何编码 AUD 患者在尝试戒烟期间的行为。结果可指导开发 新的靶向治疗。机器学习（谱聚类）还将帮助我们识别图像中的特征 这在未来可能有助于预测临床结果。