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

摘要 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 尝试戒烟期间的临床结局。 在美国，超过1400万成年人患有AUD，并且几乎没有有效的药物治疗。率 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和神经生物学之间关系的理解。 以及从AUD中恢复的行为。现有的证据表明，阿片系统，通过一个 欣快（μ-阿片样物质受体;莫尔）和烦躁（κ-阿片样物质受体; 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），这些在 酒精成瘾/复发行为健康动物中这些OR受体之间的平衡是 被酒精的消耗和成瘾的进展所破坏。人类的数据有限， 描述这种破坏。了解莫尔和大韩民国之间的不平衡， AUD患者，以及这种不平衡与渴望，情绪，退缩和时间的关联 （第一次喝酒）在戒烟尝试期间，将促进针对失衡的定制疗法的发展 并改善临床结果。 AUD患者的PET研究分别检查了莫尔和KOR的可用性。以前的研究 用MOR选择性示踪剂测量腹侧纹状体（VS）的莫尔，发现人的VS的莫尔更高 与健康受试者（HS）相比，我们使用KOR选择性PET示踪剂，观察到人们 AUD患者杏仁核和苍白球的KOR可用性显著低于HS。KOR可用性关键字 区域（例如，整个纹状体）也预测了接受阿片类药物治疗的参与者饮酒量的减少 拮抗剂纳洛酮。似乎莫尔的上调和KOR的下调发生在 AUD，两者均与临床结局相关。从来没有人把这两个目标想象成同一个人。 我们将使用PET对HS和AUD中的莫尔和KOR可用性进行成像。我们将量化 AUD患者的莫尔和KOR（单独和联合）与其治疗期间的关键临床结局之间的关系 后续退出尝试。我们将使用线性模型将每种示踪剂结合的区域值与临床 结果。我们还将使用先进的集群技术来识别不同的参与者群体 to their其imaging成像data数据.我们的目标是了解两个主要阿片受体系统的相互作用， 他们如何编码AUD患者在尝试戒烟时的行为。研究结果可指导 新的靶向治疗。机器学习（光谱聚类）也将帮助我们识别图像中的特征 这在未来可能对预测临床结果有用。

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.