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 在戒烟尝试期间，精神障碍(AUD)对临床结果的影响。 在美国，超过1400万成年人患有AUD，几乎没有有效的药物疗法。收费率 失误和复发率非常高，这仍然是成功复苏的最大障碍。 开发新的治疗方法必须基于对神经生物学之间的关系的理解 以及从澳元病中恢复的行为。现有证据表明，阿片系统通过一种 兴奋(Mu-阿片受体；MOR)和烦躁(Kappa-阿片受体；KOR)之间的平衡， 调节关键的临床结果(例如，酒精渴求、快感缺乏和戒断)，这些结果在 AUD中的酒精流失/复发行为。在健康动物中，这些OR受体之间的平衡是 被酒精的消费和成瘾的发展所干扰。人类的数据是有限的 描述一下这场颠覆。发展对韩国能源部和韩国能源部之间不平衡的认识 AUD的个体，以及这种失衡与渴望、情绪、退缩和时间流逝的关联 (第一次饮酒)在戒烟尝试期间，将促进针对失衡的量身定制疗法的发展 并改善临床结果。 对AUD患者的宠物研究分别检查了MOR和KOR的可用性。以前的研究 用MOR选择性示踪剂测定腹侧纹状体(VS)的MOR，发现人VS的MOR较高 与健康受试者(HS)进行比较。我们使用了KOR选择性的PET示踪剂，观察到人们 与HS相比，AUD患者杏仁核和苍白球的KOR利用率显著降低。密钥中的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减少。 拮抗剂纳曲酮。似乎MOR的上调和KOR的下调发生在 AUD和这两者都与临床结果有关。从来没有人在同一个人身上想象过两个目标。 我们将使用PET来显示HS和AUD的MOR和KOR可用性。我们将量化这些关系 AUD患者的MOR和KOR之间，单独和联合对关键临床结果的影响 随后的退出尝试。我们将使用线性模型将每个示踪剂结合的地区值与临床联系起来 结果。我们还将使用高级集群技术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个主要的阿片受体系统和 他们如何对AUD患者在戒烟尝试期间的行为进行编码。研究结果可指导相关领域的发展。 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 这可能在未来对临床结果的预测有用。

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.