AI-based Mapping of Complex Cannabis Extracts in Pain Pathways

基于人工智能的疼痛通路中复杂大麻提取物的绘图

基本信息

批准号：
10659413
负责人：
NIKOLAY DOKHOLYAN
金额：
$ 53.61万
依托单位：
PENNSYLVANIA STATE UNIV HERSHEY MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-14 至 2027-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10659413
关键词：
3-Dimensional Address Affinity Agonist Amino Acid Sequence Analgesics Animal Model Artificial Intelligence Beta-caryophyllene Binding Cannabinoids Cannabis Cell model Chemicals Complement Complementary Health Complementary Medicine Complex Complex Mixtures Data Set Databases Disparate Docking Drug Targeting Drug usage Evaluation Evolution Exclusion Flavonoids Health Human In Vitro Integrative Medicine Intervention Knowledge Laboratories Learning Ligands Link Luteolin Maps Marijuana Methods Modality Modeling Molecular Molecular Target Natural Products Organism Outcome Output Pain Pathway interactions Pharmaceutical Preparations Pharmacology Phenotype Physiological Physiology Process Property Proteins PubMed Quercetin Research Science Smoke Structure Techniques Terpenes Testing Therapeutic Therapeutic Uses Work antagonist chronic pain deep learning drug efficacy genome-wide analysis humulene inhibitor innovation insight interest knockout gene limonene linalool marijuana use mind/body neural network novel pain model pharmacologic pre-clinical protein data bank response small molecule text searching three dimensional structure tool web server

项目摘要

ABSTRACT There is tremendous interest in cannabis (as smoked marijuana or CBD- or THC-dominant extracts) as a therapeutic modality for a variety of health indications (including chronic pain). Given the complexity of cannabis, however, we have little insight into its mechanisms of action in complementary and integrative health approaches. Specifically, there is a prevailing notion that the 100+ cannabinoids and the various terpenoids/flavonoids that comprise cannabis act in concert to create an “Entourage Effect”. A comprehensive analysis is required to better understand the potential of cannabis agents as complementary medicines. We herein propose a novel artificial intelligence-driven approach to address this gap in our knowledge. Not surprisingly, a natural product (e.g., cannabis) that is active in an organism typically works because it acts like endogenous ligands or those known to the organism. We hypothesize that deconstructing ligand structures into specific fragments will allow us to identify targets that bind endogenous tergets containing such fragments. Moreover, we believe that disparate compounds acting in concert will maximally engage selective pathways. We have developed an artificial intelligence (AI)-driven platform, DRIFT (drug-target identification based on chemical similarity), to map ligand compounds (cannabinoids and terpenoids) to molecular targets. Thus, we can illuminate involved cellular pathways, and predict physiological response. We will use DRIFT to profile compounds in a number of different cannabis extracts (e.g., high in CBD, CBG, or THC) with varying analgesic properties to identify therapeutic combinations and their relevant targets. We will undertake three specific aims. In Specific Aim 1, use DRIFT for massive mapping of cannabis constituents to corresponding target proteins. Then, in Specific Aim 2, we will extend the DRIFT platform by evaluating binding affinities between metabolites and their proteins using a new neural network paradigm (NeuralDock). We will use text mining techniques to mine compound-protein relationships from PubMed. Finally, in Specific Aim 3, we will experimentally validate the outputs of the DRIFT platform to predict mechanisms of cannabis on pain. We will test the AI-based results using traditional pharmacology tools and a variety of preclinical animal models of pain. These same models will then be employed to test mechanisms through the complementary use of agonists, antagonists, inhibitors and (where appropriate) gene knockouts to validate mechanisms. When these studies are successful, we will have validated DRIFT as a new and valuable AI tool for studying natural products. Moreover, we will provide important insights into the growing use of cannabis in complementary and integrative health.

抽象的人们对大麻（如熏制大麻或以 CBD 或 THC 为主的提取物）作为一种鉴于其复杂性，针对各种健康适应症（包括慢性疼痛）的治疗方式。然而，我们对其在补充和综合健康方面的作用机制知之甚少具体来说，有一个普遍的观念，即 100 多种大麻素和各种大麻素。大麻中的萜类化合物/黄酮类化合物协同作用，产生全面的“随行效应”。我们需要进行分析以更好地了解大麻药物作为补充药物的潜力。本文提出了一种新颖的人工智能驱动方法来解决我们的知识差距。毫不奇怪，在生物体中具有活性的天然产物（例如大麻）通常会起作用，因为它的作用像内源性配体或生物体已知的那些我们欺负解构配体。将结构分解成特定片段将使我们能够识别结合含有此类内源靶标的靶标此外，我们相信不同的化合物协同作用将最大限度地发挥选择性。我们开发了一个人工智能（AI）驱动的平台，DRIFT（药物靶点识别）。基于化学相似性），将配体化合物（大麻素和萜类化合物）映射到分子靶点。因此，我们可以阐明相关的细胞途径，并预测生理反应。我们将使用 DRIFT 来预测。多种不同大麻提取物（例如，CBD、CBG 或 THC 含量高）中的化合物具有不同的特征我们将进行三项研究，以确定治疗组合及其相关目标的镇痛特性。在具体目标 1 中，使用 DRIFT 将大麻成分大规模映射到相应的位置。然后，在特定目标 2 中，我们将通过评估结合亲和力来扩展 DRIFT 平台。使用新的神经网络范例（NeuralDock）在代谢物及其蛋白质之间进行分析。最后，在具体目标 3 中，我们将使用挖掘技术从 PubMed 中挖掘化合物-蛋白质关系。我们将通过实验验证 DRIFT 平台的输出来预测大麻对疼痛的机制。使用传统药理学工具和各种临床前动物模型测试基于人工智能的结果然后，这些相同的模型将通过互补使用来测试机制。激动剂、拮抗剂、抑制剂和（如果适用）基因敲除以验证机制。当这些研究取得成功时，我们将验证 DRIFT 作为一种新的、有价值的人工智能工具来进行研究此外，我们还将提供有关大麻日益增长的使用的重要见解。互补和综合的健康。