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

• 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
• 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

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.

摘要 人们对大麻（作为吸食大麻或CBD或THC占主导地位的提取物）作为一种 用于各种健康适应症（包括慢性疼痛）的治疗方式。考虑到 然而，大麻，我们对其在补充和综合健康方面的作用机制知之甚少。 接近。具体来说，有一种流行的观点认为，100多种大麻素和各种大麻素， 组成大麻的萜类化合物/类黄酮协同作用以产生“随行效应”。全面 需要进行分析，以更好地了解大麻制剂作为补充药物的潜力。我们 本文提出了一种新颖人工智能驱动的方法来解决我们知识中的这一差距。 毫不奇怪，天然产物（例如，大麻）在生物体中具有活性通常是因为它的作用 如内源性配体或生物体已知的配体。我们假设解构配体 将这种结构转化为特定片段将使我们能够鉴定结合含有这种结构的内源性靶点的靶点。 片段此外，我们认为，不同的化合物协同作用将最大限度地参与选择性 途径。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），以将配体化合物（大麻素和萜类化合物）映射到分子靶标。 因此，我们可以阐明相关的细胞通路，并预测生理反应。我们将使用DRIFT 在许多不同的大麻提取物中分析化合物（例如，高CBD，CBG或THC）， 镇痛性质，以确定治疗组合及其相关靶标。我们将承担三个 具体目标。在具体目标1中，使用DRIFT将大麻成分大量映射到相应的 靶蛋白。然后，在具体目标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比较。我们将使用文本 从PubMed挖掘化合物-蛋白质关系的挖掘技术。最后，在具体目标3中，我们将 实验验证DRIFT平台的输出，以预测大麻对疼痛的机制。我们将 使用传统的药理学工具和各种临床前动物模型测试基于AI的结果， 痛苦然后，这些相同的模型将通过补充使用 激动剂、拮抗剂、抑制剂和（适当时）基因敲除以验证机制。 当这些研究成功时，我们将验证DRIFT是一种新的有价值的AI工具， 天然产品。此外，我们将提供重要的见解越来越多的大麻使用， 互补和综合健康。