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.

抽象的 对大麻（作为烟熏大麻或CBD或THC为主导提取物）非常感兴趣 各种健康适应症（包括慢性疼痛）的治疗方式。考虑到复杂的 但是，大麻几乎没有深入了解其完善和综合健康方面的作用机制 方法。具体而言，有一个普遍的观念，即100+大麻素和各种 完成大麻的萜类/类黄酮协同起作用，以创造“随行效应”。全面 需要分析以更好地理解大麻剂作为完整药物的潜力。我们 在此提出了一种新颖的人工智能驱动方法，以解决我们知识的这一差距。 毫不奇怪，在有机体中活跃的天然产品（例如，大麻）通常起作用，因为它起作用 像内源配体或生物体所知的配体。我们假设解构配体 到特定碎片的结构将使我们能够识别结合内源性tergets的目标 碎片。此外，我们认为在共同作用的不同化合物将最大程度地吸引选择性 途径。我们已经开发了一个人工智能（AI）驱动的平台，漂移（药物目标识别 基于化学相似性），将配体化合物（大麻素和萜类化合物）映射到分子靶标。 那就是我们可以照亮涉及的细胞途径，并预测物理反应。我们将使用漂移 许多不同的大麻提取物（例如，CBD，CBG或THC高）中的曲线化合物具有不同的 镇痛性特性以识别治疗组合及其相关靶标。我们将进行三个 具体目标。在特定的目标1中，使用漂移进行大麻构成的大量映射以对应 靶蛋白。然后，在特定的目标2中，我们将通过评估绑定亲和力来扩展漂移平台 使用新的神经网络范式（NeururDock）之间的代谢物及其蛋白质之间。我们将使用文字 开采PubMed的化合物蛋白质关系的采矿技术。最后，在特定的目标3中，我们将 实验验证漂移平台的输出，以预测大麻在疼痛上的机制。我们将 使用传统药理学工具和各种临床前动物模型测试基于AI的结果 疼痛。然后，这些相同的模型将通过完善的使用来测试机制 激动剂，拮抗剂，抑制剂和（在适当的情况下）基因敲除以验证机制。 当这些研究成功时，我们将验证漂移是一种新的贵重AI工具 天然产品。此外，我们将提供重要的见解，以了解大麻不断增长 完全综合的健康。