Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
ACS Chem Neurosci. 2020 Oct 21;11(20):3245-3258. doi: 10.1021/acschemneuro.0c00372. Epub 2020 Oct 7.
More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.
超过 5000 万美国成年人患有慢性疼痛。阿片类药物常用于缓解多种类型的疼痛，且效果显著。然而，阿片类药物的过度使用可能导致滥用、成瘾和死亡。非甾体抗炎药（NSAIDs）是另一种主要的镇痛药，也有许多有问题的副作用，包括头痛、头晕、呕吐、腹泻、恶心、便秘、食欲减退和嗜睡。人们迫切需要了解药物滥用和成瘾的分子机制，以帮助设计新的预防或治疗疼痛的药物。为了促进与疼痛相关的小分子信号通路研究，并预测疼痛治疗的潜在治疗靶点，我们构建了一个全面的疼痛特定化药物基因组学知识库（Pain-CKB），其中集成了数据挖掘计算工具。我们的新计算平台描述了参与疼痛调节的化学分子、基因、蛋白质和信号通路。Pain-CKB 具有友好的用户界面，可用于预测相关的蛋白质靶标，并分析和可视化输出结果，包括 HTDocking、TargetHunter、BBB 预测器和 Spider Plot。将这些与其他新工具结合使用，我们进行了三个案例研究，系统地展示了如何根据 Pain-CKB 及其算法和工具生成的数据进一步进行研究。首先，对四种 FDA 批准的镇痛药进行了系统药理学靶标映射，以鉴定已知靶标并预测潜在的靶标相互作用。随后，将靶标映射结果应用于构建对乙酰氨基酚和芬太尼的基于生理学的药代动力学（PBPK）模型，以探索这对药物之间的药物-药物相互作用（DDI）。最后，进行了药物分析以探索对乙酰氨基酚反应代谢物及其肝毒性靶标硫氧还蛋白还原酶的详细相互作用模式。
