Pei Fen, Li Hongchun, Liu Bing, Bahar Ivet
Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Front Pharmacol. 2019 Mar 8;10:191. doi: 10.3389/fphar.2019.00191. eCollection 2019.
Existing treatments against drug addiction are often ineffective due to the complexity of the networks of protein-drug and protein-protein interactions (PPIs) that mediate the development of drug addiction and related neurobiological disorders. There is an urgent need for understanding the molecular mechanisms that underlie drug addiction toward designing novel preventive or therapeutic strategies. The rapidly accumulating data on addictive drugs and their targets as well as advances in machine learning methods and computing technology now present an opportunity to systematically mine existing data and draw inferences on potential new strategies. To this aim, we carried out a comprehensive analysis of cellular pathways implicated in a diverse set of 50 drugs of abuse using quantitative systems pharmacology methods. The analysis of the drug/ligand-target interactions compiled in DrugBank and STITCH databases revealed 142 known and 48 newly predicted targets, which have been further analyzed to identify the KEGG pathways enriched at different stages of drug addiction cycle, as well as those implicated in cell signaling and regulation events associated with drug abuse. Apart from synaptic neurotransmission pathways detected as upstream signaling modules that "sense" the early effects of drugs of abuse, pathways involved in neuroplasticity are distinguished as determinants of neuronal morphological changes. Notably, many signaling pathways converge on important targets such as mTORC1. The latter emerges as a universal effector of the persistent restructuring of neurons in response to continued use of drugs of abuse.
由于介导药物成瘾及相关神经生物学紊乱发展的蛋白质 - 药物和蛋白质 - 蛋白质相互作用（PPI）网络的复杂性，现有的药物成瘾治疗方法往往无效。迫切需要了解药物成瘾背后的分子机制，以设计新的预防或治疗策略。关于成瘾药物及其靶点的快速积累的数据，以及机器学习方法和计算技术的进展，现在为系统挖掘现有数据并推断潜在的新策略提供了机会。为此，我们使用定量系统药理学方法，对涉及50种不同滥用药物的细胞途径进行了全面分析。对DrugBank和STITCH数据库中汇编的药物/配体 - 靶点相互作用的分析揭示了142个已知靶点和48个新预测的靶点，对这些靶点进行了进一步分析，以确定在药物成瘾周期不同阶段富集的KEGG途径，以及那些与药物滥用相关的细胞信号传导和调节事件有关的途径。除了被检测为“感知”滥用药物早期效应的上游信号模块的突触神经传递途径外，参与神经可塑性的途径被区分为神经元形态变化的决定因素。值得注意的是，许多信号通路汇聚于重要靶点，如mTORC1。后者成为神经元持续重组以响应持续使用滥用药物的普遍效应器。
