Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159, Mannheim, Germany.
Max Planck Institute for Biological Cybernetics, Max Planck Ring 8, 72076, Tübingen, Germany.
Nat Commun. 2018 Nov 8;9(1):4699. doi: 10.1038/s41467-018-07239-1.
Neuropsychiatric disorders are the third leading cause of global disease burden. Current pharmacological treatment for these disorders is inadequate, with often insufficient efficacy and undesirable side effects. One reason for this is that the links between molecular drug action and neurobehavioral drug effects are elusive. We use a big data approach from the neurotransmitter response patterns of 258 different neuropsychiatric drugs in rats to address this question. Data from experiments comprising 110,674 rats are presented in the Syphad database [ www.syphad.org ]. Chemoinformatics analyses of the neurotransmitter responses suggest a mismatch between the current classification of neuropsychiatric drugs and spatiotemporal neurostransmitter response patterns at the systems level. In contrast, predicted drug-target interactions reflect more appropriately brain region related neurotransmitter response. In conclusion the neurobiological mechanism of neuropsychiatric drugs are not well reflected by their current classification or their chemical similarity, but can be better captured by molecular drug-target interactions.
神经精神疾病是全球疾病负担的第三大主要原因。目前对这些疾病的药物治疗效果不佳，往往疗效不足，且存在不良副作用。造成这种情况的一个原因是，分子药物作用与神经行为药物效应之间的联系难以捉摸。我们使用大鼠中 258 种不同神经精神药物的神经递质反应模式的大数据方法来解决这个问题。包含 110674 只大鼠的实验数据在 Syphad 数据库[www.syphad.org]中呈现。对神经递质反应的计算化学分析表明，目前的神经精神药物分类与系统水平的神经递质时空反应模式之间存在不匹配。相比之下，预测的药物-靶标相互作用更能反映与大脑区域相关的神经递质反应。总之，神经精神药物的神经生物学机制不能很好地反映在它们目前的分类或化学相似性中，但可以通过分子药物-靶标相互作用更好地捕捉到。
