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Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant
European Neuropsychopharmacology ( IF 6.1 ) Pub Date : 2020-02-01 , DOI: 10.1016/j.euroneuro.2019.11.003
Mariam Bonyadi Camacho 1 , Warut D Vijitbenjaronk 2 , Thomas J Anastasio 3
Affiliation  

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed patients still fail to respond even after months to years of searching to find an effective combination. To aid in the identification of potentially effective antidepressant combinations, we created a computational model of the monoaminergic neurotransmitter (serotonin, norepinephrine, and dopamine), stress-hormone (cortisol), and male sex hormone (testosterone) systems. The model was trained via machine learning to represent a broad range of empirical observations. Neuroadaptation to chronic drug administration was simulated through incremental adjustments in model parameters that corresponded to key regulatory components of the neurotransmitter and neurohormone systems. Analysis revealed that neuroadaptation in the model depended on all of the regulatory components in complicated ways, and did not reveal any one or a few specific components that could be targeted in the design of antidepressant treatments. We used large sets of neuroadapted states of the model to screen 74 different drug and hormone combinations and identified several combinations that could potentially be therapeutic for a higher proportion of male patients than SSRIs by themselves.

中文翻译:

单胺能神经递质和男性神经内分泌系统的计算建模在对慢性抗抑郁药的治疗性神经适应分析中

二线抑郁症治疗涉及增加一种(很少是两种)额外药物,即长期服用选择性血清素再摄取抑制剂 (SSRI),这是抑郁症的一线治疗方法。不幸的是,许多抑郁症患者即使经过数月至数年的寻找有效组合仍然没有反应。为了帮助识别潜在有效的抗抑郁药组合,我们创建了单胺能神经递质(血清素、去甲肾上腺素和多巴胺)、压力激素(皮质醇)和雄性激素(睾酮)系统的计算模型。该模型通过机器学习进行训练,以表示广泛的经验观察。通过对与神经递质和神经激素系统的关键调节成分相对应的模型参数的增量调整来模拟对慢性药物给药的神经适应。分析表明,模型中的神经适应以复杂的方式依赖于所有的调节成分,并且没有揭示任何一种或几种可以在抗抑郁治疗设计中作为目标的特定成分。我们使用模型的大量神经适应状态来筛选 74 种不同的药物和激素组合,并确定了几种组合,这些组合可能比 SSRI 本身对更高比例的男性患者具有治疗作用。分析表明,模型中的神经适应以复杂的方式依赖于所有的调节成分,并且没有揭示任何一种或几种可以在抗抑郁治疗设计中作为目标的特定成分。我们使用模型的大量神经适应状态来筛选 74 种不同的药物和激素组合,并确定了几种组合,这些组合可能比 SSRI 本身对更高比例的男性患者具有治疗作用。分析表明,模型中的神经适应以复杂的方式依赖于所有的调节成分,并且没有揭示任何一种或几种可以在抗抑郁治疗设计中作为目标的特定成分。我们使用模型的大量神经适应状态来筛选 74 种不同的药物和激素组合,并确定了几种组合,这些组合可能比 SSRI 本身对更高比例的男性患者具有治疗作用。
更新日期:2020-02-01
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