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用于运动皮层肌张力障碍药物治疗的多靶点多尺度模拟

Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex.

作者信息

Neymotin Samuel A, Dura-Bernal Salvador, Lakatos Peter, Sanger Terence D, Lytton William W

机构信息

Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New YorkBrooklyn, NY, USA; Department Neuroscience, Yale University School of MedicineNew Haven, CT, USA.

Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New York Brooklyn, NY, USA.

出版信息

Front Pharmacol. 2016 Jun 14;7:157. doi: 10.3389/fphar.2016.00157. eCollection 2016.

Abstract

A large number of physiomic pathologies can produce hyperexcitability in cortex. Depending on severity, cortical hyperexcitability may manifest clinically as a hyperkinetic movement disorder or as epilpesy. We focus here on dystonia, a movement disorder that produces involuntary muscle contractions and involves pathology in multiple brain areas including basal ganglia, thalamus, cerebellum, and sensory and motor cortices. Most research in dystonia has focused on basal ganglia, while much pharmacological treatment is provided directly at muscles to prevent contraction. Motor cortex is another potential target for therapy that exhibits pathological dynamics in dystonia, including heightened activity and altered beta oscillations. We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. These models demonstrated degeneracy, meaning that there were many ways of obtaining the pathological syndrome. There was no single parameter alteration which would consistently distinguish pathological from physiological dynamics. At higher dimensions in parameter space, we were able to use support vector machines to distinguish the two patterns in different regions of space and thereby trace multitarget routes from dystonic to physiological dynamics. These results suggest the use of in silico models for discovery of multitarget drug cocktails.

摘要

大量生理机能性病变可导致皮质兴奋性过高。根据严重程度不同,皮质兴奋性过高在临床上可能表现为运动亢进性运动障碍或癫痫。我们在此聚焦于肌张力障碍,这是一种运动障碍,会导致非自主性肌肉收缩,且涉及包括基底神经节、丘脑、小脑以及感觉和运动皮质在内的多个脑区的病变。肌张力障碍的大多数研究都集中在基底神经节,而许多药物治疗是直接作用于肌肉以防止收缩。运动皮质是肌张力障碍中呈现病理动力学(包括活动增强和β振荡改变)的另一个潜在治疗靶点。我们构建了一个初级运动皮质的多尺度模型,涵盖从分子水平到细胞水平再到网络水平,包含1715个具有多个离子通道和细胞内分子动力学的房室模型神经元。我们根据从小鼠运动皮质电路映射实验获得的电生理数据连接该模型。我们通过对参数应用独立随机变化以识别病理参数集,利用该模型重现肌张力障碍中所见的活动增强模式。这些模型显示出简并性,即存在多种获得病理综合征的方式。没有单一的参数改变能够始终如一地将病理动力学与生理动力学区分开来。在参数空间的更高维度上,我们能够使用支持向量机来区分空间不同区域的两种模式,从而追踪从肌张力障碍性到生理性动力学的多靶点路径。这些结果表明可利用计算机模型来发现多靶点药物组合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7dc4/4906029/24f288208989/fphar-07-00157-g0001.jpg

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