Drug discovery in Parkinson's disease-Update and developments in the use of cellular models.

Gaia Skibinski, Steven Finkbeiner
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引用次数: 26

Abstract

Parkinson's disease (PD) is the second most common neurodegenerative disorder and is characterized by the degeneration of dopaminergic (DA) neurons within the substantia nigra. Dopamine replacement drugs remain the most effective PD treatment but only provide temporary symptomatic relief. New therapies are urgently needed, but the search for a disease-modifying treatment and a definitive understanding of the underlying mechanisms of PD has been limited by the lack of physiologically relevant models that recapitulate the disease phenotype. The use of immortalized cell lines as in vitro model systems for drug discovery has met with limited success, since efficacy and safety too often fail to translate successfully in human clinical trials. Drug discoverers are shifting their focus to more physiologically relevant cellular models, including primary neurons and stem cells. The recent discovery of induced pluripotent stem (iPS) cell technology presents an exciting opportunity to derive human DA neurons from patients with sporadic and familial forms of PD. We anticipate that these human DA models will recapitulate key features of the PD phenotype. In parallel, high-content screening platforms, which extract information on multiple cellular features within individual neurons, provide a network-based approach that can resolve temporal and spatial relationships underlying mechanisms of neurodegeneration and drug perturbations. These emerging technologies have the potential to establish highly predictive cellular models that could bring about a desperately needed revolution in PD drug discovery.

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帕金森氏症的药物发现-细胞模型使用的更新和发展。
帕金森病(PD)是第二常见的神经退行性疾病,其特征是黑质内多巴胺能(DA)神经元的退化。多巴胺替代药物仍然是最有效的PD治疗,但只能提供暂时的症状缓解。迫切需要新的治疗方法,但由于缺乏概括疾病表型的生理相关模型,对PD的疾病修饰治疗和对其潜在机制的明确理解一直受到限制。使用永生化细胞系作为药物发现的体外模型系统取得了有限的成功,因为在人体临床试验中,有效性和安全性往往无法成功转化。药物发现者正将他们的注意力转向与生理更相关的细胞模型,包括原代神经元和干细胞。最近发现的诱导多能干细胞(iPS)技术为从散发性和家族性PD患者中获得人类DA神经元提供了令人兴奋的机会。我们预计这些人类DA模型将概括PD表型的关键特征。与此同时,高含量的筛选平台可以提取单个神经元内多个细胞特征的信息,提供了一种基于网络的方法,可以解决神经变性和药物扰动机制的时空关系。这些新兴技术有可能建立高度可预测的细胞模型,从而在PD药物发现方面带来迫切需要的革命。
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