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An all-optical electrophysiology platform for the discovery of pain therapeutics

An all-optical electrophysiology platform for the discovery of pain therapeutics
用于发现疼痛疗法的全光学电生理学平台
批准号:
9930168
负责人:
Owen B. McManus
金额:
$73.19万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-07-01 至 2021-06-30

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中文摘要
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英文摘要
Project Summary Chronic pain affects over 100 million adults in the United States and is challenging to treat. Current treatments include opioids and non-steroidal inflammatory agents. However, efficacy of these drugs in chronic treatment is restricted by dose limiting toxicities, and prolonged opioid use can lead to dependency. Despite the clear, unmet medical need and significant research activity, few drugs targeting pain based on novel, non-opioid mechanisms have appeared in the past decade. Q-state has created a novel all-optical platform (Optopatch) using engineered optogenetic proteins and custom microscopes to simultaneously stimulate and record electrical activity from a variety of cell types with high sensitivity and temporal resolution. We focus our efforts on a genetically validated pain target, SCN9A (Nav1.7), a voltage-gated sodium channel that is required for pain signal transmission in sensory neurons. We will apply Optopatch technology in two formats. 1) An HTS screen of the Q-State chemical library using a heterologously expressed Nav1.7 channel assay that replicates physiological spiking activity. Counterscreens against other Nav1.x channels will be performed in the same assay format to determine compound selectivity. 2) Identified inhibitors will be evaluated in medium throughput screens that measure excitability in rodent sensory and human iPS sensory neurons that have been sensitized using inflammatory mediators. This integrated set of assays is designed to identify Nav1.7 inhibitors acting by diverse working mechanisms and prioritize compounds for further optimization using scalable in vitro models of sensory neuron function. This platform will be employed as an efficient means to select compounds for optimization using medicinal chemistry and pharmacokinetic, drug metabolism and in vivo efficacy data.
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