SBIR Phase II: Developing a platform for superior predictive analysis of HERG Ion Channel-Drug Interactions for the Comprehensive In-vitro Proarrhythmia Assay (CiPA)

SBIR II 期：开发一个平台，对 HERG 离子通道-药物相互作用进行高级预测分析，用于综合体外致心律失常测定 (CiPA)

• 批准号: 2151522
• 负责人: Leigh Korbel
• 金额: $ 100万
• 依托单位: Cytocybernetics
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151522&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Developing; platform

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve drug safety. This project advances software to identify the potential of new drugs to provoke dangerous cardiac side-effects. Because sudden cardiac death is an infrequent and relatively rare phenomenon, predicting it with conventional tools is either impractical or impossible. The proposed technology will make the development of all new classes of drugs safer, faster, and less expensive. This will be integrated into investigational new drug submission packages for submission to the FDA for drug approval. This will improve pharmaceutical safety and clinical outcomes. This Small Business Innovation Research (SBIR) Phase II project will address the critical problem of predicting the arrhythmogenic potential of new drugs seeking FDA approval. Current basic science research methods are based on trying to reproduce exact and infrequently encountered in vivo phenomena in an in vitro setting. The economic and practical constraints on drug development therefore requires industry to use proxies, primarily drug binding to the HERG potassium channel, as a predictor arrhythmogenicity. The intellectual challenge in this project is to combine mathematical modeling and quantitative analysis of rigorous experimental protocols to bridge this gap and identify underlying features which are strong predictors of arrhythmogenic behavior. Key to understanding this is the rapid development and deployment of state-dependent Markov models of drug action based on limited patch clamp data, which is both time-consuming and expensive to obtain. This project combines mathematical analysis directly to direct patch-clamp assay protocols to minimize time and expense while increasing predictive accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小企业创新研究 (SBIR) 第二阶段项目的更广泛影响/商业潜力是提高药物安全性。 该项目改进了软件，以识别新药引起危险心脏副作用的可能性。由于心源性猝死是一种罕见且相对罕见的现象，因此使用传统工具对其进行预测要么不切实际，要么不可能。拟议的技术将使所有新类别药物的开发更安全、更快、更便宜。这将被纳入研究性新药提交包中，以提交给 FDA 进行药物批准。这将提高药物安全性和临床结果。这个小企业创新研究 (SBIR) 第二阶段项目将解决预测寻求 FDA 批准的新药致心律失常潜力的关键问题。当前的基础科学研究方法是基于尝试在体外环境中重现精确且不常见的体内现象。因此，药物开发的经济和实际限制要求工业界使用代理，主要是与 HERG 钾通道结合的药物，作为心律失常的预测因子。该项目的智力挑战是将数学建模和严格实验方案的定量分析结合起来，以弥合这一差距并确定对心律失常行为具有强有力预测作用的潜在特征。 理解这一点的关键是基于有限的膜片钳数据快速开发和部署药物作用的状态依赖性马尔可夫模型，这既耗时又昂贵。该项目将数学分析直接与直接膜片钳检测方案相结合，以最大限度地减少时间和费用，同时提高预测准确性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。