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.

这项小型企业创新研究（SBIR）II期项目的更广泛的影响 /商业潜力是改善药物安全。 该项目推进了软件，以确定新药物引起危险心脏副作用的潜力。由于心脏猝死是一种很少且相对罕见的现象，因此使用常规工具预测它是不切实际的或不可能的。拟议的技术将使所有新类药物的开发更安全，更快，更便宜。这将集成到研究性新药提交包中，以提交给FDA以供药物批准。这将改善药物安全和临床结果。这项小型企业创新研究（SBIR）第二阶段项目将解决预测寻求FDA批准的新药的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试图在体外现象中重现精确且很少遇到的体内现象。因此，对药物开发的经济和实用限制要求行业使用代理，主要是药物与HERG钾渠道结合的预测性心律失常。该项目的智力挑战是结合严格的实验方案的数学建模和定量分析，以弥合这一差距并识别有力的心律失常行为预测指标的基础特征。 理解这一点的关键是基于有限的贴片夹数据，依赖状态依赖的马尔可夫动作模型的快速发展和部署，这既耗时又昂贵。该项目将数学分析直接结合在一起，以直接调整贴剂分析协议，以最大程度地减少时间和费用，同时提高预测精度。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来审查标准的评估值得支持的。