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

这一小型企业创新研究(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。该项目推进了识别可能引发危险心脏副作用的新药的软件。由于心脏性猝死是一种罕见且相对罕见的现象，用传统工具预测它要么是不切实际的，要么是不可能的。这项拟议的技术将使所有新型药物的开发更安全、更快、更便宜。这将被整合到研究用新药提交包中，提交给FDA进行药物审批。这将改善药物安全性和临床结果。这个小型企业创新研究(SBIR)第二阶段项目将解决预测寻求FDA批准的新药引发心律失常的潜力这一关键问题。目前的基础科学研究方法是基于试图在体外环境下重现体内准确和罕见的现象。因此，药物开发的经济和实际限制要求工业使用替代物，主要是与HERG钾通道结合的药物，作为预测心律失常的指标。这个项目中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烈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马尔可夫模型，这既耗时又昂贵。该项目将数学分析直接结合到直接膜片钳分析方法中，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势和更广泛的影响审查标准进行评估，被认为值得支持。

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.