SBIR Phase II: Semantic Link Association Prediction for Phenotypic Drug Discovery

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是开发一个基于信息学的软件平台，该平台将帮助制药公司在研发管道的早期创造更多新的、有效的和安全的药物。该软件平台将满足对数据集成和分析工具的需求，以帮助药物研究人员进行1)表型筛选，2)毒理学分析和3)药物再利用。它将帮助这些研究人员快速收集和解释复杂的分子和表型数据，使药物发现过程更有效，并为制药公司创造价值。将临床前药物发现过程缩短两周的经济影响估计为该行业节省了2.52亿美元的成本。通过在研发过程的早期更有效地使用数据，该软件平台还有望提高进入临床试验的药物的质量。因此，它提供了一个减少总体研发支出和增加进入市场的药物数量的机会，从而使人们能够获得价格更经济的药物。SBIR第二阶段项目建议建立一个基于信息学的软件平台，解决跨领域数据集成、分析和用户应用挑战，以便在研发过程中有效地利用数据来获得洞察力，并压缩新药或再利用药物的开发管道。使用高度可扩展的语义图技术，正在开发一个灵活的三层架构，包括1)生物医学数据层，2)计算层和3)应用层。这种架构允许系统完全可扩展和扩展到其他数据集和生物医学应用。该系统将由制药研究人员进行beta测试，并通过创建科学相关的用例进行评估。这一发展将产生一个商业软件系统，通过解决高需求的数据集成、分析和应用挑战，使重要的生物医学数据和见解可供制药组织内的所有研究人员使用。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is the development of an informatics-based software platform that will help pharmaceutical companies create more new, effective, and safe drugs earlier in the R&D pipeline. This software platform will address a need for data integration and analysis tools to aid pharmaceutical researchers in 1) phenotypic screening, 2) toxicology analysis, and 3) drug repurposing. It will help these researchers quickly gather and interpret complex molecular and phenotypic data, making the drug discovery process more efficient and creating value for pharmaceutical companies. The economic impact of reducing the preclinical drug discovery process by just two weeks is estimated to be a $252 million cost savings for the industry. By using data more effectively earlier in the R&D process, this software platform also promises to enhance the quality of drugs that enter clinical trials. Thus, it provides an opportunity to reduce overall R&D spending and increase the number of drugs that enter the market - resulting in more economically priced medicines available to the population.This SBIR Phase II project proposes to build an informatics-based software platform that solves cross-domain data integration, analysis, and user application challenges in order to effectively use data to draw insights earlier in the R&D process and compress the development pipeline for new or repurposed drugs. Using highly scalable semantic graph technologies, a flexible three-layer architecture is being developed that includes the 1) Biomedical Data Layer, 2) Computational Layer, and 3) Application Layer. This architecture allows the system be fully scalable and extensible to other datasets and biomedical applications. The system will be beta-tested by pharmaceutical researchers and evaluated though the creation of scientifically relevant use-cases. This development will result in a commercial software system that makes important biomedical data and insights available to all researchers within a pharmaceutical organization by addressing high need data integration, analysis, and application challenges.