SBIR Phase II: Digitization of Skeletal Evaluations for Developmental and Reproductive Toxicology (DART) Studies.

SBIR 第二阶段：发育和生殖毒理学 (DART) 研究骨骼评估的数字化。

• 批准号: 1852639
• 负责人: Michael Johnson
• 金额: $ 75万
• 依托单位: Visikol, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852639&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Digitization; Skeletal

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be the development of technology to improve the accuracy of skeletal evaluation within developmental and reproductive toxicology (DART) studies. The goal is to better ensure that potential therapeutics, cosmetics and agrochemicals do not cause teratogenic effects. Today, DART studies rely upon the subjective human-based manual evaluation of animal skeletons for defects, which has a low sensitivity for defects, significant inter and intra pathologist variability, and is laborious and costly. The technology under development is based on a novel imaging and automated analysis solution for this problem that will shift the paradigm of skeletal evaluation from a qualitative to quantitative approach. Through improving DART study accuracy, the objective is to better detect teratogenic effects of compounds, reduce the overall number of animals required for these studies, and reduce the cost to develop therapeutics by improving throughput and reducing study cost. The market opportunity for this technology is expected to be significant. The intellectual merit of this SBIR Phase II project is to focus on the development and optimization of an optical CT imaging device and analysis software for use with mouse, rat and rabbit fetal samples for skeletal evaluation. The specimens will be processed such that they are optically transparent with bones that are stained red. A training library of normal and abnormal fetal samples will be generated, and from this library a machine learning-based approach will be developed to automatically identify samples that are non-normal in a statistically significant manner. To achieve this, several classification methodologies will be evaluated quantitatively for accuracy and the image acquisition parameters will be optimized for imaging quality. From this work, a 21 CFR part 11 compliant software application will be developed in accordance with the ICH analytical assay guidelines such that this software can undergo IQ/OQ/PQ, which will allow for the hardware and software system to be implemented by customers in their GLP facilities. The hardware and software product that will result from this project will be one of the first validated digital pathology platforms in the marketplace, and will ultimately allow for customers to significantly reduce their operating costs while improving 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/商业潜力将是发展技术，以提高发育和生殖毒理学（DART）研究中骨骼评估的准确性。 目的是更好地确保潜在的治疗剂，化妆品和农业化学不会引起致畸作用。如今，DART的研究依赖于对动物骨架的主观人工评估缺陷，该缺陷对缺陷，显着和病理学家的显着性和病理学家的可变性具有低敏感性，并且费力且成本高昂。正在开发的技术基于针对此问题的新型成像和自动分析解决方案，该解决方案将把骨骼评估的范式从定性转变为定量方法。通过提高DART研究的准确性，目标是更好地检测化合物的致畸作用，减少这些研究所需的动物总数，并通过改善吞吐量和降低研究成本来降低发展治疗剂的成本。这项技术的市场机会预计将是重要的。该SBIR II期项目的智力优点是专注于用于用于骨骼评估的小鼠，大鼠和兔子胎儿样品的光学CT成像设备和分析软件的开发和优化。这些样品将被处理，以便它们在光学上具有红色骨骼的光学透明。将生成一个正常和异常胎儿样本的培训库，从该库中，将开发基于机器学习的方法，以自动以统计学意义的方式识别非正态分子的样本。为此，将对精确度进行定量评估几种分类方法，并且将优化图像采集参数以进行成像质量。从这项工作中，将根据ICH分析测定指南制定21 CFR第11部分符合条件的软件应用程序，以便该软件可以进行IQ/OQ/PQ，这将允许客户在其GLP设施中实现硬件和软件系统。该项目将导致的硬件和软件产品将是市场上首批验证的数字病理平台之一，并最终将允许客户显着降低其运营成本，同时提高准确性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的人。