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.

该小企业创新研究 (SBIR) 第二阶段项目的更广泛影响/商业潜力将是开发技术，以提高发育和生殖毒理学 (DART) 研究中骨骼评估的准确性。 目标是更好地确保潜在的治疗药物、化妆品和农用化学品不会造成致畸作用。如今，DART 研究依赖于对动物骨骼缺陷进行主观的、以人为基础的手动评估，这种评估对缺陷的敏感性较低，病理学家之间和内部的差异显着，而且费力且昂贵。正在开发的技术基于针对该问题的新颖成像和自动分析解决方案，该解决方案将骨骼评估的范式从定性方法转变为定量方法。通过提高 DART 研究准确性，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，并通过提高通量和降低研究成本来降低开发治疗方法的成本。该技术的市场机会预计将是巨大的。该 SBIR 二期项目的智力优势是专注于光学 CT 成像设备和分析软件的开发和优化，用于小鼠、大鼠和兔子胎儿样本的骨骼评估。这些标本将被处理成光学透明的，骨头则被染成红色。将生成正常和异常胎儿样本的训练库，并从该库中开发基于机器学习的方法，以统计显着的方式自动识别非正常样本。为了实现这一目标，将对几种分类方法的准确性进行定量评估，并针对成像质量优化图像采集参数。通过这项工作，将根据 ICH 分析测定指南开发符合 21 CFR 第 11 部分要求的软件应用程序，以便该软件可以进行 IQ/OQ/PQ，从而允许客户在其 GLP 设施中实施硬件和软件系统。该项目产生的硬件和软件产品将成为市场上首批经过验证的数字病理平台之一，最终将帮助客户显着降低运营成本，同时提高准确性。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。