STTR Phase II: Optimized manufacturing and machine learning based automation of Endothelium-on-a-chip microfluidic devices for drug screening applications.

STTR 第二阶段：用于药物筛选应用的片上内皮微流体装置的优化制造和基于机器学习的自动化。

• 批准号: 2332121
• 负责人: Chiara Federici
• 金额: $ 90.49万
• 依托单位: BIOCHIP LABS, INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332121&HistoricalAwards=false
• 关键词: STTR; Phase; II; Optimized; manufacturing

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is to address the unmet need in the companion diagnostic guided therapy market for Sickle Cell Disease (SCD). SCD is a lifelong disease affecting millions of people worldwide. Emerging therapies are estimated to be $150k-$200k per patient each year. A companion diagnostic cost in SCD anti-adhesive therapies is estimated at least $3,000 per patient. With improved accessibility to patients living in low- and middle-income countries and scalable curative therapies, the global SCD treatment market size is projected to increase to $8.75B by 2029. Additionally, companion diagnostic-guided drugs have an increased regulatory approval probability of 50% in Phase III clinical trials. The proprietary Endothelium-on-a-chip platform with human donor cells provides a physiologically relevant means to study blood-endothelium interaction. This platform can be integrated into preclinical studies to screen the effect of novel drug candidates as well as for assessment of drug toxicity. In Phase I of the STTR project, standards and quality control criteria for experimental conditions on the Endothelium-on-a-chip were established. The continuing projects with pharmaceutical companies have highlighted the need to scale the manufacturing process.This Small Business Technology Transfer (STTR) Phase II project will focus on optimizing the manufacturing process to enable the scale-up of the assay and develop the machine learning system for automated data analysis. Currently, this assay involves in-house fabrication and is limited to the central laboratory at the company’s location. Manufacturing will be optimized with proper selection of material, fabrication methods, scalable techniques, and systematic integration of different elements of the assay. This will potentially enable the commercialization and implementation of the technology at a larger scale. Current methods of analysis include counting adhesion events manually. This method will be replaced by a machine learning-based system to identify and classify adhesion events and separate those from the endothelial cells in the background. Automated data analysis will enable faster outcomes and remove user bias. Since this approach relies on enhancing the capabilities of the existing platform for scale-up and streamlined analysis, it is anticipated that it will improve its accessibility to the broader research community.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.

这一小型企业技术转移（STTR）II期项目的更广泛的影响/商业潜力是解决镰刀疾病（SCD）的伴侣诊断指导疗法市场中未满足的需求。 SCD是一种终生疾病，影响了全球数百万的人。新兴疗法估计每年患者$ 150k- $ 200k。 SCD抗粘附疗法的伴侣诊断成本估计至少每位患者$ 3,000。居住在低收入和中等收入国家和可扩展治疗疗法的患者的可及性提高，预计到2029年，全球SCD治疗市场的规模预计将增加到8.75B美元。此外，在III期临床试验中，伴随诊断诊断引导的药物的监管批准概率增加了50％。具有人类供体细胞的专有内皮片平台提供了一种与物理相关的方法来研究血内皮相互作用。该平台可以纳入临床前研究，以筛选新的候选药物的影响以及对药物毒性的评估。在STTR项目的第一阶段，建立了芯片内皮的实验条件的标准和质量控制标准。与制药公司的持续项目强调了扩展制造过程的必要性。这个小型企业技术转移（STTR）II阶段项目将着重于优化制造过程，以实现分析的扩展并开发用于自动数据分析的机器学习系统。目前，此评估涉及内部制造，仅限于公司位置的中央实验室。制造方法将通过适当选择材料，制造方法，可扩展技术以及分析元素的系统整合进行优化。这将有可能使技术的商业化和实施更大。当前的分析方法包括手动计数粘合剂事件。此方法将被基于机器学习的系统替换，以识别和分类粘合剂事件，并将其与后台内皮细胞分开。自动数据分析将使结果更快，并消除用户偏见。由于这种方法依赖于增强现有平台进行扩展和简化分析的功能，因此预计它将提高其对更广泛的研究社区的可访问性。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的智力优点和更广泛的审查标准通过评估来评估的。