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.

该小企业技术转让 (STTR) 第二阶段项目的更广泛影响/商业潜力是解决镰状细胞病 (SCD) 伴随诊断指导治疗市场中未满足的需求，镰状细胞病是一种影响全球数百万人的终生疾病。新兴疗法估计每位患者每年 15 万至 20 万美元，随着生活条件较差的患者的可及性的提高，SCD 抗粘连疗法的伴随诊断成本估计至少为 3,000 美元。和中等收入国家以及可扩展的治疗方法，预计到 2029 年，全球 SCD 治疗市场规模将增至 $8.75B。此外，伴随诊断引导药物在 III 期临床试验中的监管批准概率增加了 50%。具有人类供体细胞的内皮芯片平台提供了一种研究血液-内皮相互作用的生理学相关方法，该平台还可以整合到临床前研究中，以筛选新候选药物的效果。在 STTR 项目的第一阶段，建立了芯片上内皮细胞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大制造过程的必要性。该小型企业技术转让 (STTR) 第二阶段项目将重点关注优化制造工艺，以扩大检测规模并开发用于自动数据分析的机器学习系统。目前，该检测涉及内部制造，并且正在开发中。仅限于中心实验室将通过适当选择材料、制造方法、可扩展技术以及不同分析要素的系统集成来优化制造，这将有可能实现该技术的更大规模的商业化和实施。手动计数粘附事件。这种方法将被基于机器学习的系统所取代，以识别和分类粘附事件，并将其与背景中的内皮细胞分开，从而实现更快的结果并消除用户偏差。关于加强该奖项反映了 NSF 的法定使命，并通过利用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。