SBIR Phase I: Drug discovery using stem cell derived organoids

这个小企业创新研究（SBIR）第一阶段项目更广泛的影响/商业潜力是能够开发儿童罕见疾病的新疗法。 据估计，全球每年有3.5亿人（其中一半是儿童）患有约7，000多种已知的罕见疾病，但95%的罕见疾病仍然缺乏治疗。尽管80%的罕见疾病是遗传性的，但实际的药物发现相对较少。这是一个巨大的问题，因为30%患有罕见疾病的儿童无法活到5岁生日。为了解决这个问题，可以通过筛选与年轻器官相似并具有与患者相同基因突变的微器官（“类器官”）的研究药物来加速药物开发。在一个简单的培养皿中，类器官可以直接研究患者的基因与他/她的疾病之间的关系。 这些类器官为快速识别新的疾病机制和靶向治疗提供了机会。该团队正在将类器官技术扩展到一个自动化药物开发平台，该平台具有高通量，强大且适用于儿童多种遗传疾病。该项目通过实现两个主要目标来推进儿科罕见疾病的药物开发：（1）从人类干细胞中自动获得心脏，肝脏和大脑类器官，以及（2）自动机器学习（ML）检测来自患有影响这三个器官中任何一个的遗传疾病的患者的类器官中的疾病。为此，机器人技术和特定协议的结合将在标准化条件下区分组织和疾病特异性类器官。当类器官在组织状态之间转变时，用显微镜监测类器官。ML模型学习健康的类器官的样子，并使用这些信息来识别类器官何时表现出疾病表型。这些目标对于减少可能混淆药物发现工作的批次间类器官变异性和人为错误非常重要。在这些相同的类器官模型中，将进行药物筛选和开发，以试图逆转或延缓每种疾病特有的疾病表型。这项研究的成果将是高准确度和精确度的类器官的生产，以及用于检测心脏，肝脏和神经类器官变化的自动化方法，这对于寻找新的治疗方法至关重要。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable the development of new therapies for rare diseases in children. An estimated 350 million people worldwide - half of which are children - suffer from an estimated 7,000+ known rare diseases annually, yet 95% of rare diseases still lack treatment. Even though 80% of rare diseases are genetic in origin, there have been relatively few actual drug discoveries. This is an enormous problem as 30% of children with rare diseases will not live to see their 5th birthdays. To address this, drug development can be accelerated by screening investigational drugs against micro-organs (“organoids”) that resemble young organs and have the same gene mutations as the patients. In a simple petri dish, organoids enable the direct study of the relationship between a patient’s genes and his/her disease. Such organoids present an opportunity to rapidly identify new disease mechanisms and targeted therapies. The team is scaling organoid technology into an automated drug development platform that is high throughput, robust, and applicable to multiple genetic diseases in children. This project advances drug development for pediatric rare diseases by accomplishing two primary objectives: (1) automated heart, liver, and brain organoid derivation from human stem cells, and (2) automated machine learning (ML) detection of disease in organoids derived from patients with genetic diseases affecting any of these three organs. To do this, a combination of robotics and specific protocols will differentiate tissue- and disease-specific organoids under standardized conditions. The organoids are monitored with microscopy as they transition between tissue state. The ML model learns what healthy organoids look like and uses that information to identify when an organoid exhibits a disease phenotype. These objectives are important for reducing batch-to-batch organoid variability and human error that can confound drug discovery efforts. In these same organoid models, drug screening and development will be performed to try to reverse or retard the disease phenotype unique to each disease. The result of this research will be the production of organoids with high accuracy and precision, as well as an automated means for detecting changes in heart, liver, and neural organoids that will be essential for finding new therapies.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.