STTR Phase I: A Machine Learning Toolbox to Identify Therapeutics for Rare Genetic Disorders from Phenotypic Screens on Micropattern-Based Organoids

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力是开发一个广泛适用的创新管道，用于发现治疗罕见遗传疾病的药物。虽然每一种孤儿病只影响一小部分人，但存在数千种孤儿病，影响全世界4亿人。将目前无法治愈的疾病的疗法带到诊所的挑战，加上不断扩大的市场。 孤儿药销售额预计将在2024年达到2620亿美元。 在过去的几年里，由人类胚胎干细胞制成的类器官已经提高了通过体外重建与某种疾病有关的特定细胞群的类似排列来理解导致遗传疾病的机制的可能性。通过将健康细胞制成的类器官与携带相关基因突变的类器官进行比较，可以在体外进行药物测试以发现新的治疗方法。然而，由于技术限制，这一潜力尚未得到利用。作为实现这一目标的必要步骤，该提案计划开发基于机器学习算法的分析工具，以便在应用于类器官培养时分析药物治疗潜力和毒性。该STTR第一阶段项目的智力价值是开发和验证一种无偏倚的分析方案，以量化对微模式类器官进行的高通量表型筛选的结果。类器官有可能彻底改变药物发现管道的临床前部分，因为它们提供无偏的终点，它们与疾病相关，并且对毒性敏感。然而，在高通量平台中使用多细胞组织需要分析大量信息丰富的显微镜图像。该提案中开发的工具将允许将组织规模表型的生物复杂性转化为预测化合物临床潜力的信息。在第一个目标中，目标是证明所提出的方法允许量化化合物毒性。该计划是校准工具箱对已知的方法量化细胞活力，应用已知的有毒物质和撤回的药物。这将构成对所提出的方法在定量化合物的有害作用方面的效率的严格测试。第二，目标是优化算法以测量治疗潜力，使用基于微模式的类器官建模亨廷顿病（HD）的阳性对照。该奖项旨在展示分析旨在逆转HD类器官表型的筛选活动结果的能力，并从FDA批准的药物的小型库中检测命中分子。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to develop a widely applicable, innovative pipeline for drug discovery against rare genetic disorders. While each orphan disease affects a small fraction of individuals, thousands exist, affecting 400 million people world-wide. The challenge to bring therapies to the clinics for diseases that currently have no cure, which is coupled with an expanding market. Orphan drug sales are expected to reach $262 billion in 2024. Over the last few years, organoids made from human embryonic stem cells have raised the possibility of understanding the mechanisms leading to a genetic disorder by reconstituting, in vitro, a similar arrangement of the specific cell populations that are implicated in a certain disease. By comparing organoids made from healthy cells to those carrying the associated genetic mutation, drug testing may be performed in vitro to discover new therapeutics. However, this potential has not been harnessed due to technical limitations. In this proposal, as a necessary step towards this goal, the plan is to develop the analytical tools based on machine learning algorithms that will allow the analysis of drug therapeutic potential and toxicity when applied to organoid cultures.The intellectual merit of this STTR Phase I project is to develop and validate an unbiased analytical scheme to quantify the results of high-throughput phenotypic screens performed on micropattern organoids. Organoids have the potential to revolutionize the pre-clinical part of the drug discovery pipeline as they provide unbiased endpoints, they are disease relevant, and sensitive to toxicity. However, the use of multi-cellular organization in a high-throughput platform requires analysis of large amounts of information-rich microscopy images. The tools developed in this proposal will allow turning the biological complexity of tissue scale phenotypes into information predictive of the clinical potential of a compound. In the first aim, the goal is to demonstrate that the proposed method allows quantifying compound toxicity. The plan is to calibrate the toolbox against known methods quantifying cell viability, applying known toxic agents and withdrawn drugs. This will constitute a stringent test for the efficiency of the proposed method at quantifying deleterious effects of a compound. Second, the goal is to optimize the algorithm to measure therapeutic potential, using positive controls on micropattern-based organoids modeling Huntington's Disease (HD). The objective is to demonstrate the ability to analyze results of a screening campaign aimed at phenotypic reversal on the HD organoids, and detect hit molecules from a small library of FDA-approved drugs.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.