A Modality-Agnostic Potency Assay Enabling Both Ex Vivo and In Vivo Genome Editing Therapeutics for Sickle Cell Disease

一种与模态无关的效力测定，可实现镰状细胞病的体外和体内基因组编辑治疗

• 批准号: 10668694
• 负责人: Petros Giannikopoulos
• 金额: $ 48.95万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-28 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668694
• 关键词: Abnormal Hemoglobins; Abnormal Red Blood Cell; Address; Affect; American; Biological Assay; Biophysics; Blood capillaries; Cells; Chemistry; Clinical; Clinical Trials; Code; DNA sequencing; Data; Development; Devices; Diagnostic; Diagnostic Equipment; Disease; Erythrocytes; Event; Exhibits; FDA approved; Feedback; Genes; Health; Hematological Disease; Hematology; Hematopoietic stem cells; Hemoglobin; High Pressure Liquid Chromatography; Human; In Vitro; Individual; Inherited; Label; Laboratories; Life Expectancy; Linear Regressions; Machine Learning; Measures; Methods; Microfluidics; Modality; Modeling; Modification; Molecular; Morbidity - disease rate; Organ; Organ failure; Outcome; Output; Pain; Pathologic; Patients; Performance; Pharmaceutical Preparations; Phenotype; Polymers; Population; Positioning Attribute; Property; Protocols documentation; Reference Standards; Regulation; Research Project Grants; Resolution; Sampling; Severities; Sickle Cell Anemia; Source; Testing; Training; Validation; Variant; algorithm training; assay development; autosome; base editing; biochip; biophysical properties; cell preparation; clinical development; clinically relevant; cohort; diagnostic assay; diagnostic tool; disease phenotype; experience; gene therapy; genome editing; health assessment; improved; in vivo; innovation; laboratory equipment; machine learning model; manufacture; mortality; neural network; next generation; novel; personalized diagnostics; polymerization; primary endpoint; repaired; response; simulation; supervised learning; therapeutic genome editing; vaso-occlusive pain

ABSTRACT / PROJECT SUMMARY Sickle-cell disease (SCD) is an autosomal recessive disorder that causes considerable morbidity and mortality, affecting an estimated 100,000 individuals in the US, and millions more worldwide. Multiple editing-based cures for SCD are currently in clinical development, however, there are no clinical-grade laboratory tests available capable of characterizing the biophysical and rheological properties of RBCs derived from genome-edited SCD HSPCs. Assays capable of characterizing RBC quality are urgently needed to assess the potency of emerging editing-based genomic therapies for SCD, regardless of the editing modality. One of the central challenges that has impeded the development of a highly performant potency assay for evaluating the functional efficacy of editing-based genomic therapies for SCD has been the lack of laboratory technologies capable of sensitively, accurately, and precisely capturing the biophysical properties of SCD-RBCs utilizing only a small number of cells. In recent years, several innovations have emerged that now make the development and analytical validation of a potency assay for editing-based SCD genomic therapies feasible. One of these has been the advent of microfluidics-based diagnostic devices capable of functionally characterizing the health of RBCs at unprecedented levels of resolution and sensitivity. This proposal seeks to leverage (1) an existing suite of these aforementioned next-generation RBC biophysical and functional characterization devices, (2) conventional hematologic assays, and (3) a well-established machine learning approach to develop and analytically validate a first-in-kind potency assay for editing-based therapies for SCD. To achieve this, we will first construct a panel of comprehensively profiled, gold-standard reference samples of HSPCs that simulate a representative range of genome editing outcomes in SCD and prepare data for machine learning training. A machine learning model will then be trained to predict the percentage of RBCs that functionally exhibit a non-SCD phenotype. Once trained, we will validate the performance of the new potency assay, a panel of HSPCs affected by SCD will be therapeutically edited using at least three different modalities (e.g. homology-directed repair, base-editing, etc.).

摘要/项目摘要 镰状细胞病 (SCD) 是一种常染色体隐性遗传疾病，可导致相当大的 发病率和死亡率，影响了美国约 100,000 人，以及数百万人 全世界。多种基于编辑的 SCD 治疗方法目前正在临床开发中，但是， 没有临床级实验室测试能够表征生物物理特征 以及源自基因组编辑的 SCD HSPC 的红细胞的流变特性。检测能力 迫切需要表征红细胞质量的方法来评估新兴药物的效力 基于编辑的 SCD 基因组疗法，无论编辑方式如何。 阻碍高性能开发的核心挑战之一 评估基于编辑的 SCD 基因组疗法的功能功效的效力测定 一直缺乏能够灵敏、准确、精确地检测的实验室技术 仅利用少量细胞即可捕获 SCD-RBC 的生物物理特性。在 近年来，出现了一些创新，现在使开发和分析成为可能 验证基于编辑的 SCD 基因组疗法的效力测定的可行性。其中之一 基于微流体的诊断设备的出现，能够在功能上 以前所未有的分辨率和灵敏度表征红细胞的健康状况。 该提案旨在利用 (1) 现有的上述套件 下一代红细胞生物物理和功能表征设备，(2) 常规 血液学检测，以及（3）完善的机器学习方法来开发和 分析验证基于编辑的 SCD 疗法的首次同类效力测定。到 为了实现这一目标，我们将首先构建一个全面分析的黄金标准参考面板 模拟 SCD 中一系列代表性基因组编辑结果的 HSPC 样本 并为机器学习训练准备数据。然后将训练机器学习模型 预测功能上表现出非 SCD 表型的红细胞百分比。一旦受过训练， 我们将验证新效力测定的性能，一组受 SCD 影响的 HSPC 将使用至少三种不同的方式进行治疗性编辑（例如同源定向 修复、碱基编辑等）。