Cellular model for personalized CFTR-directed therapeutics.

项目摘要/摘要 该建议是一项NHLBI-RFA(R44)，旨在开发和验证一种新的基于人类细胞的诊断工具 用于预测囊性纤维化(CF)患者对改善其功能的药物的反应 囊性纤维化跨膜传导调节蛋白的突变形式。大号- 人鼻上皮细胞的体外扩增现在是可行的。主单元格，当 分化为气/液界面培养(ALI)，概括了患者特有的CFTR表型和 提供一个灵敏且可重复性的平台，用于测试CFTR导向疗法。跨上皮 离子转运是CFTR功能的直接测量，并在体外证明了功能的改善 突变的CFTR已被证明可以预测特定患者的临床益处。鼻腔上皮细胞是 从CF受试者中收集，培养并冷冻保存，以产生已知CFTR的细胞库 基因分型。细胞扩增，接种到滤器支架上，用于ALI培养(最多5,000个 培养/受试者)，分化，检测CFTR的表达和活性。这样做的直接目标是 项目是使用原代HNE细胞培养来：1)建立患者间CFTR的重复性- 药物反应的变异性，2)确定体外反应是否预测体内的长期临床益处，以及 3)确定对现有CFTR调节子有反应的罕见突变。我们的长期目标是证明 这一体外人体细胞模型预测了个体患者对CFTR指导的治疗的反应 开发一个诊断平台，以指导所有CF患者的个性化治疗优化。

Project Abstract/Summary This proposal is a NHLBI-RFA (R44) to develop and validate a novel human cell-based diagnostic tool for predicting individual cystic fibrosis (CF) patient responses to drugs that improve the function of mutated forms of the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Large- scale in vitro expansion of human nasal epithelial cells is now feasible. The primary cells, when differentiated as air/liquid interface cultures (ALI), recapitulate a patient-specific CFTR phenotype and provide a sensitive and reproducible platform for testing CFTR-directed therapeutics. Transepithelial ion transport is a direct measure of CFTR function and in vitro demonstration of improved function of mutated CFTR has been shown to predict clinical benefit in specific patients. Nasal epithelial cells are collected from CF subjects, cultured, and cryopreserved to generate a bank of cells of known CFTR genotype. Cells are expanded, seeded onto filter supports for ALI culture (up to ~5,000 cultures/subject), differentiated, tested for CFTR expression and activity. The immediate goals of this project are to use the primary HNE cell cultures to: 1) establish reproducibility of inter-patient CFTR- drug response variability, 2) determine if in vitro responses predict long-term in vivo clinical benefit, and 3) identify rare mutations that respond to existing CFTR modulators. The long term goal is to show that this in vitro human cell model predicts individual patient responses to CFTR-directed therapeutics and to develop a diagnostic platform to guide optimization of personalized therapies for all CF patients.