Engineered Tissue Based Phenotypic Screening of Mixture based Libraries

 描述(由申请人提供)：我们建议展示我们的基于组织的工程化药物筛选方法和系统组织的基于混合物的化学库相结合的力量。这种方法可能成为开发复杂疾病治疗方法的终极工具，例如心脏纤维化，最初没有已知的药物靶点。在65岁或以上的美国人中，心力衰竭是最常见的住院原因。射血分数保留的心力衰竭(HFPEF)在美国日益老龄化的人口中正成为一种流行病。由成纤维细胞、内皮细胞和非肌肉细胞分化而来的活化的成纤维细胞即肌成纤维细胞的存在是HFPEF心肌的标志，它们使心肌变得僵硬，从而使HFPEF进展。我们开发了一种人类心脏纤维化的疾病模型，使用含有肌成纤维细胞的工程化组织结构，概括了具有纤维化的心肌结缔组织。使用我们的表型筛选系统，在分析了42个支架排序库(每个支架样本包含化合物的混合物)后，我们识别出TPI-2049(17,340个化合物混合物)，这相当于筛选了3000万个化合物。虽然我们的最终目标是寻找一种治疗心脏纤维化的新药，但拟议的第一阶段项目将专注于对化学支架中的化合物混合物进行二次样本筛选，并确定50种单独的化合物，以挑选15种热门化合物在第二阶段研究中进行测试和优化。在一项平行研究中，我们将分析TPI-2049在缓解纤维化表型方面的作用机制，并通过执行无亲和力的靶标识别方法，用单个HIT化合物稳定靶标分子来识别潜在的药物靶标。将基于工程组织的药物发现系统和支架排序库与位置扫描技术相结合的成功示范将扩展到其他与年龄相关的疾病的药物发现项目，如骨关节炎。

 DESCRIPTION (provided by applicant): We propose to demonstrate the strength of combining our engineered tissue-based drug screening approach and a systematically organized mixture-based chemical library. This approach could become an ultimate tool to develop treatments for complex diseases, such as cardiac fibrosis, without known drug targets initially. Heart failure is the most common cause of hospitalization among Americans 65 or older. The heart failure with preserved ejection fraction (HFPEF) is becoming an epidemic among increasing population of aging Americans. Existence of activated fibroblasts, i.e. myofibroblasts, differentiated from fibroblasts, endothelial cells, and non-muscle cell is a hallmark of myocardium with HFPEF, and they stiffen myocardium to progress HFPEF. We developed a disease model for the human cardiac fibrosis using myofibroblast-containing engineered tissue constructs that recapitulate connective tissues of myocardium with fibrosis. Using our phenotypic screening system, we identified TPI-2049 (17,340 compound mixture) after analyzing a 42-scaffold scaffold ranking library (each scaffold sample contains mixtures of compounds), which is equivalent to screening >30 million compounds. While our final goal is to identify a novel drug for treating cardiac fibrosis, the proposed Phase I project will focus on performing a secondary sample screening of a compound mixture in the chemical scaffolds and identifying 50 individual compounds to pick 15 hit compounds to be tested and optimized in Phase II study. In a parallel study, we will analyze mechanisms of action of TPI-2049 in fibrosis relieving phenotypes and identify potential drug targets by performing affinity-free target identification method, stabilizing target molecules with individual hit compounds. Successful demonstration of combing engineered tissue based drug discovery system and scaffold ranking library with positional scanning technology will be extended to other drug discovery projects for age-related dieses such as osteoarthritis.