Takeda Development Center Americas, Inc., San Diego, CA 92121, USA.
Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
Cell Chem Biol. 2023 Sep 21;30(9):1169-1182.e8. doi: 10.1016/j.chembiol.2023.06.014. Epub 2023 Jul 11.
Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts. We then conducted a high-throughput small molecule chemogenomics screen of approximately 5,000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. By integrating morphological analyses and AI using pathologically relevant cells and disease-relevant stimuli, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. This phenotypic screening platform offers significant improvements over conventional methods for identifying a wide range of drug targets.
肠纤维化，常由炎症性肠病引起，可导致肠狭窄和梗阻，但目前尚无批准的治疗方法。药物研发受到缺乏可筛选的细胞表型的阻碍。为了解决这个问题，我们使用了一种名为细胞绘画的可扩展的基于图像的形态测定法，并结合机器学习算法，来识别能够逆转肠道肌成纤维细胞激活纤维化表型的小分子。然后，我们对大约 5000 种具有已知靶点或作用机制的化合物进行了高通量小分子化学基因组筛选，这些化合物已经达到了临床阶段或 FDA 批准。通过整合形态分析和 AI，使用与病理相关的细胞和与疾病相关的刺激物，我们鉴定出了一些可能能够治疗肠纤维化的化合物和靶类。与传统方法相比，这种表型筛选平台在鉴定广泛的药物靶点方面有显著的改进。
