美国格莱斯顿研究所Deepak Srivastava研究组取得最新进展。他们提出iPSC来源细胞的基于网络筛选揭示了心脏瓣膜疾病的治疗候选物。2020年12月10日出版的《科学》杂志发表了这项成果。

他们开发了一种机器学习方法，以识别可广泛纠正在人类诱发的多能干细胞（iPSC）疾病模型中失调的基因网络的小分子，该模型是涉及主动脉瓣的常见心脏病。最有效的治疗候选物XCT790进行的基因网络校正可广泛应用于患者来源的主动脉瓣膜细胞，在小鼠模型中足以预防和治疗体内的主动脉瓣膜疾病。通过人类iPSC技术，网络分析和机器学习使该策略可行，可能代表了药物发现的有效途径。

据了解，绘制人类疾病中失调的基因调控网络的图谱能够设计用于治疗核心疾病机制的网络校正疗法。但是，传统上最多只能筛选小分子对一到几个输出的影响，这会偏向发现并限制真正的疾病缓解药物候选物的可能性。

附：英文原文

Title: Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease

Author: Christina V. Theodoris, Ping Zhou, Lei Liu, Yu Zhang, Tomohiro Nishino, Yu Huang, Aleksandra Kostina, Sanjeev S. Ranade, Casey A. Gifford, Vladimir Uspenskiy, Anna Malaschicheva, Sheng Ding, Deepak Srivastava

Issue&Volume: 2020/12/10

Abstract: Mapping the gene regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve. Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary aortic valve cells and was sufficient to prevent and treat aortic valve disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery.

DOI: 10.1126/science.abd0724

Source: https://science.sciencemag.org/content/early/2020/12/09/science.abd0724