研究揭示心臟瓣膜疾病治療候選物的篩選方法
作者：
小柯機器人發布時間：2020/12/12 20:21:58
美國格萊斯頓研究所Deepak Srivastava研究組取得最新進展。他們提出iPSC來源細胞的基於網絡篩選揭示了心臟瓣膜疾病的治療候選物。2020年12月10日出版的《科學》雜誌發表了這項成果。
他們開發了一種機器學習方法，以識別可廣泛糾正在人類誘發的多能幹細胞（iPSC）疾病模型中失調的基因網絡的小分子，該模型是涉及主動脈瓣的常見心臟病。最有效的治療候選物XCT790進行的基因網絡校正可廣泛應用於患者來源的主動脈瓣膜細胞，在小鼠模型中足以預防和治療體內的主動脈瓣膜疾病。通過人類iPSC技術，網絡分析和機器學習使該策略可行，可能代表了藥物發現的有效途徑。
據了解，繪製人類疾病中失調的基因調控網絡的圖譜能夠設計用於治療核心疾病機制的網絡校正療法。但是，傳統上最多只能篩選小分子對一到幾個輸出的影響，這會偏向發現並限制真正的疾病緩解藥物候選物的可能性。
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
Science：《科學》，創刊於1880年。隸屬於美國科學促進會，最新IF：41.037