Gladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Roddenberry Stem Cell Center, Gladstone Institutes, San Francisco, CA, USA.
Science. 2021 Feb 12;371(6530). doi: 10.1126/science.abd0724. Epub 2020 Dec 10.
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 (AV). Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary AV cells and was sufficient to prevent and treat AV 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.
绘制人类疾病中失调的基因调控网络图谱，将有助于设计出针对核心疾病机制的网络校正疗法。然而，传统上，小分子药物的筛选最多只能针对一到几个靶点的效果，这会导致发现的偏倚，并限制真正的疾病修正药物候选物的可能性。在这里，我们开发了一种机器学习方法来识别能够广泛纠正人类诱导多能干细胞（iPSC）疾病模型中与涉及主动脉瓣（AV）的常见心脏病形式相关的基因网络失调的小分子药物。最有效的治疗候选药物 XCT790 的基因网络校正作用可推广到患者来源的原发性 AV 细胞，并足以在体内预防和治疗小鼠模型中的 AV 疾病。这种策略，通过人类 iPSC 技术、网络分析和机器学习成为可能，可能代表了一种有效的药物发现途径。
