Unravelling drug-gene-phenotype interactions in complex cardiovascular diseases (iCASE)

在全球范围内，心血管疾病是导致死亡的主要原因，潜在的基因突变、合并症和药物引起的脱靶心脏毒性尤其令人头疼。不恰当的测试模型掩盖了这些问题。前200种药物占美国43亿张处方的66.6%。然而，81种药物被列入黑框，82种带有心血管药物不良反应警告，七分之一在III期试验中被认为有效的许可药物已退出市场。该合作项目由诺丁汉大学和伯明翰大学、阿斯利康和葛兰素史克联合开展。跨学科的集体经验涵盖人类诱导多能干细胞（hiPSCs）、心血管谱系分化、分子/功能表型、机器人技术、药理学、转录组学和生物信息学/编码/人工智能。我们发表的研究率先在hiPSC中进行CRISPR基因编辑，以创建导致肥厚性心肌病（HCM）的变异，这是一种与相当高的发病率和死亡率相关的复杂异质性疾病。不同的技能组合和培训将结合起来，以完成3个目标，旨在未来定制和转化针对患者遗传学的药物治疗：-建立健康和病变（肥厚性心肌病）的基线结构/功能读数；HCM)微组织包括来自人类诱导多能干细胞（hiPSCs）的心血管谱系-量化患者相关药物挑战的微组织结构/功能变化-使用组学/人工智能方法获得机制洞察和途径信息，以改进药物使用。

Globally, cardiovascular disease is the leading cause of death, with underlying genetic mutations, comorbidities and drug-induced off-target cardiotoxicity being especially troublesome. Inappropriate testing models belie these issues. The top 200 drugs account for 66.6% of the 4.3bn prescriptions in the USA/pa. Yet, 81 are black boxed, 82 carry cardiovascular adverse drug reaction warnings and 1 in 7 licensed drugs deemed efficacious in phase III trials are withdrawn from the market.This collaborative project is joint between Universities of Nottingham and Birmingham, AstraZeneca and GlaxoSmithKline. Collective interdisciplinary experience spans human induced pluripotent stem cells (hiPSCs), differentiation to cardiovascular linages, molecular/functional phenotyping, robotics, pharmacology, transcriptomics and bioinformatics/coding/AI. Our published work pioneered CRISPR gene editing in hiPSC to create variants that cause hypertrophic cardiomyopathy (HCM), a complex heterogeneous disease associated with considerable morbidity and mortality.Diverse skillsets and training will be combined to complete 3 objectives, aimed at future tailoring and translating drug therapy to patient genetics:- Establish baseline structure/functional readouts for healthy and diseased (hypertrophic cardiomyopathy; HCM) microtissues comprising cardiovascular lineages derived from human induced pluripotent stem cells (hiPSCs)- Quantify changes in microtissue structure/function challenged with patient-relevant drugs- Use omics/AI approaches for mechanistic insight and pathways information to refine drug use.