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Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy

虚拟药物筛选揭示了心肌细胞肥大的情境依赖性抑制

基本信息

批准号：
10678351
负责人：
Taylor Eggertsen
金额：
$ 4.01万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10678351
关键词：
Address Agreement Behavior Biochemical Biological Cardiac Cardiac Myocytes Cause of Death Cells Clinical Complex Computer Models Data Databases Disease Drug Industry Drug Modelings Drug Screening Drug Targeting Drug usage FDA approved Grant Growth Heart Heart Diseases Heart Hypertrophy Heart failure Hypertension Hypertrophy In Vitro Literature Mechanical Stress Mediating Modeling Myocardial Ischemia Neonatal Pathway interactions Pharmaceutical Preparations Pharmacology Process Proteins Rattus Role Signal Transduction System Systems Biology Testing Therapeutic Training Work career cytokine design drug action drug efficacy drug repurposing drug testing experience experimental study follow-up network models new therapeutic target novel novel therapeutics pharmacologic predictive modeling prevent response simulation targeted treatment therapeutic target translational applications virtual

项目摘要

Proposal Abstract Heart failure is a leading cause of death worldwide. Cardiomyocyte hypertrophy is a leading predictor of heart failure as it contributes to maladaptive remodeling of the heart. Hypertrophy is therefore a good therapeutic target for preventing the onset of heart failure. Hypertrophy is mediated by complex intracellular signaling, which limits our ability to effectively model drug activity in cardiomyocytes. Currently there are no therapeutics that specifically target the intracellular signaling of cardiomyocyte hypertrophy. Previous work by the Saucerman lab has utilized computational models of signaling networks to simulate cardiomyocyte behavior in the context of hypertrophy. These simulations allow us to explore how drugs may inhibit cardiomyocyte hypertrophy using a systems biology approach. Identifying drugs that target cardiomyocyte hypertrophy would allow for translational application. The central focus of this grant is to identify drugs that inhibit cardiomyocyte hypertrophy and the mechanisms by which they act. Aim 1 will use a computational model of cardiomyocyte hypertrophy signaling to screen FDA approved drugs that inhibit hypertrophy and validate these predictions experimentally. Aim 2 will use protein interaction data to identify the mechanisms of putative antihypertrophic drugs from a separate in vitro drug screen. These aims together will result in the selection and testing of drugs repurposed for the inhibition of cardiomyocyte hypertrophy, and will lay the platform for designing virtual drug screens for heart disease.

提案摘要心力衰竭是世界范围内的主要死亡原因。心肌细胞肥大是心脏病的主要预测因素这是因为它导致心脏的适应不良重塑。因此，肥大是一种很好的治疗方法，预防心力衰竭发作的目标。肥大由复杂的细胞内信号传导介导，这限制了我们在心肌细胞中有效模拟药物活性的能力。目前还没有治疗方法专门针对心肌细胞肥大的细胞内信号传导。以前的工作 Saucerman实验室利用信号网络的计算模型来模拟心肌细胞的行为，肥大的背景。这些模拟使我们能够探索药物如何抑制心肌细胞使用系统生物学方法研究肥大。确定靶向心肌细胞肥大的药物将允许翻译应用。这项资助的中心重点是确定抑制心肌细胞增殖的药物。肥大及其作用机制。目标1将使用心肌细胞的计算模型肥大信号转导筛选FDA批准的抑制肥大的药物并验证这些预测实验性的目的2将使用蛋白质相互作用数据来确定推定的抗肥大的机制，来自单独体外药物筛选的药物。这些目标将共同导致药物的选择和测试将其重新用于抑制心肌细胞肥大，并将为设计虚拟药物奠定平台筛查心脏病