Multiscale, mechanistic, and predictive models of stroke in sickle cell disease

描述(由申请人提供) 摘要：镰状细胞病卒中的多尺度、机制和预测模型摘要镰状细胞病(SCD)是一种遗传性疾病，由一个点突变引起，改变一种细胞内一种蛋白质中的一种氨基酸。尽管SCD是独一无二的，但这种突变会导致多因素的损害、疼痛、预期寿命降低，最严重的是，会导致儿科中风。患有SCD的儿童患中风的可能性是其他儿童的200倍：超过10%的儿童将在20岁之前中风。这一加速的时间表挑战了心血管范式，即斑块形成发生在动脉损伤的30-40年内。SCD的卒中机制和亚群特别脆弱的原因都不清楚。预防性治疗有很大的副作用，需要更好地预测哪些SCD患者需要干预。我建议开发一种创新的个性化药物策略，利用实验和临床数据来训练预测镰状细胞病中风风险的数学模型，以便对儿科患者进行早期干预。为了实现这一点，我提出了一个基于假设的项目，整合了来自不同生物医学子专业的研究结果。工程师们将血流紊乱、动脉内皮功能障碍和斑块定位联系在一起。僵硬、粘稠、镰刀状的红细胞干扰和阻止血液流动，并对血管壁造成物理损害。相应地，蛋白水解酶被释放，降解动脉壁中的结构蛋白，随后细胞增殖，管腔狭窄持续存在。所有这些都因慢性炎症而加剧。为了捕捉SCD及其正反馈环路的复杂性，我建议开发多尺度动力学模型，该模型结合了SCD在细胞和组织水平上的生化和生物力学后果的蛋白分解机制，以及基于临床数据、新的生物标记物和患者预后的预测统计模型。我在细胞生物学、生物医学工程和数学建模方面的严格培训形成了一个创新的观点和工具箱，以激励新的治疗方法，建议立即干预，并降低SCD患者的成本和死亡率。 与公共卫生相关：镰状细胞病是一种遗传性疾病，会导致全身、多因素损害、疼痛、预期寿命降低，最具破坏性的是，11%的基因突变儿童会发生中风。该项目的目标是开发一种创新的个性化药物策略，使用实验和临床数据来训练预测镰状细胞病中风风险的数学模型，以便对儿科患者进行早期干预。

DESCRIPTION (Provided by the applicant) Abstract: Multiscale, mechanistic, and predictive models of stroke in sickle cell disease Abstract Sickle cell disease (SCD) is a genetic disease caused by one point mutation that changes one amino acid in one protein inside one type of cell. Despite the singularity of SCD, the mutation causes multi-factorial damage, pain, lower life expectancy, and, most devastatingly, pediatric strokes. Children with SCD are 200 times more likely to suffer a stroke than others: over 10% will stroke by the age of 20. This accelerated timeline challenges cardiovascular paradigms that plaque formation occurs over 30-40 years of arterial damage. Neither stroke mechanisms in SCD nor reasons a subset is particularly vulnerable are clear. Preventive therapies have substantial side effects, requiring better predictions of which SCD patients need intervention. I propose to develop an innovative personalized medicine strategy using experimental and clinical data to train a mathematical model predictive of sickle cell disease stroke risk for earlier intervention of pediatric patients. To achieve this, I propose a hypotheses-based project incorporating research findings from diverse biomedical subspecialties. Engineers have linked disturbed blood flow, endothelial dysfunction in arteries, and plaque localization. Stiff, sticky, sickled red blood cells disturb and block blood flow, and physically damage the vascular wall. In response, proteases are released that degrade the structural proteins in the arterial wall, cell proliferation follows, and luminal narrowing persists. All of this is exacerbated by chronic inflammation. To capture the complexity of SCD and its positive feedback loops, I propose to develop multi-scale kinetic models that incorporate proteolytic mechanistic insight from biochemical and biomechanical consequences of SCD at the cell and tissue level with predictive statistical models based on clinical data, novel biomarkers, and patient outcomes. My rigorous training in cell biology, biomedical engineering, and mathematical modeling developed an innovative viewpoint and toolbox to motivate new therapies, indicate immediate intervention, and reduce costs and deaths of SCD patients. Public Health Relevance: Sickle cell disease is a genetic disease that causes systemic, multi-factorial damage, pain, lower life expectancy, and, most devastatingly, strokes in 11% of children with the genetic mutation. The goals of this project are to develop an innovative personalized medicine strategy using experimental and clinical data to train a mathematical model predictive of sickle cell disease stroke risk for earlier intervention of pediatric patients.