Computational Approach to Personalized Anemia Management

描述(由申请人提供)：终末期肾病(ESRD)的有效贫血管理是一项复杂的任务，因为ESRD患者的红细胞生成反应存在很大差异。有证据表明，在反应迟钝的患者中使用过量的促红细胞生成药物(ESA)，会增加心血管并发症。除了健康风险外，医疗保险施加的报销限制要求欧空局管理部门提高警惕。铁是影响红细胞生成反应的最重要的元素之一。与血液透析治疗相关的持续失血、胃肠道出血风险增加、并发症以及铁吸收不良导致终末期肾病患者铁耗竭。标准的贫血治疗方案不能完全解决促红细胞生成素和铁之间相互作用的协同性质。因此，协调ESA和铁剂量的个性化策略对于实现预期的临床结果、减少患者风险暴露和提高治疗的成本效益是必要的。我们的长期目标是推进生物医学计算，使慢性病的药物和药物管理个性化。该项目的目标是解决多种药物剂量个性化的挑战，并将其具体应用于ESRD贫血的ESA和铁管理。中心假设是，可以开发一种计算方法来执行协调的个性化剂量的红细胞生成刺激剂和铁，以实现预期的临床结果，并最大限度地减少患者暴露，从而实现最佳护理。这项研究背后的理论基础是，可以通过控制论和机器学习的原理来应用量化铁和促红细胞生成素之间相互作用的计算模型，以推导出这两种制剂的个性化剂量策略。主要目标将通过三个具体目标来实现：1)通过临床相关的计算模型来量化促红细胞生成素和铁的相互作用；2)应用这种模型来导出协调的个性化ESA和铁剂量的算法；3)通过在大范围的模拟受试者和临床条件下进行的“在硅谷”试验来验证这些算法。该项目的预期成果是采用一种新的系统方法来管理终末期肾病贫血，使用多种药物，从而改善患者护理并降低成本，这将广泛适用于其他治疗领域。

DESCRIPTION (provided by applicant): Effective anemia management in End-Stage Renal Disease (ESRD) is a complex task due to large variation in erythropoietic response among the ESRD patients. Evidence shows that excessive doses of Erythropoiesis Stimulating Agents (ESA), used in hypo-responsive patients, coincide with increased cardiovascular complications. In addition to health risks, reimbursement restrictions imposed by Medicare demand additional vigilance in ESA administration. Iron is one of the most important elements affecting the erythropoietic response. The ongoing blood losses associated with hemodialysis treatments, increased risk of gastrointestinal bleeding, and access complications, as well as poor iron absorption lead to iron depletion in ESRD patients. Standard anemia management protocols do not fully address the synergistic nature of interactions between erythropoietin and iron. Personalized strategies for coordinated ESA and iron dosing are therefore necessary to achieve the desired clinical outcome, reduce patient risk exposure, and improve the cost-effectiveness of treatment. Our long-term goal is the advancement of biomedical computing to personalize medicine and pharmacologic management of chronic conditions. The objective of this project is to address the challenge of multiple drug dosing personalization with a specific application to ESA and iron management in anemia of ESRD. The central hypothesis is that a computational approach can be developed to perform coordinated personalized dosing of erythropoiesis stimulating agents and iron in order to achieve desired clinical outcomes and minimize patient exposure resulting in optimal care. The rationale behind the proposed research is that computational models quantifying the interaction between iron and erythropoietin can be applied through the principles of control theory and machine learning, to derive personalized dosing strategies for both agents. The main objective will be accomplished through three specific aims: 1) quantification of the erythropoietin-iron interaction through clinically relevant computational models, 2) application of such models to derive algorithms for coordinated personalized ESA and iron dosing, 3) validation of these algorithms through an "in sillico" trial for a wide range o simulated subjects and clinical conditions. The expected outcome of this project is a new systematic approach to the management of ESRD anemia using multiple agents resulting in improved patient care and decreased cost which will be broadly applicable to other therapeutic areas.