Computational Approach to Personalized Anemia Management

描述（由申请方提供）：由于终末期肾病（ESRD）患者的红细胞生成反应存在较大差异，因此对ESRD患者进行有效的贫血管理是一项复杂的任务。有证据表明，在低反应患者中使用过量的红细胞生成刺激剂（ESA）与心血管并发症的增加相一致。除了健康风险外，医疗保险规定的报销限制要求欧空局行政部门提高警惕。铁是影响红细胞生成反应的最重要元素之一。与血液透析治疗相关的持续失血、胃肠道出血和通路并发症风险增加以及铁吸收不良导致ESRD患者铁耗竭。标准的贫血管理方案没有完全解决促红细胞生成素和铁之间相互作用的协同作用。因此，协调ESA和铁剂量的个性化策略对于实现期望的临床结果、降低患者风险暴露并提高治疗的成本效益是必要的。我们的长期目标是生物医学计算的进步，以个性化的医疗和慢性病的药理学管理。本项目的目的是解决多种药物给药个性化的挑战，具体应用于ESA和铁管理ESRD贫血。中心假设是，可以开发计算方法来执行红细胞生成刺激剂和铁的协调的个性化给药，以实现期望的临床结果并最小化患者暴露，从而获得最佳护理。拟议研究背后的基本原理是，量化铁和促红细胞生成素之间相互作用的计算模型可以通过控制理论和机器学习的原理应用，以获得两种药物的个性化给药策略。主要目的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-铁相互作用，2）应用此类模型来导出用于协调的个性化ESA和铁给药的算法，3）通过针对广泛的模拟受试者和临床状况的“模拟”试验来验证这些算法。该项目的预期结果是使用多种药物管理ESRD贫血的新系统方法，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.