Improving red blood cell transfusion through systems biology

通过系统生物学改善红细胞输注

基本信息

批准号：
8714738
负责人：
Aarash Bordbar
金额：
$ 15万
依托单位：
SINOPIA BIOSCIENCES, INC.
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2016-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8714738
关键词：
Accounting Area Biochemical Pathway Biological Blood Blood Banks Caring Cell physiology Characteristics Chemicals Clinical Trials Complex Computer Analysis Computer Simulation Computing Methodologies Data Data Analyses Data Set Equilibrium Erythrocyte Transfusion Erythrocytes Funding Generations Hospital Costs Iceland Intellectual Property Intervention Kinetics Knowledge Lesion Life Measures Medicine Metabolic Metabolism Modeling Patients Phase Phenotype Play Positioning Attribute Proteomics Role Safety Solutions State Hospitals Systems Biology Techniques Technology Testing Time Transfusion United States Universities Work base commercial application commercialization cost design extracellular improved innovation interest metabolomics new technology novel prevent programs prospective public health relevance reconstruction research clinical testing research study statistics success

项目摘要

DESCRIPTION (provided by applicant): Red blood cells (RBC) stored in approved additive solutions undergo a set of metabolic and physicochemical changes referred to as 'storage lesions' reducing the efficacy and safety of older transfused RBC units. Though the consequences of the storage lesion are slowly becoming well documented, a major reason for delayed progress in developing new technologies for quality and safety of RBC transfusion is the lack of global understanding of metabolic decline during storage. There has been interest to utilize high-throughput metabolite profiling for global understanding of RBC metabolic decline but data analysis of complex datasets has been a daunting challenge. The proposed program will develop the first, robust computational platform involving statistical analysis, systems biology of metabolic networks, and data-driven kinetic models to fully interpret and analyze RBC metabolite-profiles in a complete network context. The program will utilize time- course global, quantitative metabolite profiling to track intracellular and extracellular RBC metabolites under standard storage conditions. Deep metabolic understanding will be obtained through computational analysis to quantitatively predict optimal additive solution strategies based on expected biological efficacy, physico- chemical considerations, compound cost, and potential regulatory hurdles. Predicted additives will be chosen for experimental testing in Phase II.

描述（由申请人提供）：储存在批准的添加剂溶液中的红细胞（RBC）会经历一系列代谢和物理化学变化，称为“储存损伤”，降低了旧输注红细胞单位的功效和安全性。尽管储存损害的后果正在慢慢得到充分记录，但红细胞输注质量和安全新技术开发进展缓慢的一个主要原因是全球缺乏对储存期间代谢下降的了解。人们一直有兴趣利用高通量代谢物分析来全面了解红细胞代谢下降，但复杂数据集的数据分析一直是一项艰巨的挑战。拟议的计划将开发第一个强大的计算平台，涉及统计分析、代谢网络的系统生物学和数据驱动的动力学模型，以在完整的网络环境中充分解释和分析红细胞代谢物概况。该计划将利用时程全局、定量代谢物分析来追踪标准储存条件下的细胞内和细胞外红细胞代谢物。将通过计算分析获得深入的代谢理解，以根据预期的生物功效、物理化学考虑因素、化合物成本和潜在的监管障碍定量预测最佳添加剂解决方案策略。将选择预测的添加剂进行第二阶段的实验测试。