Genome-wide profiling of human vascular response to oxidized lipoprotreins

人类血管对氧化脂蛋白反应的全基因组分析

基本信息

  • 批准号:
    7908422
  • 负责人:
  • 金额:
    $ 16.68万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2010
  • 资助国家:
    美国
  • 起止时间:
    2010-04-01 至 2011-07-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Atherosclerosis is a vascular inflammatory disease characterized by dramatic changes in endothelial cell (EC) and smooth muscle cell (SMC) phenotypes. The progression of atherosclerosis is dependent on interactions between vascular cells (EC/SMC), regional hemodynamic flow patterns, and environmental factors such as oxidized lipoprotein species. In line with the Mission of the NHLBI SBIR/STTR program, HemoShear's proposal will foster research on pharmaceuticals, biologics, informatics, and biotechnologies for the causes, prevention, and treatment of blood vessel disorders. Hemoshear has developed a human vascular surrogate device that uniquely mimics the vascular anatomy and hemodynamic environment during the early stages of atherosclerosis. This device enables investigation of the cellular and molecular mechanisms of human atherosclerosis and the identification of novel biomarkers and transcriptional pathways for development of drug therapies. Such cell-based biomimetic devices are being used increasingly during drug development to provide more accurate predictions of human responses than traditional cell-culture assays or animal models. The objective of this proposal is to develop a database that profiles the human vascular response (EC/SMC) to oxidized low density lipoprotein (oxLDL), which has a widely recognized role in the progression of atherosclerosis. Specifically, HemoShear will use high throughput gene arrays to determine the genome-wide profile of EC/SMC gene expression in the presence of oxLDL in the vascular surrogate model. Additionally, HemoShear will use network and pathway analyses to rigorously analyze the gene array results and identify the most oxLDL- responsive transcriptional networks. Taken together, the gene profiles and bioinformatics analyses will comprise a comprehensive genomic database that will reveal the most relevant molecular targets for therapeutic intervention of oxLDL-mediated atherosclerosis. The insight into the cellular/molecular mechanisms of atherosclerosis provided by the genomic database will serve as a basis for developing an innovative, late-stage atherosclerosis device that integrates the presence of oxLDL and macrophages into the current design (Phase II SBIR). PUBLIC HEALTH RELEVANCE: Major challenges in drug development (e.g., efficacy and safety) are due to limited pre-clinical tests that can predict human response to a drug in clinical trials. HemoShear has developed a human vascular surrogate technology for pre-clinical biodiscovery and drug efficacy/safety screening. Atherosclerosis is a blood vessel disorder responsible for more that 40% of all mortality in the United States and an estimated $9 billion in global R&D expenditures spent annually, with few new drugs entering the market. This Phase I SBIR application proposes to further develop our platform technology into a model that better represents the onset of atherosclerosis in arteries, allowing drug companies to identify new targets and test compounds for cardiovascular efficacy/safety.
描述(申请人提供):动脉粥样硬化是一种血管炎症性疾病,其特征是内皮细胞(EC)和平滑肌细胞(SMC)表型的显着变化。动脉粥样硬化的进展取决于血管细胞 (EC/SMC)、局部血流动力学模式和氧化脂蛋白等环境因素之间的相互作用。根据 NHLBI SBIR/STTR 计划的使命,HemoShear 的提案将促进药物、生物制品、信息学和生物技术的研究,以了解血管疾病的病因、预防和治疗。 Hemoshear 开发了一种人体血管替代装置,能够独特地模拟动脉粥样硬化早期阶段的血管解剖结构和血流动力学环境。该设备能够研究人类动脉粥样硬化的细胞和分子机制,并识别新的生物标志物和转录途径,以开发药物疗法。这种基于细胞的仿生装置在药物开发过程中得到越来越多的使用,以提供比传统细胞培养测定或动物模型更准确的人类反应预测。该提案的目的是开发一个数据库,描述人类血管对氧化低密度脂蛋白(oxLDL)的反应(EC/SMC),氧化低密度脂蛋白在动脉粥样硬化的进展中具有广泛认可的作用。具体来说,HemoShear 将使用高通量基因阵列来确定血管替代模型中存在 oxLDL 时 EC/SMC 基因表达的全基因组谱。此外,HemoShear 将使用网络和通路分析来严格分析基因阵列结果,并确定对 oxLDL 反应最强的转录网络。总而言之,基因谱和生物信息学分析将构成一个全面的基因组数据库,该数据库将揭示 oxLDL 介导的动脉粥样硬化治疗干预最相关的分子靶点。基因组数据库提供的对动脉粥样硬化细胞/分子机制的深入了解将作为开发创新的晚期动脉粥样硬化装置的基础,该装置将 oxLDL 和巨噬细胞的存在整合到当前的设计中(II 期 SBIR)。 公共卫生相关性:药物开发中的主要挑战(例如功效和安全性)是由于可以预测人类对临床试验中药物反应的临床前测试有限。 HemoShear 开发了一种用于临床前生物发现和药物功效/安全性筛选的人体血管替代技术。动脉粥样硬化是一种血管疾病,占美国所有死亡率的 40% 以上,全球每年的研发支出估计为 90 亿美元,但很少有新药进入市场。这一 I 期 SBIR 申请旨在进一步将我们的平台技术开发成一个模型,更好地代表动脉粥样硬化的发生,使制药公司能够确定新的靶点并测试化合物的心血管功效/安全性。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Brett R Blackman其他文献

Brett R Blackman的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Brett R Blackman', 18)}}的其他基金

Hemodynamic Co-Culture Liver Model for Drug Discovery and Assessment
用于药物发现和评估的血流动力学共培养肝脏模型
  • 批准号:
    8059220
  • 财政年份:
    2011
  • 资助金额:
    $ 16.68万
  • 项目类别:
Creating a predictive vascular system for early development
为早期发育创建预测性血管系统
  • 批准号:
    8308381
  • 财政年份:
    2011
  • 资助金额:
    $ 16.68万
  • 项目类别:
Creating a predictive vascular system for early development
为早期发育创建预测性血管系统
  • 批准号:
    8203043
  • 财政年份:
    2011
  • 资助金额:
    $ 16.68万
  • 项目类别:
Hemodynamic Adaptation of Intercelluar Junctions in Human Endothelium
人内皮细胞间连接的血流动力学适应
  • 批准号:
    7842179
  • 财政年份:
    2009
  • 资助金额:
    $ 16.68万
  • 项目类别:
Hemodynamic Adaptation of Intercellular Junctions in Human Endothelium
人内皮细胞间连接的血流动力学适应
  • 批准号:
    7391274
  • 财政年份:
    2007
  • 资助金额:
    $ 16.68万
  • 项目类别:
Hemodynamic Adaptation of Intercellular Junctions in Human Endothelium
人内皮细胞间连接的血流动力学适应
  • 批准号:
    7583964
  • 财政年份:
    2007
  • 资助金额:
    $ 16.68万
  • 项目类别:
Hemodynamic Adaptation of Intercellular Junctions in Human Endothelium
人内皮细胞间连接的血流动力学适应
  • 批准号:
    7788211
  • 财政年份:
    2007
  • 资助金额:
    $ 16.68万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 16.68万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了