CEST MRI assessment of tumor vascular permeability using non-labeled dextrans

使用非标记葡聚糖评估肿瘤血管通透性的 CEST MRI

基本信息

  • 批准号:
    9297917
  • 负责人:
  • 金额:
    $ 17.58万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-03-15 至 2019-02-28
  • 项目状态:
    已结题

项目摘要

Quantitative imaging technologies for the characterization of the size-dependent tumor vascular permeability (i.e., in the macro- to nano- size range) are of great clinical interest. Such technologies will be extremely useful for oncologists to assess the tumor vascular permeability to drugs at different sizes and, based on the drug accessibility, to stratify patients for the appropriate treatment. Moreover it can be used to monitor the tumor responses to any interventions that can potentially modulate the tumor vascular permeability and improve the drug delivery. In the current application, we propose to directly use the highly-safe, clinically-available dextrans as new MRI probes for assessing tumor vascular permeability without the need for any radioactive, paramagnetic, or super-paramagnetic label. In this approach, dextran is detected directly via its exchangeable hydroxyl (OH) protons using a recently emerged MRI contrast mechanism, Chemical Exchange Saturation Transfer (CEST), namely dextran-enhanced CEST (dexCEST). Because dextrans are available in a wide range of particle sizes-- from 5 to 54 nm for molecular weights (MW) from 10 kD to 2 MD, respectively, it is therefore feasible to use them as macro- and nano-sized MR imaging agents in a broad range of applications. As such, we hypothesize that dexCEST MRI can be used to assess the size-dependent tumor vascular permeability, and to monitor the response in the tumor vascular permeability of pancreatic cancer to stroma-targeting therapies. In particular, we will first fully optimize and validate dexCEST MRI detection to assess size-dependent tumor vascular permeability of experimental pancreatic ductal adenocarcinoma (PDAC) tumors. Then, we will use this technique to monitor the tumor response to stroma-targeting therapies in experimental PDAC tumors, which will lead to the evaluation of the use of dexCEST MRI as an imaging biomarker to quantify the efficacy of stroma-depleting drugs. The successful completion of this project will have an immediate impact on the pre-clinical development and clinical implementation of stroma-targeting therapies to treat hypo-permeable PDAC in a personalized medicine manner. Because many new drugs are in macro-size range (i.e., monocolonal antibodies) and nano-size range (nanomedicine), our approach is expected to play an important role in the clinical implementation of newly developed chemotherapy and immunotherapy, as well as their combination with stroma-targeting therapies. In addition, we expect that these developments can be easily tailored to other types of solid tumors.
定量成像技术表征大小依赖性肿瘤血管通透性

项目成果

期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Guanshu Liu其他文献

Guanshu Liu的其他文献

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{{ truncateString('Guanshu Liu', 18)}}的其他基金

MPI/MRI bimodal imaging for non-invasive tracking of extracellular vesicles targeted to infarcted myocardium
MPI/MRI 双模成像,用于无创追踪梗塞心肌细胞外囊泡
  • 批准号:
    10557225
  • 财政年份:
    2022
  • 资助金额:
    $ 17.58万
  • 项目类别:
MPI/MRI bimodal imaging for non-invasive tracking of extracellular vesicles targeted to infarcted myocardium
MPI/MRI 双模成像,用于无创追踪梗塞心肌细胞外囊泡
  • 批准号:
    10366590
  • 财政年份:
    2022
  • 资助金额:
    $ 17.58万
  • 项目类别:
Noninvasive prediction of tumor response to gemcitabine using MRI
使用 MRI 无创预测肿瘤对吉西他滨的反应
  • 批准号:
    9328948
  • 财政年份:
    2017
  • 资助金额:
    $ 17.58万
  • 项目类别:
Optimization of CEST MRI for detection of bacteria
用于细菌检测的 CEST MRI 优化
  • 批准号:
    9303352
  • 财政年份:
    2016
  • 资助金额:
    $ 17.58万
  • 项目类别:
Monitoring Prodrug Delivery in Suicide Gene Therapy Using CEST MRI
使用 CEST MRI 监测自杀基因治疗中的前药递送
  • 批准号:
    8510646
  • 财政年份:
    2012
  • 资助金额:
    $ 17.58万
  • 项目类别:
Monitoring Prodrug Delivery in Suicide Gene Therapy Using CEST MRI
使用 CEST MRI 监测自杀基因治疗中的前药递送
  • 批准号:
    8356569
  • 财政年份:
    2012
  • 资助金额:
    $ 17.58万
  • 项目类别:

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