Monitoring Photo-immunotherapy using Multi-channel Multi-modal Imaging Needles

使用多通道多模式成像针监测光免疫治疗

基本信息

  • 批准号:
    8930927
  • 负责人:
  • 金额:
    $ 7.6万
  • 依托单位:
  • 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:
    2014-09-22 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

 DESCRIPTION (provided by applicant): Photo-immunotherapy (PIT) is an emerging low-side-effect cancer therapy based on monoclonal antibody (mAb) conjugated with a near-infrared (NIR) phthalocyanine dye (IR700) that induces rapid cellular necrosis after exposure to near infrared light. Although single administration of the therapy (agent + light) was highly effective, recurrences were observed due to the inhomogeneous mAb-IR700 distribution in the tumor. Repeated therapy has shown highly effective tumor treatments owing to the redistribution of antibody into the remnant tumor over time. However, current approach for monitoring IR700 fluorescence signal (macroscopic fluorescence reflectance imaging) lacks the resolution and depth selectivity to reveal mAb-IR700 distribution heterogeneity in situ. As a result, personalized treatment regimen tailored to individual subject is not possible. Real-time monitoring of theranostic agent distribution and therapeutic effects including cellular necrosis, blood flow alterations and stromal changes within the tumor micro-environment will be critical for optimizing the effectiveness of individual PIT treatment. We hypothesize that multi-modal needle imaging technology can provide the information to predict the efficacy of PIT in situ (inside the tumor) and in real-time. The proposed multi-modal optical imaging technology is based on optical coherence tomography (OCT) and fluorescence molecular imaging (FMI). OCT enables high-resolution imaging of tissue microstructures in vivo and has been demonstrated for tumor imaging including tumor boundary detection, lymphangiography and angiography. FMI provides highly sensitive and specific information of the theranostic agent (mAB-IR700) distribution and has been demonstrated for monitoring PIT effects. Our lab has developed an integrated OCT/FMI imaging platform and miniaturized needle imaging devices for OCT and FMI. In this pilot study responding to NCI Omnibus R03, we will investigate the feasibility of multi-modal imaging needle technology for real-time monitoring intra-tumor response to optimize the efficacy of PIT. The specific aims are: 1) Develop multi-channel OCT/FMI imaging needle for real-time monitoring of tumor necrosis, blood flow alteration and tumor cell vitality during PIT at different intra-tumor locations; and 2) Investigate the feasibility of multi-modal intra-tumor imaging for optimizing the therapeutic effects of PIT. This project is a multi-disciplinary collaboration among investigators with expertise in optical imaging (Dr. Yu Chen, UMD) and photo-immunotherapy (Drs. Hisataka Kobayashi and Peter Choyke, NCI). As PIT has emerged as a promising highly selective and clinically feasible theranostic method for treatment of mAb-binding tumors with minimal off-target effects, the proposed multi-channel needle imaging technology will open a window for microscopically monitoring of tumor micro-environment, and will likely be applicable to a wide range of cancer therapies.


项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
3D mesoscopic fluorescence tomography for imaging micro-distribution of antibody-photon absorber conjugates during near infrared photoimmunotherapy in vivo.
Real-time monitoring of microdistribution of antibody-photon absorber conjugates during photoimmunotherapy in vivo.
{{ 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 }}

Yu Chen其他文献

Yu Chen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('Yu Chen', 18)}}的其他基金

Defining the role of histone H3K4 mono-methyltransferase dysfunction in urothelial carcinoma
定义组蛋白 H3K4 单甲基转移酶功能障碍在尿路上皮癌中的作用
  • 批准号:
    10522552
  • 财政年份:
    2022
  • 资助金额:
    $ 7.6万
  • 项目类别:
Automatic Wide-Field Optical Coherence Tomography for Assessment of Transplant Kidney Viability
用于评估移植肾活力的自动广域光学相干断层扫描
  • 批准号:
    10501992
  • 财政年份:
    2022
  • 资助金额:
    $ 7.6万
  • 项目类别:
Iodine Catalyzed Cross-Coupling Reactions
碘催化的交叉偶联反应
  • 批准号:
    10333396
  • 财政年份:
    2022
  • 资助金额:
    $ 7.6万
  • 项目类别:
Iodine Catalyzed Cross-Coupling Reactions
碘催化的交叉偶联反应
  • 批准号:
    10643819
  • 财政年份:
    2022
  • 资助金额:
    $ 7.6万
  • 项目类别:
Evolution and inhibition of carbapenemase in beta-lactam resistance
β-内酰胺耐药中碳青霉烯酶的进化和抑制
  • 批准号:
    10598501
  • 财政年份:
    2021
  • 资助金额:
    $ 7.6万
  • 项目类别:
Evolution and inhibition of carbapenemase in beta-lactam resistance
β-内酰胺耐药中碳青霉烯酶的进化和抑制
  • 批准号:
    10385772
  • 财政年份:
    2021
  • 资助金额:
    $ 7.6万
  • 项目类别:
Patient-Derived Models of Prostate Cancer for Personalized Medicine
用于个体化医疗的前列腺癌患者衍生模型
  • 批准号:
    10472536
  • 财政年份:
    2019
  • 资助金额:
    $ 7.6万
  • 项目类别:
Patient-Derived Models of Prostate Cancer for Personalized Medicine
用于个体化医疗的前列腺癌患者衍生模型
  • 批准号:
    10219178
  • 财政年份:
    2019
  • 资助金额:
    $ 7.6万
  • 项目类别:
Patient-Derived Models of Prostate Cancer for Personalized Medicine
用于个体化医疗的前列腺癌患者衍生模型
  • 批准号:
    10683753
  • 财政年份:
    2019
  • 资助金额:
    $ 7.6万
  • 项目类别:
Understanding the role of an aberrant hepatic nuclear transcription circuit in prostate cancer tumorigenesis and castration resistance
了解异常肝核转录回路在前列腺癌肿瘤发生和去势抵抗中的作用
  • 批准号:
    10224110
  • 财政年份:
    2017
  • 资助金额:
    $ 7.6万
  • 项目类别:

相似海外基金

ImproviNg rEnal outcomes following coronary angiograPhy and/or percuTaneoUs coroNary intErventions: a pragmatic, adaptive, patient-oriented randomized controlled trial
改善冠状动脉造影和/或经皮冠状动脉介入治疗后的肾脏结局:一项务实、适应性、以患者为导向的随机对照试验
  • 批准号:
    478732
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
    Operating Grants
SBIR Phase II: Novel size-changing, gadolinium-free contrast agent for magnetic resonance angiography
SBIR II 期:用于磁共振血管造影的新型尺寸变化、无钆造影剂
  • 批准号:
    2322379
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
    Cooperative Agreement
Neonatal Optical Coherence Tomography Angiography to Assess the Effects of Postnatal Exposures on Retinal Development and Predict Neurodevelopmental Outcomes
新生儿光学相干断层扫描血管造影评估产后暴露对视网膜发育的影响并预测神经发育结果
  • 批准号:
    10588086
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
Motion-Resistant Background Subtraction Angiography with Deep Learning: Real-Time, Edge Hardware Implementation and Product Development
具有深度学习的抗运动背景减影血管造影:实时、边缘硬件实施和产品开发
  • 批准号:
    10602275
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
Highly Accelerated Magnetic Resonance Angiography using Deep Learning
使用深度学习的高加速磁共振血管造影
  • 批准号:
    2886357
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
    Studentship
Development of a method to simultaneously obtain cerebral blood flow information and progression of cerebral white matter lesions using head MR angiography.
开发一种使用头部磁共振血管造影同时获取脑血流信息和脑白质病变进展的方法。
  • 批准号:
    23K14839
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Development of a new diagnostic method for coronary artery disease using automated image analysis with postmortem coronary angiography CT
使用死后冠状动脉造影 CT 自动图像分析开发冠状动脉疾病的新诊断方法
  • 批准号:
    23K19795
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
    Grant-in-Aid for Research Activity Start-up
Novel ultrahigh speed swept source OCT angiography methods in diabetic retinopathy
糖尿病视网膜病变的新型超高速扫源 OCT 血管造影方法
  • 批准号:
    10656644
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
Automated Machine Learning-Based Brain Artery Segmentation, Anatomical Prior Labeling, and Feature Extraction on MR Angiography
基于自动机器学习的脑动脉分割、解剖先验标记和 MR 血管造影特征提取
  • 批准号:
    10759721
  • 财政年份:
    2023
  • 资助金额:
    $ 7.6万
  • 项目类别:
SCH: A physics-informed machine learning approach to dynamic blood flow analysis from static subtraction computed tomographic angiography imaging
SCH:一种基于物理的机器学习方法,用于从静态减影计算机断层血管造影成像中进行动态血流分析
  • 批准号:
    2205265
  • 财政年份:
    2022
  • 资助金额:
    $ 7.6万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了