人工智能增强了基于寿命的组织分子异质性细观体内成像-猫眼课题宝

人工智能增强了基于寿命的组织分子异质性细观体内成像

批准号：

10585510

负责人：

Margarida Barroso

金额：

$66.08万

依托单位：

RENSSELAER POLYTECHNIC INSTITUTE

依托单位国家：

美国

项目类别：

财政年份：

2023

资助国家：

美国

项目状态：

未结题

起止时间：

2023-06-01 至 2028-05-31

来源：

https://reporter.nih.gov/project-details/10585510

关键词：

3-Dimensional Animals Antibody Therapy Binding Biochemical Blood Vessels Complex Data Set Deoxyglucose Detection Development Drug Delivery Systems Drug Receptors Drug Targeting ERBB2 gene Fluorescence Fluorescence Resonance Energy Transfer Glucose Goals Heterogeneity Human Image Imaging technology Malignant Neoplasms Mammary Neoplasms Maps Measurement Measures Mediating Metabolic Metabolism Methodology Molecular Monitor Multi-modal optical imaging Organ Patient-Focused Outcomes Patients Pharmaceutical Preparations Play Proteins Reporting Resistance Resolution Short Waves Signal Transduction Specificity Techniques Testing Therapeutic Therapeutic antibodies Three-Dimensional Image Time Tissue imaging Tissues Trastuzumab Treatment Failure Tumor Volume Tumor-Associated Process cancer cell cancer imaging deep learning deep learning model density detector drug development drug distribution drug efficacy fluorescence lifetime imaging fluorescence molecular tomography glucose metabolism high resolution imaging imaging approach imaging capabilities imaging platform improved in vivo in vivo imaging insight instrument intravital microscopy malignant breast neoplasm millimeter molecular imaging multimodality neoplastic cell non-invasive imaging novel optical imaging patient derived xenograft model pre-clinical preclinical imaging prevent response serial imaging side effect subcutaneous targeted imaging targeted treatment time use tumor tumor heterogeneity tumor microenvironment tumor xenograft user-friendly

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Abstract Quantification of drug-target engagement is recognized as the most crucial parameter in the drug development pipeline as it is central to therapeutic action. Though, such parameter can only be assessed via invasive biochemical and immunohistochemical (IHC) approaches in ex vivo tissues. Herein, we propose to integrate and optimize a multimodal optical imaging platform that can provide direct longitudinal (multiple time points) measurements of the drug-target engagement distribution across the same tissue volume in correlation with drug delivery efficacy parameters, including, tumor vasculature, and indicators of drug response, such as metabolism. The imaging platform will be validated in human breast tumor and patient derived xenografts in live animals subjected to HER2-trastuzumab therapy. Additionally, as MFMT is an indirect image formation technique relying on complex computational tasks, we will further pioneer the use of Deep Learning methodologies for fast, accurate, parameter-free and user friendly 2D and 3D MFMT image formation.

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