AI enhanced lifetime-based mesoscopic in vivo imaging of tissue molecular heterogeneity

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

基本信息

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
起止时间：
2023-06-01 至 2028-05-31
项目状态：
未结题

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

项目摘要

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.

抽象的药物靶点参与的量化被认为是药物开发中最关键的参数管道，因为它是治疗行动的核心。然而，此类参数只能通过侵入性评估离体组织中的生化和免疫组织化学 (IHC) 方法。在此，我们建议整合和优化可提供直接纵向（多个时间点）的多模态光学成像平台测量同一组织体积内与药物相关的药物靶标结合分布递送功效参数，包括肿瘤脉管系统和药物反应指标，例如代谢。该成像平台将在人类乳腺肿瘤和活体动物的患者衍生异种移植物中进行验证接受 HER2-曲妥珠单抗治疗。此外，由于 MFMT 是一种间接成像技术，依赖于在复杂的计算任务上，我们将进一步开拓使用深度学习方法来快速、准确、无参数且用户友好的 2D 和 3D MFMT 图像形成。