Project 1: Modeling the Interface between Non-invasive Imaging and Drug Distribution

项目 1：对无创成像和药物分配之间的接口进行建模

• 批准号: 9364006
• 负责人: Kristin R Swanson
• 金额: $ 28.08万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9364006
• 关键词: Affect; Affinity; Blood - brain barrier anatomy; Blood Vessels; Brain; Brain Neoplasms; Calibration; Clinical; Complex; Computer Simulation; Data; Decision Making; Development; Diffuse; Drug Delivery Systems; Drug Modelings; Gadolinium; Glioblastoma; Goals; Heterogeneity; Histology; Human; Image; Image Guided Biopsy; Imaging Techniques; Individual; Inter-tumoral heterogeneity; Invaded; Kinetics; Lead; Magnetic Resonance Imaging; Measures; Mediating; Modeling; Neoplasms; Patients; Pattern; Peripheral; Permeability; Pharmaceutical Preparations; Pharmacotherapy; Phase; Physics; Raman Spectrum Analysis; Rest; Series; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Techniques; Therapeutic; Therapeutic Agents; Tissues; Uncertainty; Variant; Vascularization; Vision; Xenograft procedure; antitumor agent; base; clinical predictors; clinically relevant; cohort; computer framework; density; drug distribution; drug efficacy; experimental study; improved outcome; in vivo; interpatient variability; lipophilicity; mathematical model; molecular subtypes; neoplastic cell; neuro-oncology; non-invasive imaging; novel therapeutics; physical science; precision medicine; response; routine imaging; serial imaging; small molecule; spectroscopic imaging; targeted treatment; tool; treatment response; tumor; tumor growth; tumor microenvironment; virtual

Project 1: Modeling the Interface between Non-invasive Imaging and Drug Distribution SUMMARY Dogma in clinical neuro-oncology holds that Gadolinium (Gd) contrast on magnetic resonance imaging (MRI) in tumor regions confirms that the blood-brain barrier (BBB) is locally compromised, and thus sufficient levels of drug are being distributed within these tumor regions. However, drug distribution data indicate the importance of the local microenvironmental heterogeneity and other physical factors that lead to differential distribution of therapeutic agents relative to Gd contrast. Non-invasively acquired imaging features can provide a snapshot of tumor microenvironment and ultimately a better understanding of drug distribution. The goal of this project is to develop and validate a “minimal” model that will capture intra- and inter-tumor heterogeneity to predict clinically relevant levels of drug distribution using routine imaging. In this project, we will use a combination of patient data, GBM patient-derived xenografts (PDXs), matrix-assisted laser desorption/ionization mass spectroscopy imaging (MALDI-MSI), and stimulated raman spectroscopy (SRS) to quantify the differences in drug distribution within and across tumors and, in doing so, develop a computational framework for predicting the efficacy of BBB-penetrant and BBB-impenetrant drugs for the treatment of GBMs. Our hypothesis is that mathematical models based on multiparametric high content imaging techniques will predict spatially distinct drug distribution patterns in invasive primary and metastatic brain tumor models for both small molecule and macromolecular therapeutics, and therefore be pivotal to predicting the in vivo efficacy of targeted therapies. The aims of this project are: Aim 1 - build a computational framework that quantitatively connects imaging features with differences in drug distribution within and across tumors and Aim 2 - build a computational framework that quantitatively connects differences in drug distribution with imageable response within and across tumors. The first aim involves experiments to quantify differences in drug distribution across tumors in a series of PDXs with MALDI MSI, physical tissue features with SRS, development/calibration of imaging-driven models for drug distribution incorporating BBB permeability, and extending our results to patients through a Phase 0 trial. The second aim involves experiments to investigate treatment response using BLI imaging, development/calibration of models of treatment response connecting drug distribution and tumor kinetics, and extending our results to patients by determining sub-cohorts of patients most likely to respond to therapies. This project will provide a quantitative connection between imaging features and drug distribution at levels sufficient to predict heterogeneous treatment response across patients. The ultimate vision is to provide clinicians an accessible decision-making tool to help choose relevant targeted therapies that will be tailored for an individual GBM patient.

项目1：无创成像和药物分配之间的接口建模 总结 临床神经肿瘤学中的教条认为，在神经系统中，磁共振成像（MRI）上的钆（Gd）造影剂在神经系统中的作用是非常重要的。 肿瘤区域证实血脑屏障（BBB）局部受损，因此足够水平的 药物分布在这些肿瘤区域内。然而，药物分布数据表明， 当地微环境的异质性和其他物理因素，导致差异分布， 相对于Gd造影剂的治疗剂。非侵入性获取的成像特征可以提供快照 肿瘤微环境，最终更好地了解药物分布。这个项目的目标是 开发和验证一个“最小”模型，该模型将捕获肿瘤内和肿瘤间的异质性， 使用常规成像的临床相关药物分布水平。在这个项目中，我们将使用以下组合： 患者数据，GBM患者源性异种移植物（PDX），基质辅助激光解吸/电离质谱 光谱成像（MALDI-MSI）和受激拉曼光谱（SRS）来量化 药物在肿瘤内和肿瘤间的分布，并在此过程中开发一个计算框架，用于预测 血脑屏障渗透剂和血脑屏障非渗透剂治疗GBM的疗效。 我们的假设是，基于多参数高内涵成像技术的数学模型将 在侵袭性原发性和转移性脑肿瘤模型中预测空间上不同的药物分布模式， 小分子和大分子治疗剂，因此是预测体内 靶向治疗的有效性。这个项目的目标是：目标1 -建立一个计算框架， 定量地将成像特征与肿瘤内和肿瘤间的药物分布差异联系起来， 2.建立一个计算框架，定量地将药物分布的差异与可成像的药物分布联系起来。 肿瘤内和肿瘤间的反应。第一个目标是通过实验来量化药物之间的差异， 在一系列PDX中的肿瘤分布与MALDI MSI，物理组织特征与SRS， 开发/校准结合BBB渗透性的药物分布的成像驱动模型，以及 通过0期试验将我们的结果推广到患者身上。第二个目标是通过实验来研究 使用BLI成像的治疗反应，开发/校准治疗反应模型， 药物分布和肿瘤动力学，并通过确定 最有可能对治疗有反应的患者。该项目将提供成像之间的定量联系 特征和药物分布水平足以预测患者的异质性治疗反应。 最终的愿景是为临床医生提供一个可访问的决策工具，以帮助选择相关的靶向治疗。 将针对单个GBM患者量身定制的治疗。