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: 9187652
• 负责人: Kristin R Swanson
• 金额: $ 28.5万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-29 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9187652
• 关键词: Affect; Affinity; Blood - brain barrier anatomy; Blood Vessels; Brain; Brain Neoplasms; Calibration; Chemicals; Clinical; Complex; Computer Simulation; Data; Decision Making; Development; Diffuse; Drug Delivery Systems; Gadolinium; Glioblastoma; Goals; Heterogeneity; Histology; Human; Image; Image Guided Biopsy; Imaging Techniques; Individual; Invaded; Kinetics; Lead; Magnetic Resonance Imaging; 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; clinically relevant; cohort; computer framework; density; drug distribution; drug efficacy; gadolinium oxide; improved outcome; in vivo; interpatient variability; lipophilicity; mathematical model; molecular subtypes; neoplastic cell; neuro-oncology; non-invasive imaging; novel therapeutics; physical science; precision medicine; research study; response; small molecule; targeted treatment; tool; treatment response; tumor; tumor growth; tumor heterogeneity; 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：对无创成像和药物分配之间的接口进行建模 概括 临床神经肿瘤学的教条认为，磁共振成像 (MRI) 上的钆 (Gd) 对比 肿瘤区域证实血脑屏障（BBB）局部受损，因此足够水平的 药物正在这些肿瘤区域内分布，但是药物分布数据表明了重要性。 局部微环境异质性和其他物理因素导致的差异分布 治疗剂相对于 Gd 造影剂的非侵入性获取的成像特征可以提供快照。 该项目的目标是了解肿瘤微环境并最终更好地了解药物分布。 开发并验证一个“最小”模型，该模型将捕获肿瘤内和肿瘤间的异质性以进行预测 在这个项目中，我们将使用常规成像的临床相关水平的药物分布。 患者数据、GBM 患者来源的异种移植物 (PDX)、基质辅助激光解吸/电离质量 光谱成像（MALDI-MSI）和受激拉曼光谱（SRS）来量化差异 肿瘤内和肿瘤间的药物分布，并在此过程中开发一个用于预测的计算框架 BBB 渗透性和 BBB 渗透性药物治疗 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 预测侵袭性原发性和转移性脑肿瘤模型中空间不同的药物分布模式 小分子和大分子治疗，因此对于预测体内疗效至关重要 该项目的目标是： 目标 1 - 建立一个计算框架 定量地将成像特征与定量肿瘤内部和之间的药物分布差异以及 Aim 联系起来 2 - 建立一个计算框架，定量地将药物分布差异与可成像联系起来 第一个目标涉及量化药物差异的实验。 一系列 PDX 中的肿瘤分布（MALDI MSI）、物理组织特征（SRS）、 开发/校准结合 BBB 渗透性的成像驱动药物分布模型，以及 通过 0 期试验将我们的结果推广到患者身上。第二个目标是进行实验研究。 使用 BLI 成像的治疗反应，连接治疗反应模型的开发/校准 药物分布和肿瘤动力学，并通过确定亚组将我们的结果扩展到患者 最有可能对治疗产生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之间的异质治疗反应。 最终的愿景是提供一个易于使用的决策工具来帮助选择相关的目标 将为每位 GBM 患者量身定制治疗方案。