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

项目1：模拟非侵入性成像和药物分配之间的接口 摘要 临床神经肿瘤学的Dogma认为Gd在磁共振成像(MRI)上的对比 肿瘤区域证实血脑屏障(BBB)局部受损，因此有足够的 药物正在这些肿瘤区域内分布。然而，药物分配数据表明了这一重要性 当地微环境的异质性和其他物理因素导致的不同的分布 与Gd造影剂相关的治疗剂。非侵入性获取的成像特征可以提供快照 了解肿瘤微环境，并最终更好地了解药物分布。这个项目的目标是 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来预测 使用常规成像的临床相关水平的药物分布。在这个项目中，我们将结合使用 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)以量化 药物在肿瘤内和肿瘤之间的分布，并在这样做的过程中开发出一种计算框架来预测 血脑屏障通透性药物与血脑屏障通透性药物治疗基底膜的疗效比较。 我们的假设是，基于多参数高内容成像技术的数学模型 预测侵袭性原发和转移性脑瘤模型中不同的药物分布模式 小分子和大分子疗法，因此在体内预测的关键 靶向治疗的疗效。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药物分布的差异联系起来 2-建立一个计算框架，将药物分布的差异与可成像量化地联系起来 肿瘤内和肿瘤间的反应。第一个目标涉及量化药物差异的实验。 使用MALDI MSI的一系列PDX中的肿瘤分布，使用SRS的物理组织特征， 开发/校准包含血脑屏障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，治疗反应连接模型的开发/校准 药物分布和肿瘤动力学，并将我们的结果扩展到患者，通过确定 最有可能对治疗有反应的患者。该项目将提供成像与图像之间的定量联系 特征和药物分布水平足以预测患者的不同治疗反应。 最终的愿景是为临床医生提供一个可访问的决策工具，以帮助选择相关的目标 将为单个GBM患者量身定做的治疗方法。

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.