# Project 1: Modeling the Interface between Non-invasive Imaging and Drug Distribution > **NIH NIH U54** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $264,879 ## Abstract 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 too... ## Key facts - **NIH application ID:** 9994242 - **Project number:** 5U54CA210180-05 - **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY - **Principal Investigator:** Kristin R Swanson - **Activity code:** U54 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $264,879 - **Award type:** 5 - **Project period:** 2016-08-29 → 2022-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9994242 ## Citation > US National Institutes of Health, RePORTER application 9994242, Project 1: Modeling the Interface between Non-invasive Imaging and Drug Distribution (5U54CA210180-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9994242. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*