# Admin-Core-001

> **NIH NIH U54** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $102,364

## Abstract

Genomics-guided precision medicine promises to identify the key therapeutic target(s) in an individual
patient to enable selection of the most efficacious therapeutic strategy. Central to the success of this strategy
needs to be the selection of relevant therapeutic agents with optimal pharmacokinetic and pharmacodynamic
properties to adequately suppress the intended target across the entire target cell population. While relevant
for all cancers, the selection of appropriate pharmacotherapies is especially challenging in brain tumors, in
which the blood-brain barrier in normal and diseased regions can significantly limit drug distribution and
efficacy for these tumors. In fact, over 95% of FDA-approved drugs have limited accumulation in the brain, and
current predictive algorithms for drug distribution into the brain, based on physico-chemical features of the
therapeutic agent, are poorly predictive. In the MIT/Mayo PS-OC, we will develop a platform for modeling drug
distribution in brain tumors across scales from organism and tissue down to sub-cellular distribution and
signaling and transcript network effect to support magnetic resonance imaging (MRI)-based modeling to
enable clinical translation. Integrated with a genomics-guided delineation of therapeutic vulnerabilities, the
proposed multi-scale model of drug distribution and efficacy could be used to select a targeted therapeutic with
an optimal predicted drug distribution based on MRI features of an individual tumor.
A key principle in oncology is that cure is only possible if a potentially curative treatment effectively targets
100% of the tumor cell population. The invasive nature of many brain tumors, with isolated tumor cells invading
into regions of normal brain, has made these tumors especially challenging to treat, and despite exciting
advances in neurosurgery, radiation therapy, cancer genomics (target identification), and cancer pharmacology
(targeted therapeutics), the prognosis for most patients with primary or metastatic brain tumors has not
significantly changed over the course of several decades. One of the central tenets of this proposal is that
failure to understand limitations in physical delivery and distribution of novel therapeutics into brain tumors is a
major reason for this lack of progress. In most brain tumors, the integrity of the vasculature and associated
BBB is heterogeneous and critically limits drug delivery to at least some parts of the tumor. Beyond
vasculature and the BBB, other physical features regulating therapeutic delivery into tumors are poorly
understood, and all of these factors ultimately result in a spatially heterogeneous range of therapeutic drug
exposure across a tumor cell population. Further, the dynamic molecular and cellular responses in a
heterogeneous tumor to temporally regulated and spatially heterogeneous molecularly targeted
therapeutics are poorly understood. Also unknown is the extent that optimizing size, affinity, and/or
chemical...

## Key facts

- **NIH application ID:** 10025667
- **Project number:** 5U54CA210180-05
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Jann N. Sarkaria
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $102,364
- **Award type:** 5
- **Project period:** 2016-08-29 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10025667

## Citation

> US National Institutes of Health, RePORTER application 10025667, Admin-Core-001 (5U54CA210180-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10025667. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*