# Core B: Preclinical Therapeutics Core

> **NIH NIH P01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2020 · $232,061

## Abstract

Summary – Core B (Preclinical Therapeutics) 
The Preclinical Therapeutics Core will leverage the expertise and infrastructure of the Center for Molecular 
Therapeutics at the Massachusetts General Hospital and of the Center for Co-Clinical Trials at the MD 
Anderson Cancer Center. High throughput drug screening of established and project derived cells 
representing the array of PDAC subtypes will be performed. A curated collection of clinically relevant 
compounds as well as novel compounds specifically relevant to the projects will be used in 2D and 3D cell 
viability assays using defined metabolic conditions. Signaling pathway and immune-modulator measurements 
will also be performed to inform mechanism of action and evaluate the impact of treatments on the interaction 
between PDAC cells and the immune system. Data that emerge from in vitro screens will be prioritized for in 
vivo efficacy studies at the Center for Co-Clinical Trials. Here, a dedicated pharmacology staff will evaluate 
single agents or drug combinations in the most relevant pre-clinical PDAC models, including human PDx and 
autochthonous GEM models. Through comprehensive pharmacologic analyses, the Core B aims to evaluate 
novel therapeutic approaches and define biomarkers that may predict responsiveness in clinical trials.

## Key facts

- **NIH application ID:** 9904488
- **Project number:** 5P01CA117969-15
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Cyril Henri Benes
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $232,061
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9904488

## Citation

> US National Institutes of Health, RePORTER application 9904488, Core B: Preclinical Therapeutics Core (5P01CA117969-15). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9904488. Licensed CC0.

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