# AI enhanced lifetime-based mesoscopic in vivo imaging of tissue molecular heterogeneity

> **NIH NIH R01** · RENSSELAER POLYTECHNIC INSTITUTE · 2024 · $618,698

## Abstract

Abstract
Quantification of drug-target engagement is recognized as the most crucial parameter in the drug development
pipeline as it is central to therapeutic action. Though, such parameter can only be assessed via invasive
biochemical and immunohistochemical (IHC) approaches in ex vivo tissues. Herein, we propose to integrate and
optimize a multimodal optical imaging platform that can provide direct longitudinal (multiple time points)
measurements of the drug-target engagement distribution across the same tissue volume in correlation with drug
delivery efficacy parameters, including, tumor vasculature, and indicators of drug response, such as metabolism.
The imaging platform will be validated in human breast tumor and patient derived xenografts in live animals
subjected to HER2-trastuzumab therapy. Additionally, as MFMT is an indirect image formation technique relying
on complex computational tasks, we will further pioneer the use of Deep Learning methodologies for fast,
accurate, parameter-free and user friendly 2D and 3D MFMT image formation.

## Key facts

- **NIH application ID:** 10844373
- **Project number:** 5R01CA271371-02
- **Recipient organization:** RENSSELAER POLYTECHNIC INSTITUTE
- **Principal Investigator:** Margarida Barroso
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $618,698
- **Award type:** 5
- **Project period:** 2023-06-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10844373

## Citation

> US National Institutes of Health, RePORTER application 10844373, AI enhanced lifetime-based mesoscopic in vivo imaging of tissue molecular heterogeneity (5R01CA271371-02). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10844373. Licensed CC0.

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