# Tumor-targeted delivery and cell internalization of theranostic gadolinium nanoparticles for image-guided nanoparticle-enhanced radiation therapy

> **NIH NIH R21** · STANFORD UNIVERSITY · 2022 · $181,122

## Abstract

The long-term objective of this project is to overcome some of the major hurdles that prevent the clinical
translation of metallic nanoparticle (NP) radiosensitization in radiation therapy (RT). Studies have shown that
the passive, enhanced permeability and retention (EPR) effect itself is not sufficient to deliver the amount of
intratumoral and intracellular NPs needed for in vivo radiosensitization with an affordable amount of injected NPs
and the conventional NPs are cleared rapidly (~minutes) in vivo. Imaging the in vivo NP biodistribution for
quantitative RT treatment planning is also an unsolved critical issue. Actively targeting and internalization into
cancer cells by gadolinium (Gd) NPs conjugated to pH-Low Insertion Peptides (pHLIPs) have the potential to
serve the dual purpose of enhancing uptake of NPs in tumor cells and selective, quantitative imaging by MRI.
pHLIP-GdNPs can actively target solid tumors’ unavoidable acidic microenvironment, which is not present in
healthy tissues. Therefore, it is superior to other biomarker targeting, such as antibody targeting, which can
become nonspecific and be evaded by selection of non-expressing subclones during treatment. pHLIPs can
also deliver the conjugated cell-impermeable cargoes inside the cancer cells via a strong non-endocytic pathway,
critical for NP-induced short-range Auger cascade and photoelectrons to reach the vital cellular targets as proved
by experiments and simulations. Complementing the rapid increasing use of MRI for RT planning and on-board
treatment-guidance, pHLIP-GdNPs can also solve the imageability problem for treatment plan optimization. Our
preliminary MRI data shows long tumor retention of NPs (>9 hours, possibly days) post pHLIP-GdNPs injection.
Specific Aims: To provide the pre-clinical foundation for more in-depth translational and clinical studies, we aim
to (i) characterize pHLIP-GdNP and evaluate its RT properties in vitro; (ii) develop a mechanistic biophysical
model of radiosensitization by GdNPs to elucidate relevant biolgocial mechanisms and facilitate quantitative RT
treatment planning; and (iii) investigate the in vivo radiosensitization and imaging properties of pHLIP-GdNP.
Research Design: (i) Characterize pHLIP-GdNP and internalization, microscopically image cellular uptake with
fluorescent tags, conduct clonogenic survival experiments in cell culture with both 250 kVp and 6 MV X-rays,
generate pH-dependent cell survival curves, and examine DNA damage. (ii) Use a Monte Carlo particle track
structure simulation to calculate microscopic dose enhancement induced by NPs. DNA damage will be modeled
to predict sensitizer enhancement ratios and compare with experimental results. (iii) Investigate the feasibility of
MR imaging to determine quantitatively in vivo NP distribution and the residence-transit time in tumor and critical
organs in mouse models, the enhanced radiosensitization in vivo in mice injected with pHLIP-GdNPs compared
to mice injected with ...

## Key facts

- **NIH application ID:** 10457237
- **Project number:** 5R21EB026553-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Guillem Pratx
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $181,122
- **Award type:** 5
- **Project period:** 2019-09-13 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10457237

## Citation

> US National Institutes of Health, RePORTER application 10457237, Tumor-targeted delivery and cell internalization of theranostic gadolinium nanoparticles for image-guided nanoparticle-enhanced radiation therapy (5R21EB026553-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10457237. Licensed CC0.

---

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