# Model-based Prediction of Redox-Modulated Responses to Cancer Treatments

> **NIH NIH U01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2021 · $626,746

## Abstract

Project Summary
While the arsenal of approaches to selectively killing cancer cells is increasing, the majority of treatments rely
on redox alterations of tumor cells and their microenvironment through chemotherapy, radiation, or some
combination thereof. Effectively predicting response to these treatments remains a significant challenge in
designing successful personalized therapeutic strategies and currently there are no biomarkers of response to
chemo/radiation therapies in clinical use. We hypothesize that the response to redox-based chemotherapeutics
can be predicted and enhanced by identifying specific metabolic network features contributing to the redox
couple NAD(P)+/NAD(P)H and associated with the specific mechanism of action. We will integrate and expand
the scope of our prior successful models of drug bioactivation networks and redox metabolic systems in a
comprehensive systems-level approach to improve understanding and enhance prediction of phenotype-specific
responses to chemotherapeutic strategies. We will investigate the NAD(P)H-driven mechanisms of response to
the quinone-based chemotherapeutic, beta-lapachone (ß-lap), in laboratory models and clinical specimens of
Head and Neck Squamous Cell Cancer (HNSCC). We propose to 1) Develop and validate a predictive model to
quantify ß-lap lethality in matched HNSCC cell lines with altered redox metabolism and response to treatment
(SCC-61/rSCC-61); 2) Enhance predictive capabilities of computational model by accounting for metabolic
diversity across HNSCC tumors in vitro and in vivo; and, 3) Test model-based predictions of therapeutic
outcomes with HNSCC clinical specimens. We anticipate our study will advance precision medicine by
accounting for the redox-dependent mechanisms of action for molecular or systemic chemotherapies.

## Key facts

- **NIH application ID:** 10247074
- **Project number:** 5U01CA215848-05
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Cristina Maria Furdui
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $626,746
- **Award type:** 5
- **Project period:** 2017-09-04 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10247074

## Citation

> US National Institutes of Health, RePORTER application 10247074, Model-based Prediction of Redox-Modulated Responses to Cancer Treatments (5U01CA215848-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10247074. Licensed CC0.

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