# Quantifying Redox Potentials for Artemisinin Resistant (ARTR) Malaria

> **NIH NIH R21** · GEORGETOWN UNIVERSITY · 2022 · $228,610

## Abstract

"Quantifying Redox Potentials for Artemisinin Resistant (ARTR) Malaria"
Capitalizing on recent characterization of coumarin - based fluorescent GSH probes we have synthesized less
expensive and more convenient probes including interesting morpholino derivatives and dextran conjugates,
have characterized their GSH - dependent and other thiol - dependent fluorescence, and in proof - of - principle
preliminary experiments have begun to quantify their localization and intensity within the live malarial parasite
DV. We are thus uniquely able to quantify DV redox potential for ARTR (artemisinin resistant) vs ARTS
(sensitive) parasites and thereby rigorously test, for the first time, the very attractive hypothesis that ferric /
ferrous heme ratios may differ for DCP/ARTR parasites. This is an essential issue that further tests the
predictions of the widely accepted concept that FPIX heme is the likely "activator" of ART - based
drugs. Furthermore, reduced Fe2+PIX / Fe3+PIX would easily explain reduced ART drug potency in ARTR
parasites. Thus, quantifying redox environment of the DV, where FPIX is released during Hb catabolism, is
essential for testing particularly attractive hypotheses for ART drug pharmacology and resistance.

## Key facts

- **NIH application ID:** 10431351
- **Project number:** 1R21AI168720-01
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** PAUL D. ROEPE
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $228,610
- **Award type:** 1
- **Project period:** 2022-04-08 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10431351

## Citation

> US National Institutes of Health, RePORTER application 10431351, Quantifying Redox Potentials for Artemisinin Resistant (ARTR) Malaria (1R21AI168720-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10431351. Licensed CC0.

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