# Targeting metabolism in AML

> **NIH VA I01** · LOUIS STOKES CLEVELAND VA MEDICAL CENTER · 2021 · —

## Abstract

Acute myeloid leukemia (AML) is the most common type of acute leukemia affecting adults, but
unfortunately there has been no change in standard therapy for most patients in over 40 years.
Traditional chemotherapeutics exhibit poor efficacy in patients over the age of 56, with a median
survival of less than one year and only 20% surviving two years. The development of novel
therapies for AML is urgently needed. We previously identified a plant derived alkaloid as a
promising agent that impaired the growth and survival of AML cells. Through medicinal
chemistry efforts, we have identified a series of securinine analogues with high potency and
promising activity in mouse models of human AML. We have identified that these compounds
inhibit the enzyme thioredoxin reductase and that this leads to direct and marked effects on
metabolism. In this grant, we will investigate how thioredoxin reductase functions as a major
regulator of cancer cell metabolism through pharmacologic and genetic studies. In addition, we
will perform lead optimization chemistry to identify securinine analogues with improved
pharmacologic properties and test them in mouse models of human AML. It is hoped that this
work will lead to improved therapies for AML and a novel understanding of how thioredoxin
reductase can directly regulate cancer cell metabolism.

## Key facts

- **NIH application ID:** 10013953
- **Project number:** 1I01BX004995-01A1
- **Recipient organization:** LOUIS STOKES CLEVELAND VA MEDICAL CENTER
- **Principal Investigator:** David Wald
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2020-10-01 → 2024-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10013953

## Citation

> US National Institutes of Health, RePORTER application 10013953, Targeting metabolism in AML (1I01BX004995-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10013953. Licensed CC0.

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