# Lead Optimization and Mechanisms of Action of Dual-Acting Antitrypanosomal Agents

> **NIH NIH SC1** · JACKSON STATE UNIVERSITY · 2022 · $362,952

## Abstract

Project Summary
Pathogenic protozoans cause several neglected, persistent, and emerging tropical diseases. These
diseases impact hundreds of millions of people worldwide, and they pose significant threat to
global public health, including that of the United States. The key problems of tropical diseases
are limited access to effective drugs, inadequate diagnostic tools, and development of drug
resistance to existing drugs by the etiological agents. Hence, continued effort to control, eliminate,
and provide safe and effective treatment options is crucial. In this SC1 project, our efforts will be
devoted to investigating new chemical entities that can serve as drug leads for human African
trypanosomiasis (HAT) and Chagas disease. The main objective is to investigate chemical entities
that can suppress the development of drug resistance by protozoan parasites to clinically used
nitroaromatic drugs. We envision that the studies outlined in this project will propel preclinical
studies of new generation antitrypanosomal agents that possess multiple mechanisms of action.
In addition, we expect that successful implementation of the aims and objectives of this project
will produce innovative and fundamental knowledge on antitrypanosomal agents.

## Key facts

- **NIH application ID:** 10445231
- **Project number:** 5SC1GM140990-02
- **Recipient organization:** JACKSON STATE UNIVERSITY
- **Principal Investigator:** Ifedayo Victor Ogungbe
- **Activity code:** SC1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $362,952
- **Award type:** 5
- **Project period:** 2021-07-09 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445231

## Citation

> US National Institutes of Health, RePORTER application 10445231, Lead Optimization and Mechanisms of Action of Dual-Acting Antitrypanosomal Agents (5SC1GM140990-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10445231. Licensed CC0.

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