# Opportunities for multi-target drug discovery in tuberculosis

> **NIH NIH R21** · BRANDEIS UNIVERSITY · 2020 · $242,770

## Abstract

Project Summary
Tuberculosis kills over 1 million people a year, and an increasing percentage of these cases
involves bacteria that are resistant to all front line and many second line therapies. New drugs
and new drug targets are needed to combat the relentless evolution of resistance. We have
discovered a scaffold, which we designate Q, that has potent in vitro antibacterial activity
against Mtb. These compounds are active in macrophage infections and do not display host
cell cytotoxicity. Some Q compounds inhibit the essential enzyme inosine monophosphate
dehydrogenase 2 (MtbIMPDH2), but other closely related compounds engage an unknown
target. Multi-target antibiotics have a greatly diminished risk of developing resistance than
single target compounds, so the observation that the structure-activity relationship (SAR) of
MtbIMPDH2 inhibition overlaps with inhibition of another essential enzyme is exciting. Indeed,
we have as yet been unable to generate strains that are resistant to these compounds. Here
we propose to further interrogate the SAR of antimycobacterial activity and to identify the
unknown target using a multi-pronged approach. These experiments will lay the foundation for
the development of multitarget antibiotics that will be “resistance-resistant”.

## Key facts

- **NIH application ID:** 9872111
- **Project number:** 5R21AI138048-02
- **Recipient organization:** BRANDEIS UNIVERSITY
- **Principal Investigator:** Lizbeth K. Hedstrom
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $242,770
- **Award type:** 5
- **Project period:** 2019-02-14 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9872111

## Citation

> US National Institutes of Health, RePORTER application 9872111, Opportunities for multi-target drug discovery in tuberculosis (5R21AI138048-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9872111. Licensed CC0.

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