# A multifactorial pipeline to dissect combinatorial drug efficacy in Tuberculosis

> **NIH NIH R56** · UNIVERSITY OF WASHINGTON · 2020 · $733,199

## Abstract

ABSTRACT
The rapid spread of multi-drug resistance has created a great need for new combination
therapies to treat a variety of conditions, including infectious diseases and cancer. In one
pressing example, multidrug resistant tuberculosis (TB) affects about 500,000 people each year
and novel drug regimens are sorely needed. However, identifying new regimens has been
daunting in part due to the inability to prioritize among a very large number of possible drug
combinations. To address this need, we have generated an experimentally grounded, machine
learning algorithm, INDIGO-MTB, which predicts the synergy or antagonism of TB drug
combinations with high accuracy. Here we propose to adapt INDIGO-MTB into a multifactorial
pipeline to dissect combinatorial drug efficacy and drive preclinical regimen development for TB.
We will build in and validate the ability to predict drug interactions under stressful environmental
conditions that mimic TB infection, and extract molecular mechanisms of drug interactions. We
will then combine synergy and efficacy measurements to create new regimen rankings, which
we will validate both in vitro and in a mouse model of TB infection. Altogether, our work will
establish a tool for rapid assessment of TB drug combinations and a framework for applying this
approach to other conditions where new multidrug therapies are needed.

## Key facts

- **NIH application ID:** 10241043
- **Project number:** 1R56AI150826-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** DAVID R SHERMAN
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $733,199
- **Award type:** 1
- **Project period:** 2020-09-03 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241043

## Citation

> US National Institutes of Health, RePORTER application 10241043, A multifactorial pipeline to dissect combinatorial drug efficacy in Tuberculosis (1R56AI150826-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241043. Licensed CC0.

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