# Optimizing Multi-drug Mycobacterium tuberculosis Therapy for Rapid Sterilization and Resistance Suppression

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2023 · $1,314,301

## Abstract

Project Summary/Abstract
In P01 AI123036, we were able to generate an algorithm that ranked single agents for Mycobacterium
tuberculosis (MTB), identified promising 2-drug combinations and, with a completely novel mathematical
approach, identified 3-drug regimens predicted to be significantly better than 2-drug regimens. These predictions
were prospectively validated in a BALB/c model (H37Rv) and in a Non-Human Primate model of MTB (Erdman
strain). In this proposal, we will extend our previous work.
There is a large number of new MTB agents, many with novel mechanisms of action. We have 4 Specific Aims
(SA) that, when complete, will allow us to identify multi-drug combinations that will optimize rate of kill for
organisms in 3 different metabolic states and will suppress resistance emergence.
In the Hollow Fiber Infection Model [HFIM] (SA#1), we will be able to rank new agents on the bases of potency
and physicochemical properties. The HFIM provides insight into the drug’s exposure-response for kill and
resistance suppression. We identified a near optimal 3-drug regimen (PMD/MFX/BDQ). With new single agents,
we can examine substituting a new agent for an older agent AND we can expand the regimens to identify a near-
optimal 4-drug regimen. This will be particularly important for patients with high bacterial burdens.
In SA #2, we will test regimens from SA#1 in two murine models (BALB/c & C3HeB/FeJ mice). These will give
somewhat different information. Both give information regarding kill and resistance suppression. Kramnik mice
have pathology more closely resembling that in humans. We will use Matrix-Assisted Laser Desorption
Ionization-MS Imaging and Laser Capture Microdissection LCMS. This allows identification of spatial distribution
and quantification of drugs. A question regarding cure is how long to wait to sacrifice animals to document
eradication. Some agents (BDQ) have long tissue half-lives. We will document rates of ingress/egress of drugs
into the infection site, allowing determination when animal cohorts may be sacrificed to document eradication.
In SA #3, we will document mechanisms of antimicrobial effect quantitatively. We have generated a first-of-a-
kind dynamic model for PBP-binding in MTB, and will link this to rates of cell kill. We have also developed
AMP/ADP/ATP intracellular assays. These will be employed for agents like diarylquinolines (e.g. BDQ) and PMD
that act as energy poisons (for PMD, this occurs under anaerobic/non-replicative conditions. We will measure
intracellular (MTB) drug concentrations, linking them to effect alone and in combination therapy experiments.
Proposal success rests on modeling of the data. In SA #4, we have written code to extend earlier analyses,
going from 3- to 4-drug regimens. For these high dimensional models, we developed several approaches to
speed up analysis making them computationally tractable. At proposal end, we shall develop a 4-drug algorithm
allowing rapid identification of near...

## Key facts

- **NIH application ID:** 10567327
- **Project number:** 1R01AI173238-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** George Louis Drusano
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,314,301
- **Award type:** 1
- **Project period:** 2023-01-25 → 2027-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10567327

## Citation

> US National Institutes of Health, RePORTER application 10567327, Optimizing Multi-drug Mycobacterium tuberculosis Therapy for Rapid Sterilization and Resistance Suppression (1R01AI173238-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10567327. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*