# Building and prospective validation of promising mono- and combination regimens that optimize killing of CRAB and CR-Klebsiella pneumoniae and for resistance suppression in murine pneumonia models

> **NIH NIH P01** · UNIVERSITY OF FLORIDA · 2024 · $152,378

## Abstract

Project Abstract/Summary Project #3
The incidence of serious infections, including pneumonia, by carbapenem-resistant Acinetobacter baumannii
(CRAB) and carbapenem-resistant Klebsiella pneumoniae (CRKP) is rising. Infections caused by CRAB and
CRKP are associated with high rates of treatment failure and mortality because only a limited number of
antibiotics are active against these multi-drug resistant bacteria and these microbes often become resistant to
the prescribed antibiotics during the treatment course. This Project will leverage mechanistic assays from our
Gram-negative Toolbox for a novel murine pneumonia model in which antibiotic-directed bacterial killing and
resistance amplification/suppression can be quantified to rationally optimize combination dosage regimens. We
will prospectively validate the efficacy of these regimens to combat CRAB and CRKP and will counter-select for
resistance. Our overarching hypothesis is that highly effective therapy for CRAB and CRKP infections requires
combination regimens that maximize bacterial killing and suppress resistance emergence. We further
hypothesize that the immune system plays an important role in enhancing bacterial killing and suppressing
resistance in a mouse pneumoniae model. To show this, we will utilize both a neutropenic and a novel
immunocompetent murine model of pneumonia. We hypothesize that it is critical to achieve rapid and extensive
initial bacterial killing by rationally optimized antibiotic combination therapies, in order to unleash the effects of
granulocytes. The mechanistic insights from Project #1 and the Mechanistic Assay Core #2 will provide an
innovative, rational path for building highly effective combination dosage regimens. These will be prospectively
validated using dynamic in vitro infection models (i.e. the hollow fiber system) in Project #2. The present project
provides a second stage in vivo validation in novel mouse pneumonia models. Taken together, this highly
integrated approach will allow us to translate mechanistic insights from our latest Gram-negative toolbox assays
via the hollow fiber and murine infection models to robust and efficacious dosage regimens against CRAB and
CRKP for future testing in clinical trials. We will employ humanized dosage regimens in mice to mirror the plasma
concentration time profiles in humans. All data from our mechanistic assays and our in vitro and mouse infection
models will be integrated into translational mathematical pharmacokinetic / pharmacodynamic models by the
Mathematical Modeling Core #3. This integrated approach will provide robust and efficacious antibiotic
combination dosage regimens that maximize bacterial killing, suppress resistance, and leverage the effect of the
immune system involving available antibiotics and those in clinical drug development. These mechanistically
optimized regimens will make a tangible difference of combating CRAB and CRKP for years to come.

## Key facts

- **NIH application ID:** 10763472
- **Project number:** 1P01AI179409-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** ARNOLD LOUIE
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $152,378
- **Award type:** 1
- **Project period:** 2024-08-08 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10763472

## Citation

> US National Institutes of Health, RePORTER application 10763472, Building and prospective validation of promising mono- and combination regimens that optimize killing of CRAB and CR-Klebsiella pneumoniae and for resistance suppression in murine pneumonia models (1P01AI179409-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10763472. Licensed CC0.

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