# Translational development of new agents alone and in combination to combat Gram-negative pathogens important in Ventilator- Associated Bacterial Pneumonia: Leveraging the Gram-negative toolbox that is

> **NIH NIH P01** · UNIVERSITY OF FLORIDA · 2024 · $2,308,174

## Abstract

Project Summary/Abstract:
Resistance to our major antibiotics has been identified by the CDC as a major threat to the health and safety of
the American public. Two of the highest threat pathogens are carbapenem-resistant Acinetobacter baumannii
(CRAB) and Klebsiella pneumoniae (CRKP). Over the last decade, we have seen the emergence of novel
resistance mechanisms, limiting the utility of our best antimicrobials. This proposal answers a call to arms from
NIAID, who set forth the tool development program (RFA-AI-16-081 in 2017) to generate mechanistic insights
that can be used to create antibiotic combinations that are rationally optimized to kill CRAB and CRKP. Further,
there has been increasing awareness of organism state(s) such as tolerance/Non-Replicative Persister (NRP)
phenotype that allows evading the lethal action of antimicrobial therapy. It is important to gain insights into this
to design approaches to suppress organism entry into NRP state and, if already present, design regimens that
can eradicate NRP. We will create novel mechanistic insights and use them to rationally optimize combination
dosing strategies to synergistically kill CRAB and CRKP, and to suppress resistance. The impact of resistance
mechanisms (e.g. efflux, β-lactamases, and porin channels) and of non-essential penicillin-binding protein (PBP)
receptors on bacterial killing and resistance emergence will be studied. To optimally suppress resistance, we will
approach this problem in 4 dimensions, and consider the changes in PBP expression over time (i.e. growth
phase) and the cellular locations of these resistance mechanisms. This P01 contains 3 Projects and 3 Cores.
Project #1 will use our tools from RFA-AI-16-081 to gain insights into how different PBP binding profiles affect
killing and resistance suppression. This project will leverage the Mechanistic Assay Core and the Mathematical
Modeling Core to design optimal, clinically feasible dosage regimens. Project #2 will examine these regimens
against CRAB and CRKP isolates in the Hollow Fiber Infection Model (HFIM). In Project #3, we will study the
best regimens (and lesser regimens, as controls) from the HFIM in two murine models of pneumonia (granulocyte
replete and granulocytopenic). This will provide insights into how granulocytes can best enhance antimicrobial
therapy. The Administrative Core will serve as the overall data repository and clearing house, and facilitate
communications. The Mechanistic Assay Core will leverage transcriptomic, proteomic, flow cytometry, and resi-
stance mechanism assays, closely integrated with PBP binding studies and isogenic strains from Project #1.
This core will generate critical insights into the mechanisms of antibiotic action, resistance and synergy. Finally,
the Mathematical Modeling Core will develop high dimensional mathematical models that will integrate all experi-
mental data from the Projects and Cores to provide robust, efficacious and clinically relevant dosage regimens...

## Key facts

- **NIH application ID:** 10763466
- **Project number:** 1P01AI179409-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jurgen Bernd Bulitta
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,308,174
- **Award type:** 1
- **Project period:** 2024-08-08 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10763466

## Citation

> US National Institutes of Health, RePORTER application 10763466, Translational development of new agents alone and in combination to combat Gram-negative pathogens important in Ventilator- Associated Bacterial Pneumonia: Leveraging the Gram-negative toolbox that is (1P01AI179409-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10763466. Licensed CC0.

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