# Core 3 Math Modeling Core

> **NIH NIH P01** · UNIVERSITY OF FLORIDA · 2024 · $204,650

## Abstract

Summary/Abstract Mathematical Modeling Core #3
The overarching Aim of this proposal is to markedly improve therapeutic regimens for patients with high burden
bacterial infections like Ventilator-Associated Bacterial Pneumonia (VABP) due to Carbapenem-resistant
Acinetobacter baumannii (CRAB) and Carbapenem-resistant Klebsiella pneumonia (CRKP). These organisms
have been declared as being among the highest priority bacterial pathogens by the World Health Organization,
the Centers for Disease Control and Prevention and by the National Institute for Allergy and Infectious Diseases.
This proposal comprises three Projects and three Cores, with the Mathematical Modeling Core being Core #3.
Translational mathematical modeling will play a critical role for this P01. Our team has had decades of experience
employing high dimensional mathematical models to generate the greatest insights into experiments where
antimicrobial therapy regimens are being sought to rapidly and completely kill pathogens while suppressing
resistance. Here we will expand our prior work substantially. In Project #1, we will create high dimensional
mathematical models from penicillin-binding protein (PBP) receptor occupancy patterns that predict the rates
and extent of killing. Mechanistic data will come from Project #1 and the Mechanistic Assay Core #2. We will
expand our datasets and mathematical models for β-lactams and β-lactamase inhibitors to studies at high
bacterial densities and will further assess novel non-β-lactam-PBP-binders. Previously, this work has been
performed with lysed bacteria. We now developed a novel approach where we can examine the PBP receptor
binding in intact Gram-negative bacterial cells over time. This has been paired with the ability to look at
penetration and outer membrane permeability independently. All this has already been inserted into our models.
A preliminary analysis simultaneously fit 23 β-lactams and β-lactamase inhibitors, as well as the 2- and 3-drug
combinations, and yielded an R2 = 0.89 in the Pre-Bayesian (population fits) step and an R2 of 0.99 in the
Bayesian (individual fits) step. We will prospectively study regimens optimized by Project 1 in the HFIM (Project
2) and in two different murine models (Project 3), one granulocyte-replete and the other granulocytopenic.
Models will include drug concentrations, total bacterial burden and less-susceptible populations for antibiotic
monotherapy regimens and combination regimens of 2, 3 or 4 drugs. In addition, mechanistic insights will be
provided by Core #2. The models that incorporate all these data will be very high dimensional. To optimize the
insights gained and provided back to all Projects, the analyses will be done in real time. As a major part of this
Mathematical Modeling Core (#3), our team is developing new algorithms to generate substantial speed-up (e.g.
Random Parametric Expectation Maximization [RPEM] and Non-Parametric Simulated Annealing [NPSA]). To
reiterate, these innovati...

## Key facts

- **NIH application ID:** 10763469
- **Project number:** 1P01AI179409-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Michael N. Neely
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $204,650
- **Award type:** 1
- **Project period:** 2024-08-08 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10763469

## Citation

> US National Institutes of Health, RePORTER application 10763469, Core 3 Math Modeling Core (1P01AI179409-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10763469. Licensed CC0.

---

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