# Mathematical Modeling Core

> **NIH NIH P01** · UNIVERSITY OF FLORIDA · 2020 · $386,969

## Abstract

Project Summary 
Patients suffering from Mycobacterium tuberculosis (MTB) infections frequently require lengthy 
(18-24 months) treatments with multiple drugs. Adverse drug events in addition to high death 
rates and therapeutic failures represent a substantial challenge for these patients. A major 
hypothesis of this project is that drug regimen sequencing will markedly shorten the length of TB 
treatments and prevent the emergence of resistance. To test this hypothesis, we will use three 
different preclinical experimental systems as well as metabolic state data of MTB: i) to identify 
switch regimens that target MTB populations that have been metabolically altered and made 
less responsive to drug treatment over time due to the primary regimen and ii) to select follow- 
on regimens that have resistance mechanisms that are independent of those employed by the 
primary regimen. The mathematical modeling core will play an essential role for the successful 
achievement of these goals as it will allow for the integration of data from all three projects and 
the Assay Core through the use of cutting edge mathematical modeling and simulation 
approaches into an overarching mathematical framework. Once established and qualified, this 
mathematical framework will allow us to compare and contrast different drug regimens in terms 
of bacterial cell kill and suppression of resistance and, hence, serve as the engine that drives 
the whole proposal. Data used for this innovative analysis will originate from in vitro (Project #1) 
and in vivo animal systems (Project #2: Mouse; Project #3: cynomolgus macaque), which will be 
used to identify the most promising drug and dosing regimen that will (hopefully) effectively treat 
MTB infections in humans in a more expeditious and rational manner as follows: 1) 
concentrations representative of the surrogate target site, i.e. ELF concentrations, will be 
characterized for all three projects using a population pharmacokinetic (pop-PK) analysis 
approach and 2) linked to their corresponding antimicrobial effect considering different 
susceptibilities to drugs for different bacterial subpopulations. Finally, simple and Monte Carlo 
simulations will be performed to determine the effects of between-subject variability on cell kill 
and resistance suppression. The non-parametric adaptive grid (NPAG) algorithm within 
Pmetrics will provide the computational engine behind this proposal. As part of the 
mathematical core activities, NPAG will be enhanced with new parallel computational 
capabilities, as well as the ability to model data that are categorical, discrete, or time-to-event. 
These updates will increase the speed of the analysis and extend it to properly handle bacterial 
colony counts and animal survival data generated in Projects 1-3.

## Key facts

- **NIH application ID:** 9993193
- **Project number:** 5P01AI123036-05
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Stephan Schmidt
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $386,969
- **Award type:** 5
- **Project period:** 2016-08-20 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9993193

## Citation

> US National Institutes of Health, RePORTER application 9993193, Mathematical Modeling Core (5P01AI123036-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9993193. Licensed CC0.

---

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