# Personalized Antimicrobial Combinations to Combat Resistance

> **NIH NIH R01** · UNIVERSITY OF HOUSTON · 2024 · $802,525

## Abstract

Abstract
The prevalence of multidrug resistance in Gram-negative bacteria (e.g., Pseudomonas aeruginosa,
Acinetobacter baumannii) is rising at an alarming rate, rendering many (if not all) antibiotics ineffective when
used alone. The rate of new drug development is unlikely to keep pace with the increase in multidrug
resistance. Combination therapy is often used clinically as a last resort. However, considering the numerous
possibilities, combination therapy are selected by clinicians mostly based on anecdotal experience and
intuition. A robust method to guide rational selection of combination therapy would be crucial to delay returning
to the pre-antibiotic era.
Our long-term goal is to optimize clinical use of antibiotics to combat the emergence of resistance. The
objective of this application is to refine a novel precision medicine platform (monitoring device and data
processing algorithm) that will guide the design of combination therapy. If short-term experimental data can be
used to predict the response of patient-specific bacteria to clinically relevant antibiotic exposures, effective
treatment strategies could be formulated rationally by identifying the best possible combination. Clinicians
could be guided in the selection of combination therapy, without comprehensively knowledge of the resistance
mechanism(s) involved. We plan to accomplish the objective of the application as follows: (1) identify useful
antibiotic combinations against multidrug resistant bacteria; and (2) validate the mathematical model
predictions with clinical outcomes.
In this application, the proposed approach will be illustrated by experimental data with P. aeruginosa, A.
baumannii and Klebsiella pneumoniae. However, the proposed model-based system is not confined to a
specific antimicrobial agent - pathogen combination. It could be extrapolated to other antimicrobial agents
(e.g., antibacterials, antifungals and antiretrovirals) with different mechanisms of action, as well as to other
pathogens (e.g., Neisseria gonorrhoeae, Candida auris, and HIV) with different microbiological characteristics.

## Key facts

- **NIH application ID:** 10947250
- **Project number:** 2R01AI140287-06
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** VINCENT H TAM
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $802,525
- **Award type:** 2
- **Project period:** 2018-08-15 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10947250

## Citation

> US National Institutes of Health, RePORTER application 10947250, Personalized Antimicrobial Combinations to Combat Resistance (2R01AI140287-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10947250. Licensed CC0.

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