# Novel Antibiotic Susceptibility Testing Platform for Detecting Resistant Subpopulations

> **NIH VA I01** · VETERANS HEALTH ADMINISTRATION · 2024 · —

## Abstract

ABSTRACT
Objectives
Antibiotic resistant bacteria are an immense and rapidly growing medical problem affecting Veterans, combat
warriors, and the broader population. Some infections are now considered “untreatable,” threatening to return
us to the pre-antibiotic era. Heteroresistance (HR) is an under-recognized yet widespread form of antibiotic
resistance in which a bacterial strain harbors a minor subpopulation of resistant cells co-existing with a majority
susceptible population. We showed that even when as infrequent as 1 in 1 million, these resistant cells can
rapidly replicate despite antibiotic treatment and thus cause treatment failure. However, we discovered a way
to exploit HR as the basis of effective combination therapy; when two drugs to which an isolate exhibits HR are
combined, each drug kills the cells resistant to the other and together they eradicate the infection. To avoid
monotherapy failures and select effective combination regimens, it is important to identify HR during clinical
antibiotic susceptibility testing (AST). However, current AST cannot detect and distinguish HR. This proposal
will develop a novel AST platform to detect HR, potentially leading to improved patient outcomes.
Research Plan
Preliminary data suggest that optical interferometry can accurately and rapidly (1.5 hours) detect HR, as well
as outperforming current diagnostics in differentiating standard resistance (in which 100% of cells are resistant)
from susceptibility. We will elucidate optimal functional parameters for interferometry-based AST to balance
sensitivity, accuracy, and speed. We will then test the efficacy of the interferometry AST on a broad panel of
clinical bacterial isolates with diverse antibiotic resistance profiles, enhancing the system through an iterative
process. We will extend the indications of the system, determining if it is effective on spiked blood cultures,
abrogating the need for testing of pure colonies and further enhancing sample processing speed. Finally, we
will retrospectively study a collection of bacteria from 2 clinical trials, testing the ability of interferometry to
accurately detect HR as well as the association of HR with clinical outcomes.
Methods
This proposal is based on the innovative use of optical interferometry, a physics and material sciences
technology, to detect antibiotic resistance including HR. Interferometry generates a topographic map of the
surface of a bacterial population grown on agar with different respective antibiotics. “Peaks” of growing,
resistant cells are easily distinguished from “valleys” of non-growing, susceptible cells.
Clinical Relevance
The research in this proposal has the potential to provide clinicians with the most accurate and comprehensive
antibiotic resistance profile of bacterial strains. This could significantly improve the way antibiotics are selected
for therapy, helping to avoid unnecessary failures, and identifying effective combination therapies to treat
infections c...

## Key facts

- **NIH application ID:** 10805814
- **Project number:** 1I01BX005985-01A2
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** DAVID S WEISS
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-04-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10805814

## Citation

> US National Institutes of Health, RePORTER application 10805814, Novel Antibiotic Susceptibility Testing Platform for Detecting Resistant Subpopulations (1I01BX005985-01A2). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10805814. Licensed CC0.

---

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