# AstraDx: Sub-Hour AST Via Computational Image Processing

> **NIH NIH R43** · ASTRADX, INC. · 2022 · $299,633

## Abstract

PROJECT SUMMARY/ABSTRACT
Antimicrobial susceptibility testing (AST) for bacterial bloodstream infections (BSIs) is dangerously slow.
Starting from a positive blood culture, AST currently takes a median of 15 hours, and often takes days. Until
results are available, there is no choice but to treat patients with empiric antibiotics and hope the pathogen is
susceptible. This is unacceptable. BSIs are serious, rapidly fatal infections that kill a quarter-million Americans
each year, including a third of all hospital deaths. Empiric antibiotics destroy patients’ normal flora,
predisposing them to additional, untreatable infections. Every hour a patient is on the wrong antibiotic
increases risk of death by 7.6%. This key problem has made rapid, affordable AST a global priority.
To address this problem, AstraDx has developed a low-cost new AST device that we hypothesize can perform
AST in under an hour. The purpose of this proposal is to test this hypothesis. Preliminary studies involving
multiple experiments on 19 strains vs. 8 standard antibiotics have demonstrated growth detection in 31±18
minutes (95% range, 20-63 minutes). Strains tested included methicillin-resistant S. aureus (MRSA),
vancomycin-resistant Enterococci (VRE), carbapenem-resistant A. baumannii (CRAB), carbapenem-resistant
Enterobacterales (CRE), multidrug-resistant P. aeruginosa—all high priority per the CDC—as well as
corresponding susceptible strains. The innovation that makes these results possible is a new computational
image-processing pipeline, which enables extremely sensitive and robust detection of an antibiotic’s effect on
growth, often in less than the strain’s doubling time. A second innovation is the use of inexpensive standard
components, leading to an affordable device and consumables. These innovations support our long-term goal
of making rapid AST universally available. We will test our hypothesis through three tightly focused specific
aims. Aim 1 will generate training data on diverse bug-drug combinations, with bacteria from simulated-
positive blood cultures grown in the presence of doubling dilutions of antibiotic on 384-well plates, similar to the
current phenotypic gold standard, with multiple antibiotics on each plate. Aim 2 will use the images from Aim 1
to create a unified picture of each strain’s behavior in the presence of each antibiotic, which we will use to
refine how we determine minimum inhibitory concentrations (MICs) in order to maximize categorical agreement
with the CLSI gold standard. Finally, Aim 3 will validate the method from Aim 2 by testing additional bacterial
strains and measuring categorical agreement with the gold standard, per FDA standard guidelines.
The expected outcome is a device proven to achieve sub-hour AST with FDA-level performance on a wide
variety of pathogens, establishing technical merit, feasibility, and commercial potential for an SBIR Phase II.
Our advanced technology, a clearly identified product, will help meet the cri...

## Key facts

- **NIH application ID:** 10547198
- **Project number:** 1R43AI172561-01
- **Recipient organization:** ASTRADX, INC.
- **Principal Investigator:** Jonathan Florez
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $299,633
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10547198

## Citation

> US National Institutes of Health, RePORTER application 10547198, AstraDx: Sub-Hour AST Via Computational Image Processing (1R43AI172561-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10547198. Licensed CC0.

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