Resistell offers a rapid antibiotic resistance testing solution utilizing molecular techniques to identify bacterial resistance profiles. This enables healthcare providers to make informed treatment decisions, minimizing the likelihood of ineffective antibiotic prescriptions.
Funding
$24.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current antibiotic susceptibility testing (AST) methods rely on bacterial growth, which can take days to yield results, delaying appropriate treatment and contributing to the misuse of broad-spectrum antibiotics. This delay increases the risk of complications, extends hospital stays, and accelerates the development of antimicrobial resistance (AMR).
Solution
Resistell offers an ultra-rapid antibiotic susceptibility testing (AST) platform that delivers accurate results in as little as 2 hours, bypassing the need for bacterial growth. The platform utilizes proprietary nanomotion sensors to detect vibrations caused by living bacterial cells, providing phenotypic AST results significantly faster than traditional culture-based methods. Advanced machine learning algorithms analyze the nanomotion data to determine antibiotic susceptibility with high accuracy. This rapid turnaround enables healthcare providers to make informed treatment decisions quickly, promoting antibiotic stewardship, improving patient outcomes, and combating the global threat of AMR.
Target Audience
The primary target audience includes clinical microbiology laboratories, hospitals, and healthcare providers seeking rapid and accurate antibiotic susceptibility testing to improve patient care and combat antimicrobial resistance.
Features
- Rapid AST results in approximately 2 hours, enabling timely treatment decisions
- Nanomotion technology detects bacterial vibrations to determine antibiotic susceptibility without relying on bacterial growth
- Machine learning algorithms trained on a large dataset of nanomotion recordings for accurate susceptibility prediction
- Demonstrated accuracy rates between 90.5% and 100% during training phases and 89.5% to 98.9% in independent tests
- Validated in an international multisite clinical study with results in approximately 4 hours and 97.6% accuracy
- Applicable to both fast-growing and slow-growing bacteria, including Mycobacterium tuberculosis
- Potential to reduce the use of broad-spectrum antibiotics and promote targeted treatment strategies