I-Corps: Translation Potential of Point of Care Diagnostics for Rapid Antibiotic Susceptibility Tests

This I-Corps project is based on the development of a sensor system that mimics the process of odor detection by the nose. Similar to the biological sensor system used for the sense of smell that can detect and distinguish individual odors amongst many others, this technology utilizes sensor to detect and analyze molecules in complex environments such as biological fluids or recycled water.

The goal is to provide an alternative to instruments that require highly trained technicians and expensive infrastructure for operation. This approach has been used to determine the presence of a bacterial infection along with appropriate antibiotic therapy, impurities in treated water, and aerosolized chemicals. The technology may address unmet needs in medical diagnostics assessing the susceptibility of bacteria to antibiotics.

The solution could rapidly determine appropriate antibiotic therapies for patients and monitor chemical byproducts in a manufacturing process and water treatment applications.

This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a molecular sensor system sensor using surface chemistry and artificial neural networks. The technology is based on a nanophotonic sensing platform that uses a combination of vibrational spectroscopy and machine learning analysis to analyze complex chemical mixtures.

Molecular scale control of surface chemistry has led to reproducible sensor detection at single molecule detection limits allowing for quantification of analytes down to femtomolar concentrations using surface enhanced Raman scattering (SERS). SERS spectral data is then analyzed with machine learning algorithms to “fingerprint” complex spectral response.

The sensitivity, coupled with the ability to rapidly gather large SERS datasets, provides a means to measure changes in multiple chemical signals rather than only detect a single analyte in typical sensor platforms. Chemical profiles of the metabolic response of bacterial cells exposed to antibiotics provide a fingerprint to discriminate between resistance and susceptibility of bacteria after exposure to antibiotics.

Using this platform, metabolic responses to antibiotics in Escherichia coli and Pseudomonas aeruginosa, two common pathogens associated with urinary tract infections, are observed below the conventional minimum inhibitory concentrations of antibiotics in 5 minutes with 99.3% accuracy, discriminating between susceptible versus resistant bacteria. This platform may have potential for providing rapid antibiotic susceptibility tests that may mitigate the rise in antimicrobial resistance in pathogenic bacteria or inform the user of toxicants in the environment.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.