STTR Phase I: Leveraging Sequencing to Identify and Predict Multidrug Resistance

Award ID: 2421262

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to develop a platform for detecting genetic antibiotic resistance motifs that are predictive of current and future susceptibility to therapeutics. Antibiotic resistance is a global health crisis that is predicted to overtake cancer and heart disease as the leading cause of death by 2050, taking 10 million lives annually. Antibiotic-resistant infections directly cause 1.2 million deaths and play a significant role in an additional 4.95 million globally. In the US, there are over 2.8 million antibiotic-resistant infections annually, which result in 35,000 deaths which cost the US health system >$20B in direct medical costs. Contributing to this crisis, it is estimated that half of antibiotic prescriptions are unnecessary or misused. There are currently 6,129 hospitals in the U.S. that have created a total available market for antibiotic resistance diagnostics of $3.9B. With the adoption of next-generation sequencing (NGS) techniques making the required data inputs more available every day, this project will expand the 10% market share already garnered by NGS technologies for antibiotic susceptibility testing (AST). This Small Business Technology Transfer (STTR) Phase I project will establish the feasibility of leveraging mutational signatures found in the DNA of bacteria and predict current and future drug resistance status. Mutational signatures are highly specific global patterns associated with mutational processes in cells. We have shown they can be indicative of past antibiotic exposure leading to an understanding of current resistance status. Additionally, we have shown specific signatures are indicative of rapid future multidrug resistance acquisition, lending to insights into the likelihood or lability of an infection to mutate and become resistant to treatment unlike any current product on the market. This approach offers two significant advantages: 1) no reliance on specific genes/mutations to identify a genotype/phenotype, enabling detection of emerging, uncharacterized resistance mechanisms, and 2) species agnosticism due to high evolutionary conservation of signatures. The current project will build upon signature analysis of Pseudomonas aeruginosa that lead to a near 100% accuracy in predicting antimicrobial susceptibility and extend the approach to Acinetobacter baumannii, the second most burdensome resistant infection. Whole genome sequences of historical clinical samples, which have undergone extended AST with known exposure and resistance profiles, will be used to identify new signatures in a new bacterial species. These will then be replicated in the lab and finally validated in the clinic by the collection of prospective clinical samples to assess the predictive utility of the newly identified signatures. 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.

NSF Program Director: Henry Ahn