CARB-X awards $1.1 million to phage-based product for E coli bloodstream infections

News brief

Bacteriophage illustration — iLexx / iStock

CARB-X (Combating Antibiotic-Resistant Bacteria Biopharmaceutical Accelerator) announced today that it is awarding $1.1 million to biotechnology company Phiogen to advance its novel bacteriophage-based treatment and preventive for extraintestinal pathogenic Escherichia coli (ExPEC) bloodstream infections.

The money will enable Houston-based Phiogen to evaluate PHI-BI-01, a product that uses bacteriophages—bacteria-killing viruses—to eliminate ExPEC in the bloodstream and activate an immune response that helps prevent recurrence. ExPEC strains, which typically originate in the gastrointestinal system or urinary tract, have become a more frequent cause of invasive bloodstream infections and have grown increasingly resistant to antibiotics.

PHI-BI-01 is among the phage-based drug candidates delivered through Phiogen's discovery platform, which uses a bacteriophage mass-capturing device, high-throughput screening, and directed evolution to develop phages with antibacterial and immunogenic properties.

"Our team's discovery redefines what phages can do, opening the door to a new class of live biologics capable of addressing both acute infection and recurrent disease," Phiogen CEO Amanda Burkardt, MBA, said in a CARB-X press release.

Novel approach for preventing E coli infections

Phiogen will work with CARB-X to explore PHB-BI-01's immunogenic properties, generate pre-clinical data, and advance the project to first-in-human clinical trials.

"PHIOGEN offers a potential novel approach to the prevention of invasive disease caused by E. coli," said CARB-X research and development director Erin Duffy, PhD. "We are excited to evaluate the immune harnessing potential of this project."

The award is the latest from CARB-X's 2024 funding round. Since its founding in 2016, CARB-X has funded 116 early-stage R&D products designed to prevent, treat, and diagnose antibiotic-resistant infections.

AI tool can help identify patients who may have H5N1 avian flu, researchers say

News brief

Mary Van Beusekom, MS

Doctor examining woman for H5N1 infection — *Zinkevych / iStock*

An artificial intelligence (AI) tool can quickly scan electronic medical records to identify high-risk patients who may be infected with H5N1 avian flu, University of Maryland (UM) researchers write in Clinical Infectious Diseases.

The team used a generative AI large-language model to analyze 13,494 adult emergency department visits at the UM Medical System in 2024. The patients had sought care for an acute respiratory illness featuring conjunctivitis ("pink eye") or symptoms such as cough, fever, and congestion, which are common in early H5N1 infections.

More-advanced systems needed

Requiring only 26 minutes of human input and 3 cents per patient note, the AI tool identified 76 records that mentioned a risk factor for H5N1, such as working as a butcher or at a farm with livestock.

It's vital for healthcare systems to monitor for potential human exposure and to act quickly on that information.

Katherine Goodman, PhD, JD

After the researchers reviewed the flagged records, 14 patients were confirmed to have had recent exposure to animals that transmit H5N1, such as poultry, wild birds, and livestock. The patients weren't tested for H5N1, so their infections couldn't be confirmed, but the tool zeroed in on the relevant cases among thousands of patients with suspected flu or another routine respiratory illness.

"Because we are not tracking how many symptomatic patients have potential bird flu exposures, and how many of those patients are being tested, infections could be going undetected," lead author Katherine Goodman, PhD, JD, said in a UM news release. "It's vital for healthcare systems to monitor for potential human exposure and to act quickly on that information."

In a commentary in the same journal, Erica Shenoy, MD, PhD, of Massachusetts General Hospital, and colleagues said that the study tool represents a more efficient screening method.

"However, screening for H5-specific exposure requires already knowing what to look for—in other words, these tools detect chickens, not eggs," they wrote. "To find (golden) eggs, that is, identifying risks in the earliest stages of disease emergence or as an epidemic evolves, will entail more advanced systems in which algorithms are trained based on past outbreaks for what to look for."