Pathogen Finder

Courtesy Photo | The top row shows the vaccine platforms tested in RAPTER: Vesicular stomatitis virus delta G (VSVdeltaG), Nucleic acids that include both DNA and mRNA platforms, Adenovirus, nanolipoproteins (NLP), Outer membrane vesicles (OMV), Protein conjugates, and Nanosponge. The bottom row shows the pilot pathogens: SARS-CoV-2 and Ebola viruses, and Burkholderia pseudomallei bacteria. The center section shows the immune responses each vaccine platform can generate, and the immune response required to protect against each pathogen. Machine learning combines this information to identify which platform is most compatible with a new pathogen. (Los Alamos National Laboratory image)  

FT. BELVOIR, VA, UNITED STATES

05.10.2023

Courtesy Story

Defense Threat Reduction Agency's Chemical and Biological Technologies Department

The COVID-19 pandemic highlighted the need to shrink the timeline of effective vaccine design from bench research to Food and Drug Administration approval. Vaccine design, testing, and manufacturing are not only time consuming but also expensive. One approach to developing a predictive capability for matching a specific bacteria, virus, or toxin with the most appropriate vaccine platform is the Rapid Assessment of Platform Technologies to Expedite Response (RAPTER) program.

The Defense Threat Reduction Agency’s (DTRA) Chemical and Biological Technologies Department in its role as the Joint Science and Technologies Office (JSTO) for Chemical and Biological Defense is investing with researchers across multiple fields, such as computational biology, virology, bacteriology, and vaccinology, to develop this machine learning (ML)-based capability to rapidly choose a suitable vaccine platform for any viral and bacterial pathogen. This would allow the Department of Defense to rapidly counter current, new, and emerging biological threats.

Currently, there are several approaches vaccinologists use to develop vaccines that generate appropriate immune responses, including:

• Date Taken: 05.10.2023
• Date Posted: 05.10.2023 16:44
• Story ID: 444483
• Location: FT. BELVOIR, VA, US