Fast Pandemic Detection Tool Ready to Fight Flu
Agilent Technologies and Los Alamos Develop First High-Throughput System to Be Deployed at UCLA’s New Global Bio Lab
Agilent Technologies Inc. announced researchers are developing new tools for rapidly characterizing biological pathogens that could give rise to potentially deadly pandemics such as Influenza A (H1N1). This is part of a joint effort by national laboratory-, university- and private-sector institutions.
The first tool, an automated genotyping system, is a joint effort between Los Alamos National Laboratory, the University of California at Los Angeles (UCLA) School of Public Health and Agilent. This system will be utilized in the Global Bio Lab at UCLA and will use high-throughput technology for automated global-public-health surveillance.
The automated genotyping system, built to specification by Agilent, was delivered to Los Alamos in late May for verification of design and capability testing. The $1.7 million BioCel Automation System was designed in collaboration by Los Alamos and UCLA researchers, and professionals at Agilent’s Automation Solutions division, previously known as Velocity11. The system will be able to automatically determine the genetic sequence of viruses such as influenza hundreds of times faster than any other method available today.
According to the company public-health officials will be able to rapidly and reliably determine the strain of a virus, allowing more time for mitigation or containment strategies to be employed if necessary by using this system and future high-throughput tools in pandemic response mode. Moreover, these BioCel systems will be useful in research mode for monitoring animal populations for the emergence of new and potentially deadly pathogens before the pathogens are able to infect humans. The UCLA Global Bio Lab will become part of the High Throughput Laboratory Network (HTLN). When built out, the network will provide an international and interconnected capacity that enables uniformity in testing methods - reducing the potential for errors or confusion arising from variable testing methodologies currently used.
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