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Computer simulation captures immune response to flu
Date:5/18/2009

e ability of dendritic cells to present viral antigens to T cells.

Experiments on the effects of anti-viral drugs on a "flu-infected" virtual immune system also accurately predicted that lowering the viral load or spread within two days of symptoms enables rapid control of the infection. These results match known limitations of neuraminidase inhibitors like the widely stockpiled Tamiflu and Relenza, which inhibit viral reproduction in infected cells, but are only effective if given soon after infection. The model also predicted that antiviral therapies may be more effective if taken in combination, but only if administered within two days of infection. Unlike chronic HIV infection, the acute nature of influenza means there is a narrow time window during early infection when interfering with viral replication can reduce viral load.

"Heterosubtypic immunity" refers to the ability of human immune defenses primed to fight one strain of flu to provide broad protection should other strains of flu be encountered later. Whether or not a single vaccine can protect against any of the many H1N1 Influenza viruses would depend on this property. The team's model confirmed that heterosubtypic immunity to influenza depends on a specific quality, the number of killer T cells present in the lungs of a given individual at the start of infection. Thus, near-future vaccines against swine flu for instance may work better if designed to evoke local immune memory cells in the lungs, rather than administered through the bloodstream.

In addition, the model argues that a strong antibody response, along with local T cell expansion, will be important to ensure protection against future pandemics. The findings suggest that some unconventional therapies that rapidly boost immune responses might be effective in a worst case scenario of a pandemic influenza virus. One possibility is the treatment of those front line of the epidemic (e.g. healthcare workers) with an injec
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Contact: Greg Williams
Greg_Williams@urmc.rochester.edu
585-273-1757
University of Rochester Medical Center
Source:Eurekalert

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