A new way to study this
important issue is to use the tools of statistical physics. At the
APS meeting, Michael Deem of Rice University (email@example.com)
described a new way of predicting the flu vaccine's efficacy (a
higher efficacy means that fewer vaccinated individuals get the flu
relative to unvaccinated individuals). To predict efficacy,
researchers examine each strain's hemagglutinin (H) protein, the
major protein on the surface of influenza A virus that is recognized
by the immune system.
In one standard approach, researchers study
all the mutations in the entire H protein from one season to the
next. In another approach, researchers study the ability of
antibodies produced in ferrets to recognize either the vaccine
strain or the mutated flu strain, which had been thought to be a
good method for predicting flu vaccine efficacy in humans.
these approaches are only modestly reliable indications of the
vaccine's efficacy. Deem and his Rice University colleagues point
out that each H protein has 5 "epitopes," antibody-triggering
regions mutating at different rates. The Rice team refers to the
one that mutates the most as the "dominant" epitope. Drawing upon
theoretical tools originally developed for nuclear and
condensed-matter physics, the researchers focus on the fraction of
amino acids that change in the dominant epitope from one flu season
to the next.
Analyzing 35 years of epidemiological efficacy data,
the researchers believe that their focus on epitope mutations
correlates better with vaccine efficacy than do the traditional
approaches. Deem and his colleagues Vishal Gupta and Robert Earl
believe that this new measure may prove useful in designing the
annual flu vaccine and in interpreting vaccine efficacy studies.