1. Data sharing
You've performed a study and produced masses of data. What do you do with it now? Share it…
Researchers at Benaroya Research Institute at Virginia Mason (BRI) and the Baylor Institute for Immunology Research (BIIR) analyzed the molecular and cellular responses following vaccination. The researchers found that they generated an enormous amount of data, but much of it was going to see little investigation. In order to extend the value of data generated in this study, the authors developed web applications to allow exploration of the data by the broader scientific community. The article therefore links directly to interactive figures (http://www.interactivefigures.com/dm3/vaccine-paper/vaccine-landing.gsp) so that the readers can interact with and customize the article's figures by adjusting variables and parameters, and thus investigate additional hypotheses with the full dataset.
“The ease with which findings can be shared puts the enormous amount of data collected by these types of systems biology studies just a few clicks away from thousands of immunology experts,” said Gerald Nepom, MD, PhD, Director, BRI. “This publication and web portal liberate the data, which can be reanalyzed in new ways by scientists anywhere in the world to help accelerate discoveries.”
How do you develop vaccines for priority targets without knowing what those priorities are? Use data as a decision-support tool for prioritizing new vaccines…
How do you prioritize vaccines targets? Do you use a single criterion like infant mortality equivalents (which were used in the 1985/1986 study New Vaccine Development) or $/QALY? It would make sense to include a broad range of criteria to influence decision making in vaccine development, and that is the aim of the Institute of Medicine's new software called Strategic Multi-Attribute Ranking Tool for Vaccines (or SMART Vaccines). Dr Guru Madhavan, Senior Program Officer, Institute of Medicine, National Academy of Sciences, spoke about SMART Vaccines at the World Vaccine Congress USA 2013. Users are offered a choice of up to 29 attributes drawn from broad categories including health burden considerations, economic considerations, demographic considerations, public concerns, scientific and business considerations, programmatic considerations, and policy considerations. The data helps support decision-making (although does not make decisions) and thus drive the development of priority vaccines.
3. Predictive vaccinology
Why expose someone to the risk and cost of a vaccine, if there's no hope of a response? Bring on the era of personalized vaccinology 2.0…
In the 21st century, does it make sense for us to give the same drug and dose to everyone? Not really, so why do we do it with vaccines? This is what researchers like Dr Gregory Poland of the Mayo Clinic have been asking. In a paper published in PLoS Pathogens, researchers predict that the future of vaccine development will use vaccinomics and predictive vaccinology to abandon the "one size and dose fits all" vaccine approach. By understanding host genetics and immune "signature profiles" as drivers of the immune response, we can predict whether to give a vaccine based on likelihood of response. Using data to understand how immune responses are generated across age, gender, race and medical condition may mean we can deliver vaccines in a directed, rather than empiric, approach. If we can predict aberrant response – that is, adverse effects or no response – then we might not expose someone to the risk and cost of a vaccine. Read more about what Dr Poland had to say when he spoke at the World Vaccine Congress USA 2013.
What do you think? What other exciting uses of data in vaccine development are there? You can join our discussion on LinkedIn or leave a comment below, I'd love to hear what you think. If you want to know more about strategy and innovation in vaccines, you might be interested in attending the World Vaccine Congress Asia 2013, 17-20 June 2013, Singapore.
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