Philippe Guérin: Sharing data to fight malaria
Over 250 Institutions participate in the effort of sharing data on the efficacy of antimalarial drugs, which involves standardising and re-analysing data. Bringing all this data together creates new evidence that can be translated into policy practice, offering new therapeutic options for particular populations.
This is a podcast from the Nuffield Department of Medicine. Today we ask Professor Philippe Guérin to talk to us about sharing data to fight malaria.
Q: What is the WorldWide Antimalarial Resistance Network and what are its aims?
Philippe Guérin: The WorldWide Antimalarial Resistance Network is a scientifically independent network of scientists who are working together to assemble data on the efficacy of antimalarials.
We started in 2009 and we are engaging the research community to bring together all data which assess the efficacy of drugs, specifically antimalarials. This allows us to gain power in the way we are interpreting all of this data and this is already also creating strong evidence that can be translated into practice.
Q: What kind of work does the network do?
PG: We are working with the research community and as of today we have more than 250 institutions participating in this effort. They are sharing data in the network and we are collating all this information, putting it into common repositories, standardising the data and reanalysing all this information: going back to the clinical trial data and individual patient data and trying to make sense of all of that.
This is gaining power in the evidence that we can draw from this pooled analysis, and also generating all sorts of collaborations between different partners working together. Together, these 250 groups are almost the majority of all the groups working on antimalarials in the world, and we have assembled more than two-thirds of the clinical data on artemisinin combination therapy which has been generated in the last 15 years.
Q: What does your own line of research focus on?
PG: Currently we are trying to answer two different scientific questions. We are working on malaria and malnutrition for instance. We are gathering data on children between six months and five years who have experienced malaria and who are also either chronically or acutely malnourished: we are trying to understand if the drugs that we are normally using for children are working as well. We already know that this is not the case, and so the point would be eventually to draw evidence about what could be an optimised drug or treatment that could be adapted for a child with this kind of co-morbidities.
We are also working with laboratories to try to enhance the way the quality of the data and the way people are standardising their data can be improved. We are working with 60 laboratories in 28 countries, providing external quality assurance and this is making people work together. This network is an central point to facilitate that.
Q: What are the most important lines of research that have emerged over the last 5-10 years?
PG: In our space there is an engagement and a movement into data sharing. There is a lot of push in data sharing from funders and policy makers, journals, etc.
In the space of infectious diseases, I think we have pioneered a data sharing platform in malaria, and gathering all of these groups working together. Usually scientists compete: here they are working collectively for the same goal! We are translating the swish and the movement of big data into reality, addressing all the sensibilities around that and trying to ensure that the investigator gains in that experience, and that we are all gaining by providing evidence that can be translated into practice.
Q: Why does this line of work matter and why should we put money in to it?
PG: Bringing all this data together creates new evidence. There are fantastic groups working in the field of antimalarial efficacy and testing drugs. They are generating great evidence, but none of these groups can answer some particular questions: it is only by assembling all this data together that we are able to do so. Therefore it is complementary to the work done by our colleagues and it is also a nice additional outcome of their work that we are managing to generate by this data sharing effort and data sharing platform.
Q: How does your work fit into translational medicine within the department?
PG: The kind of evidence from the meta-analysis that we are doing generate new guidance and new evidence that can be translated into policy practice. Some of the work that we have done has been taken on board by expert committee, and the World Health Organisation guidelines are using this kind of data to propose new therapeutic options for particular population. This is a great outcome of direct consequences of our work into practice for physicians in the field treating particular populations.
This interview was recorded in September 2015.