What do science, culture, and policy have in common? In order to improve the quality and affordability of health care, all three have to change. This message is central to Sage Bionetworks‘ mission and the theme from this year’s Sage Commons Congress held April 15th and 16th in San Francisco.
What’s the problem?
Biology is complex. This complexity makes it difficult to understand why some people are healthy and why others get sick. In some cases we have a clear understanding of the biochemical origins of health conditions and their treatments. Unfortunately, most drugs are effective for only a fraction of the people they treat, and in the cases where drugs are effective, their effectiveness is diminished by side effects. The most striking problems being adverse events, which are the sixth leading cause of death in the U.S.
One way to cost effectively improve health care is to increase the efficacy of treatments in greater numbers of individuals. Also referred to as personalized medicine, the idea is that future treatments are accompanied by diagnostic tests that indicate the treatment’s effectiveness. Accomplishing this goal requires that we understand the ways in which drugs affect their specific and non-specific targets with much higher precision. We also need to understand each target’s role in its biological pathway. However, as we attempt to break systems down into pathways and their component parts, a problem emerges. The components participate in multiple pathways and pathways interact with other pathways to form higher-ordered networks, and these interactions vary within individuals.
When visualized, biological networks look a lot like LinkedIn, Facebook, or Twitter networks. In these social networks, individuals participate in many groups and have connections to one another. Unlike social networks, which are easy to dissect, our biological networks comprise millions of interactions between proteins, DNA, RNA, chemicals, and microorganisms. Studying these networks requires advanced data collection technologies, computer programs, software systems, and social interactions. Therein lies the rub.
More data isn’t enough
Turning the vision of personalized therapies into reality requires a large numbers of scientists who understand the power of global analyses and can work together in research communities. Hence, one part of the Sage mission is to get greater numbers of scientists to adopt new approaches. Another part is getting them to share their data in useful ways.
Accomplishing this goal requires changing the research culture from one that emphasizes individual contributions to one that promotes group participation. Our publish or perish paradigm, combined with publication business models, discourages open-access and data sharing. It also reduces innovation. According to data presented by Aled Edwards, when faced with the opportunity to look at something completely new, we focus on well-known research problems. Why? Because funding is conservative and doing the same thing as your peers has less risk.
Bottom line, we need to take more risk in our research and take more risk sharing data pre and post publication. Taking more risk means we need to trust each other more.
Simply increasing data availability, however, is not enough. We also need to change
