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VBCR - June 2015, Volume 4, No 3 - CCR Conference Highlights
Chase Doyle

Rheumatologists know that disease-free remission is the surest sign of long-term success—less disability, decreased morbidity and mortality, and a greater likelihood of employment. However, fewer than 1 in 10 patients actually achieve this state. The translational research presented at the 2015 annual Congress of Clinical Rheumatology calls for a revolution in the discovery, development, and utilization of pharmaceutical treatments.

“In the pharmaceutical industry, there are unacceptably high late-failure rates,” said Iain B. McInnes, MD, PhD, professor of medicine and director of the Institute of Infection, Immunity and Inflammation at the University of Glasgow, Scotland. “Drug discovery failures are not cheap, and a late failure costs even more money. The trouble is that we’re not really getting any better. Far too few [drugs]…are translating into actual clinical benefit.”

The revolution McInnes spoke of is the rejection of an antiquated system, whereby single molecules are selected and put through a ponderous, and frequently unsuccessful, discovery paradigm. As he sees it, part of the problem lies in a publication bias—“editors love the individual pathway”—and a reductionist model in science, which has instinctively driven investigators.

“We can no longer afford this approach,” said McInnes. “If we continue to look for drugs this way, then we’ll never get truly transformational alterations in the way we treat human beings with rheumatic diseases.”

His answer: a systematic approach to drug discovery—capturing and integrating global sets of data from as many hierarchical levels of information as possible. McInnes reminded the audience that this is already happening in other fields of medicine.

“The cancer doctors are miles ahead of us,” he said. “They have mathematicians working on their large-scale data sets.”

One of the conundrums of so-called big data, however, is that not all information is informative, reproducible, or insightful. Nor is there a single formula for working this out. While big data should lead to more knowledge and insight, if you wish to obtain life-saving answers, asking the right questions is critical. In McInnes’ opinion, these questions will come from hypothesis-generating models and the scientific property of “emergence.”

“The point is, you need theories,” he said. “Looking molecule by molecule is not useful; you need to understand the behavior of groups of molecules…A systems-wide approach may reveal what an individual molecule cannot—emergent properties when considering the whole.”

Learning how to navigate multilayers of data in a meaningful way and building in the clinical question is very much a work in progress, but McInnes and his team remain inspired by the possibilities. They are presently collecting data from around the world, adding biology to the bioinformatics to glean insight. Or as he put it: “using existing knowledge systematically to change the future.”

“The problem with a lot of science at the moment,” said McInnes, “is that we spend a lot of time and money targeting, with brilliant molecules, a therapeutic target that was never there.”

Fixing this problem will require the collaboration of research scientists and clinicians alike—treating patients, contributing to trials, and generating data sets—along with the resolve to approach decisions systematically, before countless resources are squandered.

“If you put all of these things together in a proper, formally structured systematic approach, then we will have a virtual cycle of discovery,” McInnes concluded. “We will still fail, of course, but we will fail less often, and if we fail, we will have understood why we failed so that we do it better next time. And that is the secret for future success.”

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Last modified: June 29, 2015
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