Advances in genomic technologies have led many researchers, including those in pharma, to focus on generating data instead of trying to understand complex disease mechanisms, according to a panel discussion held at the Beyond Genome conference this summer.
“I think one of the problems that we have as scientists is that, for better or worse, we tend to be seduced by technology,” said panelist Michael Liebman, executive director of the Windber Research Institute in Pennsylvania. This causes researchers to “lose the perspective of what the real scientific question is we’re trying to address,” he said. “As a result we believe that a collection of a lot of data is going to lead to an answer, and what it leads to [instead] is a collection of a lot more data and not an answer to the question because we forgot to ask the question to start with.”
Panelist Francois Iris, president and chief scientific officer of Bio-Modeling Systems, a French systems biology company, agreed and cautioned that researchers must not confuse technological innovations with possible answers. “We should be a little more humble,” he said. “We thought [new technologies] would save us from thinking. Tough luck.”
The panelists’ comments come at a perennial crossroads for genomic technologies. Several Beyond Genome attendees lamented that these tools have not had an effect on drug discovery or development. But a study on pharma’s productivity released earlier this year by the Tufts Center for the Study of Drug Development found that genomic technologies helped drug makers increase by 52 percent the number of candidates that make it into the clinic.
Liebman, who stressed that his comments are not an indictment of new technologies or of technological advancement, said the problem is especially evident in systems biology, a particularly complex corner of genomic research.
Some scientists in this field “tend to try to minimize what we look at in systems to try to make it simpler,” he said. “We have a tendency to try to make the system small enough so we can manage it, but the problem is that it interacts with things outside of [it]. When we don’t take that into consideration, it makes that model fail.”
Liebman said academia could play a role in reversing this trend, though it would likely be an uphill battle in part because the NIH is entrenched in the existing data-generation model.
“The NIH system tends to favor a lot of technology development” instead of enabling research that uses existing tools to shed light on disease pathways and mechanisms, he said. “It’s a very deep-rooted problem in society.”