Catalyzing drug development for the team sport of translational science

Today there are many efforts under way to create drug development tools – from therapeutic area data standards to preclinical safety biomarkers to patient-reported outcomes instruments – needed by the field involving the pre-competitive sharing of data and expertise and leading to the development of standards that are then qualified by the Food and Drug Administration (FDA) and other regulatory bodies.

At this year’s Partnering for Cures meeting, a panel of experts discussed the role of rules, tools, and data pools in the team sport that is translational science. The panel agreed that the efficiency of the drug development process needs to be improved, and the key to doing this will be to reform the system so that the rules are the same for all players involved.

Carolyn Compton discussed the role of the Critical Path Institute (C-Path) in this reformative process. Compton explained that one of the primary goals of C-Path is to form consortia around standards creation to streamline the rules so that they are applicable to all sponsors submitting new drug applications to the FDA. The development of these standards, she noted, will increase the workflow efficiency of both the sponsors and the FDA.

Eric Perakslis of FDA reiterated the need for this type of streamlining. Perakslis explained that currently the system by which submitted applications are checked for completion can take months. In addition, the task of reviewing data that have been collected and measured in numerous ways significantly hinders the workflow. He argued that the creation of thoughtful and meaningful data standards would go a long way in streamlining the review process.

George Vradenburg of USAgainstAlzheimer's pointed out that the value to patients, taxpayers, and industry of modestly compressing drug pipelines will result in billions of dollars in savings; however, he said that we spend too much time discussing the “trivial underbrush” and that we really need to set priorities that will create real value and “aim at those like a laser light.” Vradenburg also highlighted that the path to alleviate some of the process congestion will require a “focus on some big implementation steps with clear action goals that can be taken on jointly by the team,” which includes government agencies, industry, research communities, and patient advocacy organizations.

Some of the major implementation changes that Vradenburg referred to are evident in precompetitive research initiatives among leading biopharmaceutical companies. Marc Bonnefoi of Sanofi US explained that TransCelerate BioPharma and Project Data Sphere, initiated by the CEO Roundtable on Cancer’s Life Sciences Consortium, are examples of successful precompetitive research initiatives among pharmaceutical companies where data are shared with the goal of using data more efficiently to improve the quality of clinical studies and accelerate drug development. Bonnefoi stressed, however, that the success of these precompetitive initiatives is highly dependent on contribution from each organization’s leadership team to support the aims of the initiatives.

Dana Ball of T1D Exchange redirected the discussion toward asking the right questions of the data that we have: “Data for the sake of having data is not helpful. We have to think … down the line to ask what problems we are trying to solve with this information; all of this will [determine] the tools that we will need to build solutions to these problems and the rules [of using] the data pools.”

Panelists agreed that the primary limitation now is not the data; but rather the handling and interpreting of the data. In order to address this problem, there needs to be a major investment in infrastructure. As Compton said, “[This type of investment] is not sexy, but it is absolutely necessary.” We need this infrastructure to make the data powerful enough to turn it into medical solutions for patients.