Clinical Researcher—April 2023 (Volume 37, Issue 2)

ON THE JOB

Richard Young

As clinical science advances, data professionals will lead in trial optimization by solving a Rubik’s Cube of people-, process-, and technology-related challenges.

After more than two years of scrambling to adjust to rapid-fire changes in clinical trial operations, life sciences are moving on to address new challenges. More companies are developing long-term plans for their clinical programs, while sponsors strive to become more efficient and make technology less burdensome for patients and research sites. Those on the leading edge are working to unify data and connect more closely with partners. The goal is to speed up access to information and improve patient value.

At the same time, the science driving clinical trials is changing rapidly. Trials that were once one-dimensional are now multidimensional, with more varied data sources being considered daily. Adaptive designs are now being used in basket and umbrella studies; for example, adaptive trials for sickle cell disease and beta-thalassemia treatments involving CRISPR. In some cases, trials are amended after every patient visit, driving a more performant study and protecting patient safety.

These approaches are still considered novel, but will likely become more familiar over time. The same applies to trials involving more automation and new strategies leveraging artificial intelligence (AI). Will our existing data infrastructure be able to handle them?

Linear Approaches Won’t Work for Multivariate Problems

As an industry, we have to stop thinking linearly about clinical trials. Instead, we should examine them from the people, processes, and technology perspective. I think running a trial today is akin to solving a puzzle: a Rubik’s Cube. For those unfamiliar with a Rubik’s Cube, the once phenomenally popular game introduced in the 1980s requires players to rotate six squares that come in six different mixed-up colors as part of an overall cube up and down and back and forth until each side of the cube is a single color—all without disassembling or damaging the cube in any way.

Imagine that one side of the cube represents patients, another sites, another regulators, another data management, another clinical research, and another statistics (and that is leaving out other potential sides for pharmacovigilance and medical writing).

The first thing you will appreciate about the cube is that you can only see three sides at any time, no matter how you orient yourself or the cube. Consequently, when you make a change, you have no idea what impact that change will have on the other three sides outside your vantage point.

However, like any good clinical trial, there is an additional complexity to consider because you are not the only person trying to solve the Rubik’s Cube. Imagine other people are trying to solve it at the same time. Do your moves work in concert with or against those of the other players? Are they undoing your good work? Are they making things easier or better for you?

If we accept this analogy of a Rubik’s Cube, our next thought is to review whether we are solving this effectively, or if at all. As we explore the growing complexity of clinical trials, another idea comes to mind—what if, rather than solving the Rubik’s Cube, we are unintentionally making it even more complicated?

Rather than nine pieces per side, what if there are 16 or 25; what if the puzzle is no longer a cube but a dodecahedron? The truth is that, instead of solving this puzzle, we’re only making it larger and more complicated. This is where clinical data professionals come in and why the industry needs to move from merely managing clinical data to thinking more strategically and adopting science-based approaches.

For years, the clinical data manager’s role has been invisible. Practitioners reconciled and cleaned data quietly in the back office, separated from colleagues in clinical operations and other departments. It is time for a change.

New and Changing Roles in Clinical Data

With data and scientific complexity changing trial design and execution, clinical data specialists are moving from the back office and taking centerstage. Clinical data managers are now helping to solve some of the most challenging problems, such as optimizing protocols, improving trial flexibility for patients, and collaborating across functions. As Mayank Anand, outgoing Chair of the Society for Clinical Data Management (SCDM), put it, “Who else will be able to clean the terabytes of clinical data that the industry is currently generating?”

I recently discussed the evolution of clinical data management with Mayank and Luis Torres, head of programming and the full-service provider business program at Labcorp Drug Development, in the State of Digital Trials podcasts.

As Mayank recalled, clinical data management has often been an accidental career path for those who had studied life sciences, biotechnology, or genomics-related fields in graduate school. He said that data professionals took a back seat for years in strategic planning and business meetings.

Similarly, Luis shared recollections of his first job during the paper days of clinical data management. “Ten or 12 of us worked on manual clinical data entry and similar tasks in a room that we called Guam because it was so isolated from the other departments and so hot from the computers we used,” he said.

As their professional careers took off, both saw how vital data management would become to improving clinical trial efficiency. This became clear once companies adopted electronic data capture systems. Now, 20 years later, disruptive changes necessitated by the pandemic have made it an exciting time to work in the field.

Luis sees data managers becoming the “glue” that holds trials and their many varied stakeholders together. Mayank agreed, adding that the ability to bring different groups together, understand other perspectives, and develop a cross-functional approach to optimizing trials is already improving results at companies that include GSK.

Taking on the Cube

Cross-functional problem solving and adopting a “we” rather than an “I” approach to technology selection is crucial to trial success today, said Mayank. Teams can be as large and varied as needed without bogging down decision-making, as long as a decisionmaker has been designated to move processes forward.

In the future, having standardized practices across the industry will also improve efficiency, commented Luis, who appreciates the work that SCDM has been doing in this area. Currently, contract research organizations may use their own standard operating procedures in some situations, those of their customers in others, or some hybrid of the two. He noted that industry-wide standards and guides promise to clarify best practices and help the industry advance.

SCDM has also become a global voice for clinical data specialists, said Mayank. The society provides a forum for discussion and debate as the role of the data specialist and clinical data continue to change.

Conclusion

The industry is clearly moving from data management to data science as strategies evolve and data professionals’ roles become more prominent. Closer collaboration between clinical data and clinical operations and more cross-functional efforts involving other stakeholders should accelerate study advances in the future.