Structuring epidemiology teams to maximize impact of RWE: Q&A with Dr. Nicolle Gatto
Nicolle Gatto, Ph.D., is the Senior Vice President of Scientific Research at Aetion, bringing with her 20 years of experience from Pfizer where she led epidemiology teams focused on regulatory and safety initiatives, and provided strategic leadership on methods-related work. Now at Aetion, Nicolle serves as a senior scientific voice within the organization, advancing research with our team of pharmacoepidemiologists, clients, and partners.
In her two decades in biopharma, Dr. Gatto drove forward observational research projects, with a focus on advancing on standing cohorts—groups of patients in real-world data (RWD) that are similar to an anticipated indicted population which can help fill knowledge gaps left by clinical trial or postmarket data. She speaks more about that work in part one of our Q&A series, including how external comparator groups can support research on COVID-19 treatments and vaccines.
Here, Dr. Gatto lends her expertise and advice to biopharma organizations on structuring epidemiology teams to maximize the impact of their work, and facilitating cross-team collaboration to create a path forward for real-world evidence (RWE) research initiatives. Read on to learn more.
Responses have been edited for clarity and length.
Q: What advice would you give to growing epidemiology teams as they build influence within biopharma organizations?
A: In my view, an epidemiology team, or any team working on an RWD analysis—whether they’re epidemiologists, statisticians, or data scientists—will be best served when structured with flexibility in mind.
Large biopharma companies, for example, have many different therapeutic area focuses—and these can change over time. When epidemiology teams can be flexible, they can allocate epidemiologists to work across therapeutic areas in a way that meets the business’s needs. This not only improves the team’s experience, but also helps with resourcing: for example, if everyone on the oncology team is fully booked, and there’s a new oncology product that needs an epidemiologist’s support, you can pull in someone from the infectious disease team to staff the project.
I also think it’s important for epidemiologists, statisticians, and data scientists to work together as much as possible, and for epidemiology team members to have a seat at the organization’s decision-making table. While it’s great if these teams have a dedicated team study budget, it often doesn’t work that way. They need the opportunity to get in front of internal stakeholders who control the budget for their studies, otherwise it can be difficult to advance their work.
It’s also important that the person designing the study has a complete, first-hand understanding of the indication (e.g., natural history and unmet need in a disease area) and regulatory and commercialization strategies under consideration—without this, it can be difficult to propose work that can supplement trials or fill knowledge gaps.
Q: Where is RWE adoption within biopharma today? What challenges do organizations face as they build RWE programs, and how can they mitigate them?
A: I think adoption varies depending on the size of the company. In large biopharma companies, or in larger mid-sized biopharma, people are generally familiar with epidemiology—especially post-COVID-19. They’re familiar with real-world data and real-world evidence largely because the FDA has made it part of the vernacular for drug development.
Biopharma companies have tried a lot of organizational structures, but one of the main challenges I’ve seen is that when various, fragmented groups are all working with RWD, it can be difficult to ensure efficiency. For example, different teams could be working to answer the same research question in multiple studies, when it’d be more efficient to find a way to answer that question in one study.
Perhaps more importantly, biopharma companies also must navigate the different levels of evidence required to fulfill the purpose of a study, and understand that the evidence needs for one stakeholder may not be well understood by those working on other RWD use cases. For example, someone in market access may ask the same research question as a regulatory epidemiologist, but for very different reasons. Therefore, the level of evidence they need can vary greatly (appropriately resulting in different data needs, and methodological approaches), as can the speed at which they need to answer the question.
Additionally, as companies continue to grapple with how to centralize data and the expertise needed to analyze it, they’re faced with the decision of where the best place is for RWD experts to sit within the organization. Is it within a therapeutic area team, that way the epidemiologists have a seat at that table from the beginning? Or is it more centralized, so that data experts can learn from each other as they conduct analyses?
I think biopharma would benefit from bringing all stakeholders together early at the decision-making table. Everyone’s research questions could be discussed and weighed to look for efficiencies or opportunities for collaboration, and budget could be allocated accordingly.
The sooner that stakeholders can come together and align on needs and a unified goal, the more efficiently they can use RWE to help meet that goal.