Drug Discovery & Development (DDD) caught up with Jim Carroll, VP of Real World Evidence, ICON, at the Public Workshop: A Framework for Regulatory Use of Real-World Evidence Convened by the Duke- Margolis, Center for Health Policy at Duke University in Washington, DC last month. Here’s what he had to say about the applications of real-word data and its implications for healthcare.

DDD: What’s involved in turning real-world data into real-world evidence?

Jim Carroll: There’s a big technology component to this, and it’s the crux of what we covered in a recent white paper, “Clinical Research and Clinical Practice Converge through Real-World Evidence.”

Many companies spend a fair amount of money subscribing to different real-world data (RWD) sets. But, they typically don’t have a great mechanism for consolidating all of those different data sets into a single environment and linking those data standards in a suitable form for analytical application across the enterprise. Rather, they rely on ad hoc approaches where there’s an analytic solution that sits up on top of a large, albeit singular RWD set.

Today, with more and more data flowing through the healthcare system, there’s a need to link those data assets to come up with a more complete picture of a patient’s healthcare utilization. So we’ve focused on delivering a solution that can serve the broader biopharmaceutical enterprise and speed time to insight. We believe that by taking a platform approach, the requirements across clinical and commercial domains can be supported through the aggregation of multiple, large and disparate data sets into a single data environment.

Once they are in such an environment, the datasets are standardized and readied to support a more comprehensive real-world evidence (RWE) strategy in a way that provides maximum economic leverage for the sponsor. Key components of the platform include tools for consuming, aggregating, and processing the data while making it available for a wide array of analyses, all within a well-defined governance structure. Such platforms can support a wide range of applications—from patient recruitment and protocol feasibility ... to virtual registries and comparative effectiveness research ... to commercial applications related to market share and real-world treatment patterns.

DDD: Is there a value in the drug development process?

JC: Absolutely. Although many companies acquire data for a singular purpose to address a singular objective, we see a need is to expand the utility of these real-world data assets to be able to support and inform decisions throughout the R&D continuum ... and into commercialization.

First, RWE can be infused into the drug development process to increase the likelihood of commercial success. RWE can be put to use in early-phase modeling activities to better understand the factors that affect the cost effectiveness of a new treatment. These models can then be applied to develop an early pricing strategy, to design the Phase III trial, as well as to determine product positioning and shape the market. Often these models are used to develop key assumptions around which population segments will be most commercially attractive. And, these RWE-informed models can become a starting point for understanding what safety and efficacy thresholds may be required to support favorable reimbursement and optimal patient access.

Second, RWE can shorten enrollment timeframes by identifying and quantifying the number of patients that would be suitable for a particular clinical trial. Through cohort modeling with RWD, analysts are able to see the implications of various inclusion and exclusion criteria on the universe of available patients. This can lead to modifications to the protocol or an understanding of how the enrollment timeframe assumptions need to be adjusted.

Third, with a real-world view into investigators who are treating the highest volume of patients that align with the protocol, you can bring those specific investigators into the trial. That addresses another big challenge within clinical research today: the cost of starting up sites that under enroll.

DDD: What other applications does real world data have?

JC: The RWD-driven models I mentioned above can be used to inform other critical decisions when you’re in Phase II of the development life cycle, answering around pricing strategy and the optimal price for your product when it ultimately comes to market. They can help you understand how to position the drug when it gets to market and ultimately what type of a product profile you’re going to need in order to be successful in the marketplace. These early economic models can help to set the stage for commercial success down the road.

DDD: What specific role does real world evidence play?

JC: Often when we build an early economic model, it’s important to develop a baseline understanding or base case of the current standard of care—what particular treatment options are used in the standard of care and what are the typical outcomes of patients that you see. Once that base case model has been developed through the use of real-world evidence, you can start to understand the implications of the investigational drug’s intended therapeutic benefit. You can use that model to understand the degree to which the drug will need to change specific outcomes in order to provide a more meaningful therapeutic benefit to patients and command a higher price and greater market access.

Once that’s established, you can modify various inputs into these models to better understand and categorize the types of thresholds you’re going to need to achieve during the clinical development process. Ultimately, the goal is to have a product that can be priced in a way that’s economically attractive to not only patients, but also to payers so you can maximize reimbursement for that drug.

DDD: Can you talk about CROs and their role?

JC: As a CRO, we have the opportunity to work with multiple sponsor companies, and through that process— this is something our competitors do as well—we have an opportunity to see how our customers are using RWE. Also, the gaps we see across our customer base can help to inform our investments in RWE as well as in tools that can help inform the decision-making process.

With respect to RWE, CROs are providing value to sponsors to different degrees. Some are more progressive in investing in RWE and RWE generation tools. Others provide value with RWE in a more localized and isolated manner.

Within ICON, we’ve made sizeable investments in RWD assets and tools to inform decision-making, a concept that is firmly ingrained in our processes and how we approach clinical research. In addition, we’ve centralized RWE strategy development so that we can work with customers in a highly coordinated manner. We use an intelligent, multi-disciplinary approach to understand the business objective and how RWE can be used to accomplish it, as well as identify the most economical RWD sources to support the strategy. A lot of confusion in how and where RWE can be applied relates to knowing what RWD are available, how they can be accessed, and how they can be leveraged for early-stage R&D and post-marketing research.

DDD: Was there overlap to what was discussed during the? Public Workshop: A Framework for Regulatory Use of Real-World Evidence panels and ICON’s white paper

JC: We’ve really seen a convergence of clinical research and clinical practice. A lot of what the FDA is looking to do is to have large-real world studies done in a more pragmatic fashion, and the only way we can do that is in the actual clinical care setting. And that degree of convergence is starting to expand significantly. That helps to provide a shorter innovation cycle between how physicians may treat patients today and what the best practices are so that the health system and individual physicians can continue to learn.

That was a big aspect of the panel discussions earlier today—how these two realms are coming together to drive much more of a learning health system. And this is largely enabled through RWD as well as pathology-driven analytic capabilities. Without the data and the appropriate analytics, it’s difficult to understand and measure what the impact is of certain treatment decisions. The pathology and the data really need to be the vehicle to fuel these iterations and these learnings and again, the convergence of clinical research and clinical practice.

DDD: Do you see this as the continued focus?

JC: What’s interesting is that the FDA is now mandated by law with the 21st Century Cures Act and also PDUFA 6 to develop the RWE framework for expanded use of RWE. Both of these pieces of legislation have specific requirements around how RWE is going to be used to inform regulatory decision-making. That’s what a lot of the talk today was about—presenting what some of the issues and challenges are around those and getting other comments on these challenges and continuing to move the dialogue along. Since RWD flows throughout healthcare system, discussions around expanded use of RWE for regulatory decision-making sometimes evolve to solving broader challenges within healthcare system, which, in many respects RWE has the power to do.