RWD's Impact on Clinical Trial Recruitment Costs

One of FDA’s main focuses at the moment is streamlining the drug approval process. In June of this year, the agency announced a new pilot program designed specifically to shorten the early-stage drug trial timelines.

A key factor of this is the growing acceptance of RWD in trial design. The agency hopes that by allowing RWD collected by qualified research institutions, drug developers will not only be able to move through early stages faster, but also reduce the need for late-stage trials as well.

Pharmaceutical Executive spoke with Jen Lamppa, VP of commercial strategy, how RWD impacts trial design, specifically during the patient eligibility screening phase. The addition of this data is expected to impact recruitment timelines, although it may have other unintended impacts as well. As such, pharma and biotech companies will likely need to adjust their recruitment models to properly take advantage of the new environment.

Pharmaceutical Executive: What are the costly ways that RWD can impact clinical trial recruitment?
Jen Lamppa: Two things stand out when it comes to self-reported data in clinical trial enrollment. The first is the sheer volume of self-reported data that needs to be collected. The second — and by far the more costly — is the accuracy of that data.

When a patient walks into a doctor's office and is asked about their medical history, the honest truth is that it is often easier to ask the clinician, "What did I say last time? What medications are on my chart?" There is a significant burden placed on patients at the moment of enrollment: they are expected to accurately recall their current health status, their medical history, and what has happened to them, often under time pressure and in an unfamiliar clinical environment.

Real-world data tells the true story. It contains the documented record of what actually happened to a patient historically — and that record is essential for validating whether a patient coming into a study is who they say they are and whether they fit the intended study population.

The consequences of inaccurate self-reported data can be extremely costly. You can end up enrolling a patient who should not have been in the study — someone who doesn't fit the right patient profile, or who carries undue risks that the protocol was designed to exclude. There are documented examples of trials where the wrong patients were enrolled or where inaccurate information was provided at baseline.

In a best-case scenario, the consequence is a time-consuming audit and the potential exclusion of that patient from the analysis. In a worst-case scenario, there are safety implications — and those can be completely detrimental, in some cases becoming a showstopper for the entire study.