The challenges facing the Sponsor required the Real Life Sciences and the Sponsor teams work through complex issues together. Working with rare disease populations requires compliance strategies be thought through carefully in advance. Small populations, like those found in rare and ultra-rare disease populations can increase the likelihood of patient re-identification if advanced methods are not applied.
Regulators may initially be hesitant to approve anonymization that strips out or redacts large portions of pertinent information. Working together, the Sponsor and Real Life Sciences performed anonymization and quantifiable risk modelling ahead of the first meeting with regulators. This provided near immediate alignment with regulator expectations and avoided downstream rework and time consuming iterations (a frequently reported issue across the industry).
This Sponsor cares deeply about data transparency in its commitment to advance life-changing treatments. Rare Disease populations that the Sponsor targets have a variety of data anonymization challenges. Small trial populations limit the scope of what can be disclosed and incomplete anonymization carries with it a large risk to re-identify individuals.
Conversely, regulatory bodies and pharmaceutical organizations are seeing an increase in data sharing -- and Rare Disease populations are not exempt. New regulations such as EU 536 will continue to make it even more critical for sponsors to maintain a reliable method of data transformation, and a business process that is repeatable, efficient and applicable across trials and adheres to the requirements of multiple governing bodies.
To achieve sustained success, cross functional teams need to be aligned when solving transparency and anonymization challenges. This is particularly true in environments with advanced data anonymization complexity, such as rare disease trial populations. Bringing together Medical Writing, Compliance, Data Security, Biostatistics, Regulatory and Transparency teams to a single process will reduce the risk of missteps that small patient population clinical trial data sets can bring during the process of anonymization.
Real Life Sciences generated a whitepaper for the Sponsor that defined best practices including roles and responsibilities needed in transforming small population data on a trial by trial basis. The Sponsor was able to leverage these best practices to streamline processes and align internal teams.
Much of the success surrounding data anonymization for the unique populations at the Sponsor organization stemmed from the ability to create a detailed process to review with regulators at the outset. Cross functional clarity and detail accelerated the regulatory process and minimized the need for rework in populations where the margin for error is exceedingly small.