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On December 6th, 2017, ArisGlobal held a webcast with DIA, “Is Regulatory Affairs Ready for Automation?” In this webinar, our presenter, David Scanlon, Senior Director of Regulatory Strategy, posed three related polling questions to the audience, and their responses to each came up with some interesting information. This is the second in a 3-part blog series that has taken these audience responses, and shares our thinking about what this means to our clients in the Regulatory space.

In part 1 of this blog series, we gave feedback regarding the top regulatory challenges faced by our webinar audience. Nearly 70% of attendees identified lack of system integration and overall data quality as their top 2 regulatory challenges.

As evident from our second polling question, ”How far along the road would you say you are for a ‘new’ technological revolution,” the journey to capitalize on the next industrial revolution is just beginning for most participants

  • A small percentage (4%) of our webinar attendees rated their organization as early adopters with fully engaged business leaders driving the agenda
  • Thirteen percent (13%) of our webinar attendees indicated that their company’s IT leadership as being on top of new technology engagement
  • Eighty three percent (83%) of our webinar attendees are just beginning, starting to understand the possibilities, or have just started early investigation.

For David Scanlon, our webinar presenter, the results to this question were not surprising. Before joining ArisGlobal a few months ago, he was a short-listed ‘Inspirational Leader’ within AstraZenca’s Global Medicines Development Unit for his visionary strategy and execution, which today has AstraZenca at the forefront edge of Regulatory data science, after completion of a number of automation projects.

“Finding people who can bridge the gap between technology and business practices in what I call ‘Regulatory Analytics and Informatics’ was the hardest task,” David comments. “With the right capacities in place, convincing leadership to invest is not difficult as they can quickly see the benefits from their investment.”

Learning from other industries and other functions in pharma, including pharmacovigilance, regulatory functions are beginning to realize the ‘gold mines’ of information they are responsible for managing. Learning to leverage the information in their databases, both structured (RIM platforms) and unstructured (document management platforms) through ‘Regulatory Data Science’ will become a core competency.  Early opportunities include the identification and extraction of relevant data elements where regulatory automation can lead to meeting regulatory demands for improved data integrity and supporting the movement toward getting data right for SPOR implementation in Europe.

The final polling question asked attendees to rate their organization’s data science capabilities. Results showed that ‘Regulatory Data Science’ is still very much in the early stages of development for nearly 60% and, given the solid attendance at the webinar, is of great interest to many companies:

  • Nine percent (9%) have maturing or advanced capabilities
  • Thirty three percent (33%) are at an intermediate level
  • Fifty-nine percent (59%) are just beginning or have not yet started

Again, David was not surprised by these results, which confirm other benchmarking activities in which he has been involved. He shared, “This capability will differentiate companies and will support their move towards the core pharma mission of investing money in science in medicine to treat more life-threatening diseases. The capability I built in AstraZeneca is in safe hands, and I now look forward to helping build the technology platforms that will support this much-needed transformation.”

Real-time, simplified regulatory decision making will be enhanced by Regulatory analytics and informatics and robotic automation, whereas natural language processing (NLP), machine learning and data mining will drive much needed improvements in data quality and improvements in data integrity between documents and databases. The machine-learning algorithms can be applied to complex data gathering tasks, normally performed manually, like preparation for SPOR Implementation (where between 70-80% of full ISO standards data are locked in documents – the approved regulatory documentation). In fact, our polling results show that the majority of participants would consider adopting NLP and machine learning-based tools to solve their regulatory related problems.

That, in turn, means more time can be freed up for important activities such as finding innovative ways to get treatments faster to patients and ensuring no harm is done to patients with faster implementation of required changes to labels.

We encourage you to view the on-demand webcast for more comparisons and insights into how automation is coming to life in Regulatory today. Our final blog in this series will be published shortly, and will focus on the starting conditions that are in place to drive success.

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