The potential for generative AI (GenAI) technology to digest, assess and summarize key insights and findings from across vast and diverse sources of content and data make the technology well-suited to addressing the demanding everyday regulatory workloads. It’s no coincidence that targeted applications are now taking form to address the function’s biggest process pain points.

Contract Pharma (CP): What’s behind this new wave of expectation for GenAI in a regulatory context?

Agnes Cwienczek (AC): GenAI is a form of artificial intelligence which, through deep learning drawing on vast banks of data, can rapidly generate new insights and content summaries in an easily digestible form comprising text, graphics/charts, audio, or other media.

Already the technology can be used to help pharma companies to pre-empt agency queries and build stronger marketing authorization applications, building on what’s gone before. That’s assuming there is a decent knowledge base to draw on, to distil what good looks like.

And GenAI tools can help build and enrich this kind of knowledge base, very efficiently. Early pilots of using GenAI to automatically inject health authority interactions into a database, or regulatory information management (RIM) system, have seen 12 fields of data extracted with 90% accuracy—with 80% faster processing and three times fewer handovers compared to when teams of professionals have had to go back through historical submissions and agency interactions manually.¹

With ready access to this information, GenAI tools can automatically check across the global organization what has been objected to by other agencies (relating to the same submission, but in a different country) and flag this within the current content.

GenAI can also help to create summaries for the various reports in the submission as well as any administrative documents such as cover letters or forms; it can help to check that data is consistent between CMC documents and labelling, for instance, minimizing the risk of deficiencies.

Renato Rjavec (RR): GenAI is also demonstrating powerful potential in monitoring and proactively using the latest global regulatory intelligence—for example, as part of impact assessment/change management. Early pilot projects here too have yielded 50- 80% faster processing, and in this case half the handovers compared to manual lookup and intervention.

And this is just the beginning. The real potential is still ahead—on course to be realized within the next two years. This will be the point at which regulatory teams are able to lean on GenAI technology to generate and cross-check entire regulatory submissions automatically, with a quality review from RA professionals requiring just a fraction of the effort needed now.

CP: What problems does this solve for pharma?

RR: The ability to generate and cross-check entire regulatory submissions automatically will be particularly powerful in transforming regulatory submission lifecycle management, which today consumes significant time and budget.

Despite the increasing trend of data-oriented submissions, the reality of content-based submissions is here to stay for the foreseeable future. At a conservative estimate, large pharma organizations typically generate around 600-800 submissions per month. Even a very modest time saving, of just 1-2 hours per submission, would make a substantial difference to associated resource allocation. And that is the minimum saving I expect once GenAI is harnessed in earnest to automate the collation and assembly of content—extrapolating from initial regulatory use cases of the technology.

CP: Can you give a sense of how developments in GenAI technology are making all of this possible?

RR: It’s a culmination, really, of GenAI’s pace of advancement, its steady maturation, and the technology’s fast-growing acceptance and perceived reliability.

Already, the technology is being used widely and with confidence to analyze and infer meaning from data and content in a wide range and formats and distil what is needed into whatever the desired new format for the target context. Even within the strictly regulated life sciences industry, GenAI is now seen as a viable solution to the cost/performance challenge of marketing authorization and license maintenance, as well as real-world product safety monitoring.

The next wave of developments will enable end-to-end process transformation. Future use cases will include the provision of inline regulatory guidance to help users in submission compilation; generation of new draft submission content based on existing content; and cross-validation of final content against regulatory guidance and data.

These targeted GenAI applications will be able to identify and draw from the latest correct sources, to collate and repurpose the relevant information and fill the respective submission outline. This will automatically involve cross-checking with the company’s RIM system, assess what has previously been submitted, ensuring that the new submission is accurate and consistent.

In due course we can also expect GenAI to enable more efficient and effective maintenance of international labelling compliance across the product lifecycle, via automated cross-referencing.

AC: Ultimately this is about delivering better and faster submissions, more cost-efficiently, in any market. Via rapid, automated cross-referencing (to ensure that the correct excipient/ingredient information has been included, for instance), GenAI will not only expedite submissions compilation; it will also improve the quality, accuracy, and success rate of submission updates.

In the meantime, skilled teams will be free to focus more of their attention on scientific work—activities that add more value for the organization.

CP: Is the opportunity concentrated in more advanced markets?

RR: No. Advanced automation in regulatory submissions generation could transform the efficiency of dealing with less developed markets too. As more mature markets lean toward well-defined electronic submissions, it is a stark reality that the rest of the world continues to rely heavily on non-electronic files; for submission to authorities whose requirements are less standardized. The ability to streamline associated submissions with advanced end-to-end automation promises to be very powerful in this context, to help companies navigate the differing requirements,² deduce “what good looks like,” and swiftly collate and format what’s needed.

This is important, given that emerging markets together account for a sizeable proportion of the global life sciences opportunity.³ Growth in pharma sales in emerging markets is set to accelerate over the next decade, with medicine use in Latin America and Asia expected to rise faster than other regions over the next five years.⁴

CP: What does all of this imply for pharma companies’ data infrastructure and data assets? Are they robust enough to support these ambitious process automation opportunities?

RR: Absolutely. Simply adding a GenAI capability alone is no magic bullet. The more robust the assets GenAI can draw from, the more reliable and transformational associated process automation initiatives will be. The more diverse the available checkpoints, meanwhile, the more confidence there will be in the newly generated output.

So, companies will absolutely need to work on bolstering their regulatory intelligence knowledge bases, and recommit to existing initiatives to clean up, standardize, and unify their product data. All of this is crucial groundwork that is needed anyway and will optimize the success and impact of GenAI-based process automation. In fact, GenAI can help with automated cross-checks to identify discrepancies and anomalies in data and its formatting, as part of companies’ efforts to get their IDMP data standardization in order, by honing and formalizing associated data governance.

The sooner companies start experimenting with the possibilities, the better. Testing out the possibilities will give pharma companies the best feel for how far GenAI can go, how quickly results can be honed, and how much time and budget this could buy back for regulatory teams. 

References
1. This is ArisGlobal’s own data from early customer pilots. Separately, McKinsey estimates that deploying next-generation AI to improve HA responses and their impact can reduce Agency follow-up by 50%.
2. Navigating Global Regulatory Requirements for Generic Drugs: A Comparative Study of MIST, BRICS, and ICH Countries, International Journal of Pharmaceutical Investigation, December 2023/updated February 2024: https://jpionline.org/article/32579
3. Emerging Markets Offer Pharma Its Next Growth Opportunity – National governments and global non-governmental organizations are trying to expand access to essential medicines and treatments in developing countries: Pharmacy Times, February 2024 https://www.pharmacytimes.com/view/emerging-markets-offer-pharma-its-next-growth-opportunity
4. Latin America is one of the fastest-growing pharmaceutical markets in the world. With an increasing and aging population of 660 million people, it is forecast to grow at a compound annual growth rate (CAGR) of seven to ten percent between 2023 and 2027 (Statista, July 2024: https://www.statista.com/topics/12539/pharmaceutical-industry-in-latin-america/) In 2024, the projected revenue for the Pharmaceuticals market in Asia is expected to reach a staggering US$238.10bn. The largest market within this industry is Oncology Drugs, with a projected market volume of US$40.67bn in 2024. (Statista, June 2024: https://www.statista.com/outlook/hmo/pharmaceuticals/asia)

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Renato Rjavec is Senior Director of Product Management at ArisGlobal, with a keen focus on AI as a means for targeted automation of critical but labor-intensive processes where accuracy and precision are paramount. Renato has almost two decades of experience in ideation, development and implementation of regulatory and quality solutions for the life sciences industry.

Agnes Cwienczek is Director of Product Management at ArisGlobal, specializing in regulatory information management, document management, submission management and labelling management. She previously worked at Merck in global regulatory and quality assurance, during a career spanning two decades at the frontline of regulatory information management.