The Evolution of Data-First Regulatory Operations

PharmTech recently spoke with Remco Munnik, founder and owner, Arcana Life Sciences, to get his perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. Munnik, who has 25 years of experience in regulatory affairs, discusses the industry's "decisive, swift shift towards data-first regulatory operations.” Driven by initiatives from the European Medicines Agency, Munnik explains that the implementation of electronic application forms and product databases marks a turning point where "digitalization is no longer optional, but really a permanent fixture of regulatory practices."

A major focus of the discussion is the integration of AI. While the industry aims to use AI to automate document generation and enhance quality oversight, Munnik identifies significant barriers, including fragmented legacy systems and limited data standardization. He argues that successful AI adoption requires not just technical tools, but a robust data governance setup and a significant culture shift within organizations.

This digital evolution has also resulted in a widening skills gap. Munnik notes that the role of the pharmaceutical professional is changing; where they once focused on documentation, they must now act as data scientists capable of extracting insights from complex datasets. As Munnik describes the transition: "In the past, when you worked in pharma, you had to be a scientist, and the scientist then became an administrator to document everything." To bridge this gap, he advocates for on-the-job rotations and a leadership mindset that embraces inevitable change as a competitive advantage.

Looking toward 2026, Munnik emphasizes the need for investment in data-driven infrastructure to ensure traceability from the point of data creation to final registration. He highlights promising innovations, such as the work of the Regulatory Optimisation Group, which seeks to replace resource-intensive document submissions for lifecycle changes with direct database updates. These advancements, combined with upcoming European pharmaceutical legislation, are set to redefine how the industry maintains compliance and achieves speed to market.

Transcript

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

My name is Remco Munnik. I'm a consultant at Arcana. I started that company in July of this year. [I have] 25 years of experience in regulatory operations and regulatory affairs, a subject matter expert in electronic submissions.

In the regulatory operations space, I really saw a decisive, swift shift towards data-first regulatory operations. In Europe, the European Medicines Agency is really setting there the tone with the product database go live, which is also complemented by the rollout of processes that use data.

So, the electronic application form of shortage reporting. I think that really showed that digitalization is no longer optional, but really a permanent fixture of regulatory practices.

Across the industry, I think there's a strong desire to apply AI, whether to accelerate regulatory intelligence, automate document generation, or enhance quality oversight. And I think the challenge is not as to where to use AI but how to integrate it responsibly and effectively.

And I think the unresolved barriers to full [adoption] with AI, in practice, I see a lot of challenges still with data integrity and interoperability. So there's still fragmented legacy systems that are not connected, limited data standardization that hinders scalable AI and cross-departmental collaboration. I think that requires a robust data governance setup where roles and responsibilities are defined. And last but not least, I think it's also a talent and a culture shift. There's, from a talent point of view, people need AI skills. There might be an even digital maturity between departments, and change management is also really required to make sure that the AI can be scalable on an enterprise level.

I think there's a widening skills gap with digitalization. In the past, when you worked in pharma, you had to be a scientist, and the scientist then became an administrator to document everything. And I think what we see now is a shift that you need to be almost a data analyst or data scientist. So, still a scientist, but way more into data driven and really then extracting insights from complex datasets using statistics, machine learning, and data modeling.

I think here data governance and stewardship is key to bridge that gap, but I think best practices also from a selection, training on the job, learning on the job… so really embed the learning, on-the-job rotations, cross-functional team learning, I think that's very important. And last but not least, we also need a change perception within the leadership, because one thing that remains the same is change. So really embrace the change; it is inevitable, but try to leverage that as an advantage.

One of the most promising innovations that I've come across is the work that we have been doing at the Regulatory Optimisation Group. So this is a group that has been working with the European regulators to optimize life cycle management changes. So, traditionally, if somebody changes their name or address, you need to submit all kinds of documents, all kinds of submission to the regulators that needs to be assessed and processed. Now, this process is very time-consuming, resource-intensive, but also prone to inefficiencies. And what we've been now talking with the regulators [about] is also to submit this directly into a database. So, a name change, an address change, let's submit that directly into a database and everybody gets better because you have a cost efficiency, you have a quality improvement, everybody's informed at the same time, there's a speed to market, but also compliance, and I think that's really a win-win situation that we want to achieve.

In 2026, I still believe that companies need to invest quite a bit in data-driven infrastructure. So upgrade core systems, whether that's manufacturing execution systems or laboratory information systems, ERP platforms, how they are integrated with master data, but also regulatory data as part of IDMP. The linking between what is registered from a regulatory point of view and what is manufactured and marketed from a supply chain point of view will be a lot more important to capture. I think another investment is needed in governance [to] really have that clear traceability from the source where a data element is created until the submission and registration of the data element with the regulator there is important.

So, that's really important to define the data ownership across the functions, and I think last but not least, it's also the workforce enablement. Digital training needs to be standard, but also giving all the employees confidence with data standardization to really ensure that operators can interact and interpret fluently with standardized structured data.

I think one more thing in Europe that will be very important is the new European pharmaceutical legislation that will have quite an impact on the entire way of operating for pharma, but more to be shared in 2026 when the new pharmaceutical legislation draft is available.