How to Build a Labeling Process That Adapts With Regulatory Change
Every regulatory affairs professional knows the feeling. A new requirement lands, be it a UDI update, a revised CLP classification, or a market specific mandate, and what follows is a familiar scramble. Someone pulls together a list of affected products, another person starts working through label versions, and a chain of emails and approval requests begins that will take weeks, sometimes months, to resolve.
The instinct is to treat this as a resourcing problem and focus on more people, more hours, faster turnaround. But for most organizations operating across multiple markets with complex product portfolios, that approach has a ceiling and regulatory affairs teams hit it regularly. The real issue here isn't the regulation itself, but rather the labeling process underneath it, and if you’re looking to break out of this cycle, the only solution is to re-think your process.
The Regulatory Landscape Is Only Getting More Complex
The volume and pace of regulatory change affecting labeling is not slowing down. Medical device recall events in the US increased by 8.6% in 2024, rising from 975 events in 2023 to 1,059 – with Class I severity recalls reaching a 15 year high. While recalls have multiple causes, labeling errors and non-compliance remain persistent contributors across pharmaceutical and medical device sectors.
At the same time, the regulatory environment itself continues to expand. EU MDR and IVDR implementation continues to evolve, IDMP standards are reshaping how medicinal product information is structured and submitted, DSCSA serialization requirements remain a live operational challenge for pharmaceutical supply chains in the US, and markets across Latin America and Asia are progressively tightening their own labeling frameworks.
For regulatory affairs teams, this creates a structural problem as the regulations keep coming and the label variants keep multiplying. And the question of how quickly and accurately your organization can respond becomes a competitive and compliance issue simultaneously.
Why Document Centric Processes Break Under Regulatory Pressure
Most labeling operations in regulated industries were built on a document centric model. Label content lives inside Word documents, PDFs, InDesign templates etc., stored across shared drives, local folders, or legacy content management systems. Each label is its own discrete object, managed independently. This model may work for smaller businesses with low volume, but at scale, it becomes a serious bottleneck and could end up costing enterprises a lot more than just time.
Consider a straightforward scenario. A pharmaceutical company markets a product across 15 countries, each requiring its own label variant to reflect local regulatory content, language, and symbol requirements. Under IDMP, a change to the way a medicinal product's ingredients or indications are structured may require updates across every one of those variants. Under a document centric model, that means 15 separate files to locate, update, review, and re-approve with the very real risk that a version somewhere in the chain gets missed.
Research published in PMC found that labeling issues accounted for close to 15% of drug recalls reviewed in a cross sectional FDA study – a figure that reflects how often the label, rather than the product itself, becomes the compliance failure point. In document driven environments, the conditions for exactly that kind of failure are structurally built in.
The same pattern plays out across medical devices. Medical device labeling most often fails when it comes to missed translation updates, version mismatches, and documentation gaps, leading to shipping bottlenecks and audit issues. These are simply the result of a process that was not designed to handle regulatory change at speed.
The Difference Between Updating a Document and Changing a Data Asset
The shift that makes labeling processes genuinely adaptable is moving from managing documents to managing data.
In a data driven labeling model, label elements (regulated phrases, hazard statements, ingredient declarations, symbols, translations) exist as version controlled data assets held in a centralized system. A label is assembled from those assets rather than created from scratch each time.
Therefore, when a regulatory requirement changes, the response is simple. Instead of hunting down every label that contains the affected content and editing each one manually, you update the asset once. The system identifies every label where that asset appears – using functionality like Kallik's Where Used search – and the update becomes one simple, controlled and traceable click.
This might just sound like a faster version of the same process, but it is in fact a fundamentally different relationship between your team and regulatory change. The question shifts from "which labels do we need to fix and have we found them all?" to "we've updated the asset, now what does the system show us needs review?"
That distinction matters enormously when you are dealing with a time sensitive regulatory deadline, a market withdrawal risk, or a post market surveillance finding that requires urgent label correction.
Structured Data Is Also the Foundation AI Needs
Labeling data is also crucial when it comes to the use of AI tools. Regulatory affairs functions are being asked to evaluate AI solutions that promise to accelerate content generation, translation review, or regulatory submission drafting. Some of these tools have serious potential, but only if the structured, controlled and accurate data is there to begin with.
For regulatory affairs teams evaluating AI adoption, this is key. The enterprises that will get the most from AI in labeling are those that have already built the structured data foundation to support it.
What a Regulation Ready Labeling Process Actually Looks Like
There is no single definition of a regulation ready labeling process, but certain characteristics show up consistently in organizations that handle regulatory change well.
- A single source of truth
- Built in approval workflows
- Full and accurate version history (key for meeting the requirements of 21 CFR Part 11 and EU GMP Annex 11)
- Asset search functionality
- Electronic signatures
These each allow organizations to stay regulation ready while reducing risk and operational friction.
Where to Start
For most regulatory affairs teams, the move toward a data driven labeling model begins with an honest assessment of where content currently lives, how change is managed today, and what the real cost of the current approach is in time, risk, and resource.
If your team is spending meaningful time on each regulatory update just locating and auditing affected labels before any actual updating has begun, that is a reasonable place to start the conversation.
Kallik’s leading enterprise labeling and artwork management platform was built to support exactly this kind of transition, giving regulatory affairs teams a centralized, structured platform for managing labeling content across markets, products, and regulatory frameworks. To learn more about how it works in practice, visit our How Kallik Works page or get in touch with our team at enquiries@kallik.com or fill in a form here.
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