Reg impact assessment next in GenAI’s sights, experts claim

AI-powered process automation is ideally suited to this vital front-end aspect of product change control, say Preeya Beczek and ArisGlobal’s Agnes Cwienczek.

As confidence builds in Regulatory AI use with practical experience of the technology’s potential, domain leaders are seeing regulatory impact assessment (as part of product change control) as the next obvious candidate for transformation. The process is notoriously complex and labor-intensive, while adding little value beyond its critical role in maintaining quality and compliance. It is no coincidence that, in a 2024 industry survey, regulatory impact assessment drew significant interest among senior regulatory professionals.

Core AI capabilities, particularly harnessing Generative AI (GenAI), are already established in a life sciences regulatory context. Examples include using AI to hone marketing authorization applications and maintain registrations, a satisfying solution to soaring Regulatory workloads which sets a strong precedent.

The regulatory implications of product change control

Fronting any process involving a product change, regulatory impact assessment is not only labor-intensive; it also carries an inherent time pressure which AI could help alleviate.

If an urgent safety change comes in, the associated regulatory impact assessment typically needs to be performed within hours, not days. That’s irrespective of the extensive scouring and reviewing of information this will entail. Among the immediate considerations are: “What did we present to the authority last time?”; “What does our label say?”, “How soon must the change be implemented/within what timeframe, and which documentation is required?”

All of this requires extensive searching and referencing of diverse and often unconnected sources, including manual lookup of non-indexed (unstructured) data buried in static documents. Investigative work often extends beyond central operations too, spanning feedback loops from affiliates about the current status and local regulations – information which may be recorded in different languages.

When a product change triggers a regulatory impact assessment, typically this will happen initially above an individual country level. That assessment then has to be repeated to some degree by the local operation, where national licenses are involved. Each country will then decide whether and when they will need to make a change (e.g. reflect it in product labelling), and update their registration/notify the relevant health authority. Is it a case of “do first, then tell”, for instance? And what of the manufacturing sites where the product is held? When will the change be rolled out? Will a grace period be required? How urgent is it: can it wait for the next print run?

Much of this activity will need to take place in parallel too, to support forward planning – demand planning, supply planning, materials availability, and so on. And the associated safety/regulatory changes will need to follow this chain of events very promptly. All of this adds to the complexity of product change control, and the work it generates. In the 2024 survey, 55% of senior regulatory professionals actively expressed interest in an advanced, AI-enabled technology support for the task, to relieve the intensity of the workload and speed up delivery. As many as 97% agreed that AI-enabled automation would be useful in identifying the direct impact of product changes.

Planning the shift

A practical approach to applying AI in regulatory impact assessment is to break the end-to-end process down, and consider the individual stages where intelligent automation could really make a difference. Data- and technology-readiness will have a bearing on what’s possible now and what is likely to deliver best results. Starting small is advisable, focusing on 1-2 particular product lines, or a specific region or country. They key is to identify a painful problem that needs to be overcome, where AI could present a solution.

As AI assumes the detailed exploratory work, process stakeholders (central regulatory professionals, local regulatory representatives, plus those operating at a manufacturing level, demand, and supply chain level, and in Quality and Safety) can start to align more closely and collaborate more effectively on next actions.

Even just speeding up the review process in the initial assessment (locating and searching all of the information, and determining where efforts need to be concentrated), will empower teams to move more swiftly in determining and executing next moves. The ability to automatically scan the latest regulatory intelligence in different markets, and consult previous Agency exchanges, can then help further expedite next steps – or at least pinpoint where supplementary insights may be needed where the latest local requirements are less clear.

The more embedded AI becomes in the end-to-end process, the more the gains will multiply. Where an AI tool is pulling information from several sources into one place, teams can be ready to review and validate the findings. Generative AI tools can help with structured content authoring, meanwhile, or swiftly bring a document from version zero to a solid first draft, knowing what data to pull in – and where to find it.

Prioritizing governance

As regulatory teams turn increasingly to AI to take over the administrative heavy lifting, the role of tight governance becomes paramount.

However smart and well trained the AI capabilities may be, professionals should not be deferring to the technology to make the decisions for them. Cross-functional teams will still need to agree whether and where a change is applicable, whether it needs to be made now, or whether it can be deferred, and when it should be reported to the relevant regulatory body, for instance.

There should be a clear plan to share the vision with and upskill relevant teams too, so that they harness the technology reliably and responsibly to optimal and appropriate effect.