UK Replacing Animals in Science Strategy
UK NAMs
How to align with the UK’s replacing animals in science strategy, launched November 2025, and navigate the shift to predictive toxicology and data-driven safety.
The challenge you're facing
The UK Government’s “Replacing Animals in Science” strategy marks a structural shift for life sciences. Backed by significant national funding, the strategy moves the UK beyond simple assay replacements and instead targets a systemic transition toward predictive toxicology, AI pipelines, multi-organ human models, and data-sharing frameworks.
What do you need to do?
You will need to know how to leverage expansive data and predictive computational models to prove safety for diverse endpoints across the drug discovery pipeline and deploy in a timeframe that derisks regulatory submissions.
The UK Roadmap now demands that organisations integrate multiple evidence streams into predictive safety assessments.
Meet UK NAMs requirements in three steps:
Predict
1. Build in silico workflows
Identify potential safety risks before experimental testing using modern in silico approaches.
QSAR
Mechanistic integration
Read across
Leverage
2. Use existing evidence first
Start every safety assessment by leveraging existing data before generating new experimental evidence.
Datasets
Weight of evidence / Context of use
Data integration & collaboration
Characterise
3. Advance analytical safety protocols
Build deeper analytical insight across compounds, impurities and degradation mechanisms to influence safety.
Impurity control
High throughput
Product characterisation
Regulatory Requirements
Mechanistic Human Relevance:
Expectations from the MHRA
The MHRA is encouraging and supporting the use of mechanistically informed, human-relevant evidence. Their focus on mechanistic human relevance reflects the landscape transition away from relying solely on traditional animal models.
Organisations that can explain why an adverse effect occurs, demonstrate its relevance to human biology and integrate multiple evidence sources, will be better positioned to meet the evolving NAMs requirements now and in the next 10 years.
Mechanistic human relevance isn’t achieved through a single technology—it’s built by combining three core capabilities: predict, leverage, characterise.
Meeting MHRA guidance:
Combine computational, experimental and existing evidence using Integrated Approaches to Testing and Assessment (IATA) and Weight of Evidence principles.
Define the boundaries of your non-animal methods and establish human relevance by mapping to Adverse Outcome Pathways (AOPs) under the rationale for clear Context of Use.
Present your predictive, human-relevant data structure before formal submission to align regulatory expectations early and removes bottlenecks from your approval timelines.
The MHRA’s vision puts mechanistic human relevance at the centre of modern safety assessments. Rather than relying on a single test or model, pharmaceutical organisations are expected to build integrated, scientifically justified evidence packages.
Offshoring & Global Readiness
Securing regulatory acceptance
across the EU & US supply chains
The UK is not evolving in isolation. Regulatory agencies across Europe and the United States are increasingly recognising the value of predictive, human-relevant safety assessment. Discover the EU Roadmap and US Strategy.
As the MHRA, FDA and EMA continue to advance the use of New Approach Methodologies (NAMs), organisations should develop safety strategies that are adaptable across multiple regulatory jurisdictions.
By integrating chemical databases, computational modelling and mechanistic human biology into a single evidence package, organisations can build submissions which align closely with regulatory expectations across global development programmes.
Navigating global supply:
A well-integrated evidence package is easier to adapt across different regulatory jurisdictions than isolated datasets.
A clearly articulated Context of Use improves confidence during regulatory review.
Internationally accepted methodologies are more likely to support global development programmes.
The organisations that will benefit most from the transition to NAMs are likely to be those that build globally reusable, mechanistically informed evidence packages that can support regulatory decision-making across multiple jurisdictions, rather than those that simply replace animal tests.