For decades, animal testing has been the foundation of chemical safety assessment. This paradigm is changing. EU regulators are increasingly moving towards non-animal methods and an approach based on human-relevant evidence – data that are useful for decision-making in the protection of human health and the environment.
In June 2026, the European Commission adopted a roadmap for the gradual phase-out of animal testing in chemical safety assessment. This is a clear signal that NAMs are becoming a new standard.
The scale of the challenge is significant. According to an analysis by the Regulatory Affairs Professionals Society (RAPS), more than 15 million animals were used for regulatory testing in the EU between 2015 and 2023, with almost 40% of these procedures related to chemical safety assessment. The EU’s actions are therefore not a niche adjustment, but part of a broader transformation in how safety evidence is generated and assessed.
Beyond REACH and CLP – a broad scope of change
The roadmap covers not only chemicals regulated under REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) and CLP (Classification, Labelling and Packaging), but also pharmaceuticals, biocides, plant protection products, and food and feed additives.
For companies, the message is clear: NAM-ready evidence packages will become increasingly important for efficient registration and evaluation processes.
From a “reference test” to “fit for purpose”
This shift is changing what is considered relevant evidence for regulatory purposes. Instead of asking, “Does an alternative method reproduce the result of an animal test?”, the question becomes: “Is this evidence sufficient for a specific decision in a defined context of use? Can it be assessed, understood and defended?”
For computational methods, including QSAR, this change is both an opportunity and a test of maturity. It is not enough for a model to produce statistically accurate predictions. For a result to become part of a regulatory evidence package, it must be possible to assess, understand and defend it within a specific context of use.
The areas that will matter most include:
- Model transparency – clearly defined assumptions, a documented applicability domain and robust uncertainty analysis. A result without context is not regulatory evidence.
- Alignment with a specific application – not every model is suitable for every type of assessment. It will be essential to demonstrate that a method addresses a specific regulatory question and meets the requirements of the relevant safety assessment process.
- Integration with other data sources – in silico data will increasingly be assessed as part of a broader evidence base rather than in isolation. Their value will grow when they are consistently integrated with in vitro data, historical study results and other available evidence.
AI, compliance and digital evidence: a key challenge for companies
Over the next 24–36 months, regulatory expectations around guidelines, validation criteria and documentation are likely to become clearer. Companies that start preparing now will be in a stronger position to adapt. Building expertise in NAMs, machine learning and AI in toxicology, together with strong reporting and data management practices, can help reduce the risk of regulatory delays as new requirements are introduced.
If you are interested in the latest developments in NAMs and their applications in regulatory toxicology, subscribe to QSAR Lab’s LinkedIn newsletter, All NAMs in One Place, and stay up to date with key developments, trends and practical insights.
Source: RAPS
https://www.raps.org/resource/eu-publishes-roadmap-to-reduce-reliance-on-animal-testing.html
