Non-Animal Methods (NAMs)
Building better NAMs through data sharing
Sharing existing data can reduce duplicated testing while building the evidence needed to develop and apply alternative approaches to safety assessment.
20 years. Hundreds of experts. One shared mission.
of top pharmaceutical companies contributing
shared data records
What if the data you need already exists?
Organisations can end up generating similar toxicity and analytical data independently, at considerable cost.
Precompetitive data sharing changes this.
By securely pooling data that doesn’t provide a competitive advantage individually but has collective scientific value, organisations can reuse existing evidence, reduce duplicated testing and build stronger datasets together.
If the relevant evidence already exists, this means:
No new Ames test. tolerability study. nitrite analysis.
Data sharing can support NAMs in two ways
A non-animal approach in itself
Using relevant existing data can reduce the need to generate new study data, helping avoid unnecessary duplication and making better use of existing evidence.
The foundation for other NAMs
High-quality shared datasets can support approaches such as read-across, QSAR models, defined approaches and weight of evidence.
From shared data to better safety assessment
Data sharing in practice
Our toxicology data sharing initiatives show both roles in action: reusing existing evidence and building the foundation for other NAMs.
Complex nitrosamines
Anonymously sharing Ames data on structurally complex nitrosamines to build the evidence base for read-across and computational approaches.
Discover more →Aromatic amines
Advancing the understanding of Ames mutagenicity assays for primary aromatic amines.
Discover more →Intermediates
Sharing Ames mutagenicity results for common intermediates and in vitro mammalian cell assays.
Discover more →AI/PDE
Bringing acceptable intake and permitted daily exposure data together to support compound-specific limits.
Discover more →Ready to bring data sharing into your NAMs workflow?
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Why Lhasa?
Trusted host
Neutral, not-for-profit governance.
Data governance
Quality, consistency and confidentiality.
Expert-driven
Toxicologists, data scientists and regulatory specialists.
Maintained & sustainable
Active curation and long-term stewardship.
Lhasa provides the neutral environment, scientific expertise and infrastructure needed to turn shared data into knowledge that organisations can use.