Non-Animal Methods (NAMs)
Using in silico methods in NAMs
Every confident chemical safety decision starts
with the right evidence.
Regulators around the world are increasingly encouraging the use of NAMs to reduce reliance on animal testing. In silico methods are one of the most established and widely accepted NAMs, but knowing when and how to use them is just as important as the technology itself.
Expand your knowledge
Trusted by regulators. Used by industry.
Regulators are increasingly accepting and expecting scientifically justified combinations of evidence.
Industry is building on knowledge already generated, rather than starting from scratch.
We are an experienced in silico and read-across (RAX) provider
member organisations working with Lhasa Limited globally
of members renew their sponsorship year after year
regulatory bodies trained on Lhasa tools
What in silico and RAX methods bring to the assessment
Uses existing data
Builds on knowledge already generated and fills gaps where no experimental data exists yet.
Fast
Predictions in minutes, not weeks, and can run before a compound is even synthesised.
Cost-efficient
No reagents, animals or lab time, with many compounds assessed in parallel.
Transparent
No laboratory animals required, with traceable, data-rich reasoning behind every prediction.
Toxicologist, Analytical Development Department
Aché
I've been using Lhasa solutions in my day-to-day work for the past four years. What stands out most is the confidence they give us. Their scientific validity and robustness strengthen the documentation we submit to regulatory authorities, and the intuitive interface makes even complex assessments straightforward.
Find your use case
Filter by sector and discover the chemical safety use cases in silico methods are most commonly used to answer.
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A practical pathway to NAM adoption
Adopting NAMs works best as a gradual, staged process, introducing each new method alongside your existing assays, not instead of them. The steps below move you from your current testing baseline toward routine NAM integration.
Assess your current testing practices
Understand where your existing safety assessment relies on in vivo and in vitro methods, and identify where computational approaches could add value without disrupting ongoing projects. Our scientific experts can help you explore appropriate use cases and understand where in silico approaches can complement your existing evidence.
Audit and curate your existing data
Bring together historical toxicity data around key endpoints to understand what evidence you already have, identify data gaps and create a reference set for evaluating new approaches. With over 20 years of experience running data-sharing consortia and toxicity data management systems, we can help you build on high-quality shared evidence rather than starting from scratch.
Define clear objectives and context of use
Define the specific question your NAM needs to answer, the context in which it will be used and what constitutes sufficient evidence. Our experience developing and applying in silico methodologies can help you select an appropriate approach and understand what its outputs can, and cannot, tell you.
Run hybrid testing and validation in parallel
Introduce appropriate in silico methods alongside existing evidence, comparing predictions with experimental data and assessing where the approach performs well and where uncertainty remains. Our in silico solutions combine predictive models with scientific knowledge and transparent reasoning, giving your team a practical way to evaluate their performance before expanding their use.
Train your team and build expertise
Make sure scientists and reviewers understand what each method can and cannot do, so they can recognise uncertainty and apply expert judgement with confidence. As an educational charity, we provide training, documentation and scientific support to help teams understand and interpret computational predictions.
Align with regulatory expectations
Understand the regulatory expectations relevant to your intended use and ensure your evidence and interpretation can be appropriately justified. Our long-standing involvement in regulatory science and experience supporting internationally recognised approaches can help you understand how computational evidence fits into regulatory decision-making.
Monitor outcomes and iterate
Track performance and decision impact over time, identify gaps and use what you learn to strengthen your approach and expand NAM use as confidence grows. Through our collaborative data-sharing initiatives, we provide opportunities to contribute to and benefit from growing industry evidence bases, helping computational approaches evolve alongside scientific and regulatory expectations.
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