NAM Testing FAQ

A NAM is a New Approach Methodology or Novel Alternative Methodology used to study biology, disease, safety, or efficacy with approaches that can reduce, refine, or replace animal use. NAMs can include cell-based systems, organoids, microphysiological systems, computational models, and integrated workflows.

You test a NAM when you need stronger evidence that the method is accurate, biologically relevant, reproducible, and fit for the question you want to answer. Testing a NAM helps clarify what the method can do well, where its boundaries are, and whether the outputs line up with the biology you are trying to model.

A NAM can be useful for mechanistic research, translational biology, toxicology, drug repurposing, biomarker work, and study design decisions. The right NAM depends on the biological system, endpoint, and level of evidence you need.

Testing a NAM usually involves asking whether the method reflects the biology it claims to represent, whether it gives consistent outputs, and whether it performs well enough for a specific research or decision context. In practice, that can involve benchmarking against relevant in vivo systems, evaluating repeatability, and comparing outputs under defined conditions.

Verification asks whether your NAM is working accurately and reproducibly as a model system. Validation asks whether the specific findings from your NAM-based project hold up when compared with carefully designed in vivo work.

Repurposing is useful when a NAM already works in one setting and you want to adapt it to a new organ system, disease context, exposure window, or decision problem. Instead of rebuilding from scratch, you test whether the method still performs well under the new conditions.

Co-development makes sense when you are designing a new NAM, combining multiple components, or trying to build biological context into the method from the start. That approach can reduce redesign later and align the method more closely with translational or regulatory goals.

Use the NAM Directory when you want to find a NAM test, compare method types, review validation status, or scan available approaches by biomarker, organ system, or domain. It is the best starting point when you are trying to understand what methods already exist.

Use the AI/ML datasets page when you need supporting data sources for computational toxicology, translational modeling, machine learning workflows, or hypothesis generation that sits alongside NAM testing.

If you need to know whether your NAM is fundamentally accurate, start with verification. If you need to confirm findings from a specific project, start with validation. If you need to adapt an existing method, look at repurposing. If you are building a method from the ground up or combining approaches, review co-development.