New Approach Methodologies: A New Terrain for Antibody Testing

Regulatory authorities support the use of animal alternatives for testing monoclonal antibodies, but how will this process play out? Six questions to consider.

In April, the US Food and Drug Administration released a roadmap outlining how to reduce animal testing in preclinical safety studies, with the journey beginning with monoclonal antibodies (mAbs)—large-molecule drugs that account for approximately 50% of new approvals. Singling out mAbs as a starting point for new approach methodologies (NAMs) wasn’t surprising. New Approach Methodologies (NAMs) are innovative testing strategies, ranging from in vitro assays to computational models, that aim to generate safety data without relying solely on traditional animal studies.

Compared to small-molecule drugs, fewer animal studies are needed to advance an mAb. The toxicology of mAbs is already limited relative to small-molecule drugs, as explained in the ICH S6 guidelines.

Carcinogenicity tests may be waived in some mAb studies, and specific reproductive toxicology tests can also be delayed.  Additionally, the FDA and sponsors can engage in discussions to address challenges such as the immunogenicity of human proteins in animal models, an issue that frequently arises but typically does not obscure potential toxicological signals

Here are six key questions around the implementation of NAMs to propel antibody discovery and accelerate regulatory approval.

Why are antibodies a good frontier to explore new approach methodologies?

Antibodies, especially humanized mAbs, antibody drug conjugates (ADCs), and bispecific antibodies, often fail in animal models due to species differences in target binding, immune architecture, and toxicity. NAMs (in vitro human cells, organoids, in silico) offer better predictive human-relevant translatability, reducing reliance on animal data.

How does using NAMs in the antibody space help us to meet our 3Rs objectives?

NAMs advance the 3Rs in antibody development by replacing several in vivo studies with human-relevant cell and computational models, reducing animal use through early high-throughput screening and virtual control groups, and refining approaches with organoids and microphysiological systems (MPS). Together, they enhance mechanistic understanding, streamline development, and align with the growing regulatory acceptance of the 3Rs.

How much are animals used in different antibody modalities (ADCs, bispecifics, mAbs)?

The more complex and human-tailored the antibody modality, the less relevant animal models tend to be. mAbs generally have well-validated animal models (mice, rats, and some large animal species). ADCs often need multi-species toxicology owing to payload toxicity. It becomes trickier for bispecifics, as many may lack relevant animal models if targets are human-specific, forcing reliance on transgenic/surrogate models, which may not fully recapitulate the human system. More extensive in vitro human cell-based models help fill the gap. 

How are NAMs being integrated to de-risk antibody programs and accelerate timelines?

Human cell assays and computational models identify off-target liabilities and immunogenic hotspots early, reducing late-stage failures. AI-driven candidate screening, combined with organ-on-chip toxicity platforms, can reduce preclinical pipelines from years to under a few months. Such approaches cut costs, enhance decision-making speed, and de-risk investment by flagging safety issues before animal- or clinical studies commence.

Are there any challenges with using new approaches now in terms of their viability, translatability, or even availability?

Key hurdles include the limited validation of novel NAMs against established animal datasets, regulatory alignment and acceptance, which are still evolving, as well as gaps in standardized protocols for complex systems, such as organoids. Access to high-quality human primary cells and specialized bioengineering platforms also remains variable. Ongoing initiatives, such as the FDA’s 2025 roadmap, are harmonizing guidelines and fostering partnerships to improve NAMs' adoption.

What will the NAMs pipeline look like in the future?

The future pipeline is likely to be dominated by integrated platforms. AI-driven in silico screening will triage better candidates faster, while multi-organ microphysiological systems will be able to assess efficacy and safety in parallel. Additionally, models such as digital twins and biomarker identification will improve the prediction of clinical outcomes. By 2030, regulatory acceptance of NAMs as stand-alone dossiers is expected, enabling faster, ethical, and cost-effective antibody development. 

Check out this webinar on how NAMS can transform discovery to IND and improve translational outcomes. The webinar was presented by Namrata Jayanth and Jan Iyer, Director of the AI/ML Platform Product at Absci.

Namrata Jayanth, PhD, is a Research Leader in Advanced Modalities at Charles River’s Early Discovery site in Leiden, Netherlands. She has several years of experience in industrial and academic settings, focusing on Cancer Cell Biology, In Vitro Pharmacology, and Drug Discovery. Previously at the Bristol-Myers Squibb Research Centre, she served as a Discovery Biology Team Lead, responsible for the early-stage discovery and preclinical progression of small molecules and therapeutic antibodies for the oncology and immunology portfolios.

References:

1. https://nagibio.ch/news/embracing-ethical-and-efficient-toxicology-with-nams
2. https://cn-bio.com/why-the-fda-animal-testing-phase-out-for-monoclonal-antibodies/
3. https://www.axionbiosystems.com/new-approach-methodologies-nams-advances-preclinical-safety-studies
4. https://www.greenlabs-nl.eu/projects/animal-free-science/
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6. https://pmc.ncbi.nlm.nih.gov/articles/PMC7839014/
7. https://www.sciencedirect.com/science/article/abs/pii/S0273230025001175
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9. https://www.fda.gov/news-events/press-announcements/fda-announces-plan-phase-out-animal-testing-requirement-monoclonal-antibodies-and-other-drugs
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