Recommendation 013/2026 of the German Federal Institute for Risk Assessment (BfR) considers a reduction of concurrent control animals possible when historical control data demonstrate stability. However, the recommendation provides no method for quantifying stability or determining the magnitude of reduction. We present a framework that addresses this gap for designs whose control group size follows from a power calculation on pre-specified continuous endpoints, each assessed separately. Designs whose group size is prescribed by a test guideline, as in standard repeated-dose toxicity studies, fall outside its scope. The framework offers two approaches scaled to the available expertise. Both begin with a shared planning step that estimates how much the control-group mean of an endpoint varies across studies of the same design and assigns that endpoint, not the laboratory, to one of three stability zones (stable, moderate, or unstable). In the stable zone, a frequentist approach maintains 80% power while accepting a modestly larger minimum detectable effect and requires no Bayesian expertise. A full Bayesian approach achieves a greater reduction at constant power and effect size, provided concurrent and historical controls agree, but requires statistical expertise. We demonstrate, using 89 artificial historical control animals across 8 studies, that the frequentist approach saves 12 of 37 control animals per study and the Bayesian approach up to 32, compared with zero under the status quo. Naive pooling, the intuitive but incorrect alternative, can inflate the false positive rate from 5% to over 30%. A freely available planning tool (C*Power) supports the planning step. Plain language summaryWhen researchers test new substances in animals or run intervention and behavioral studies, they need an untreated control group. Its size is often set by a statistical formula that ignores all previous experiments, although many laboratories have years of consistent control data. We developed a framework that uses this evidence to reduce control animal numbers for measured outcomes such as body weight, wherever such a formula sets the group size. It does not apply when a testing guideline prescribes how many animals each group must contain. The framework offers two options: a simpler approach that needs no specialist statistics, and a more powerful one for laboratories with statistical support that saves the most animals. A free online tool guides researchers through the planning and drafts the documentation for ethics applications. This supports the 3R principle of reduction, as encouraged by the German Federal Institute for Risk Assessment (Recommendation 013/2026).
Vaccines are administered to large populations of healthy individuals, including vulnerable groups, making an exceptionally rigorous safety profile a prerequisite for their approval and use. Nonclinical vaccine safety assessment has traditionally relied on standardized animal testing methodologies, which face increasing challenges: the ethical imperative to reduce animal use, the need for faster vaccine development, and the limitations of animal models in predicting human-specific immune responses. The advent of modern vaccine platforms such as mRNA technologies and viral-vector vaccines necessitates a re-evaluation of safety frameworks. This article explores emerging strategies to modernize vaccine safety assessment, integrating new approach methodologies (NAMs), including in vitro systems, artificial intelligence, and adverse outcome pathways (AOPs). Ethical refinements to animal husbandry practices, including group housing, humane endpoints, pain management and scaled blood sampling volumes, are also discussed to align with the principles of the 3Rs (replacement, reduction, refinement). The adoption of molecular and omics-based techniques, along with innovative validation strategies, offers opportunities to improve predictive accuracy and mechanistic insight. The article highlights a strategic framework for implementing NAMs across the three stages at which animals are still used – early screening, late nonclinical development, and batch release – while ensuring alignment with regulatory requirements. Implementation must be careful enough to ensure that the use of NAMs in the safety evaluation of vaccines maintains or exceeds current safety assessment standards. By addressing scientific, ethical, and regulatory challenges, NAMs have the potential to transform vaccine safety assessment, enhancing human relevance and accelerating innovation in vaccine development. Plain language summaryThis article examines the evolving landscape of vaccine safety assessment, focusing on opportunities to modernize testing approaches through new approach methodologies (NAMs). Drawing on recent discussions with stakeholders across industry, academia, and regulatory bodies, we outline key challenges and strategic pathways for implementing NAMs in vaccine development while maintaining robust safety standards. Special attention is given to emerging technologies, such as human-relevant test systems, artificial intelligence, and virtual control groups, to the replacement of animal-based potency, pyrogen and adventitious agent tests used for batch release, and to considerations for regulatory acceptance and implementation strategies.
Validation underpins confidence in test methods and integrated testing strategies, yet while the methods under evaluation have been transformed, the design of validation studies remains governed by convention and feasibility rather than prospective optimization. I propose model-informed validation design (MIVD), a quantitative framework that treats a validation study as a decision problem to be designed before resources are committed. This expands on our e-validation concept from 2024. Inspired by model-informed drug development, MIVD represents the pathway from target substance population and reference construct through biological and laboratory variability, analysis, and decision rules as an executable computational model, a digital validation twin. Candidate designs are then compared by simulation using assurance, information economics, adaptive rules, and multi-objective optimization, with the aim of maximizing the probability of the correct, appropriately scoped validation decision rather than minimizing effort or maximizing the chance that a method will pass. Because reference standards are imperfect and contexts of use evolve, a validation becomes cumulative and can be updated along a lifecycle. A mean-based proxy reconstruction of the 1996-1997 EPISKIN skin corrosion validation shows how the preferred validation design shifts with the acceptance criteria, reference uncertainty, and chemical selection, while demonstrating that additional testing does not necessarily produce proportionately greater decision value. MIVD reframes validation from the evaluation of methods to the prospective design of the evidence by which those methods are judged. Plain language summaryBefore a new safety test can be used, it must be checked, or validated, to show that it produces reliable and correct results. Such validation studies are expensive, because many chemicals are tested in several laboratories. Today validation studies are mostly designed based on past practice, rather than by working out in advance which experiments would settle the important questions. This article proposes a way to design validation studies deliberately. A digital twin imitates and compares different study designs before the laboratory work begins. Revisiting a skin corrosion validation study done in 1996-1997, the approach shows that the amount of testing needed depends strongly on how much confidence in the results is required, how reliable the reference classifications are, and which chemicals are selected. Designing validation studies, rather than assembling them from templates, is the next step for safety science.
The increasing application of non-animal new approach methodologies (NAMs) in regulatory decision-making requires regulatory professionals to develop new competencies to confidently interpret data derived from this expanding array of methods. Tailored educational programs could facilitate this, but little research has examined the specific educational needs and preferences of regulatory professionals across sectors. This cross-sectional survey aims to address this gap and generate actionable insights for designing such programs. An online questionnaire was administered to regulatory professionals worldwide between July and December 2025. Quantitative data were analyzed descriptively, including bivariate descriptive analyses; thematic analysis was conducted on qualitative data. Among the 123 respondents, regulatory professionals employed by industry reported having used/assessed NAMs more often than those employed by academia or regulatory authorities. Frequent use/assessment of NAMs was highest in respondents from the cosmetics sector, and lowest in the veterinary and human pharmaceuticals and medical devices sectors. Experience levels were lowest for advanced in silico tools, large data sets, and human stem cell models. Respondents learned about NAMs most frequently through online courses and webinars, while interactive workshops, hands-on sessions, case studies, and mentoring were considered the most effective formats. Funding considerations were frequently mentioned in open-format answers. These results support the co-design of educational and training opportunities tailored to regulatory professionals’ different roles and levels of expertise on NAMs, integrating practical sessions and real-world case studies, and including strategies to ensure widespread affordability and accessibility. Such opportunities may help strengthen confidence in application of NAM data to regulatory decision-making. Plain language summaryNon-animal new approach methodologies (NAMs) are methods that generate evidence on safety and efficacy of substances without using live animals. They have great potential in supporting the replacement and reduction of animal testing. Regulatory professionals, i.e., those responsible for making decisions based on the interpretation of this evidence, should receive appropriate training to interpret NAM-derived evidence confidently. This study surveyed 123 regulatory professionals worldwide regarding their experience with NAMs and their training needs. Respondents from industry reported more experience with NAMs than those from academia or regulatory authorities. Respondents had the least experience with advanced computer-based tools, large datasets, and human stem cell models. While many had learned through online courses and webinars, they generally preferred interactive workshops, hands-on training, case studies, and mentoring. The findings suggest that affordable, accessible, and practice-oriented training is needed to support the wider use of NAMs in regulatory decisions.
