Validation by design: Model-informed validation design (MIVD) as a quantitative design science

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.