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 gov-erned 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. Can-didate 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 recon-struction 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 dem-onstrating 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.