{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T19:54:43Z","timestamp":1791921283825,"version":"4.3.4"},"reference-count":92,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020098","name":"Innovative Health Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Toxicol."],"abstract":"<jats:p>\n                    Next generation risk assessment (NGRA) demands a fundamental transformation in how toxicological test methods are validated. Traditional validation approaches, designed for animal tests and mainly for simple\n                    <jats:italic>in vitro<\/jats:italic>\n                    methods, are increasingly inadequate for evaluating complex New Approach Methodologies (NAMs), artificial intelligence (AI)-based approaches, and integrated testing strategies (ITS). This paper presents a comprehensive framework for \u201c\n                    <jats:italic>next-generation validation<\/jats:italic>\n                    \u201d that leverages artificial intelligence and modern computational capabilities to create more efficient, thorough, and dynamic validation processes. The proposed framework emphasizes human relevance over simple concordance with animal data and emphasizes key innovations including e-validation, mechanistic validation, and post-validation companion AI agents. Because AI can inherit biases, obscure failure modes, and drift over time, the framework treats AI as both a tool for, and a subject of, validation, requiring transparent performance criteria, uncertainty quantification, and explicit governance for model updates and lifecycle monitoring. To make the framework actionable, we define a method as \u201c\n                    <jats:italic>NGV-validated for a stated context of use<\/jats:italic>\n                    \u201d when it meets pre-specified acceptance criteria across five domains, i.e., technical reliability, biological relevance, predictive performance, uncertainty quantification, and data integrity, supported by defined governance roles, version control, and lifecycle re-review triggers. e-validation employs sophisticated algorithms for reference chemical selection, study simulation, and continuous performance monitoring, while mechanistic validation evaluates whether methods accurately capture relevant biological pathways and mechanisms of toxicity. The paper addresses critical implementation challenges including data quality standardization, regulatory acceptance, and international harmonization, providing specific recommendations for various stakeholders. Looking forward, validation will increasingly embrace dynamic, adaptive approaches that evolve alongside scientific understanding and technological capabilities. The integration of artificial intelligence will enhance analysis of complex data, enable real-time monitoring of method performance, and support more sophisticated uncertainty quantification. Success in this transformation requires coordinated effort across regulatory agencies, industry partners, and academic institutions. In summary, this paper emphasizes a five-pillar framework integrating mechanistic, probabilistic, and AI-driven elements to reform toxicological validation. The proposed framework, exemplified here for tests for developmental neurotoxicants and virtual control groups, represents a crucial step toward more efficient and accurate chemical safety assessment while maintaining necessary standards for public health protection.\n                  <\/jats:p>","DOI":"10.3389\/ftox.2026.1790669","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:29:50Z","timestamp":1777634990000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Next generation validation for next generation risk assessment"],"prefix":"10.3389","volume":"8","author":[{"given":"Karolina","family":"Kopa\u0144ska","sequence":"first","affiliation":[{"name":"Bloomberg School of Public Health and Whiting School of Engineering, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University","place":["Baltimore, MD, United States"]},{"name":"CAA Tevents","place":["Solingen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Hartung","sequence":"additional","affiliation":[{"name":"Bloomberg School of Public Health and Whiting School of Engineering, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University","place":["Baltimore, MD, United States"]},{"name":"CAA Tevents","place":["Solingen, Germany"]},{"name":"Doerenkamp-Zbinden Chair for Evidence-Based Toxicology","place":["Baltimore, MD, United States"]},{"name":"CAAT-Europe, University of Konstanz","place":["Konstanz, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"123821","DOI":"10.1016\/J.LFS.2025.123821","article-title":"The role of machine learning in predictive toxicology: a review of current trends and future perspectives","volume":"378","author":"Ajisafe","year":"2025","journal-title":"Life Sci."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1480845","DOI":"10.3389\/FNCEL.2024.1480845\/EPUB","article-title":"Organoid intelligence for developmental neurotoxicity testing","volume":"18","author":"Alam El Din","year":"2024","journal-title":"Front. 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