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Updated: May 19, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

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Published on: September 16, 2022

Next generation validation for next generation risk assessment.

Karolina Kopańska1,2, Thomas Hartung1,2,3,4

  • 1Bloomberg School of Public Health and Whiting School of Engineering, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, United States.

Frontiers in Toxicology
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

Next-generation risk assessment requires transforming toxicological test validation. A new framework uses artificial intelligence (AI) for dynamic, human-relevant validation, ensuring reliable chemical safety assessment.

Keywords:
artificial intelligencee-validationintegrated testing strategiesmechanistic validationnew approach methodologiesnext-generation risk assessmenttoxicologyvalidation

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Area of Science:

  • Toxicology and Risk Assessment
  • Computational Toxicology
  • Regulatory Science

Background:

  • Traditional toxicological test validation methods are inadequate for complex New Approach Methodologies (NAMs), AI-based approaches, and integrated testing strategies (ITS).
  • Next-generation risk assessment (NGRA) necessitates a fundamental shift in validation paradigms.
  • Existing validation approaches, primarily designed for animal testing and simple in vitro methods, fail to address the complexities of modern toxicological tools.

Purpose of the Study:

  • To present a comprehensive framework for "next-generation validation" (NGV) that incorporates artificial intelligence (AI) and advanced computational capabilities.
  • To establish criteria for methods to be considered "NGV-validated for a stated context of use" based on five key domains.
  • To address challenges and provide recommendations for the implementation of NGV in toxicological testing.

Main Methods:

  • Development of a five-pillar framework for NGV, integrating mechanistic, probabilistic, and AI-driven elements.
  • Emphasis on human relevance, e-validation, mechanistic validation, and post-validation AI companion agents.
  • Defining actionable criteria for NGV, including technical reliability, biological relevance, predictive performance, uncertainty quantification, and data integrity.

Main Results:

  • The proposed NGV framework enables more efficient, thorough, and dynamic validation processes.
  • AI is treated as both a tool and a subject of validation, requiring transparency, uncertainty quantification, and lifecycle monitoring.
  • The framework is exemplified for tests on developmental neurotoxicants and virtual control groups.

Conclusions:

  • The NGV framework represents a crucial step toward more efficient and accurate chemical safety assessment.
  • Dynamic, adaptive validation approaches integrating AI are essential for evolving scientific understanding and technological capabilities.
  • Successful transformation requires coordinated efforts among regulatory agencies, industry, and academia to maintain public health standards.