Abstract

LEVERAGING ARTIFICIAL INTELLIGENCE FOR NON-ANIMAL TOXICITY TESTING: A NEW ERA OF ETHICAL TOXICOLOGY

Sunil Kumar* and Sahana V. M. Vats

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Abstract

The field of toxicology stands at a pivotal crossroads, shifting from reliance on traditional animal testing- fraught with ethical dilemmas, high costs, and limited human relevance-toward innovative, artificial intelligence (AI)-driven, animal-free approaches. This narrative review offers a comprehensive conceptual framework for this paradigm transition, critically analyzing the inherent limitations of in-vivo animal models, including species-specific biological discrepancies and scalability issues. It highlights groundbreaking AI-powered alternatives such as advanced Quantitative Structure-Activity Relationship (QSAR) models, organ-on-chip (OoC) technologies augmented with machine learning, and sophisticated in silico virtual screening techniques that integrate multi-omics and big data analytics for improved toxicity prediction. The review further explores the evolving regulatory landscape, acknowledging cautious but growing acceptance of AI tools by global agencies, while identifying key challenges related to model validation, transparency, ethical AI deployment, and data bias. Emphasizing interdisciplinary collaboration and scientific transparency, this article advocates for a future toxicology framework that balances technological innovation with ethical responsibility. Ultimately, the fusion of AI and toxicology represents more than a methodological upgrade, it is a transformative step toward a humane, efficient, and scientifically strong approach to toxicological safety assessment.

Keywords: Artificial Intelligence (AI), Toxicology, Non-Animal Testing, QSAR Models, Organ-on-Chip, In Silico Methods, Virtual Screening, Ethical Toxicology, Regulatory Science, Computational Toxicology, Predictive Modeling, 3Rs Principle (Replace, Reduce, Refine)