Document Type

Article

Publication Date

2024

DOI

10.3389/ftox.2024.1461587

Publication Title

Frontiers in Toxicology

Volume

6

Pages

1461587 (1-21)

Abstract

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, highlighting successful applications and case studies across diverse sectors. From predicting genotoxicity and mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer a comprehensive framework for understanding chemical exposures and mitigating associated health risks. Future perspectives for advancing chemical risk assessment and cancer prevention through data integration, advanced machine learning techniques, translational research, and policy implementation are discussed. By implementing the predictive capabilities of AI/ML and omics technologies, researchers and policymakers can enhance public health protection, inform regulatory decisions, and promote sustainable development for a healthier future.

Rights

© 2024 Singh, Bhardwaj, Laux, Pradeep, Busse, Luch, Hirose, Osgood and Stacey.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (CC BY 4.0). The use, distribution or reproduction in other forums is permitted, provided the original authors and the copyright owners are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Original Publication Citation

Singh, A. V., Bhardwaj, P., Laux, P., Pradeep, P., Busse, M., Luch, A., Hirose, A., Osgood, C. J., & Stacey, M. W. (2024). AI and ML-based risk assessment of chemicals: Predicting carcinogenic risk from chemical-induced genomic instability. Frontiers in Toxicology, 6, 1-21, Article 1461587. https://doi.org/10.3389/ftox.2024.1461587

ORCID

0009-0001-8330-2703 (Osgood), 0000-0002-3807-6233 (Stacey)

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