Systematic update to the mammalian relative potency estimate database and development of best estimate toxic equivalency factors for dioxin-like compounds

Fitch, S.; Blanchette, A.; Haws, L.C.; Franke, K.; Ring, C.; DeVito, M.; Wheeler, M.; Walker, N.; Birnbaum, L.; Van Ede, K.I.; Antunes Fernandes, E.C.; Wikoff, D.S.

doi:10.1016/j.yrtph.2024.105571

Regulatory Toxicology and Pharmacology

2024

DOI: 10.1016/j.yrtph.2024.105571

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Systematic update to the mammalian relative potency estimate database and development of best estimate toxic equivalency factors for dioxin-like compounds

S. Fitch,

A. Blanchette,

L.C. Haws

et al.

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AI in Predictive Toxicology

Yingngam

2024

Advances in Medical Technologies and Clinical Practice

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The field of toxicology is undergoing a significant transformation due to the integration of artificial intelligence (AI). In addition to traditional reliance on empirical studies and animal testing, AI-powered predictive toxicology is now used to predict the toxic effects of chemicals and drugs. This chapter examines the role of AI in enhancing the accuracy, efficiency, and breadth of toxicological assessments by bridging the gap between traditional approaches and advanced AI techniques. It explores various AI methodologies, such as machine learning, deep learning, and neural networks, focusing on their application in toxicity prediction. Furthermore, this chapter investigates the integration of AI with toxicological databases and the development and empirical validation of predictive models. It also addresses various challenges associated with AI-powered toxicology, including data quality, model interpretability, and scalability. The chapter concludes that despite facing challenges, AI is a powerful tool in modern toxicological analysis.

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AI in Predictive Toxicology

Yingngam

2024

Advances in Medical Technologies and Clinical Practice

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Systematic update to the mammalian relative potency estimate database and development of best estimate toxic equivalency factors for dioxin-like compounds

Cited by 1 publication

References 15 publications

AI in Predictive Toxicology

AI in Predictive Toxicology

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