Darina G. Yordanova , Chanita D. Kuseva , Hristiana Ivanova , Terry W. Schultz , Vanessa Rocha , Andreas Natsch , Heike Laue , Ovanes G. Mekenyan
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引用次数: 0
Abstract
Justifying read-across predictions for subchronic effects, such as no observed adverse effect levels (NOAEL), is challenging. The scarcity of suitable experimental data hampers such predictions, such that a conservative approach is often employed where the structural similarity between target and the tested source substances is very high. A less stringent interpretation of structural similarity may be used to expand data gap-filling by read-across if other types of similarity (e.g., toxicokinetic and toxicodynamic consideration) are factored into the justification. Herein, qualitative and quantitative in silico-assisted procedures are described and demonstrated for those instances where no structurally similar analogues are identified. In the qualitative approach, the toxicity classification of the most toxic metabolite is assigned directly to the target compound. While simple, this approach may lead to an over-classification of the target compound and a false positive result. In contrast, the quantitative approach is more complicated. In addition to identifying those metabolites causing toxicity, it examines the quantitative information for the amount of the most toxic metabolite. The maximum dose of the parent chemical is estimated which will not result in the generation of toxic metabolites sufficient to cause harmful effects. This quantitative approach permits a calculation of the margin of exposure, is noteworthy for industrial assessment purposes.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs