Incorporation of chemical and toxicological availability into metal mixture toxicity modeling: State of the art and future perspectives

Bing Gong, Hao Qiu, Ana Romero-Freire, Cornelis A.M. Van Gestel, Erkai He*

*Corresponding author for this work

    Research output: Contribution to JournalReview articleAcademicpeer-review

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    Abstract

    In the real world, metals are generally present as mixtures, but evaluating their mixture toxicity is still a daunting challenge. The classic conceptual models of concentration addition (CA) and independent action (IA) have been widely used by simply adding doses and responses to predict mixture effects assuming there is non-interaction. In cases where interactions do occur in a mixture, both CA and IA are no longer applicable for quantifying the toxicity, because interpretation of the observed joint effects is often limited to overall antagonism or synergism. In metal mixtures, interactive effects may occur at various levels, such as the exposure level, the uptake level, and the target level. A comprehensive understanding of the mechanisms of joint toxicity is therefore needed to incorporate the interactive effects of mixture components in predicting mixture toxicity. With this in mind, numerous bioavailability-based methods may be considered, with diverse mechanistic perspectives, such as the biotic ligand model (BLM), the electrostatic toxicity model (ETM), the WHAM-F tox approach, a toxicokinetic-toxicodynamic (TK-TD) and an omics-based approach. This review therefore timely summarizes the representative predictive tools and their underlying mechanisms and highlights the importance of integrating mixture interactions and bioavailability in assessing the toxicity and risks of metal mixtures.

    Original languageEnglish
    Pages (from-to)1730-1772
    Number of pages43
    JournalCritical Reviews in Environmental Science and Technology
    Volume52
    Issue number10
    Early online date25 Dec 2020
    DOIs
    Publication statusPublished - 2022

    Bibliographical note

    Funding Information:
    This study was supported by the National Key R&D Program of China (No. 2018YFC1800600), the National Natural Science Foundation of China (Nos. 41701571, 41701573, 41877500, 41977115, and 42022057), and Shanghai Rising-Star Program (No. 20QA1404500), Science and Technology Program of Guangzhou, China (No. 201904010116).

    Publisher Copyright:
    © 2020 Taylor & Francis Group, LLC.

    Funding

    This study was supported by the National Key R&D Program of China (No. 2018YFC1800600), the National Natural Science Foundation of China (Nos. 41701571, 41701573, 41877500, 41977115, and 42022057), and Shanghai Rising-Star Program (No. 20QA1404500), Science and Technology Program of Guangzhou, China (No. 201904010116).

    Keywords

    • Biotic ligand model
    • electrostatic
    • mixture effects
    • omics
    • toxicokinetic-toxicodynamic
    • WHAM

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