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 articlepeer-review

13 Scopus citations

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 In cases where interactions do occur in a mixture, both CA and IA are no longer applicable for quantifying the toxicity, because interpretation of the observed joint effects is often limited to overall antagonism or synergism.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
DOIs
StatePublished - 2022

Keywords

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

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