TY - JOUR
T1 - Incorporation of chemical and toxicological availability into metal mixture toxicity modeling
T2 - State of the art and future perspectives
AU - Gong, Bing
AU - Qiu, Hao
AU - Romero-Freire, Ana
AU - Van Gestel, Cornelis A.M.
AU - He, Erkai
N1 - Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - 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 In cases where interactions do occur in a mixture, both CA and IA are no longer applicable for quantifying the toxicity, because interpretation of the observed joint effects is often limited to overall antagonism or synergism.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.
AB - 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 In cases where interactions do occur in a mixture, both CA and IA are no longer applicable for quantifying the toxicity, because interpretation of the observed joint effects is often limited to overall antagonism or synergism.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.
KW - Biotic ligand model
KW - WHAM
KW - electrostatic
KW - mixture effects
KW - omics
KW - toxicokinetic-toxicodynamic
UR - https://www.scopus.com/pages/publications/85098524806
U2 - 10.1080/10643389.2020.1862560
DO - 10.1080/10643389.2020.1862560
M3 - 文献综述
AN - SCOPUS:85098524806
SN - 1064-3389
VL - 52
SP - 1730
EP - 1772
JO - Critical Reviews in Environmental Science and Technology
JF - Critical Reviews in Environmental Science and Technology
IS - 10
ER -