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 In cases where interactions do occur in a mixture, both CA and IA are no longer applicable for quantifying the toxicity, because interpretation of the observed joint effects is often limited to overall antagonism or synergism.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 language | English |
|---|---|
| Pages (from-to) | 1730-1772 |
| Number of pages | 43 |
| Journal | Critical Reviews in Environmental Science and Technology |
| Volume | 52 |
| Issue number | 10 |
| Early online date | 25 Dec 2020 |
| DOIs | |
| Publication status | Published - 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