Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.envint.2022.107680 |
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| Title (Primary) | Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput oxicokinetics and mixture toxicity modeling |
| Author | Braun, G.; Escher, B.I. |
| Source Titel | Environment International |
| Year | 2023 |
| Department | ZELLTOX |
| Volume | 171 |
| Page From | art. 107680 |
| Language | englisch |
| Topic | T9 Healthy Planet |
| Supplements | https://www.sciencedirect.com/science/article/pii/S0160412022006079#s0110 |
| Keywords | Biomonitoring; High-throughput kinetics; Mixture toxicity; Human health; Neurotoxicity |
| Abstract | Modern society continues to pollute the environment with larger quantities of chemicals that have also become more structurally and functionally diverse. Risk assessment of chemicals can hardly keep up with the sheer numbers that lead to complex mixtures of increasing chemical diversity including new chemicals, substitution products on top of still abundant legacy compounds. Fortunately, over the last years computational tools have helped us to identify and prioritize chemicals of concern. These include toxicokinetic models to predict exposure to chemicals as well as new approach methodologies such as in-vitro bioassays to address toxicodynamic effects. Combined, they allow for a prediction of mixtures and their respective effects and help overcome the lack of data we face for many chemicals. In this study we propose a high-throughput approach using experimental and predicted exposure, toxicokinetic and toxicodynamic data to simulate mixtures, to which a virtual population is exposed to and predict their mixture effects. The general workflow is adaptable for any type of toxicity, but we demonstrated its applicability with a case study on neurotoxicity. If no experimental data for neurotoxicity were available, we used baseline toxicity predictions as a surrogate. Baseline toxicity is the minimal toxicity any chemical has and might underestimate the true contribution to the mixture effect but many neurotoxicants are not by orders of magnitude more potent than baseline toxicity. Therefore, including baseline-toxic effects in mixture simulations yields a more realistic picture than excluding them in mixture simulations. This workflow did not only correctly identify and prioritize known chemicals of concern like benzothiazoles, organochlorine pesticides and plasticizers but we were also able to identify new potential neurotoxicants that we recommend to include in future biomonitoring studies and if found in humans, to also include in neurotoxicity screening. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26872 |
| Braun, G., Escher, B.I. (2023): Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput oxicokinetics and mixture toxicity modeling Environ. Int. 171 , art. 107680 10.1016/j.envint.2022.107680 |
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