Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput toxicokinetics and mixture toxicity modeling

被引:5
|
作者
Braun, Georg [1 ]
Escher, Beate I. [1 ,2 ]
机构
[1] UFZ Helmholtz Ctr Environm Res, Dept Cell Toxicol, Leipzig, Germany
[2] Eberhard Karls Univ Tubingen, Dept Geosci, Environm Toxicol, Tubingen, Germany
关键词
Biomonitoring; High-throughput kinetics; Mixture toxicity; Human health; Neurotoxicity; CRYOPRESERVED HUMAN HEPATOCYTES; IN-VIVO EXTRAPOLATION; DEVELOPMENTAL NEUROTOXICITY; ENVIRONMENTAL CHEMICALS; CYTOCHROMES P450; PREDICTION; VITRO; EXPOSURE; DRUG; METABOLISM;
D O I
10.1016/j.envint.2022.107680
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Modeling Signaling Networks Using High-throughput Phospho-proteomics
    Terfve, Camille
    Saez-Rodriguez, Julio
    ADVANCES IN SYSTEMS BIOLOGY, 2012, 736 : 19 - 57
  • [32] Targeted gene enrichment and high-throughput sequencing for environmental biomonitoring: a case study using freshwater macroinvertebrates
    Dowle, Eddy J.
    Pochon, Xavier
    Banks, Jonathan C.
    Shearer, Karen
    Wood, Susanna A.
    MOLECULAR ECOLOGY RESOURCES, 2016, 16 (05) : 1240 - 1254
  • [33] ACTIVE HIGH-THROUGHPUT MICROMIXER USING INJECTED MAGNETIC MIXTURE UNDERNEATH MICROFLUIDIC CHANNEL
    Surendran, Athira N.
    Zhou, Ran
    PROCEEDINGS OF THE ASME 2020 18TH INTERNATIONAL CONFERENCE ON NANOCHANNELS, MICROCHANNELS, AND MINICHANNELS (ICNMM2020), 2020,
  • [34] Estimating Margin of Exposure to Thyroid Peroxidase Inhibitors Using High-Throughput in vitro Data, High-Throughput Exposure Modeling, and Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling
    Leonard, Jeremy A.
    Tan, Yu-Mei
    Gilbert, Mary
    Isaacs, Kristin
    El-Masri, Hisham
    TOXICOLOGICAL SCIENCES, 2016, 151 (01) : 57 - 70
  • [35] Identifying environmental chemicals as agonists of the androgen receptor by using a quantitative high-throughput screening platform
    Lynch, Caitlin
    Sakamuru, Srilatha
    Huang, Ruili
    Stavreva, Diana A.
    Varticovski, Lyuba
    Hager, Gordon L.
    Judson, Richard S.
    Houck, Keith A.
    Kleinstreuer, Nicole C.
    Casey, Warren
    Paules, Richard S.
    Simeonov, Anton
    Xia, Menghang
    TOXICOLOGY, 2017, 385 : 48 - 58
  • [36] High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures
    Franzosa, Jill A.
    Bonzo, Jessica A.
    Jack, John
    Baker, Nancy C.
    Kothiya, Parth
    Witek, Rafal P.
    Hurban, Patrick
    Siferd, Stephen
    Hester, Susan
    Shah, Imran
    Ferguson, Stephen S.
    Houck, Keith A.
    Wambaugh, John F.
    NPJ SYSTEMS BIOLOGY AND APPLICATIONS, 2021, 7 (01)
  • [37] High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures
    Jill A. Franzosa
    Jessica A. Bonzo
    John Jack
    Nancy C. Baker
    Parth Kothiya
    Rafal P. Witek
    Patrick Hurban
    Stephen Siferd
    Susan Hester
    Imran Shah
    Stephen S. Ferguson
    Keith A. Houck
    John F. Wambaugh
    npj Systems Biology and Applications, 7
  • [38] Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling
    Nyffeler, Johanna
    Willis, Clinton
    Lougee, Ryan
    Richard, Ann
    Paul-Friedman, Katie
    Harrill, Joshua A.
    TOXICOLOGY AND APPLIED PHARMACOLOGY, 2020, 389
  • [39] High-throughput screening of chemicals as functional substitutes using structure-based classification models
    Phillips, Katherine A.
    Wambaugh, John F.
    Grulke, Christopher M.
    Dionisio, Kathie L.
    Isaacs, Kristin K.
    GREEN CHEMISTRY, 2017, 19 (04) : 1063 - 1074
  • [40] 3D-Suspension culture platform for high throughput screening of neurotoxic chemicals using LUHMES dopaminergic neurons
    Tong, Zhi-Bin
    Huang, Ruili
    Braisted, John
    Chu, Pei-Hsuan
    Simeonov, Anton
    Gerhold, David L.
    SLAS DISCOVERY, 2024, 29 (03)