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
  • [41] Using high-throughput pharmacokinetic simulation and in silico property estimates to anticipate mammalian toxicity
    Clark, Robert
    Lawless, Michael
    Daga, Pankaj
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 256
  • [42] A high-throughput method for assessing chemical toxicity using a Caenorhabditis elegans reproduction assay
    Boyd, Windy A.
    McBride, Sandra J.
    Rice, Julie R.
    Snyder, Daniel W.
    Freedman, Jonathan H.
    TOXICOLOGY AND APPLIED PHARMACOLOGY, 2010, 245 (02) : 153 - +
  • [43] High-Throughput Metabolic Toxicity Assessment of Compounds Using the Metachip-Datachip Platform
    Asuri, Prashanth
    Ryan, Jessica
    Lee, Moo-Yeal
    Zhu, Guangyu
    Hogg, Michael
    Clark, Douglas S.
    Dordick, Jonathan S.
    Rozzell, David
    CHEMICAL RESEARCH IN TOXICOLOGY, 2010, 23 (01) : 279 - 280
  • [44] Round table - Molecular modeling and high-throughput experimentation (HTE): Meeting the challenges of catalysts, chemicals and materials design
    Raybaud, Pascal
    Toulhoat, Herve
    Morin, Stephane
    Farrusseng, David
    Norskov, Jens
    Baron, Gino
    Wolverton, Chris
    Chianelli, Russel
    Ungerer, Philippe
    Stern, David
    Henry, Claude
    Daage, Michel
    Mortier, Wilfried
    OIL & GAS SCIENCE AND TECHNOLOGY-REVUE D IFP ENERGIES NOUVELLES, 2006, 61 (04): : 579 - 592
  • [45] Optimization of Multicomponent Photopolymer Formulations Using High-Throughput Analysis and Kinetic Modeling
    Johnson, Peter M.
    Bowman, Christopher N.
    Stansbury, Jeffrey W.
    AICHE JOURNAL, 2010, 56 (05) : 1262 - 1269
  • [46] Kinetic modeling of a comonomer photopolymerization system using high-throughput conversion data
    Johnson, Peter M.
    Stansbury, Jeffrey W.
    Bowman, Christopher N.
    MACROMOLECULES, 2008, 41 (01) : 230 - 237
  • [47] Identifying Chemicals and Mixtures of Potential Biological Concern Detected in Passive Samplers from Great Lakes Tributaries Using High-Throughput Data and Biological Pathways
    Alvarez, David A.
    Corsi, Steven R.
    De Cicco, Laura A.
    Villeneuve, Daniel L.
    Baldwin, Austin K.
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2021, 40 (08) : 2165 - 2182
  • [48] Risk-Based High-Throughput Chemical Screening and Prioritization using Exposure Models and in Vitro Bioactivity Assays
    Shin, Hyeong-Moo
    Ernstoff, Alexi
    Arnot, Jon A.
    Wetmore, Barbara A.
    Csiszar, Susan A.
    Fantke, Peter
    Zhang, Xianming
    McKone, Thomas E.
    Jolliet, Olivier
    Bennett, Deborah H.
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2015, 49 (11) : 6760 - 6771
  • [49] Iron oxide nanoparticle toxicity testing using high-throughput analysis and high-content imaging
    Harris, Georgina
    Palosaari, Taina
    Magdolenova, Zuzana
    Mennecozzi, Milena
    Gineste, Jean Michel
    Saavedra, Luis
    Milcamps, Anne
    Huk, Anna
    Collins, Andrew Richard
    Dusinska, Maria
    Whelan, Maurice
    NANOTOXICOLOGY, 2015, 9 : 87 - 94
  • [50] High-throughput screening and classification of chemicals and their effects on neuronal gene expression using RASL-seq
    Jeremy M. Simon
    Smita R. Paranjape
    Justin M. Wolter
    Gabriela Salazar
    Mark J. Zylka
    Scientific Reports, 9