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In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index
Umeå University, Faculty of Science and Technology, Department of Chemistry.ORCID iD: 0000-0002-3154-6642
Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Department of Environmental Science, Stockholm University, Stockholm, Sweden.
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2022 (English)In: Environmental Science and Technology, ISSN 0013-936X, E-ISSN 1520-5851, Vol. 56, no 12, p. 8363-8372Article in journal (Refereed) Published
Abstract [en]

Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2022. Vol. 56, no 12, p. 8363-8372
Keywords [en]
conformal prediction, endocrine disruption, environmental health, QSAR
National Category
Environmental Sciences Other Chemistry Topics Occupational Health and Environmental Health
Identifiers
URN: urn:nbn:se:umu:diva-196482DOI: 10.1021/acs.est.1c07762ISI: 000815124300001PubMedID: 35561338Scopus ID: 2-s2.0-85131097293OAI: oai:DiVA.org:umu-196482DiVA, id: diva2:1671850
Funder
Swedish Research Council Formas, 2018-02264Swedish Environmental Protection Agency, 215-20-010Mistra - The Swedish Foundation for Strategic Environmental Research, 2018/11Available from: 2022-06-17 Created: 2022-06-17 Last updated: 2024-03-22Bibliographically approved
In thesis
1. Development of in silico methods to aid chemical risk assessment: focusing on kinetic interactions in mixtures
Open this publication in new window or tab >>Development of in silico methods to aid chemical risk assessment: focusing on kinetic interactions in mixtures
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Utveckling av in silico-metoder för att underlätta kemisk riskbedömning : med fokus på kinetiska interaktioner i blandningar
Abstract [en]

The environment and biota are constantly exposed to numerous chemicals through contaminated food, soil, water, and air. These chemicals can be taken up and distributed to reach sensitive tissues where they may cause various effects. Many of these chemicals lack data on their environmental and human health effects. Traditional toxicological tests relying on animal experiments are today being phased out in favor of cell-based and computational methods for early hazard detection and exposure assessment. This thesis focuses on developing computational tools for various stages of chemical risk assessment with a particular focus on bisphenols and per- and polyfluoroalkyl substances (PFAS). In Paper I, quantitative structure-activity relationship (QSAR) models covering molecular targets of the thyroid hormone (TH) system were developed and applied to two data sets to prioritize chemicals of concern for detailed toxicological studies. In Papers II and III, experimental and computational approaches were combined to study toxicokinetics and maternal transfer in zebrafish. Our main focus was to study potential mixture effects on administration, distribution, metabolism, and elimination (ADME) processes, i.e., to reveal if co-exposed chemicals impact each other’s ADME. Physiologically based kinetic (PBK) mixture models were developed to allow translation of external exposure concentrations into tissue concentrations and modelling plausible mechanisms of chemical interactions in a mixture.

Main findings of this thesis are summarized as follows:

• Application of QSAR models (Paper I) to two chemical inventories revealed that chemicals found in human blood could induce a large iirange of pathways in the TH system whereas chemicals used in Sweden with predicted high exposure index to consumers showed a lower likelihood to induce TH pathways.

• Two zebrafish experiments (Paper II and Paper III) did not reveal statistically significant mixture effects on ADME of chemicals.

• In Paper II, a PBK mixture model for PFAS accounting for competitive plasma protein binding was developed. The model demonstrated good predictive performance. Competitive plasma protein binding did not affect the predicted internal concentrations.

• In Paper III we developed a binary PBK model parametrized for two bisphenols and PFOS showing that competitive plasma protein binding has an effect on ADME of bisphenols at PFOS concentrations at μg/L levels. At these levels internal concentrations of bisphenols were shown to decrease, implying that PFOS outcompeted bisphenols from studied plasma proteins resulting in higher excretion rates.

Developed QSAR models showed good predictive power and the ability to identify and prioritize chemicals of concern with confidence. Additionally, PBK models aid in hypotheses testing and predicting exposure concentrations at which co-exposed chemicals could potentially influence each other’s ADME properties. These tools will provide overall early tier data on exposure and effects using non-testing methods in assessment of risks of chemicals. 

Place, publisher, year, edition, pages
Umeå: Umeå University, 2024. p. 69
Keywords
QSAR, physiologically based kinetic, PBK, zebrafish, ADME, mixture, bisphenols, PFAS, toxicokinetics
National Category
Environmental Sciences
Research subject
environmental science; Toxicology
Identifiers
urn:nbn:se:umu:diva-222550 (URN)978-91-8070-340-6 (ISBN)978-91-8070-341-3 (ISBN)
Public defence
2024-04-19, Lilla Hörsalen, KBE301, KBC-huset, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2024-03-28 Created: 2024-03-22 Last updated: 2024-03-25Bibliographically approved

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Dracheva, ElenaRydén, PatrikAndersson, Patrik L.

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