Toward In Silico NAMs Analysis of Thyroid Disruption Leading to Developmental Neurotoxicity─A Collection of AOP-Anchored Computational ModelsClick to copy article linkArticle link copied!
- Beata JudzinskaBeata JudzinskaLaboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza St. 63, Gdańsk 80-308, PolandMore by Beata Judzinska
- Wiktor NisterenkoWiktor NisterenkoLaboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza St. 63, Gdańsk 80-308, PolandDepartment of Physical Chemistry, Medical University of Gdańsk, Al. Gen. Hallera 107, Gdańsk 80-416, PolandMore by Wiktor Nisterenko
- Natalia BulawskaNatalia BulawskaLaboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza St. 63, Gdańsk 80-308, PolandMore by Natalia Bulawska
- Dominika KowalskaDominika KowalskaQSAR Lab Ltd., Grunwaldzka Ave. 103A, Gdańsk 80-244, PolandMore by Dominika Kowalska
- Karolina Jagiello*Karolina Jagiello*Email: [email protected]Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza St. 63, Gdańsk 80-308, PolandMore by Karolina Jagiello
- Anita Sosnowska*Anita Sosnowska*Email: [email protected]Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza St. 63, Gdańsk 80-308, PolandMore by Anita Sosnowska
- Tomasz PuzynTomasz PuzynLaboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza St. 63, Gdańsk 80-308, PolandMore by Tomasz Puzyn
Abstract
Thyroid disruption (TD) plays a critical role in developmental neurotoxicity (DNT), given the essential functions of thyroid hormones in brain development. The identification and assessment of DNT caused by TD have become a significant focus in regulatory toxicology, necessitating the use of innovative approaches that are both predictive and efficient. This study provides a comprehensive examination of in silico new approach methodologies, with a particular emphasis on (quantitative) structure–activity relationship ((Q)SAR) models. Models anchored in the adverse outcome pathway framework offer mechanistic insights and predictive capabilities for assessing DNT linked to TD. By integrating knowledge of molecular initiating events and key events associated with thyroid hormone disruption, quantitative structure–activity relationships models provide a streamlined approach for predicting DNT. This systematic review identified 44 relevant studies documenting a total of 178 predictive models. The distribution of models across endpoints reveals that the most dominant endpoints are PXR (72), TTR (45), and TPO (21). A rigorous quality assessment showed that only 32 models are fully compliant with the OECD QAF (Quality Assessment Framework) criteria. This highlights the urgent need for more robust, endpoint-specific modeling tools.
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