Advances in Machine Learning Models for Toxicity Prediction in Aquatic Systems: Trends and Perspectives

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dc.creatorDiéguez-Santana, Karel
dc.date2026-06-10
dc.date.accessioned2026-06-11T06:30:12Z
dc.descriptionEcotoxicological risk assessment in aquatic systems is a fundamental pillar of contemporary environmental management. The presence of compounds of industrial, agricultural, and pharmaceutical origin in water bodies poses urgent challenges for environmental regulation and monitoring. Entities such as the Organisation for Economic Co-operation and Development (OECD), the United States Environmental Protection Agency (US-EPA), and the European Union, through the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation, require the toxicological assessment of thousands of substances in aquatic organisms. However, experimental testing is costly, time-consuming, and raises ethical dilemmas when it involves living organisms.en-US
dc.descriptionLa evaluación del riesgo ecotoxicológico en sistemas acuáticos constituye un pilar fundamental de la gestión ambiental contemporánea. La presencia de compuestos de origen industrial, agrícola y farmacéutico en cuerpos de agua plantea desafíos urgentes para la regulación y el monitoreo ambiental. Entidades como la Organización para la Cooperación y el Desarrollo Económicos (OCDE), la Agencia de Protección Ambiental de los Estados Unidos (US-EPA, por sus siglas en inglés) y la Unión Europea, a través del reglamento sobre el registro, la evaluación, la autorización y la restricción de sustancias químicas (REACH), exigen la evaluación toxicológica de miles de sustancias en organismos acuáticos. Sin embargo, los ensayos experimentales son costosos, consumen tiempo y plantean dilemas éticos cuando involucran organismos vivos.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/4049
dc.identifier.urihttps://hdl.handle.net/20.500.12622/8173
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/4049/4109
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dc.relation/*ref*/K. Diéguez Santana, M. M. Nachimba-Mayanchi, A. Puris, R. Torres Gutiérrez, and H. González-Díaz, “Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches,” Environ. Res., vol. 214, no. Part 3, p. 113984, Nov. 2022. https://doi.org/10.1016/j.envres.2022.113984
dc.relation/*ref*/K. Diéguez-Santana, G. M. Casanola-Martin, R. Torres-Gutiérrez, B. Rasulev y H. González-Díaz, “AQUA Tox: A web tool for predicting aquatic toxicity in rotifer species using intrinsic explainable models,” J. Hazard. Mater., vol. 492, p. 138050, Jul. 2025. https://doi.org/10.1016/j.jhazmat.2025.138050
dc.relation/*ref*/K. Diéguez-Santana, H. Pham-The, P. J. Villegas-Aguilar, H. Le-Thi-Thu, J. A. Castillo-Garit, and G. M. Casañola-Martin, “Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database,” Chemosphere, vol. 165, pp. 434-441, Dec. 2016. https://doi.org/10.1016/j.chemosphere.2016.09.041
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dc.relation/*ref*/K. Diéguez-Santana, B. Rasulev, and H. González-Díaz, “Towards Rational Nanomaterial Design by Prediction of Drug-Nanoparticle Systems Interaction vs. Bacteria Metabolic Networks,” Environ. Sci.: Nano, vol. 9, pp. 1391-1413, Apr. 2022. https://doi.org/10.1039/D1EN00967B
dc.relation/*ref*/J. A. Castillo-Garit et al., “Aplicaciones y potencialidades de los métodos de diseño computacional en estudios ambientales y farmacocinéticos,” Anales de la ACC, vol. 11, no. 1, Aug. 2021. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2304-01062021000100013
dc.relation/*ref*/H. Tan et al., “Deep learning in environmental toxicology: Current progress and open challenges,” ACS ES&T Water, vol. 4, no. 3, pp. 805–819, Jun. 2024. https://doi.org/10.1021/acsestwater.3c00152
dc.relation/*ref*/L. D. Burgoon, F. M. Kluxen, and M. Frericks. “Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints–a pesticide perspective for regulatory toxicologists with a focus on machine learning models,” Regulatory Toxicology and Pharmacology, vol. 137, p. 105311. Jan. 2023. https://doi.org/10.1016/j.yrtph.2022.105311
dc.relation/*ref*/A. Gajewicz-Skretna, A. Furuhama, H. Yamamoto, and N. Suzuki, “Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods,” Chemosphere, vol. 280, p. 130681, Oct. 2021. https://doi.org/10.1016/j.chemosphere.2021.130681
dc.rightsCopyright (c) 2026 TecnoLógicasen-US
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 29 No. 66 (2026)en-US
dc.sourceTecnoLógicas; Vol. 29 Núm. 66 (2026)es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectmodelos de aprendizaje automáticoes-ES
dc.subjecttoxicidad ambientales-ES
dc.subjectsistemas acuáticoses-ES
dc.subjectriesgo ecotoxicológicoes-ES
dc.subjectpredicción de toxicidades-ES
dc.subjectmachine learning modelsen-US
dc.subjectenvironmental toxicityen-US
dc.subjectaquatic systemsen-US
dc.subjectecotoxicological risken-US
dc.subjecttoxicity predictionen-US
dc.titleAdvances in Machine Learning Models for Toxicity Prediction in Aquatic Systems: Trends and Perspectivesen-US
dc.titleAvances en modelos de aprendizaje automático para la predicción de toxicidad en sistemas acuáticos: tendencias y perspectivases-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeEditorialen-US
dc.typeEditoriales-ES

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