Publicación:
Estimación de la toxicidad acuática de compuestos químicos usados en fracturación hidráulica mediante modelado cuantitativo de relaciones estructura-actividad (QSAR) en peces de aguas continentales.

dc.audience
dc.contributor.advisorEnsuncho Muñoz, Adolfo Enrique
dc.contributor.authorRamírez León, Diana Berónica
dc.contributor.juryAlcala Varilla, Luis Arturo
dc.contributor.juryRobles González, Juana Raquel
dc.date.accessioned2024-08-09T15:18:30Z
dc.date.available2026-08-08
dc.date.available2024-08-09T15:18:30Z
dc.date.issued2024-08-07
dc.description.abstractLa contaminación de aguas dulces por sustancias químicas es un problema de gran impacto que afecta tanto a los ecosistemas como a la salud humana. En este contexto, el estudio se enfoca en la toxicidad de compuestos químicos utilizados en la fracturación hidráulica para la extracción de petróleo y gas, y su efecto en peces de aguas continentales como Pimephales promelas, Oncorhynchus mykiss y Oreochromis niloticus. El objetivo del estudio es estimar la toxicidad de treinta y cinco compuestos utilizando el método de relación cuantitativa estructura-actividad (QSAR) mediante QSAR Toolbox. A partir de estos resultados obtenidos por Toolbox, se construyeron modelos QSAR con los paquetes computacionales E-Dragon y QSAR DTC, utilizando análisis de regresión lineal y validándolos estadísticamente para predecir la toxicidad acuática de sustancias químicas no estudiadas. Los resultados del estudio muestran que los compuestos químicos utilizados en la fracturación hidráulica tienen un impacto significativo en la toxicidad de los peces, y las moléculas con menores valores de log Kow presentan una mayor respuesta tóxica. Los tres modelos QSAR estudiados cumplieron con los criterios de validación interna, con valores de R^2>0.7 y Q_(LOO )^2>0.6, demostrando así una adecuada aceptabilidad. En conclusión, el estudio destaca la importancia de la toxicología en la evaluación y regulación de los peligros presentes en el ambiente. La información obtenida puede ser utilizada para anticipar perfiles de toxicidad y predecir los efectos de los compuestos químicos en los ecosistemas acuáticos, lo cual es fundamental para la conservación de las fuentes hídricas y la protección de las especies acuáticas.spa
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ciencias Químicas
dc.description.modalityTrabajos de Investigación y/o Extensión
dc.description.tableofcontentsINTRODUCCIÓN GENERALspa
dc.description.tableofcontentsPARTE I. FUNDAMENTOS TEÓRICOSspa
dc.description.tableofcontentsCAPÍTULO 1. Modelación toxicológica In Silicospa
dc.description.tableofcontents1.1. Introducciónspa
dc.description.tableofcontents1.2. Toxicologíaspa
dc.description.tableofcontents1.3. Toxicología In Silicospa
dc.description.tableofcontents1.3.1. Protocolos de Toxicología In Silicospa
dc.description.tableofcontents1.4. Modelados In Silico en Toxicología Predictivaspa
dc.description.tableofcontents1.4.1. Modelos de Factor de Incertidumbre (UF)spa
dc.description.tableofcontents1.4.2. Modelos de Dosis/Tiempo-Respuestaspa
dc.description.tableofcontents1.4.3. Alertas Estructurales (SA) y Modelos Basados en Reglasspa
dc.description.tableofcontents1.4.4. Read-Acrossspa
dc.description.tableofcontents1.4.5. Relación Cuantitativa Estructura-Actividad (QSAR)spa
dc.description.tableofcontents1.4.6. Modelos Farmacocinéticos y Modelos Farmacodinámicosspa
dc.description.tableofcontentsCAPÍTULO 2. Estudios Toxicológicos Asistidos por QSARspa
dc.description.tableofcontents2.1. Introducciónspa
dc.description.tableofcontents2.2. QSARspa
dc.description.tableofcontents2.2.1. Tipos de Estudios QSARspa
dc.description.tableofcontents2.2.1.1 QSAR Tradicionalspa
dc.description.tableofcontents2.2.2 QSAR/QSTRspa
dc.description.tableofcontents2.2.3. Modelos QSTRspa
dc.description.tableofcontents2.2.3.1. Toxicidad Sistémica en Humanosspa
dc.description.tableofcontents2.2.3.2. Toxicidad en Humanos a Nivel Localspa
dc.description.tableofcontents2.2.3.3. Distribución Ambientalspa
dc.description.tableofcontents2.2.3.4. Ecotoxicidadspa
dc.description.tableofcontents2.2.4. QSTRS en las Normativas Regulatorias Internacionalesspa
dc.description.tableofcontents2.2.5. Fuentes de Datos Toxicológicosspa
dc.description.tableofcontents2.2.6. Programas Especializados para el Desarrollo de QSTRsspa
dc.description.tableofcontents2.3. Validación de Modelos QSARspa
dc.description.tableofcontents2.3.1. Medidas de Validación Basadas en Regresiónspa
dc.description.tableofcontents2.4. Descriptores Molecularesspa
dc.description.tableofcontents2.5. QSAR Toolboxspa
dc.description.tableofcontentsCAPÍTULO 3. La Fracturación Hidráulica y la Contaminación en Sistemas Acuáticosspa
dc.description.tableofcontents3.1. Introducciónspa
dc.description.tableofcontents3.2. La Fracturación Hidráulicaspa
dc.description.tableofcontents3.2.1. Procesos Asociados a la Explotación del Gas de Lutitaspa
dc.description.tableofcontents3.2.1.1. Exploraciónspa
dc.description.tableofcontents3.2.1.2. Construcciónspa
dc.description.tableofcontents3.2.1.3. Perforaciónspa
dc.description.tableofcontents3.2.1.4. Fracturaciónspa
dc.description.tableofcontents3.2.1.5. Producción y Distribuciónspa
dc.description.tableofcontents3.2.1.6. Recubrimiento y Finalización del Pozospa
dc.description.tableofcontents3.3. Contaminación de los Ecosistemasspa
dc.description.tableofcontents3.3.1. Ecosistemas Acuáticosspa
dc.description.tableofcontents3.3.2. Ecotoxicología y Pecesspa
dc.description.tableofcontents3.3.2.1. Oncorhynchus Mykissspa
dc.description.tableofcontents3.3.2.2. Pimephales Promelasspa
dc.description.tableofcontents3.3.2.3. Oreochromis Niloticusspa
dc.description.tableofcontentsPARTE II. APLICACIONESspa
dc.description.tableofcontentsCAPÍTULO 4. Metodología Computacionalspa
dc.description.tableofcontents4.1. Compuestos Químicos Relacionados con la Técnica de Fracturación Hidráulicaspa
dc.description.tableofcontents4.2. Flujo de Trabajo en QSAR Toolboxspa
dc.description.tableofcontents4.3. Construcción de Modelos QSARspa
dc.description.tableofcontents4.4. Validación de los Modelosspa
dc.description.tableofcontentsCAPÍTULO 5. Resultados y Discusiónspa
dc.description.tableofcontents5.1. Predicción de LC50 para Pimephales Promelas por QSAR Toolboxspa
dc.description.tableofcontents5.2. Predicción de LC50 para Oncorhynchus Mykiss por QSAR Toolboxspa
dc.description.tableofcontents5.3. Predicción de LC50 para Oreochromis Niloticus por QSAR Toolboxspa
dc.description.tableofcontents5.4. Construcción y Descripción de Modelos QSAR para la Predicción de Toxicidad a Sustancias Químicas en P. Promelas, O. Niloticus y O. Mykissspa
dc.description.tableofcontents5.5. Validación de los Modelos QSAR para las especies Acuáticas: P. Promelas, O. Niloticus y O. Mykissspa
dc.description.tableofcontentsCAPÍTULO 6. Conclusionesspa
dc.description.tableofcontents6.1. Consideracionesspa
dc.description.tableofcontentsREFERENCIASspa
dc.format.mimetypeapplication/pdf
dc.identifier.instnameUniversidad de Córdoba
dc.identifier.reponameRepositorio Institucional Unicórdoba
dc.identifier.repourlhttps://repositorio.unicordoba.edu.co
dc.identifier.urihttps://repositorio.unicordoba.edu.co/handle/ucordoba/8485
dc.language.isospa
dc.publisherUniversidad de Córdoba
dc.publisher.facultyFacultad de Ciencias Básicas
dc.publisher.placeMontería, Córdoba, Colombia
dc.publisher.programMaestría en Ciencias Químicas
dc.relation.referencesAczel, M. R., & Makuch, K. E. (2018). Environmental Impact Assessments and Hydraulic Fracturing: Lessons from Two U.S. States. Case Studies in the Environment, 2(1), 1-11.
dc.relation.referencesAguilar, A. A. V., & Condori, R. F. (2021). Estudios in Silico, Simulando Vida en un Entorno Virtual. Gaceta Médica Boliviana, 44(2), 278-278.
dc.relation.referencesAlbánese, S., & Cicchella, D. (2012). Legacy Problems in Urban Geochemistry. Elements, 8, 423 - 428.
dc.relation.referencesAnkley, G. T., & Villeneuve, D. L. (2006). The fathead minnow in aquatic toxicology: past, present and future. Aquatic toxicology, 78(1), 91-102.
dc.relation.referencesAranguren-Campos, F., Calderón-Carrillo, Z., & Usuriaga-Torres, J. (2017). A Selection Methodology Of Flowback Treatment Technologies And Water Reuse In Hydraulic Fracturing In Source Rocks - A Strategy To Reduce The Environmental Impacts In Colombia. CT&F - Ciencia, Tecnología y Futuro, 7(1), 5-30.
dc.relation.referencesArranz, J. C. E., García, J. A. P., & Velar, R. C. (2020). Introducción al diseño racional de fármacos. Editorial Universitaria (Cuba).
dc.relation.referencesBenigni, R., Battistelli, C. L., Bossa, C., Colafranceschi, M., & Tcheremenskaia, O. (2013). Mutagenicity, carcinogenicity, and other end points. Computational Toxicology: 2, 67-98.
dc.relation.referencesBensusan, M. (2018). El Fracking: Un preocupante dilema entre la independencia energética y el impacto socio-ambiental. Foro Jurídico, 17, 192 - 214.
dc.relation.referencesBlack, K. J., Boslett, A. J., Hill, E. L., Ma, L., & McCoy, S. J. (2021). Economic, environmental, and health impacts of the fracking boom. Annual Review of Resource Economics, 13, 311-334.
dc.relation.referencesBouarra, N., Nadji, N., Kherouf, S., Nouri, L., Boudjemaa, A., Bachari, K., & Messadi, D. (2022). QSER modeling of half-wave oxidation potential of indolizines by theoretical descriptors. Journal of the Turkish Chemical Society Section A: Chemistry, 9(3), 709-720.
dc.relation.referencesBrooks, B. W. (2018). Urbanization, environment and pharmaceuticals: Advancing comparative physiology, pharmacology and toxicology. Conservation physiology, 6(1), cox079.
dc.relation.referencesBrooks, B. W., Sabo-Attwood, T., Choi, K., Kim, S., Kostal, J., LaLone, C. A., Langan, L. M., Margiotta-Casaluc, L., You, Y., & Zhang, X. (2020). Toxicology advances for 21st century chemical pollution. One Earth, 2(4), 312-316.
dc.relation.referencesCapó, M. A. (2002). Principios de Ecotoxicología. Diagnóstico, Tratamiento y Gestión del Medio Ambiente. Ed- McGraw-Hill. Madrid.
dc.relation.referencesCharry-Ocampo, S. (2018). Efectos de la estimulación hidráulica (fracking) en el recurso hídrico: Implicaciones en el contexto colombiano. ciencia e ingenieria neogranadina, 28(1), 135-164.
dc.relation.referencesChatterjee, M., & Roy, K. (2021). Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors. Journal of Hazardous Materials, 408, 124936.
dc.relation.referencesComunidad Andina. (2019, 15 de mayo). Resolución N° 2075. Secretaria General. https://www.nuevalegislacion.com/files/susc/cdj/conc/r_ca_2075_19.pdf.
dc.relation.referencesDabade, S. J., Mandloi, D., & Bajaj, A. (2020). Molecular docking and QSAR studies of coumarin derivatives as NMT inhibitors: simple structural features as potential modulators of antifungal activity. Letters in Drug Design & Discovery, 17(10), 1293-1308.
dc.relation.referencesD'Agaro, E., Gibertoni, P. P., & Esposito, S. (2022). Recent trends and economic aspects in the rainbow trout (Oncorhynchus mykiss) sector. Applied Sciences (Switzerland), 12(17), 8773.
dc.relation.referencesDe, P., Kar, S., Ambure, P., & Roy, K. (2022). Prediction reliability of QSAR models: an overview of various validation tools. Archives of Toxicology, 96(5), 1279-1295.
dc.relation.referencesDeGraeve, G. M., Elder, R. G., Woods, D. C., & Bergman, H. L. (1982). Effects of naphthalene and benzene on fathead minnows and rainbow trout. Archives of Environmental Contamination and Toxicology, 11(4), 487-490.
dc.relation.referencesDesai, K., & Aminzadeh, F. (2019). Flowback of Fracturing Fluids with Upgraded Visualization of Hydraulic Fractures and Its Implications on Overall Well Performance. En F. Aminzadeh (Ed.), Hydraulic Fracturing and Well Stimulation (1.a ed., pp. 271-283). Wiley.
dc.relation.referencesDevillers, J. (2013). Methods for building QSARs. In Computational toxicology, 930, 3-27. Humana Press, Totowa, NJ.
dc.relation.referencesDimitrov, S. D., Diderich, R., Sobanski, T., Pavlov, T. S., Chankov, G. V., Chapkanov, A. S., Karakolev, Y. H., Temelkov, S. G., Vasilev, R. A., Gerova, K. D., Kuseva, C. D., Todorova, N. D., Mehmed, A. M., Rasenberg, M., & Mekenyan, O. G. (2016). QSAR Toolbox–workflow and major functionalities. SAR and QSAR in Environmental Research, 27(3), 203-219.
dc.relation.referencesDris, R., H. Imhof, W. Sánchez, J. Gásperi, F. Galgani, B. Tassin, & C. Laforsch (2015). Beyond the ocean: Contamination of freshwater ecosystems with (micro-) plastic particles. Environmental Chemistry, 12, 539 - 550.
dc.relation.referencesEfron, B. (1982). The Jackknife, the Bootstrap and other resampling plans. In CBMS-NSF regional conference series in applied mathematics 1982. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
dc.relation.referenceseia. (2021, diciembre). Short-Term Energy Outlook. U.S. Energy Information Administration. Recuperado 11 de enero de 2022, de https://www.eia.gov/outlooks/steo/pdf/steo_full.pdf.
dc.relation.referencesEnsuncho, A. E., Robles, J. R., & López, J. M. (2022). Modelación 3D-QSAR de los derivados de 5-(nitroheteroaril)-1, 3, 4-tiadiazol con actividad leishmanicida. Información tecnológica, 33(4), 41-52.
dc.relation.referencesEnsuncho, A., López, J., & Robles, J. (2017). Herramienta Computacional para la Construcción de Modelos QSAR. Editorial Palomo.
dc.relation.referencesEl Asely, A. M., Reda, R. M., Salah, A. S., Mahmoud, M. A., & Dawood, M. A. (2020). Overall performances of Nile tilapia (Oreochromis niloticus) associated with using vegetable oil sources under suboptimal temperature. Aquaculture Nutrition, 26(4), 1154-1163.
dc.relation.referencesElliott, E. G., Ettinger, A. S., Leaderer, B. P., Bracken, M. B., & Deziel, N. C. (2017). A systematic evaluation of chemicals in hydraulic-fracturing fluids and wastewater for reproductive and developmental toxicity. Journal of Exposure Science & Environmental Epidemiology, 27(1), 90-99.
dc.relation.referencesEl‐Sayed, A. F. M., & Fitzsimmons, K. (2023). From Africa to the world—The journey of Nile tilapia. Reviews in Aquaculture, 15, 6-21.
dc.relation.referencesEscalona, J., Carrasco, R., & Padrón, J. (2008). Introducción al diseño racional de fármacos, 1st ed., Editorial Universitaria: Ciudad de la Habana.
dc.relation.referencesEuldji, I., Si‐Moussa, C., Hamadache, M., & Benkortbi, O. (2022). QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide. Molecular Informatics, 41(10), 2200026.
dc.relation.referencesEuropean Chemicals Agency (ECHA). (2008). In Guidance on information requirements and Chemical Safety Assessment: Chapter R.6 QSARs and grouping of chemicals; ECHA: Helsinki.
dc.relation.referencesFerrari, L. (2015). La ecotoxicología aplicada a la evaluación de la contaminación de los ríos: El caso del rio Reconquista. Ciencia e Investigación. 65, 17 - 35.
dc.relation.referencesFischer, I., Milton, C., & Wallace, H. (2020). Toxicity testing is evolving! Toxicology Research, 9(2), 67-80.
dc.relation.referencesGallagher, A., Kar, S., & Sepúlveda, M. S. (2023). Computational Modeling of Human Serum Albumin Binding of Per-and Polyfluoroalkyl Substances Employing QSAR, Read-Across, and Docking. Molecules, 28(14), 5375.
dc.relation.referencesGbeddy, G., Egodawatta, P., Goonetilleke, A., Ayoko, G., & Chen, L. (2020). Application of quantitative structure-activity relationship (QSAR) model in comprehensive human health risk assessment of PAHs, and alkyl-, nitro-, carbonyl-, and hydroxyl-PAHs laden in urban road dust. Journal of hazardous materials, 383, 121154.
dc.relation.referencesGleesson, M. P., Modi, S., Bender, A., L Marchese Robinson, R., Kirchmair, J., Promkatkaew, M., Hannongbua, S., & Glen, R. (2012). The challenges involved in modeling toxicity data in silico: a review. Current pharmaceutical design, 18(9), 1266-1291.
dc.relation.referencesGordalla, B. C., Ewers, U., & Frimmel, F. H. (2013). Hydraulic fracturing: a toxicological threat for groundwater and drinking-water? Environmental earth sciences, 70(8), 3875-3893.
dc.relation.referencesGozalbes, R., de Julián-Ortiz, J. V., & Fito-López, C. (2014). Métodos computacionales en toxicología predictiva: aplicación a la reducción de ensayos con animales en el contexto de la legislación comunitaria REACH. Revista de Toxicología, 31(2), 157-167.
dc.relation.referencesGramatica, P. (2020). Principles of QSAR modeling: comments and suggestions from personal experience. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 5(3), 61-97.
dc.relation.referencesGranados-Tavera, Kevin, Tilvez, Elkin A., & Ahumedo-Monterrosa, Maicol. (2019). Modelling of the Structure-Activity Quantitative Relationships (QSAR) of Tipifarnib Analogues with Antichagasic Activity. Información tecnológica, 30(1), 3-14.
dc.relation.referencesHaneef, T., Mustafa, M. R. U., Wan Yusof, K., Isa, M. H., Bashir, M. J. K., Ahmad, M., & Zafar, M. (2020). Removal of Polycyclic Aromatic Hydrocarbons (PAHs) from Produced Water by Ferrate (VI) Oxidation. Water, 12(11), 3132.
dc.relation.referencesHe, Y., Folkerts, E. J., Zhang, Y., Martin, J. W., Alessi, D. S., & Goss, G. G. (2017). Effects on Biotransformation, Oxidative Stress, and Endocrine Disruption in Rainbow Trout (Oncorhynchus mykiss) Exposed to Hydraulic Fracturing Flowback and Produced Water. Environmental Science & Technology, 51(2), 940-947.
dc.relation.referencesHemmerich, J., & Ecker, G. F. (2020). In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. Wiley Interdisciplinary Reviews: Computational Molecular Science, 10(4), e1475.
dc.relation.referencesHill, E., & Ma, L. (2021). The fracking concern with water quality. Science, 373(6557), 853-854.
dc.relation.referencesHines, I. S., Marshall, M. A., Smith, S. A., Kuhn, D. D., & Stevens, A. M. (2023). Systematic literature review identifying bacterial constituents in the core intestinal microbiome of rainbow trout (Oncorhynchus mykiss). Aquaculture, Fish and Fisheries, 3(5), 393-406.
dc.relation.referencesHong, S., Yoon, S. J., Kim, T., Ryu, J., Kang, S. G., & Khim, J. S. (2020). Response to oiled wildlife in the management and evaluation of marine oil spills in South Korea. Regional Studies in Marine Science. 40, 101 – 542.
dc.relation.referencesHoover, G., Kar, S., Guffey, S., Leszczynski, J., & Sepúlveda, M. S. (2019). In vitro and in silico modeling of perfluoroalkyl substances mixture toxicity in an amphibian fibroblast cell line. Chemosphere, 233, 25-33.
dc.relation.referencesHuisinga, M., Bertrand, L., Chamanza, R., Damiani, I., Engelhardt, J., Francke, S., Freyberger, A., Harada, T., Harleman, J., Kaufmann, W., Keane, K., Köhrle, J., Lenz, B., Marty, M., Melching-Kollmuss, S., Palazzi, X., Pohlmeyer-Esch, G., Popp, A., Rosol, T., Strauss, V., Brink-Knol, H., Wood, Ch., & Yoshida, M. (2020). Adversity considerations for thyroid follicular cell hypertrophy and hyperplasia in nonclinical toxicity studies: results from the 6th ESTP International Expert Workshop. Toxicologic Pathology, 48(8), 920-938.
dc.relation.referencesIyaniwura, T. T. (1991). Non-target and environmental hazards of pesticides. Reviews on environmental health, 9(3), 161-176.
dc.relation.referencesKar, S., Sanderson, H., Roy, K., Benfenati, E., & Leszczynski, J. (2020). Ecotoxicological assessment of pharmaceuticals and personal care products using predictive toxicology approaches. Green Chemistry, 22(5), 1458-1516.
dc.relation.referencesKhan, K., Kumar, V., Colombo, E., Lombardo, A., Benfenati, E., & Roy, K. (2022). Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors. Environment International, 170, 107625.
dc.relation.referencesKhan, K., Roy, K., & Benfenati, E. (2019). Ecotoxicological QSAR modeling of endocrine disruptor chemicals. Journal of Hazardous Materials, 369, 707 - 718.
dc.relation.referencesKhan, P. M., & Roy, K. (2022). Chemometric modelling of heat release capacity, total heat release and char formation of polymers to assess their flammability characteristics. Molecular Informatics, 41(1), 2000030.
dc.relation.referencesKolawole, O., & Ispas, I. (2020). Interaction between hydraulic fractures and natural fractures: Current status and prospective directions. Journal of Petroleum Exploration and Production Technology, 10, 1613 - 1634.
dc.relation.referencesKrithiga, T., Sathish, S., Renita, A. A., Prabu, D., Lokesh, S., Geetha, R., Karthick Raja Namasivayam, S., & Sillanpaa, M. (2022). Persistent organic pollutants in water resources: Fate, occurrence, characterization and risk analysis. Science of The Total Environment, 831, 154808.
dc.relation.referencesKunal, R. (2018). Quantitative structure-activity relationships (QSARs): A few validation methods and software tools developed at the DTC laboratory. Journal of the Indian Chemical Society, 95, 1497-1502.
dc.relation.referencesLabute, P. (2000). A widely applicable set of descriptors. Journal of Molecular Graphics and Modelling, 18(4-5), 464-477.
dc.relation.referencesLerat, J. G., Sterpenich, J., Mosser-Ruck, R., Lorgeoux, C., Bihannic, I., Fialips, C. I., Schovsbo, N. H., Pironon, J., & Gaucher, É. C. (2018). Metals and radionuclides (MaR) in the Alum Shale of Denmark: Identification of MaR-bearing phases for the better management of hydraulic fracturing waters. Journal of Natural Gas Science and Engineering, 53, 139-152.
dc.relation.referencesLiu, Z., Gao, J., Li, C., Xu, L., Lv, X., Deng, H., Gao, Y., Wang, H., Li, H., & Wang, Z. (2023). Application of QSAR models for acute toxicity of tetrazole compounds administrated orally and intraperitoneally in rat and mouse. Toxicology, 500, 153679.
dc.relation.referencesLombardo, A., Manganaro, A., Arning, J., & Benfenati, E. (2022). Development of new QSAR models for water, sediment, and soil half-life. Science of The Total Environment, 838, 156004.
dc.relation.referencesMaeshima, T., Yoshida, S., Watanabe, M., & Itagaki, F. (2023). Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR. Pharmaceutical Research, 40(3), 711-719.
dc.relation.referencesMagouz, F. I., Mahmoud, S. A., El-Morsy, R. A., Paray, B. A., Soliman, A. A., Zaineldin, A. I., & Dawood, M. A. (2021). Dietary menthol essential oil enhanced the growth performance, digestive enzyme activity, immune-related genes, and resistance against acute ammonia exposure in Nile tilapia (Oreochromis niloticus). Aquaculture, 530, 735944.
dc.relation.referencesMaguire, K., & Papeş, M. (2021). Oil and gas development and its effect on bird diversity in the high plains of Colorado (2003–2018). Biological Conservation, 263, 109358.
dc.relation.referencesMahalakshmi, P. S., & Jahnavi, Y. (2020). A review on QSAR studies. International Journal of Advances in Pharmacy and Biotechnology, 6(2), 19-23.
dc.relation.referencesMahmoud, M. A., Abd El-Rahim, A. H., Mahrous, K. F., Abdelsalam, M., Abu-Aita, N. A., & Afify, M. (2019). The impact of several hydraulic fracking chemicals on Nile tilapia and evaluation of the protective effects of Spirulina platensis. Environmental Science and Pollution Research, 26(19), 19453-19467.
dc.relation.referencesMajumdar, S., & Basak, S. C. (2018). Beware of external validation!-a comparative study of several validation techniques used in QSAR modelling. Current computer-aided drug design, 14(4), 284-291.
dc.relation.referencesManiloff, P., & Mastromonaco, R. (2017). The local employment impacts of fracking: A national study. Resource and Energy Economics, 49, 62-85.
dc.relation.referencesMartínez, G. R. S., Martínez, P. S., & Buitrago, O. R. (2015). Residuos sólidos y líquidos en el deterioro del ambiente y la salud de la comunidad educativa de la escuela Los Toldos, Popayán, Cauca. Revista Nodo, 10(19), 25-41.
dc.relation.referencesMetian, M., Troell, M., Christensen, V., Steenbeek, J., & Pouil, S. (2020). Mapping diversity of species in global aquaculture. Reviews in Aquaculture, 12(2), 1090–1100.
dc.relation.referencesMohamed, N. A., Saad, M. F., Shukry, M., El-Keredy, A. M., Nasif, O., Van Doan, H., & Dawood, M. A. (2021). Physiological and ion changes of Nile tilapia (Oreochromis niloticus) under the effect of salinity stress. Aquaculture reports, 19, 100567.
dc.relation.referencesMombelli, E., & Pandard, P. (2021). Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow. Regulatory Toxicology and Pharmacology, 122, 104893.
dc.relation.referencesMuratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., Isayev, O., Curtalolo, S., Fourches, D., Cohen, Y., Aspuru-Guzik, A., Winkler, D. A., Agrafiotis, D., Cherkasov, A., & Tropsha, A. (2020). QSAR without borders. Chemical Society Reviews, 49(11), 3525-3564.
dc.relation.referencesMyatt, G. J., Ahlberg, E., Akahori, Y., Allen, D., Amberg, A., Anger, L. T., Aptulaf. A., Auerbachg, S., Beilkeh, L., Bellion, P., Benigni, P., Bercu, Ewan D. Booth, Dave Bower, Alessandro Brigo, Natalie Burden, Zoryana Cammerer, J., Cronin, M., Cross, K., Custer. L., Dettwiler, M., Dobo, k., Ford, K., Fortin, M., Gad-McDonald, S., Gellatly, N., Gervais, V., Glover, K., Glowienke, S., Gompel, J., Gutsell, S., Hardy, B., Harvey, J., Hillegass, J., Honma, M., Hsieh, J-H., Hsu, Ch-W., Hughes, K., Johnson, C., Jolly, R., Jones, D., Kemper, R., Kenyon, M., Kim, M., Kruhlak, N., Kulkarni, S., Kümmerer, K., Leavitt, P., Majer, B., Masten, S., Miller, S., Moser, J., Mumtaz, M., Muster, W., Neilson, L., Oprea, T., Patlewicz, G., Paulino, A., Piparo, E., Powley, M., Quigley, D., Reddy, M., Richarz, A-D., Ruiz, P., Schilter, B., Serafimova, R,. Simpson, W., Stavitskaya, L., Stidl, R., Suarez-Rodriguez, D., Szabo, D., Teasdale, A., Trejo-Martin, A., Valentin, J-P., Vuorinen, A., Wall, B., Watts, P., White, A., Wichard, J., Witt, K., Woolley, A., Woolley, D., Zwickl, C. & Hasselgren, C. (2018). In silico toxicology protocols. Regulatory Toxicology and Pharmacology, 96, 1-17.
dc.relation.referencesNayak, S., Dash, S. N., Pati, S. S., Priyadarshini, P., & Patnaik, L. (2021). Lipid peroxidation and antioxidant levels in Anabas testudineus (Bloch) under naphthalene (PAH) stress. Aquaculture Research, 52(11), 5739-5749.
dc.relation.referencesPapoulias, D., & Velasco, A. (2013). Histopathological Analysis of Fish from Acorn Fork Creek, Kentucky, Exposed to Hydraulic Fracturing Fluid Releases. Southeastern Naturalist, 12, 92-111.
dc.relation.referencesPatel, H. M., Noolvi, M. N., Sharma, P., Jaiswal, V., Bansal, S., Lohan, S., Kumar, S. S., Abbot, V., Dhiman, S., & Bhardwaj, V. (2014). Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery. Medicinal chemistry research, 23, 4991-5007.
dc.relation.referencesPatlewicz, G., Helman, G., Pradeep, P., & Shah, I. (2017). Navigating through the minefield of read-across tools: A review of in silico tools for grouping. Computational Toxicology, 3, 1-18.
dc.relation.referencesPerez Santin, E., Rodríguez Solana, R., González García, M., García Suárez, M. D. M., Blanco Díaz, G. D., Cima Cabal, M. D., Moreno Rojas, J. M., & Lopez Sanchez, J. I. (2021). Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdisciplinary Reviews: Computational Molecular Science, 11(5), e1516.
dc.relation.referencesPinto-Valderrama, J., & Idrovo, A. J. (2019). Fracking, yacimientos en roca generadora y salud humana: Entre la incertidumbre y la precaución. Revista de la Universidad Industrial de Santander. Salud, 51(2), 100-103.
dc.relation.referencesRaies, A. B., & Bajic, V. B. (2016). In silico toxicology: Computational methods for the prediction of chemical toxicity: Computational methods for the prediction of chemical toxicity. Wiley Interdisciplinary Reviews: Computational Molecular Science, 6(2), 147-172.
dc.relation.referencesREACH, A. (2006). Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency. Edited by UNION EPATCOTE2006.
dc.relation.referencesRivera-Parra, J. L., Vizcarra, C., Mora, K., Mayorga, H., & Dueñas, J. C. (2020). Spatial distribution of oil spills in the north eastern Ecuadorian Amazon: A comprehensive review of possible threats. Biological Conservation. 252, 108 - 820.
dc.relation.referencesRodríguez, J., Heo, J., & Kim, K. H. (2020). The Impact of Hydraulic Fracturing on Groundwater Quality in the Permian Basin, West Texas, USA. Water, 12(3), 796.
dc.relation.referencesRotella, D. (2011). New horizons in predictive toxicology: current status and application. Royal Society of Chemistry. https://books.google.es/books?hl=es&lr=&id=qnIoDwAAQBAJ&oi=fnd&pg=PA9&dq=Cronin+MTD+(2012)+In+silico+tools+for+toxicity+prediction.+In:+New+horizons+in+predictive+toxicology:+current+status+and+application.+R+Soc+Chem+9%E2%80%9325&ots=jK-g7rI4l1&sig=DcFg1QVEBlf5AN5HcE3r4qzRu5k#v=onepage&q&f=false.
dc.relation.referencesRoy, K., Mitra, I., Kar, S., Ojha, P. K., Das, R. N., & Kabir, H. (2012). Comparative studies on some metrics for external validation of QSPR models. Journal of chemical information and modeling, 52(2), 396-408.
dc.relation.referencesSayyadi kord Abadi, R., Alizadehdakhel, A., & Dorani Shiraz, S. (2017). Ab initio and QSAR study of several etoposides as anticancer drugs: Solvent effect. Russian Journal of Physical Chemistry B, 11, 307-317.
dc.relation.referencesSchultz, T. W., Diderich, R., Kuseva, C. D., & Mekenyan, O. G. (2018). The OECD QSAR toolbox starts its second decade. In Computational Toxicology, 1800, 55-77. Humana Press, New York, NY.
dc.relation.referencesSchuurmann, G., Ebert, R. U., Chen, J., Wang, B., & Kuhne, R. (2008). External validation and prediction employing the predictive squared correlation coefficient - Test set activity mean vs training set activity mean. Journal of chemical information and modeling, 48(11), 2140-2145.
dc.relation.referencesSeth, A., & Roy, K. (2020). QSAR modeling of algal low level toxicity values of different phenol and aniline derivatives using 2D descriptors. Aquatic Toxicology, 228, 105627.
dc.relation.referencesSigurnjak Bureš, M., Cvetnić, M., Miloloža, M., Kučić Grgić, D., Markić, M., Kušić, H., Bolanča, T., Rogošić, M., & Ukić, Š. (2021). Modeling the toxicity of pollutants mixtures for risk assessment: a review. Environmental chemistry letters, 19, 1629-1655.
dc.relation.referencesSingh, S., Ahmad, D., Alam, D., & Shamsher, M. (2021). Synthesis Of Heterocyclic Nitrogen Containing Compounds Including In Silico Toxicity And Structural Activity Relationship. European Journal of Molecular & Clinical Medicine, 7(7), 6633-6645.
dc.relation.referencesSmith, Y. ¿Cuál es la toxicología? News medical life sciences. Recuperado 10 de enero de 2022, de https://www.news-medical.net/health/What-is-Toxicology-(Spanish).aspx.
dc.relation.referencesSoeder, D. J. (2020). Fracking and the environment: a scientific assessment of the environmental risks from hydraulic fracturing and fossil fuels. Springer Nature.
dc.relation.referencesSong, X., Chai, L., & Zhang, J. (2020). Graph signal processing approach to QSAR/QSPR model learning of compounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 1963-1973.
dc.relation.referencesTeles, H. R., Ferreira, L. L., Valli, M., Coelho, F., & Andricopulo, A. D. (2022). Hierarchical Clustering and Target-Independent QSAR for Antileishmanial Oxazole and Oxadiazole Derivatives. International Journal of Molecular Sciences, 23(16), 8898.
dc.relation.referencesTolentino, L. K. S., De Pedro, C. P., Icamina, J. D., Navarro, J. B. E., Salvacion, L. J. D., Sobrevilla, G. C. D., Villanueva, A. A., Amado, T. M., Padilla, M. V. C., & Madrigal, G. A. M. (2020). Weight prediction system for nile tilapia using image processing and predictive analysis. International Journal of Advanced Computer Science and Applications, 11(8).
dc.relation.referencesToropov, A. A., Toropova, A. P., & Benfenati, E. (2020). QSAR model for pesticides toxicity to Rainbow Trout based on “ideal correlations”. Aquatic Toxicology, 227, 105589.
dc.relation.referencesUrista, D. V., Carrué, D. B., Otero, I., Arrasate, S., Quevedo-Tumailli, V. F., Gestal, M., González-Díaz, H., & Munteanu, C. R. (2020). Prediction of antimalarial drug-decorated nanoparticle delivery systems with random forest models. Biology, 9(8), 198.
dc.relation.referencesUS EPA. (2015). Assessment of the Potential Impacts of Hydraulic Fracturing for Oil and Gas on Drinking Water Resources (External Review Draft). U.S. Environmental Protection Agency (N.o 600; U.S. Environmental Protection Agency). EPA.
dc.relation.referencesVeerasamy, R., Rajak, H., Jain, A., Sivadasan, S., Varghese, C. P., & Agrawal, R. K. (2011). Validation of QSAR models-strategies and importance. Int. J. Drug Des. Discov, 3, 511-519.
dc.relation.referencesVenkatapathy, R., & Wang, N. C. Y. (2013). Developmental toxicity prediction. Computational Toxicology, 2, 305-340.
dc.relation.referencesVijayanand, M., Ramakrishnan, A., Subramanian, R., Issac, P. K., Nasr, M., Khoo, K. S., Rajagopal, R., Greff, B., Wan Azelee, N. I., Jeon, B-H., Chang, S. W., & Ravindran, B. (2023). Polyaromatic hydrocarbons (PAHs) in the water environment: A review on toxicity, microbial biodegradation, systematic biological advancements, and environmental fate. Environmental research, 227, 115716.
dc.relation.referencesWang H. (2019). Hydraulic fracture propagation in naturally fractured reservoirs: complex fracture or fracture networks. Journal of Natural Gas Science & Engineering. 68, 102 - 911.
dc.relation.referencesWang, Z., Walker, G. W., Muir, D. C., & Nagatani-Yoshida, K. (2020). Toward a global understanding of chemical pollution: a first comprehensive analysis of national and regional chemical inventories. Environmental Science & Technology, 54(5), 2575-2584.
dc.relation.referencesWu, Y., & Wang, G. (2018). Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. International journal of molecular sciences, 19(8), 2358.
dc.relation.referencesXiao, C., Tian, L., Zhang, Y., Hou, T., Yang, Y., Deng, Y., Wang, Y., & Chen, S. (2017). A novel approach to detect interacting behavior between hydraulic fracture and natural fracture by use of semianalytical pressure-transient model. SPE Journal, 22(06), 1834-1855.
dc.relation.referencesXu, Y. (2022). Deep neural networks for QSAR. Artificial intelligence in drug design, 233-260.
dc.relation.referencesYan, X. F., Xiao, H. M., Gong, X. D., & Ju, X. H. (2005). Quantitative structure–activity relationships of nitroaromatics toxicity to the algae (Scenedesmus obliguus). Chemosphere, 59(4), 467-471.
dc.relation.referencesYang, L., Wang, Y., Hao, W., Chang, J., Pan, Y., Li, J., & Wang, H. (2020). Modeling pesticides toxicity to Sheepshead minnow using QSAR. Ecotoxicology and Environmental Safety, 193, 110352.
dc.relation.referencesYordanova, D., Schultz, T. W., Kuseva, C., Tankova, K., Ivanova, H., Dermen, I., Pavlov, T., Temelkov, S., Chapkanov, A., Georgiev, M., Gissi, A., Sobanski, T., & Mekenyan, O. G. (2019). Automated and standardized workflows in the OECD QSAR Toolbox. Computational Toxicology, 10, 89 - 104.
dc.relation.referencesYost, E. E., Stanek, J., DeWoskin, R. S., & Burgoon, L. D. (2016a). Overview of Chronic Oral Toxicity Values for Chemicals Present in Hydraulic Fracturing Fluids, Flowback, and Produced Waters. Environmental Science & Technology, 50(9), 4788-4797.
dc.relation.referencesYost, E. E., Stanek, J., DeWoskin, R. S., & Burgoon, L. D. (2016b). Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure–Activity Relationship Modeling. Environmental Science & Technology, 50(14), 7732-7742.
dc.relation.referencesZambrano, M., Casanova, R., Prada, J., Arencibia, G., Vidal, A., & Capetillo, N. (2012). Bioacumulación de hidrocarburos aromáticos policíclicos en Anadara tuberculosa (Sowerby, 1833) (Arcoida: Arcidae). Gayana (concepción), 76(1), 1-9.
dc.relation.referencesZhou, X., Zheng, Z., Lu, T., Xu, P., Chang, T., Li, M., & Lu, W. (2023). Interpretable machine learning assisted multi-objective optimization design for small molecule hole transport materials. Journal of Alloys and Compounds, 966, 171440.
dc.relation.referencesZhu, T., & Tao, C. (2022). Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. Journal of Hazardous Materials, 423, 127037.
dc.relation.referencesZuriaga, E., Giner, B., Valero, M. S., Gómez, M., García, C. B., & Lomba, L. (2019). QSAR modelling for predicting the toxic effects of traditional and derived biomass solvents on a Danio rerio biomodel. Chemosphere, 227, 480 - 488.
dc.rightsCopyright Universidad de Córdoba, 2024
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_f1cf
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordsChemical substances
dc.subject.keywordsToxicity
dc.subject.keywordsHydraulic fracturing
dc.subject.keywordsAquatic ecosystems
dc.subject.keywordsQuantitative structure-activity relationship
dc.subject.proposalSustancias químicas
dc.subject.proposalToxicidad
dc.subject.proposalFracturación hidráulica
dc.subject.proposalEcosistemas acuáticos
dc.subject.proposalRelación cuantitativa estructura-actividad
dc.titleEstimación de la toxicidad acuática de compuestos químicos usados en fracturación hidráulica mediante modelado cuantitativo de relaciones estructura-actividad (QSAR) en peces de aguas continentales.spa
dc.typeTrabajo de grado - Maestría
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
Archivos
Bloque original
Mostrando 1 - 2 de 2
No hay miniatura disponible
Nombre:
RamírezLeónDianaBerónica.pdf
Tamaño:
2.42 MB
Formato:
Adobe Portable Document Format
No hay miniatura disponible
Nombre:
AutorizaciónPublicación_RamírezLeónDianaBerónica.pdf
Tamaño:
838.68 KB
Formato:
Adobe Portable Document Format
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
15.18 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Colecciones