Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry
- Autores
- Domínguez Romero, Juan C.; García Reyes, Juan F.; Martínez Romero, Rubén; Berton, Paula; Martínez Lara, Esther; Del Moral Leal, María L.; Molina Díaz, Antonio
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The development of comprehensive methods able to tackle with the systematic identification of drug metabolites in an automated fashion is of great interest. In this article, a strategy based on the combined use of two complementary data mining tools is proposed for the screening and systematic detection and identification of urinary drug metabolites by liquid chromatography full-scan high resolution mass spectrometry. The proposed methodology is based on the use of accurate mass extraction of diagnostic ions (compound-dependent information) from in-source CID fragmentation without precursor ion isolation along with the use of automated mass extraction of accurate-mass shifts corresponding to typical biotransformations (non compound-dependent information) that xenobiotics usually undergo when metabolized. The combined strategy was evaluated using LC-TOFMS with a suite of nine sport drugs representative from different classes (propranolol, bumetanide, clenbuterol, ephedrine, finasteride, methoxyphenamine, methylephedrine, salbutamol and terbutaline), after single doses administered to rats. The metabolite identification coverage rate obtained with the systematic method (compared to existing literature) was satisfactory, and provided the identification of several non-previously reported metabolites. In addition, the combined information obtained helps to minimize the number of false positives. As an example, the systematic identification of urinary metabolites of propranolol enabled the identification of up to 24 metabolites, 15 of them non previously described in literature, which is a valuable indicator of the usefulness of the proposed systematic procedure.
Fil: Domínguez Romero, Juan C.. Universidad de Jaén; España
Fil: García Reyes, Juan F.. Universidad de Jaén; España
Fil: Martínez Romero, Rubén. Universidad de Jaén; España
Fil: Berton, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Científico Tecnológico Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Fil: Martínez Lara, Esther. Universidad de Jaén; España
Fil: Del Moral Leal, María L.. Universidad de Jaén; España
Fil: Molina Díaz, Antonio. Universidad de Jaén; España - Materia
-
Liquid Chromatography
High Resolution Mass Spectrometry
Drug Metabolites
Sport Drug Testing
Propranolol - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/2722
Ver los metadatos del registro completo
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Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometryDomínguez Romero, Juan C.García Reyes, Juan F.Martínez Romero, RubénBerton, PaulaMartínez Lara, EstherDel Moral Leal, María L.Molina Díaz, AntonioLiquid ChromatographyHigh Resolution Mass SpectrometryDrug MetabolitesSport Drug TestingPropranololhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The development of comprehensive methods able to tackle with the systematic identification of drug metabolites in an automated fashion is of great interest. In this article, a strategy based on the combined use of two complementary data mining tools is proposed for the screening and systematic detection and identification of urinary drug metabolites by liquid chromatography full-scan high resolution mass spectrometry. The proposed methodology is based on the use of accurate mass extraction of diagnostic ions (compound-dependent information) from in-source CID fragmentation without precursor ion isolation along with the use of automated mass extraction of accurate-mass shifts corresponding to typical biotransformations (non compound-dependent information) that xenobiotics usually undergo when metabolized. The combined strategy was evaluated using LC-TOFMS with a suite of nine sport drugs representative from different classes (propranolol, bumetanide, clenbuterol, ephedrine, finasteride, methoxyphenamine, methylephedrine, salbutamol and terbutaline), after single doses administered to rats. The metabolite identification coverage rate obtained with the systematic method (compared to existing literature) was satisfactory, and provided the identification of several non-previously reported metabolites. In addition, the combined information obtained helps to minimize the number of false positives. As an example, the systematic identification of urinary metabolites of propranolol enabled the identification of up to 24 metabolites, 15 of them non previously described in literature, which is a valuable indicator of the usefulness of the proposed systematic procedure.Fil: Domínguez Romero, Juan C.. Universidad de Jaén; EspañaFil: García Reyes, Juan F.. Universidad de Jaén; EspañaFil: Martínez Romero, Rubén. Universidad de Jaén; EspañaFil: Berton, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Científico Tecnológico Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Martínez Lara, Esther. Universidad de Jaén; EspañaFil: Del Moral Leal, María L.. Universidad de Jaén; EspañaFil: Molina Díaz, Antonio. Universidad de Jaén; EspañaElsevier Science2013-01-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/2722Domínguez Romero, Juan C.; García Reyes, Juan F.; Martínez Romero, Rubén; Berton, Paula; Martínez Lara, Esther; et al.; Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry; Elsevier Science; Analytica Chimica Acta; 761; 25-1-2013; 1-100003-2670enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2012.11.049info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0003267012017266info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:32:48Zoai:ri.conicet.gov.ar:11336/2722instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:32:48.352CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry |
title |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry |
spellingShingle |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry Domínguez Romero, Juan C. Liquid Chromatography High Resolution Mass Spectrometry Drug Metabolites Sport Drug Testing Propranolol |
title_short |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry |
title_full |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry |
title_fullStr |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry |
title_full_unstemmed |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry |
title_sort |
Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry |
dc.creator.none.fl_str_mv |
Domínguez Romero, Juan C. García Reyes, Juan F. Martínez Romero, Rubén Berton, Paula Martínez Lara, Esther Del Moral Leal, María L. Molina Díaz, Antonio |
author |
Domínguez Romero, Juan C. |
author_facet |
Domínguez Romero, Juan C. García Reyes, Juan F. Martínez Romero, Rubén Berton, Paula Martínez Lara, Esther Del Moral Leal, María L. Molina Díaz, Antonio |
author_role |
author |
author2 |
García Reyes, Juan F. Martínez Romero, Rubén Berton, Paula Martínez Lara, Esther Del Moral Leal, María L. Molina Díaz, Antonio |
author2_role |
author author author author author author |
dc.subject.none.fl_str_mv |
Liquid Chromatography High Resolution Mass Spectrometry Drug Metabolites Sport Drug Testing Propranolol |
topic |
Liquid Chromatography High Resolution Mass Spectrometry Drug Metabolites Sport Drug Testing Propranolol |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.4 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
The development of comprehensive methods able to tackle with the systematic identification of drug metabolites in an automated fashion is of great interest. In this article, a strategy based on the combined use of two complementary data mining tools is proposed for the screening and systematic detection and identification of urinary drug metabolites by liquid chromatography full-scan high resolution mass spectrometry. The proposed methodology is based on the use of accurate mass extraction of diagnostic ions (compound-dependent information) from in-source CID fragmentation without precursor ion isolation along with the use of automated mass extraction of accurate-mass shifts corresponding to typical biotransformations (non compound-dependent information) that xenobiotics usually undergo when metabolized. The combined strategy was evaluated using LC-TOFMS with a suite of nine sport drugs representative from different classes (propranolol, bumetanide, clenbuterol, ephedrine, finasteride, methoxyphenamine, methylephedrine, salbutamol and terbutaline), after single doses administered to rats. The metabolite identification coverage rate obtained with the systematic method (compared to existing literature) was satisfactory, and provided the identification of several non-previously reported metabolites. In addition, the combined information obtained helps to minimize the number of false positives. As an example, the systematic identification of urinary metabolites of propranolol enabled the identification of up to 24 metabolites, 15 of them non previously described in literature, which is a valuable indicator of the usefulness of the proposed systematic procedure. Fil: Domínguez Romero, Juan C.. Universidad de Jaén; España Fil: García Reyes, Juan F.. Universidad de Jaén; España Fil: Martínez Romero, Rubén. Universidad de Jaén; España Fil: Berton, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Científico Tecnológico Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina Fil: Martínez Lara, Esther. Universidad de Jaén; España Fil: Del Moral Leal, María L.. Universidad de Jaén; España Fil: Molina Díaz, Antonio. Universidad de Jaén; España |
description |
The development of comprehensive methods able to tackle with the systematic identification of drug metabolites in an automated fashion is of great interest. In this article, a strategy based on the combined use of two complementary data mining tools is proposed for the screening and systematic detection and identification of urinary drug metabolites by liquid chromatography full-scan high resolution mass spectrometry. The proposed methodology is based on the use of accurate mass extraction of diagnostic ions (compound-dependent information) from in-source CID fragmentation without precursor ion isolation along with the use of automated mass extraction of accurate-mass shifts corresponding to typical biotransformations (non compound-dependent information) that xenobiotics usually undergo when metabolized. The combined strategy was evaluated using LC-TOFMS with a suite of nine sport drugs representative from different classes (propranolol, bumetanide, clenbuterol, ephedrine, finasteride, methoxyphenamine, methylephedrine, salbutamol and terbutaline), after single doses administered to rats. The metabolite identification coverage rate obtained with the systematic method (compared to existing literature) was satisfactory, and provided the identification of several non-previously reported metabolites. In addition, the combined information obtained helps to minimize the number of false positives. As an example, the systematic identification of urinary metabolites of propranolol enabled the identification of up to 24 metabolites, 15 of them non previously described in literature, which is a valuable indicator of the usefulness of the proposed systematic procedure. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-01-25 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/2722 Domínguez Romero, Juan C.; García Reyes, Juan F.; Martínez Romero, Rubén; Berton, Paula; Martínez Lara, Esther; et al.; Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry; Elsevier Science; Analytica Chimica Acta; 761; 25-1-2013; 1-10 0003-2670 |
url |
http://hdl.handle.net/11336/2722 |
identifier_str_mv |
Domínguez Romero, Juan C.; García Reyes, Juan F.; Martínez Romero, Rubén; Berton, Paula; Martínez Lara, Esther; et al.; Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry; Elsevier Science; Analytica Chimica Acta; 761; 25-1-2013; 1-10 0003-2670 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2012.11.049 info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0003267012017266 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614342071287808 |
score |
13.070432 |