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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/2722
Acceder
id CONICETDig_1afdf4450443a03471836f1eb320060d
oai_identifier_str oai:ri.conicet.gov.ar:11336/2722
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling 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 Técnicas2025-10-22T12:05:13Zoai: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:34982025-10-22 12:05:14.091CONICET 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
_version_ 1846782405118001152
score 12.982451

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