A supervised machine-learning approach for the efficient development of a multi method (LC-MS) for a large number of drugs and subsets thereof: focus on oral antitumor agents.

Kehl, Niklas; Gessner, Arne; Maas, Renke; Fromm, Martin F; Taudte, R Verena

Kehl, Niklas; Gessner, Arne; Maas, Renke; Fromm, Martin F; Taudte, R Verena.

Afiliación

Kehl N; Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Gessner A; Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Maas R; Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Fromm MF; FAU NeW - Research Center for New Bioactive Compounds, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Taudte RV; Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Clin Chem Lab Med ; 62(2): 293-302, 2024 Jan 26.

Article en En | MEDLINE | ID: mdl-37606251

Asunto(s)

Antineoplásicos; Humanos; Cromatografía Liquida/métodos; Espectrometría de Masas en Tándem/métodos; Antineoplásicos/farmacología; Monitoreo de Drogas/métodos; Aprendizaje Automático

Palabras clave

artificial neural networks; liquid chromatography coupled to mass spectrometry; oral antitumor drugs; plasma concentration; regression analysis; supervised machine-learning

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Antineoplásicos Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Clin Chem Lab Med Asunto de la revista: QUIMICA CLINICA / TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2024 Tipo del documento: Article País de afiliación: Alemania

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