Machine learning guided prediction of liquid chromatography-mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products.

Chromatography, Liquid DNA Damage Humans Mass Spectrometry Pharmaceutical Preparations Support Vector Machine
Genetic algorithm Genotoxic impurities Ionization efficiency Liquid chromatography-mass spectrometry Molecular descriptor Support vector machine

Journal

Journal of pharmaceutical and biomedical analysis
ISSN: 1873-264X
Titre abrégé: J Pharm Biomed Anal
Pays: England
ID NLM: 8309336

Informations de publication

Date de publication:
05 Feb 2021
Historique:
received: 17 06 2020
revised: 14 11 2020
accepted: 16 11 2020
pubmed: 8 12 2020
medline: 22 6 2021
entrez: 7 12 2020
Statut: ppublish

Résumé

The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to cause cancer, low-level quantitation is required to protect patients. A quick and easy method of determining the liquid chromatography-mass spectrometry (LC/MS) conditions for high-sensitivity analysis of GTIs may prospectively accelerate pharmaceutical development. In this study, a quantitative structure-property relationship (QSPR) model was developed for predicting the ionization efficiency of compounds using liquid-chromatography-mass spectrometry (LC/MS) parameters and molecular descriptors. Before implementing the QSPR prediction model, linear regression analysis was performed to model the relationship between the ionization efficiency and the LC/MS parameters for each compound. Comparison of the predicted peak areas with the experimentally observed peak areas showed good agreement based on the coefficient of determination (R

Identifiants

DOI: 10.1016/j.jpba.2020.113781 PMID: 33280999
pubmed: 33280999
pii: S0731-7085(20)31667-8
doi: 10.1016/j.jpba.2020.113781
pii:
doi:

Substances chimiques

Pharmaceutical Preparations 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

113781

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors report no declarations of interest.

Auteurs

Kohei Miyamoto (K)

Analytical Research Laboratories, Astellas Pharma Inc., 180 Ozumi, Yaizu, Shizuoka 425-0072, Japan; Department of Analytical and Bioanalytical Chemistry, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan. Electronic address: kohei.miyamoto@astellas.com.

Hajime Mizuno (H)

Department of Analytical and Bioanalytical Chemistry, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.

Eiji Sugiyama (E)

Department of Analytical and Bioanalytical Chemistry, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.

Toshimasa Toyo'oka (T)

Department of Analytical and Bioanalytical Chemistry, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.

Kenichiro Todoroki (K)

Department of Analytical and Bioanalytical Chemistry, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan. Electronic address: todoroki@u-shizuoka-ken.ac.jp.

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Classifications MeSH

questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Chromatographie › Chromatographie en phase liquide › Machine learning guided prediction of liquid...
questionsmedicales.fr › Phénomènes génétiques › Altération de l'ADN › Machine learning guided prediction of liquid...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Machine learning guided prediction of liquid...
questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Spectrométrie de masse › Machine learning guided prediction of liquid...
questionsmedicales.fr › Préparations pharmaceutiques › Machine learning guided prediction of liquid...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Apprentissage machine supervisé › Machine à vecteur de support › Machine learning guided prediction of liquid...