Computational prediction of retention times of veterinary antibiotics obtained by liquid chromatography‐mass spectrometry

BACKGROUND Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (tR) in liquid chromatography–mass spectrometry (LC–MS). One strategy to predict the tR of new v...

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Bibliographic Details
Published in:Journal of the science of food and agriculture 2024-08, Vol.104 (11), p.6724-6732
Main Authors: Rojas, Cristian, Sarmiento, Nicole, Ayora, Emilia, Pis Diez, Reinaldo
Format: Article
Language:English
Subjects:
Animal diseases
Animal health
animal source foods
Antibiotics
Chemical compounds
Chromatography
Columnar structure
Computer applications
in silico methods
Industrial development
LC–MS
Lipophilic
Liquid chromatography
Mass spectrometry
Mass spectroscopy
Octanol
Prediction models
Predictions
QSPR
Regression models
Retention
retention time
Scientific imaging
veterinary antibiotic residues
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Summary:BACKGROUND Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (tR) in liquid chromatography–mass spectrometry (LC–MS). One strategy to predict the tR of new veterinary antibiotics is the development of predictive quantitative structure–property relationships (QSPRs), which were used in this study. RESULTS A database of 122 antibiotics was selected in which the tR was measured using a Hypersil GOLD column. An optimal three‐feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection, and multiple linear regression. The negligible differences among the coefficient of determination and the root‐mean‐square error for the training set (R2 = 0.902 and RMSEC = 0.871) and test set (Q2 = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a more in‐depth explanation of the mechanism of action of each descriptor in predicting the tR is provided, with the construction of the theoretical chemical space for accurate predictions of new antibiotics. CONCLUSION The in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol–water partition coefficient, and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC–MS. The computational model was developed in accordance with five principles outlined by the Organization for Economic Co‐operation and Development. © 2024 Society of Chemical Industry.
ISSN:0022-5142
1097-0010
1097-0010
DOI:10.1002/jsfa.13499