Urinary chemical fingerprint left behind by repeated NSAID administration: Discovery of putative biomarkers using artificial intelligence

Autor: Pablo Piñeyro, Jennifer E. Slovak, Martin A. Suarez, Nicolas F. Villarino, Liam E. Broughton-Neiswanger, Sol M. Rivera-Velez, Julianne K. Hwang
Rok vydání: 2020
Předmět:
0301 basic medicine
Taurine
Physiology
Urine
Biochemistry
Mass Spectrometry
Sugar Alcohols
0302 clinical medicine
Drug Metabolism
Metabolites
Medicine and Health Sciences
Cluster Analysis
Medicine
030212 general & internal medicine
Xylitol
Mammals
chemistry.chemical_classification
Chromatography
Kidney
Multidisciplinary
CATS
Organic Compounds
Anti-Inflammatory Agents
Non-Steroidal
Monosaccharides
Eukaryota
Body Fluids
Butyrates
Chemistry
Meloxicam
medicine.anatomical_structure
Physical Sciences
Vertebrates
Female
Anatomy
Research Article
medicine.drug
Science
Urinary system
Carbohydrates
Sugar acids
03 medical and health sciences
Metabolomics
Artificial Intelligence
Metabolome
Animals
Humans
Pharmacokinetics
Pharmacology
business.industry
Organic Chemistry
Chemical Compounds
Organisms
Biology and Life Sciences
Sugar Acids
Kidneys
Renal System
Metabolism
030104 developmental biology
ROC Curve
chemistry
Amniotes
Cats
Tyrosine
Artificial intelligence
business
Acids
Biomarkers
Pseudouridine
Zdroj: PLoS ONE
PLoS ONE, Vol 15, Iss 2, p e0228989 (2020)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0228989
Popis: Prediction and early detection of kidney damage induced by nonsteroidal anti-inflammatories (NSAIDs) would provide the best chances of maximizing the anti-inflammatory effects while minimizing the risk of kidney damage. Unfortunately, biomarkers for detecting NSAID-induced kidney damage in cats remain to be discovered. To identify potential urinary biomarkers for monitoring NSAID-based treatments, we applied an untargeted metabolomics approach to urine collected from cats treated repeatedly with meloxicam or saline for up to 17 days. Applying multivariate analysis, this study identified a panel of seven metabolites that discriminate meloxicam treated from saline treated cats. Combining artificial intelligence machine learning algorithms and an independent testing urinary metabolome data set from cats with meloxicam-induced kidney damage, a panel of metabolites was identified and validated. The panel of metabolites including tryptophan, tyrosine, taurine, threonic acid, pseudouridine, xylitol and lyxitol, successfully distinguish meloxicam-treated and saline-treated cats with up to 75-100% sensitivity and specificity. This panel of urinary metabolites may prove a useful and non-invasive diagnostic tool for monitoring potential NSAID induced kidney injury in feline patients and may act as the framework for identifying urine biomarkers of NSAID induced injury in other species.
Databáze: OpenAIRE
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