The 10th Asia-Oceania Mass Spectrometry Conference (AOMSC2025) - organized by the Mass Spectrometry Society of Japan

Abstract

Timetable
Download Conference Program
Download All Abstracts
Zoom Access
Corporate Program

Poster Presentations

Day 2, June 23(Mon.) 

Room P (Maesato East, Foyer, Ocean Wing)

A Metabolomic LC-HRMS Approach for the Administration Route Classification of Altrenogest in Racehorses

(1University of Technology Sydney, 2Racing Analytical Services Limited, 3Australian Racing Forensic Laboratory)
oMadysen Elbourne1, Adam Cawley2, John Keledjian3, Shanlin Fu1

Altrenogest is a synthetic form of progesterone used therapeutically to suppress unwanted symptoms of oestrus in mares by mimicking the increase of progesterone and decreasing levels of estrogen production.[1,2] This results in more manageable mares for training and competition, while improving the workplace safety.[3,4] However, when altrenogest is administered, prohibited steroid impurities (trendione, trenbolone, and epitrenbolone) can be detected.[1,5,6] It has been assumed that greater concentrations of these steroid impurities are present in injectable preparations and, therefore, pose a greater risk of causing anabolic effects when administered.[7] For this reason, and due to the necessity of this therapeutic substance for the safety of thoroughbred racing participants, a metabolomic approach investigating the differentiation of two main administration routes was conducted.
Liquid chromatography high-resolution mass spectrometry analysis of equine urine samples found five sulfated compounds, estrone sulfate, testosterone sulfate, 2-methoxyestradiol sulfate, pregnenolone sulfate, and cortisol sulfate, with the potential to differentiate between orally administered and intramuscularly injected altrenogest using a random forest classification model. The best model results, comparing datasets from the administration of two horses, gave an AUC score of 0.965. This study proposes a new potential application for metabolomic multi-tool workflows and machine learning models in a forensic toxicological context.