The Metabolomic Investigation of Urinary Biomarkers for Progestogenic and Dopaminergic Substance Misuse in Racehorses

Elbourne, Madysen

The Metabolomic Investigation of Urinary Biomarkers for Progestogenic and Dopaminergic Substance Misuse in Racehorses

Elbourne, Madysen

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Elbourne, Madysen
dc.date.accessioned	2026-03-04T04:40:03Z
dc.date.available	2026-03-04T04:40:03Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/193763
dc.description	University of Technology Sydney. Faculty of Science.	en_US.UTF-8
dc.description.abstract	This thesis investigates progestogenic and dopaminergic substance misuse in racehorses, applying metabolomic analyses and machine learning to enhance biomarker discovery for anti-doping. Focused on altrenogest, a therapeutic progesterone analogue, the study examined differences between oral and intramuscular administration, noting distinct impurity excretion patterns for trendione and epitrenbolone. Samples from a ten-mare administration study revealed that impurities persisted longer after intramuscular dosing. Untargeted LC-HRMS analysis identified five sulfated steroids capable of distinguishing administration routes, achieving high classification accuracy (AUC = 0.965). For dopaminergic misuse, attention centred on levodopa-containing drugs, such as Stalevo, potentially used to manipulate race performance. Current 3-methoxytyramine thresholds detect large doses but miss micro-dosing or complex medication misuse. A validated LC-QTOF-MS method quantified key metabolites (3-methoxytyramine and tyramine), while metabolomic analysis identified conjugated catecholamines as potential novel biomarkers. Analysis of samples from a 12-horse administration study yielded metabolite profiles supporting extended detection windows via machine learning models. Overall, the work demonstrates integrated targeted/untargeted metabolomic strategies for detecting complex doping cases, proposing new biomarkers and predictive models to strengthen equine anti-doping efforts.	en_US.UTF-8
dc.format	Thesis(PhD)
dc.language.iso	en	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/193763/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© Madysen Elbourne
dc.rights	au.edu.uts.lib/cph
dc.title	The Metabolomic Investigation of Urinary Biomarkers for Progestogenic and Dopaminergic Substance Misuse in Racehorses	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This thesis investigates progestogenic and dopaminergic substance misuse in racehorses, applying metabolomic analyses and machine learning to enhance biomarker discovery for anti-doping. Focused on altrenogest, a therapeutic progesterone analogue, the study examined differences between oral and intramuscular administration, noting distinct impurity excretion patterns for trendione and epitrenbolone. Samples from a ten-mare administration study revealed that impurities persisted longer after intramuscular dosing. Untargeted LC-HRMS analysis identified five sulfated steroids capable of distinguishing administration routes, achieving high classification accuracy (AUC = 0.965). For dopaminergic misuse, attention centred on levodopa-containing drugs, such as Stalevo, potentially used to manipulate race performance. Current 3-methoxytyramine thresholds detect large doses but miss micro-dosing or complex medication misuse. A validated LC-QTOF-MS method quantified key metabolites (3-methoxytyramine and tyramine), while metabolomic analysis identified conjugated catecholamines as potential novel biomarkers. Analysis of samples from a 12-horse administration study yielded metabolite profiles supporting extended detection windows via machine learning models. Overall, the work demonstrates integrated targeted/untargeted metabolomic strategies for detecting complex doping cases, proposing new biomarkers and predictive models to strengthen equine anti-doping efforts.

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http://hdl.handle.net/10453/193763