Novel Algorithms for Comprehensive Untargeted Detection of Doping Agents in Biological Samples.
Abstract: To address the limitations of current targeted analytical methods that can only detect known doping agents, a novel methodology that permits untargeted drug detection (UDD) has been developed to help in the fight against doping in sports. Fifty-seven drugs were spiked into blank equine plasma and were treated as unknowns since their exact masses and chromatographic retention times were not utilized for detection. The spiked drugs were extracted from the plasma samples and were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The acquired LC-HRMS raw data files were processed using metabolomic software for compound detection and identification. For UDD with the resultant data, a mathematical model was created, and two algorithms were generated to calculate the ratio of the mean (ROM) and outlier index (OLI). Using ROM and OLI, the majority of the 57 drugs were accurately detected by name (52 of 57) or chemical formula (1 of 57). The limit of detection for the drugs was from tens of picograms to nanograms per milliliter. Xenobiotics and endogenous substances relevant to doping control were also identified using this untargeted approach following their extraction from real-world race samples, thus validating the UDD methodology. To the authors' knowledge, this is the first completely UDD methodological approach and represents significant advance toward using artificial intelligence for the detection of both known and emerging doping agents in sports.
Publication Date: 2021-05-21 PubMed ID: 34018396DOI: 10.1021/acs.analchem.1c01273Google Scholar: Lookup
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- Journal Article
- Research Support
- Non-U.S. Gov't
Summary
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The researchers developed a new approach to detect the use of drugs for performance enhancement in sports. They tested fifty-seven drugs by spiking them into blank horse blood plasma and then used liquid chromatography and high-resolution mass spectrometry to analyze the composition. The majority of the drugs were accurately detected by this method, which is the first time such a comprehensive undirected drug detection method has been developed. As such, it represents a significant advance for artificial intelligence applications in sports doping control.
Objective of the study
- The aim was to address the difficulties of targeted analytical methods that are only capable of detecting known doping substances. The researchers sought to create a methodology that permits unreliable drug detection to bolster anti-doping measures in sports.
Methods used in the study
- Fifty-seven drugs were added to blank equine plasma and treated as unknowns, disregarding their exact mass and chromatographic retention times.
- The drugs in the plasma samples were then extracted and analyzed using a scientific process called liquid chromatography, coupled with high-resolution mass spectrometry (LC-HRMS).
- The raw data generated from the LC-HRMS was dealt with using metabolomics software that permitted compound detection and identification.
Results of the study
- A mathematical model was built on the resultant data and two algorithms were constructed to calculate the ratio of the mean (ROM) and the outlier index (OLI).
- The majority of the spiked drugs were accurately detected by name (52 of 57) or by chemical formula (1 of 57), showcasing the precision of the methodology.
- The detection limit for the drugs ranged from the tens of picograms to nanograms per milliliter, demonstrating the sensitivity of the new method.
- Xenobiotics and endogenous substances, substances that are made inside the body, relevant to doping control were also successfully identified using this untargeted method after they had been extracted from real-world sports samples. This validated the integrity and utility of the unreliable drug detection (UDD) methodology.
Significance of the study
- This research represents a significant advance towards using artificial intelligence for the detection of both known and emerging doping agents in sports.
- This is the first entirely unreliable drug detection (UDD) methodological approach, indicating a pioneering development in the field.
Cite This Article
APA
Guan F, You Y, Fay S, Li X, Robinson MA.
(2021).
Novel Algorithms for Comprehensive Untargeted Detection of Doping Agents in Biological Samples.
Anal Chem, 93(21), 7746-7753.
https://doi.org/10.1021/acs.analchem.1c01273 Publication
Researcher Affiliations
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, Pennsylvania 19348, United States.
- Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, Pennsylvania 19382, United States.
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, Pennsylvania 19348, United States.
- Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, Pennsylvania 19382, United States.
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, Pennsylvania 19348, United States.
- Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, Pennsylvania 19382, United States.
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, Pennsylvania 19348, United States.
- Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, Pennsylvania 19382, United States.
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, Pennsylvania 19348, United States.
- Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, Pennsylvania 19382, United States.
MeSH Terms
- Algorithms
- Animals
- Artificial Intelligence
- Chromatography, Liquid
- Doping in Sports
- Horses
- Mass Spectrometry
- Substance Abuse Detection
Citations
This article has been cited 3 times.- Lu Y, Yan J, Ou G, Fu L. A Review of Recent Progress in Drug Doping and Gene Doping Control Analysis.. Molecules 2023 Jul 18;28(14).
- Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics.. Methods Mol Biol 2023;2571:207-239.
- Klingberg J, Keen B, Cawley A, Pasin D, Fu S. Developments in high-resolution mass spectrometric analyses of new psychoactive substances.. Arch Toxicol 2022 Apr;96(4):949-967.
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