FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Anal Bioanal Chem / Generic approach for the discovery of drug metabolites in ho...
Analytical and bioanalytical chemistry2022; 414(28); 8125-8142; doi: 10.1007/s00216-022-04347-2

Generic approach for the discovery of drug metabolites in horses based on data-dependent acquisition by liquid chromatography high-resolution mass spectrometry and its applications to pharmacokinetic study of daprodustat.

Abstract: In drug metabolism studies in horses, non-targeted analysis by means of liquid chromatography coupled with high-resolution mass spectrometry with data-dependent acquisition (DDA) has recently become increasingly popular for rapid identification of potential biomarkers in post-administration biological samples. However, the most commonly encountered problem is the presence of highly abundant interfering components that co-elute with the target substances, especially if the concentrations of these substances are relatively low. In this study, we evaluated the possibility of expanding DDA coverage for the identification of drug metabolites by applying intelligently generated exclusion lists (ELs) consisting of a set of chemical backgrounds and endogenous substances. Daprodustat was used as a model compound because of its relatively lower administration dose (100 mg) compared to other hypoxia-inducible factor stabilizers and the high demand in the detection sensitivity of its metabolites at the anticipated lower concentrations. It was found that the entire DDA process could efficiently identify both major and minor metabolites (flagged beyond the pre-set DDA threshold) in a single run after applying the ELs to exclude 67.7-99.0% of the interfering peaks, resulting in a much higher chance of triggering DDA to cover the analytes of interest. This approach successfully identified 21 metabolites of daprodustat and then established the metabolic pathway. It was concluded that the use of this generic intelligent "DDA + EL" approach for non-targeted analysis is a powerful tool for the discovery of unknown metabolites, even in complex plasma and urine matrices in the context of doping control.
© 2022. Springer-Verlag GmbH Germany, part of Springer Nature.
Get Full Text
Publication Date: 2022-10-01 PubMed ID: 36181513PubMed Central: 7438806DOI: 10.1007/s00216-022-04347-2Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article describes a new methodology for drug metabolism analysis in horses using data-dependent acquisition (DDA) and liquid chromatography high-resolution mass spectrometry. With Daprodustat as a model compound, the researchers developed exclusion lists to efficiently identify major and minor metabolites in a single run, which is key in doping control.

Methodology

  • The researchers used non-targeted analysis by means of liquid chromatography coupled with high-resolution mass spectrometry with data-dependent acquisition (DDA). This method is growing in popularity for quickly identifying potential biomarkers in post-administration biological samples from horses.
  • The main issue usually encountered in such analyses is the presence of highly abundant interfering components that co-elute with the target substances. This is an issue, especially if the concentrations of these substances are relatively low.

New Approach and its Application

  • The research team assessed the possibility of expanding DDA coverage for the identification of drug metabolites. They did this by applying intelligently created exclusion lists (ELs) comprised of a set of chemical backgrounds and endogenous substances.
  • Daprodustat, a compound used for its relatively lower administration dose in comparison to other hypoxia-inducible factor stabilizers, was used as a model. The high demand in the sensitivity of its metabolites detection at the anticipated lower concentrations made it a suitable model.
  • The entire DDA process, after applying the ELs, could efficiently identify both major and minor metabolites in a single run, while excluding 67.7-99.0% of the interfering peaks. This resulted in a much higher chance of triggering DDA to cover the analytes of interest.
  • This approach successfully identified 21 metabolites of daprodustat and established the metabolic pathway.

Conclusion

  • The researchers concluded that this generic intelligent “DDA + EL” approach for non-targeted analysis is a powerful tool for discovering unknown metabolites. This is extremely useful, even in the detection of complex plasma and urine matrices in the context of doping control.

Cite This Article

APA
Ishii H, Shibuya M, Kusano K, Sone Y, Kamiya T, Wakuno A, Ito H, Miyata K, Sato F, Kuroda T, Yamada M, Leung GN. (2022). Generic approach for the discovery of drug metabolites in horses based on data-dependent acquisition by liquid chromatography high-resolution mass spectrometry and its applications to pharmacokinetic study of daprodustat. Anal Bioanal Chem, 414(28), 8125-8142. https://doi.org/10.1007/s00216-022-04347-2

Publication

Analytical and bioanalytical chemistry
ISSN: 1618-2650
NlmUniqueID: 101134327
Country: Germany
Language: English
Volume: 414
Issue: 28
Pages: 8125-8142

Researcher Affiliations

Ishii, Hideaki
  • Drug Analysis Department, Laboratory of Racing Chemistry, 1731-2 Tsuruta-machi, Utsunomiya, Tochigi, 320-0851, Japan. h-ishii@lrc.or.jp.
  • Department of Pharmaceutical Sciences, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan. h-ishii@lrc.or.jp.
Shibuya, Mariko
  • Drug Analysis Department, Laboratory of Racing Chemistry, 1731-2 Tsuruta-machi, Utsunomiya, Tochigi, 320-0851, Japan.
Kusano, Kanichi
  • Veterinarian Section, Equine Department, Japan Racing Association, 6-11-1 Roppongi, Minato-ku, Tokyo, 105-0003, Japan.
Sone, Yu
  • Veterinarian Section, Equine Department, Japan Racing Association, 6-11-1 Roppongi, Minato-ku, Tokyo, 105-0003, Japan.
Kamiya, Takahiro
  • Equine Veterinary Clinic, Horse Racing School, Japan Racing Association, 835-1 Ne, Shiroi, Chiba, 270-1431, Japan.
Wakuno, Ai
  • Equine Veterinary Clinic, Horse Racing School, Japan Racing Association, 835-1 Ne, Shiroi, Chiba, 270-1431, Japan.
Ito, Hideki
  • Equine Veterinary Clinic, Horse Racing School, Japan Racing Association, 835-1 Ne, Shiroi, Chiba, 270-1431, Japan.
Miyata, Kenji
  • JRA Equestrian Park Utsunomiya Office, 321-4 Tokamicho, Utsunomiya, Tochigi, 320-0856, Japan.
Sato, Fumio
  • Clinical Veterinary Medicine Division, Equine Research Institute, Japan Racing Association, 1400-4, Shiba, Shimotsuke, Tochigi, 329-0412, Japan.
Kuroda, Taisuke
  • Clinical Veterinary Medicine Division, Equine Research Institute, Japan Racing Association, 1400-4, Shiba, Shimotsuke, Tochigi, 329-0412, Japan.
Yamada, Masayuki
  • Drug Analysis Department, Laboratory of Racing Chemistry, 1731-2 Tsuruta-machi, Utsunomiya, Tochigi, 320-0851, Japan.
Leung, Gary Ngai-Wa
  • Drug Analysis Department, Laboratory of Racing Chemistry, 1731-2 Tsuruta-machi, Utsunomiya, Tochigi, 320-0851, Japan.

MeSH Terms

  • Animals
  • Chromatography, Liquid / methods
  • Doping in Sports
  • Horses
  • Mass Spectrometry / methods
  • Pharmaceutical Preparations
  • Substance Abuse Detection / methods

Grant Funding

  • 2022 / Japan Racing Association

References

This article includes 50 references
  1. Thevis M, Kuuranne T, Geyer H. Annual banned-substance review: Analytical approaches in human sports drug testing 2020/2021.. Drug Test Anal 2022 Jan;14(1):7-30.
    doi: 10.1002/dta.3199pubmed: 34788500google scholar: lookup
  2. Maekawa M, Mano N. Cutting-edge LC-MS/MS applications in clinical mass spectrometry: Focusing on analysis of drugs and metabolites.. Biomed Chromatogr 2022 May;36(5):e5347.
    doi: 10.1002/bmc.5347pubmed: 35073598google scholar: lookup
  3. Luo X, Wang Z, Yang L, Gao T, Zhang Y. A review of analytical methods and models used in atmospheric microplastic research.. Sci Total Environ 2022 Jul 1;828:154487.
    doi: 10.1016/j.scitotenv.2022.154487pubmed: 35278538google scholar: lookup
  4. Dou X, Zhang L, Yang R, Wang X, Yu L, Yue X. Mass spectrometry in food authentication and origin traceability. Mass Spectrom Rev 2022:e21779.
    doi: 10.1002/mas.21779google scholar: lookup
  5. Yamanaka T, Yamada M, Tsujimura K, Kondo T, Nagata S, Hobo S, Kurosawa M, Matsumura T. Clinical pharmacokinetics of oseltamivir and its active metabolite oseltamivir carboxylate after oral administration in horses.. J Vet Med Sci 2007 Mar;69(3):293-6.
    doi: 10.1292/jvms.69.293pubmed: 17409647google scholar: lookup
  6. Tsujimura K, Yamada M, Nagata S, Yamanaka T, Nemoto M, Kondo T, Kurosawa M, Matsumura T. Pharmacokinetics of penciclovir after oral administration of its prodrug famciclovir to horses.. J Vet Med Sci 2010 Mar;72(3):357-61.
    doi: 10.1292/jvms.09-0350pubmed: 19959884google scholar: lookup
  7. Ishii H, Leung GN, Yamashita S, Yamada M, Kushiro A, Kasashima Y, Okada J, Kawasaki K, Kijima-Suda I. Doping control analysis of GW1516 in equine plasma using liquid chromatography/electrospray ionization Q-Exactive high-resolution mass spectrometry.. Rapid Commun Mass Spectrom 2020 Dec 15;34(23):e8920.
    doi: 10.1002/rcm.8920pubmed: 32776613google scholar: lookup
  8. Ishii H, Shibuya M, Leung GN, Yamashita S, Yamada M, Kushiro A, Kasashima Y, Okada J, Kawasaki K, Kijima-Suda I. Metabolic study of GW1516 in equine urine using liquid chromatography/electrospray ionization Q-Exactive high-resolution mass spectrometry for doping control.. Rapid Commun Mass Spectrom 2021 Mar 13;35(5):e9028.
    doi: 10.1002/rcm.9028pubmed: 33319421google scholar: lookup
  9. Ishii H, Shibuya M, So YM, Wong JKY, Ho ENM, Kusano K, Sone Y, Kamiya T, Wakuno A, Ito H, Miyata K, Yamada M, Leung GN. Comprehensive metabolic study of IOX4 in equine urine and plasma using liquid chromatography/electrospray ionization Q Exactive high-resolution mass spectrometer for the purpose of doping control.. Drug Test Anal 2022 Feb;14(2):233-251.
    doi: 10.1002/dta.3172pubmed: 34612014google scholar: lookup
  10. Penner N, Klunk LJ, Prakash C. Human radiolabeled mass balance studies: objectives, utilities and limitations.. Biopharm Drug Dispos 2009 May;30(4):185-203.
    doi: 10.1002/bdd.661pubmed: 19544285google scholar: lookup
  11. Spracklin DK, Chen D, Bergman AJ, Callegari E, Obach RS. Mini-Review: Comprehensive Drug Disposition Knowledge Generated in the Modern Human Radiolabeled ADME Study.. CPT Pharmacometrics Syst Pharmacol 2020 Aug;9(8):428-434.
    doi: 10.1002/psp4.12540pubmed: 32562380pmc: 7438806google scholar: lookup
  12. Lee JY, Mushtaq S, Park JE, Shin HS, Lee SY, Jeon J. Radioanalytical techniques to quantitatively assess the biological uptake and in vivo behavior of hazardous substances. Molecules 2020;25(17):3985.
    doi: 10.3390/molecules25173985pmc: 7504758google scholar: lookup
  13. Ishii H, Obara T, Kijima-Suda I. Investigation of plasma concentrations of paracetamol, metacetamol, and orthocetamol in Japanese racehorses using liquid chromatography-electrospray ionisation-tandem mass spectrometry.. Drug Test Anal 2020 Jul;12(7):929-937.
    doi: 10.1002/dta.2792pubmed: 32187884google scholar: lookup
  14. Ishii H, Obara T, Kusano K, Kijima-Suda I. Quantitative analysis of paracetamol, metacetamol, and orthocetamol in equine urine from racehorses in Japan using liquid chromatography-electrospray ionization-tandem mass spectrometry.. Drug Test Anal 2020 Aug;12(8):1196-1202.
    doi: 10.1002/dta.2860pubmed: 32436292google scholar: lookup
  15. Ishii H, Leung GN, Yamashita S, Nagata SI, Kushiro A, Sakai S, Toju K, Okada J, Kawasaki K, Kusano K, Kijima-Suda I. Comprehensive metabolic study of nicotine in equine plasma and urine using liquid chromatography/high-resolution mass spectrometry for the identification of unique biomarkers for doping control.. J Chromatogr B Analyt Technol Biomed Life Sci 2022 Feb 1;1190:123100.
    doi: 10.1016/j.jchromb.2022.123100pubmed: 35032890google scholar: lookup
  16. Ishii H, Leung GN, Yamashita S, Nagata SI, Kushiro A, Sakai S, Toju K, Okada J, Kawasaki K, Kusano K, Kijima-Suda I. Identification of potential biomarkers in urine and plasma after consumption of tobacco product in horses.. Drug Test Anal 2022 May;14(5):902-914.
    doi: 10.1002/dta.3242pubmed: 35195357google scholar: lookup
  17. Ishii H, Shibuya M, Kusano K, Sone Y, Kamiya T, Wakuno A. Pharmacokinetic study of vadadustat and high-resolution mass spectrometric characterization of its novel metabolites in equines for the purpose of doping control. Crurrent Drug Metabolism Accepted: 22 June 2022.
    doi: 10.2174/1389200223666220825093945google scholar: lookup
  18. Zhang H, Zhang D, Ray K. A software filter to remove interference ions from drug metabolites in accurate mass liquid chromatography/mass spectrometric analyses.. J Mass Spectrom 2003 Oct;38(10):1110-2.
    doi: 10.1002/jms.521pubmed: 14595861google scholar: lookup
  19. Zhang H, Zhang D, Ray K, Zhu M. Mass defect filter technique and its applications to drug metabolite identification by high-resolution mass spectrometry.. J Mass Spectrom 2009 Jul;44(7):999-1016.
    doi: 10.1002/jms.1610pubmed: 19598168google scholar: lookup
  20. Zelesky V, Schneider R, Janiszewski J, Zamora I, Ferguson J, Troutman M. Software automation tools for increased throughput metabolic soft-spot identification in early drug discovery.. Bioanalysis 2013 May;5(10):1165-79.
    doi: 10.4155/bio.13.89pubmed: 23721441google scholar: lookup
  21. Dhurjad PS, Marothu VK, Rathod R. Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.. Bioanalysis 2017 Aug;9(16):1265-1278.
    doi: 10.4155/bio-2017-0046pubmed: 28816067google scholar: lookup
  22. Defossez E, Bourquin J, von Reuss S, Rasmann S, Glauser G. Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics.. Mass Spectrom Rev 2023 Jan;42(1):131-143.
    doi: 10.1002/mas.21715pubmed: 34145627google scholar: lookup
  23. Fenaille F, Barbier Saint-Hilaire P, Rousseau K, Junot C. Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?. J Chromatogr A 2017 Dec 1;1526:1-12.
    doi: 10.1016/j.chroma.2017.10.043pubmed: 29074071google scholar: lookup
  24. Benton HP, Ivanisevic J, Mahieu NG, Kurczy ME, Johnson CH, Franco L, Rinehart D, Valentine E, Gowda H, Ubhi BK, Tautenhahn R, Gieschen A, Fields MW, Patti GJ, Siuzdak G. Autonomous metabolomics for rapid metabolite identification in global profiling.. Anal Chem 2015 Jan 20;87(2):884-91.
    doi: 10.1021/ac5025649pubmed: 25496351google scholar: lookup
  25. Lawson TN, Weber RJ, Jones MR, Chetwynd AJ, Rodrı Guez-Blanco G, Di Guida R, Viant MR, Dunn WB. msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics.. Anal Chem 2017 Feb 21;89(4):2432-2439.
    doi: 10.1021/acs.analchem.6b04358pubmed: 28194963google scholar: lookup
  26. Cho K, Schwaiger-Haber M, Naser FJ, Stancliffe E, Sindelar M, Patti GJ. Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics.. Anal Chim Acta 2021 Mar 8;1149:338210.
    doi: 10.1016/j.aca.2021.338210pubmed: 33551064pmc: 8189644google scholar: lookup
  27. Ueda T, Tozaki T, Nozawa S, Kinoshita K, Gawahara H. Identification of metabolomic changes in horse plasma after racing by liquid chromatography-high resolution mass spectrometry as a strategy for doping testing.. J Equine Sci 2019 Sep;30(3):55-61.
    doi: 10.1294/jes.30.55pubmed: 31592223pmc: 6773618google scholar: lookup
  28. Ohnuma K, Uchida T, Leung GN, Ueda T, Obara T, Ishii H. Establishment of a post-race biomarkers database and application of pathway analysis to identify potential biomarkers in post-race equine plasma.. Drug Test Anal 2022 May;14(5):915-928.
    doi: 10.1002/dta.3041pubmed: 33835667google scholar: lookup
  29. David L, Kang J, Chen S. Untargeted Metabolomics of Arabidopsis Stomatal Immunity.. Methods Mol Biol 2021;2200:413-424.
    doi: 10.1007/978-1-0716-0880-7_20pubmed: 33175390google scholar: lookup
  30. Beuck S, Schänzer W, Thevis M. Hypoxia-inducible factor stabilizers and other small-molecule erythropoiesis-stimulating agents in current and preventive doping analysis.. Drug Test Anal 2012 Nov;4(11):830-45.
    doi: 10.1002/dta.390pubmed: 22362605google scholar: lookup
  31. Thevis M, Milosovich S, Licea-Perez H, Knecht D, Cavalier T, Schänzer W. Mass spectrometric characterization of a prolyl hydroxylase inhibitor GSK1278863, its bishydroxylated metabolite, and its implementation into routine doping controls.. Drug Test Anal 2016 Aug;8(8):858-63.
    doi: 10.1002/dta.1870pubmed: 26361079google scholar: lookup
  32. Johnson BM, Stier BA, Caltabiano S. Effect of food and gemfibrozil on the pharmacokinetics of the novel prolyl hydroxylase inhibitor GSK1278863.. Clin Pharmacol Drug Dev 2014 Mar;3(2):109-17.
    doi: 10.1002/cpdd.83pubmed: 27128455google scholar: lookup
  33. Yamada M, Osamura M, Ogura H, Onoue T, Wakamatsu A, Numachi Y, Caltabiano S, Mahar KM. A Single-Dose, Open-Label, Randomized, Two-Way Crossover Study in Healthy Japanese Participants to Evaluate the Bioequivalence and the Food Effect on the Pharmacokinetics of Daprodustat.. Clin Pharmacol Drug Dev 2020 Nov;9(8):978-984.
    doi: 10.1002/cpdd.793pubmed: 32250021pmc: 7687240google scholar: lookup
  34. Dhillon S. Daprodustat: First Approval.. Drugs 2020 Sep;80(14):1491-1497.
    doi: 10.1007/s40265-020-01384-ypubmed: 32880805pmc: 7471535google scholar: lookup
  35. . International Agreement on Breeding, Racing and Wagering And Appendixes (January 2022). International Federation of Horseracing Authorities 2022.
  36. . 2022 Equine Prohibited Substances List. Fédération Équestre, Internationale 2022.
  37. Markham A. Vadadustat: First Approval.. Drugs 2020 Sep;80(13):1365-1371.
    doi: 10.1007/s40265-020-01383-zpubmed: 32852744google scholar: lookup
  38. Haase VH, Chertow GM, Block GA, Pergola PE, deGoma EM, Khawaja Z, Sharma A, Maroni BJ, McCullough PA. Effects of vadadustat on hemoglobin concentrations in patients receiving hemodialysis previously treated with erythropoiesis-stimulating agents.. Nephrol Dial Transplant 2019 Jan 1;34(1):90-99.
    doi: 10.1093/ndt/gfy055pubmed: 29672740google scholar: lookup
  39. Nangaku M, Kondo K, Takabe S, Ueta K, Kaneko G, Otsuka M, Kawaguchi Y, Komatsu Y. Vadadustat for anemia in chronic kidney disease patients on peritoneal dialysis: A phase 3 open-label study in Japan.. Ther Apher Dial 2021 Oct;25(5):642-653.
    doi: 10.1111/1744-9987.13611pubmed: 33283981google scholar: lookup
  40. Eckardt KU, Agarwal R, Farag YM, Jardine AG, Khawaja Z, Koury MJ, Luo W, Matsushita K, McCullough PA, Parfrey P, Ross G, Sarnak MJ, Vargo D, Winkelmayer WC, Chertow GM. Global Phase 3 programme of vadadustat for treatment of anaemia of chronic kidney disease: rationale, study design and baseline characteristics of dialysis-dependent patients in the INNO2VATE trials.. Nephrol Dial Transplant 2021 Nov 9;36(11):2039-2048.
    doi: 10.1093/ndt/gfaa204pubmed: 33188693google scholar: lookup
  41. Akizawa T, Macdougall IC, Berns JS, Bernhardt T, Staedtler G, Taguchi M, Iekushi K, Krueger T. Long-Term Efficacy and Safety of Molidustat for Anemia in Chronic Kidney Disease: DIALOGUE Extension Studies.. Am J Nephrol 2019;49(4):271-280.
    doi: 10.1159/000499111pubmed: 30852574google scholar: lookup
  42. Ishii H, Yamaguchi H, Mano N. Shifting the linear range in electrospray ionization by in-source collision-induced dissociation. Chem Pharm Bull 2016;64(4):356–9.
    doi: 10.1248/cpb.c15-00741google scholar: lookup
  43. Ishii H, Shimada M, Yamaguchi H, Mano N. A simultaneous determination method for 5-fluorouracil and its metabolites in human plasma with linear range adjusted by in-source collision-induced dissociation using hydrophilic interaction liquid chromatography-electrospray ionization-tandem mass spectrometry.. Biomed Chromatogr 2016 Nov;30(11):1882-1886.
    doi: 10.1002/bmc.3743pubmed: 27078498google scholar: lookup
  44. Ishii H, Yamaguchi H, Mano N. Expanding the versatility of a quantitative determination range adjustment technique using in-source CID in LC/MS/MS. Chromatography 2017;38(2):59–63.
    doi: 10.15583/jpchrom.2017.004google scholar: lookup
  45. Takasaki S, Tanaka M, Kikuchi M, Maekawa M, Kawasaki Y, Ito A, Arai Y, Yamaguchi H, Mano N. Simultaneous analysis of oral anticancer drugs for renal cell carcinoma in human plasma using liquid chromatography/electrospray ionization tandem mass spectrometry.. Biomed Chromatogr 2018 Jun;32(6):e4184.
    doi: 10.1002/bmc.4184pubmed: 29283445google scholar: lookup
  46. Hirasawa T, Kikuchi M, Shigeta K, Takasaki S, Sato Y, Sato T, Ogura J, Onodera K, Fukuhara N, Onishi Y, Maekawa M, Mano N. High-throughput liquid chromatography/electrospray ionization-tandem mass spectrometry method using in-source collision-induced dissociation for simultaneous quantification of imatinib, dasatinib, bosutinib, nilotinib, and ibrutinib in human plasma.. Biomed Chromatogr 2021 Aug;35(8):e5124.
    doi: 10.1002/bmc.5124pubmed: 33772839google scholar: lookup
  47. Sato T, Suzuka M, Sato Y, Iwabuchi R, Kobayashi D, Ogura J, Takasaki S, Yokota M, Tsukamoto T, Hayakawa Y, Kikuchi M, Maekawa M, Mano N. Development of a simultaneous analytical method for clozapine and its metabolites in human plasma using liquid chromatography/electrospray ionization tandem mass spectrometry with linear range adjusted by in-source collision-induced dissociation.. Biomed Chromatogr 2021 Jul;35(7):e5094.
    doi: 10.1002/bmc.5094pubmed: 33599311google scholar: lookup
  48. Mazzarino M, Perretti I, Stacchini C, Comunità F, de la Torre X, Botrè F. UPLC-MS-Based Procedures to Detect Prolyl-Hydroxylase Inhibitors of HIF in Urine.. J Anal Toxicol 2021 Feb 13;45(2):184-194.
    doi: 10.1093/jat/bkaa055pubmed: 32435795google scholar: lookup
  49. Ishii H, Shibuya M, Leung GN, Nozawa S, Yamashita S, Yamada M, Kushiro A, Kasashima Y, Okada J, Kawasaki K, Kijima-Suda I. Detection and longitudinal distribution of GW1516 and its metabolites in equine hair for doping control using liquid chromatography/high-resolution mass spectrometry.. Rapid Commun Mass Spectrom 2021 Apr 30;35(8):e9050.
    doi: 10.1002/rcm.9050pubmed: 33470485google scholar: lookup
  50. Al-Rifai Z, Mulvey D. Principles of total intravenous anaesthesia: basic pharmacokinetics and model descriptions. BJA Education 2016;16(3):92–7.
    doi: 10.1093/bjaceaccp/mkv021google scholar: lookup

Citations

This article has been cited 2 times.
  1. Zhao J, Wang Y, Liu B. Doping Detection Based on the Nanoscale: Biosensing Mechanisms and Applications of Two-Dimensional Materials. Biosensors (Basel) 2025 Apr 3;15(4).
    doi: 10.3390/bios15040227pubmed: 40277541google scholar: lookup
  2. Ishii H, Shigematsu R, Takemoto S, Ishikawa Y, Mizobe F, Nomura M, Arima D, Kunii H, Yuasa R, Yamanaka T, Tanabe S, Nagata SI, Yamada M, Leung GN. Quantification of osilodrostat in horse urine using LC/ESI-HRMS to establish an elimination profile for doping control. Bioanalysis 2024;16(17-18):947-958.
    doi: 10.1080/17576180.2024.2385848pubmed: 39235065google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.