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Record W3021122917 · doi:10.1093/jat/bkaa049

Influence of Pain Killers on the Urinary Anabolic Steroid Profile

2020· article· en· W3021122917 on OpenAlexfundno aff
Anna Stoll, Michele Iannone, Giuseppina De Gregorio, Xavier de la Torre, Francesco Molaioni, Francesco Botrè, Maria Kristina Parr

Bibliographic record

VenueJournal of Analytical Toxicology · 2020
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsSteroidAnabolic steroidMetaboliteIbuprofenPharmacologyChemistryAnabolismIn vivoTestosterone (patch)UrineNandroloneGlucuronideInternal medicineEndocrinologyMedicineHormoneBiology

Abstract

fetched live from OpenAlex

Anabolic androgenic steroids (AAS) are prohibited as performance-enhancing drugs in sports. Among them, testosterone and its precursors are often referred to as "pseudoendogenous" AAS, that is, endogenous steroids that are prohibited when administered exogenously. To detect their misuse, among other methods, the World Anti-Doping Agency-accredited laboratories monitor the steroid profile (concentrations and concentration ratios of endogenous steroids, precursors and metabolites) in urine samples collected from athletes in and out of competition. Alterations in steroid profile markers are used as indicators for misuse of anabolic steroids in sports. Therefore, especially their metabolic pathways with possible interactions are crucial to elucidate. As steroid metabolism is very complex, and many enzymes are involved, certain non-prohibited drugs may influence steroid metabolite excretion. One important group of steroid-metabolizing enzymes is aldo-keto reductases (AKRs). An inhibition of them by non-steroidal anti-inflammatory drugs (NSAIDs), which are neither prohibited nor monitored, but frequently used drugs in sports, was demonstrated in vitro. Thus, this work aims to investigate the influence of NSAID intake on the urinary steroid profile. Kinetic and inhibitory studies were performed using 5α-dihydrotestosterone as substrate. The results obtained from in vitro experiments show that ibuprofen inhibits AKR1C2 and thus influences steroid biotransformation. For in vivo investigations, urine samples prior, during and postadministration of ibuprofen were analyzed using routine methods to monitor the steroid profile. Changes in markers of the steroid profile of volunteers were observed. The combination of in vitro and in vivo results suggests that monitoring of ibuprofen may be useful in doping control analysis. The presented work illustrates the importance to consider co-administration of (non-prohibited) drugs during antidoping analysis. Intake of multiple substances is likely leading to interfering effects. Divergent results in antidoping analysis may therefore be observed and misinterpretation of analytical data may occur. Similar considerations may be appropriate for other fields of forensic applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.300
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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