EPOR-Based Purification and Analysis of Erythropoietin Mimetic Peptides from Human Urine by Cys-Specific Cleavage and LC/MS/MS
Bibliographic record
Abstract
The development of a new class of erythropoietin mimetic agents (EMA) for treating anemic conditions has been initiated with the discovery of oligopeptides capable of dimerizing the erythropoietin (EPO) receptor and thus stimulating erythropoiesis. The most promising amino acid sequences have been mounted on various different polymeric structures or carrier molecules to obtain highly active EPO-like drugs exhibiting beneficial and desirable pharmacokinetic profiles. Concomitant with creating new therapeutic options, erythropoietin mimetic peptide (EMP)-based drug candidates represent means to artificially enhance endurance performance and necessitate coverage by sports drug testing methods. Therefore, the aim of the present study was to develop a strategy for the comprehensive detection of EMPs in doping controls, which can be used complementary to existing protocols. Three model EMPs were used to provide proof-of-concept data. Following EPO receptor-facilitated purification of target analytes from human urine, the common presence of the cysteine-flanked core structure of EMPs was exploited to generate diagnostic peptides with the aid of a nonenzymatic cleavage procedure. Sensitive detection was accomplished by targeted-SIM/data-dependent MS(2) analysis. Method characterization was conducted for the EMP-based drug peginesatide concerning specificity, linearity, precision, recovery, stability, ion suppression/enhancement, and limit of detection (LOD, 0.25 ng/mL). Additionally, first data for the identification of the erythropoietin mimetic peptides EMP1 and BB68 were generated, demonstrating the multi-analyte testing capability of the presented approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".