A HPLC-SRM-MS based method for the detection and quantification of methotrexate in urine at doses used in clinical practice for patients with rheumatological disease: a potential measure of adherence
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a common autoimmune disease that causes significant disability and reduced life expectancy. The folate antagonist methotrexate (MTX) is first-line therapy for RA when used weekly at low doses (5-25 mg). However, the true rate of adherence to MTX is uncertain. This is in part due to the different methods of measurement of adherence employed with no biochemical test currently available to determine adherence to low dose MTX. Common methods of MTX measurement include immunoassays in patients with high dose therapy, but these assays cross-react with MTX metabolites and lack the sensitivity required to measure adherence to low dose MTX. HPLC-SRM-MS (selected reaction monitoring-mass spectrometry) has several theoretical advantages over immunoassays with improved specificity, minimal cross-reaction and higher sensitivity. The aim of this study was to develop an assay to measure MTX and its major metabolite 7-OH-MTX in urine as a tool to monitor adherence to low dose MTX in clinic. As a proof of concept, urine samples from 4 participants with RA were measured after directly observed therapy. The assay showed improved sensitivity compared to that reported by immunoassays, with low carryover and high within-run precision. In participant samples, MTX was measurable in the urine for up to 105 hours after administration and 7-OH-MTX was detectable up to 98 hours after administration, suggesting that this assay is suitable for the measurement of adherence to therapy. The assay requires minimal sample preparation and can be adopted by other laboratories with minimal study set up.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".