Methotrexate Drug Interactions in the Treatment of Rheumatoid Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) often have comorbidities that require multiple medications. Several of these medications may alter the efficacy or increase the toxicity of methotrexate (MTX). The purpose of our study was to determine which drugs used in combination with MTX (excluding disease modifying antirheumatic drugs, folic and folinic acid, corticosteroids, and biologic agents) enhance side effects or toxicity of MTX or lower its efficacy. METHODS: A systematic literature search was performed with Medline, Embase, Cochrane Register and Database, and abstracts from the 2006/2007 annual congresses of the American College of Rheumatology and the European League Against Rheumatism. A manual search of the citation lists of retrieved publications was performed. RESULTS: Of the 1172 articles identified, 67 were included: 21 pharmacokinetics studies, 5 observational studies, and 78 case reports. Most medications do not significantly affect the pharmacokinetics profile of MTX. Among the clinical studies, cytopenia and elevation of liver enzymes were the main reported toxicities. The use of trimethoprim-sulfamethoxazole (TMP-SMX) was mentioned as a risk factor for developing cytopenia in one observational study and in 17 case reports. Thirty case reports of cytopenia were attributed to the use of concomitant nonsteroidal antiinflammatory drugs, including acetylsalicylic acid. Two studies described mild abnormalities of liver enzymes with the use of isoniazid, and one study with the use of high-dose ASA. CONCLUSION: Based on the published literature, MTX has limited drug interactions, with the exception of TMP-SMX and high-dose ASA, which can exacerbate toxicity of MTX. The clinical significance of these interactions has not been substantiated by extensive clinical observations.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".