Safety and Efficacy of Febuxostat Treatment in Subjects with Gout and Severe Allopurinol Adverse Reactions
Bibliographic record
Abstract
OBJECTIVE: Allopurinol, a purine base analog inhibitor of xanthine oxidase (XO) activity, remains the standard for pharmacologic urate-lowering management of gout. Allopurinol is efficacious and safe in most patients, but intolerance is estimated to occur in up to 10% of treated patients. Severe or life-threatening allopurinol adverse reactions (AE) occur much less frequently, and include severe cutaneous allopurinol reactions, vasculitis, and/or a multisystem allopurinol hypersensitivity syndrome. During clinical development of febuxostat (FEB), a recently approved non-purine analog inhibitor of XO, subjects with severe allopurinol intolerance were excluded from randomized double-blind FEB/allopurinol comparative trials. METHODS: In this retrospective study, safety and urate-lowering efficacy of FEB was assessed in 13 successively encountered gout patients with prior documented severe allopurinol reactions. RESULTS: FEB was well tolerated in 12 of 13 patients, each of whom remains on treatment. One patient previously hospitalized with documented exfoliative erythroderma during allopurinol treatment, developed biopsy-confirmed cutaneous leukocytoclastic vasculitis. None of the other 12 patients treated with FEB showed rash, worsening hepatic function, blood cytopenia or eosinophilia. CONCLUSION: In 12 of our 13 gout patients with previously documented severe allopurinol AE, FEB treatment was safe. However, the development of a hypersensitivity type cutaneous vasculitis (likely but not definitively FEB-related) early in treatment mandates caution, careful dose escalation, and close monitoring when FEB urate-lowering therapy of allopurinol-intolerant patients is considered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".