Pharmacokinetic and Pharmacodynamic Interaction Between Allopurinol and Probenecid in Patients with Gout
Bibliographic record
Abstract
OBJECTIVE: To investigate the pharmacokinetic and pharmacodynamic interaction between probenecid and oxypurinol (the active metabolite of allopurinol) in patients with gout. METHODS: This was an open-label observational clinical study. Blood and urine samples were collected to measure oxypurinol and urate concentrations. We examined the effects of adding probenecid to allopurinol therapy upon plasma concentrations and renal clearances of urate and oxypurinol. RESULTS: Twenty patients taking allopurinol 100-400 mg daily completed the study. Maximum coadministered doses of probenecid were 250 mg/day (n = 1), 500 mg/day (n = 19), 1000 mg/day (n = 7), 1500 mg/day (n = 3), and 2000 mg/day (n = 1). All doses except the 250 mg daily dose were divided and dosing was twice daily. Estimated creatinine clearances ranged from 28 to 113 ml/min. Addition of probenecid 500 mg/day to allopurinol therapy decreased plasma urate concentrations by 25%, from mean 0.37 mmol/l (95% CI 0.33-0.41) to mean 0.28 mmol/l (95% CI 0.24-0.32) (p < 0.001); and increased renal urate clearance by 62%, from mean 6.0 ml/min (95% CI 4.5-7.5) to mean 9.6 ml/min (95% CI 6.9-12.3) (p < 0.001). Average steady-state plasma oxypurinol concentrations decreased by 26%, from mean 11.1 mg/l (95% CI 5.0-17.3) to mean 8.2 mg/l (95% CI 4.0-12.4) (p < 0.001); and renal oxypurinol clearance increased by 24%, from mean 12.7 ml/min (95% CI 9.6-15.8) to mean 16.1 ml/min (95% CI 12.0-20.2) (p < 0.05). The additional hypouricemic effect of probenecid 500 mg/day appeared to be lower in patients with renal impairment. CONCLUSION: Coadministration of allopurinol with probenecid had a significantly greater hypouricemic effect than allopurinol alone despite an associated reduction of plasma oxypurinol concentrations. Australian Clinical Trials Registry ACTRN012606000276550.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".