Evaluation of the Potential for Drug Interactions With Patiromer in Healthy Volunteers
Bibliographic record
Abstract
Introduction: Patiromer is a potassium-binding polymer that is not systemically absorbed; however, it may bind coadministered oral drugs in the gastrointestinal tract, potentially reducing their absorption. Methods: Twelve randomized, open-label, 3-period, 3-sequence crossover studies were conducted in healthy volunteers to evaluate the effect of patiromer (perpetrator drug) on absorption and single-dose pharmacokinetics (PK) of drugs (victims) that might be commonly used with patiromer. Subjects received victim drug alone, victim drug administered together with patiromer 25.2 g (highest approved dose), and victim drug administered 3 hours before patiromer 25.2 g. The primary PK endpoints were area under the curve (AUC), extrapolated to infinity (AUC 0-∞ ), and maximum concentration ( C max ). Results were reported as 90% confidence intervals (CIs) about the geometric mean AUC 0-∞ and C max ratios with prespecified equivalence limits of 80% to 125%. Results: Overall, 370 subjects were enrolled, with 365 receiving ≥1 dose of patiromer; 351 subjects completed the studies and all required treatments. When coadministered with patiromer, the 90% CIs for AUC 0-∞ remained within 80% to 125% for 9 drugs (amlodipine, cinacalcet, clopidogrel, furosemide, lithium, metoprolol, trimethoprim, verapamil, and warfarin). The AUC 0-∞ point estimate ratios for levothyroxine and metformin with patiromer coadministration were ≥80%, with the lower bounds of the 90% CIs at 76.8% and 72.8%, respectively. For ciprofloxacin, the point estimate for AUC 0-∞ was 71.5% (90% CI: 65.3-78.4). For 8 of 12 drugs, point estimates for C max were ≥80% with patiromer coadministration; for ciprofloxacin, clopidogrel, metformin, and metoprolol, the point estimates were <80%. When patiromer was administered 3 hours after each victim drug, the 90% CIs for AUC 0-∞ and C max for each drug were within the prespecified 80% to 125% limits. Conclusion: For 9 of the 12 drugs coadministered with patiromer, there were no clinically significant drug–drug interactions. For 3 drugs (ciprofloxacin, levothyroxine, and metformin), a 3-hour separation between patiromer and their administration resulted in no clinically significant drug–drug interactions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".