Food effects on abiraterone pharmacokinetics in healthy subjects and patients with metastatic castration‐resistant prostate cancer
Bibliographic record
Abstract
Food effect on abiraterone pharmacokinetics and safety on abiraterone acetate coadministration with low-fat or high-fat meals was examined in healthy subjects and metastatic castration-resistant prostate cancer (mCRPC) patients. Healthy subjects (n = 36) were randomized to abiraterone acetate (single dose, 1000 mg) + low-fat meal, + high-fat meal, and fasted state. mCRPC patients received repeated doses (abiraterone acetate 1000 mg + 5 mg prednisone twice daily; days 1-7) in a modified fasting state followed by abiraterone acetate plus prednisone within 0.5 hours post-low-fat (n = 6) or high-fat meal (n = 18; days 8-14). In healthy subjects, geometric mean (GM) abiraterone area under plasma concentration-time curve (AUC) increased ∼5- and ∼10-fold, respectively, with low-fat and high-fat meals versus fasted state (GM [coefficient of variation], 1942 [48] and 4077 [37] ng · h/mL vs 421 [67] ng · h/mL, respectively). In mCRPC patients, abiraterone AUC was ∼2-fold higher with a high-fat meal and similar with a low-fat meal versus modified fasting state (GM [coefficient of variation]: 1992 [34] vs 973 [58] ng · h/mL and 1264 [65] vs 1185 [90] ng · h/mL, respectively). Adverse events (all grade ≤ 3) were similar, with high-fat/low-fat meals or fasted/modified fasting state. Short-term dosing with food did not alter abiraterone acetate safety.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".