Effect of ketoconazole administration on the pharmacokinetics (PK) and pharmacodynamics (PD) of bortezomib in patients with advanced solid tumors
Bibliographic record
Abstract
13016 Background: Bortezomib (btz; VELCADE) is a first-in-class small molecule proteasome inhibitor used to treat patients with multiple myeloma and mantle cell lymphoma. In vitro and in vivo studies indicate that btz is primarily metabolized by CYP3A4 and CYP2C19. We conducted a study to evaluate the effect of inhibition of CYP3A4 with ketoconazole (keto) on the PK of btz in humans. Methods: The study enrolled patients with advanced malignancies for whom standard therapy was not available. Patients received btz 1.0mg/m2 (IV) on days 1, 4, 8, and 11 of a 21-day cycle, and were randomized to receive keto 400mg (PO) on days 6, 7, 8, and 9 of either the first or second cycle of treatment. Blood samples for plasma btz determination were collected over 72 hours following the Day 8 dose in Cycles 1 and 2. PK parameters were computed non-compartmentally. PD parameters were derived from an Emax model of percentage proteasome inhibition in whole blood. Results: Of the 21 patients enrolled, 13 had sufficient PK sampling in Cycles 1 and 2 to assess the effect of keto on the PK of btz. No statistically significant difference in AUC0–72h for btz ± keto was observed (p=0.2248). The mean AUC0–72h ratio was 1.22 (90% CI, 0.92–1.61). The exposure-PD relationships for btz ± keto were similar (Table). Adverse events were similar in the presence and absence of keto. Conclusions: Although the AUC0–72h difference was not statistically significant, the 90% CI for the AUC0–72h ratio extends beyond the FDA- specified range of 0.80–1.25 for DDI studies, precluding a declaration of no effect. The presence of a strong CYP3A4 inhibitor increased mean btz exposure by only 22% and had no apparent effect on the exposure-PD relationship. [Table: see text] No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".