A pharmacokinetics and safety phase 1/1b study of oral ixazomib in patients with multiple myeloma and severe renal impairment or end‐stage renal disease requiring haemodialysis
Bibliographic record
Abstract
Renal impairment (RI) is a major complication of multiple myeloma (MM). This study aimed to characterize the single-dose pharmacokinetics (PK) of the oral proteasome inhibitor, ixazomib, in cancer patients with normal renal function [creatinine clearance (CrCl) ≥90 ml/min; n = 20), severe RI (CrCl <30 ml/min; n = 14), or end-stage renal disease requiring haemodialysis (ESRD; n = 7). PK and adverse events (AEs) were assessed after a single 3 mg dose of ixazomib. Ixazomib was highly bound to plasma proteins (~99%) in all renal function groups. Unbound and total systemic exposures of ixazomib were 38% and 39% higher, respectively, in severe RI/ESRD patients versus patients with normal renal function. Total ixazomib concentrations were similar in pre- and post-dialyser samples collected from ESRD patients; therefore, ixazomib can be administered without regard to haemodialysis timing. Except for anaemia, the incidence of the most common AEs was generally similar across groups, but grade 3 and 4 AEs were more frequent in the severe RI/ESRD groups versus the normal group (79%/57% vs. 45%), as were serious AEs (43%/43% vs. 15%). The PK and safety results support a reduced ixazomib dose of 3 mg in patients with severe RI/ESRD.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".