Effect of a strong CYP3A4 inhibitor and inducer on the pharmacokinetics of senaparib (IMP4297) in healthy volunteers: A drug–drug interaction study
Bibliographic record
Abstract
Aims A phase I open‐label study assessed the effect of multiple oral doses of a potent CYP3A4 inhibitor (itraconazole) and inducer (rifampicin) on the pharmacokinetic profile of a single oral dose of senaparib, a novel, highly potent poly‐(ADP‐ribose) polymerase 1/2 inhibitor and CYP3A4 substrate, in Chinese healthy male volunteers (HMV). Methods Adult HMV were enrolled to the itraconazole or rifampicin group (n = 16 each). In Period 1, all participants received a single oral dose of senaparib 40 mg (itraconazole group) or 100 mg (rifampicin group). In Period 2, the same dose was coadministered with itraconazole (200 mg) and rifampicin (600 mg), respectively. The primary endpoints were senaparib exposure parameters. Results Coadministration with itraconazole significantly increased exposure of senaparib and decreased that of its major metabolites M9 and M14. Maximum plasma senaparib concentration (Cmax) was increased by ~79% and area under the concentration–time curve (AUC) increased by ~2.8‐fold. Coadministration with rifampicin significantly reduced the Cmax and AUC of senaparib by ~59 and 83%, respectively. The Cmax for both M9 and M14 was slightly increased, although AUC was decreased. All treatment‐emergent adverse events were grade ≤2, regardless of the treatment administered. Conclusion In Chinese HMV, the exposure of senaparib was significantly increased when coadministered with itraconazole and significantly decreased when coadministered with rifampicin. It is recommended to avoid concomitant use of senaparib and strong inhibitors or inducers of CYP3A4.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".