Acalabrutinib CYP3A‐mediated drug–drug interactions: Clinical evaluations and physiologically based pharmacokinetic modelling to inform dose adjustment strategy
Bibliographic record
Abstract
AIMS: Clinical drug interaction studies with itraconazole and rifampicin have demonstrated that acalabrutinib is a sensitive substrate of CYP3A. A physiologically based pharmacokinetic (PBPK) model was developed based on the data of these studies. One of the active CYP3A metabolites, ACP-5862, was identified but never studied in a drug interaction scenario. This study aims to evaluate both parent and metabolite exposure change with coadministration of moderate CYP3A inhibitors and its impact on safety and efficacy. METHODS: In an open label, randomized, 2-period study, we investigated the effect of coadministration of fluconazole or isavuconazole on the pharmacokinetics of acalabrutinib. Bruton tyrosine kinase receptor occupancy and safety were compared between different treatments. Experimental data were compared to PBPK simulation results. RESULTS: Least square means of acalabrutinib maximum plasma concentration and area under the curve increased 1.37 (1.14-1.64) and 1.60 (1.45-1.77)-fold in the presence of isavuconazole and 1.48 (1.10-1.98) and 2.16 (1.94-2.40)-fold in the presence of fluconazole, respectively. For ACP-5862, these values are 0.72 (0.63-0.82) and 0.91 (0.86-0.97) fold for isavuconazole and 0.65 (0.49-0.87) and 0.95 (0.91-0.99) fold for fluconazole coadministration. The PBPK model was able to recover acalabrutinib and ACP-5862 PK profiles in the study. Bruton tyrosine kinase receptor occupancy change was minimal in the presence of isavuconazole. There were no deaths, serious adverse events (AEs), or subject discontinuation due to AEs in this study. Only mild (Grade 1) AEs were reported during the study, by 17% of the study population. CONCLUSION: Our results demonstrate the impact of fluconazole and isavuconazole on the pharmacokinetics of acalabrutinib and ACP-5862, and suggest that no dose adjustment is needed for concomitant administration with moderate CYP3A inhibitors. the current PBPK model can be used to propose dose adjustment for drug interactions via CYP3A.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".