Aprepitant and fosaprepitant drug interactions: a systematic review
Bibliographic record
Abstract
AIMS: Aprepitant and fosaprepitant, commonly used for the prevention of chemotherapy-induced nausea and vomiting, alter cytochrome P450 activity. This systematic review evaluates clinically significant pharmacokinetic drug interactions with aprepitant and fosaprepitant and describes adverse events ascribed to drug interactions with aprepitant or fosaprepitant. METHODS: We systematically reviewed the literature to September 11, 2016, to identify articles evaluating drug interactions involving aprepitant/fosaprepitant. The clinical significance of each reported pharmacokinetic drug interaction was evaluated based on the United States Food and Drug Administration guidance document on conducting drug interaction studies. The probability of an adverse event reported in case reports being due to a drug interaction with aprepitant/fosaprepitant was determined using the Drug Interaction Probability Scale. RESULTS: A total of 4377 publications were identified. Of these, 64 met inclusion eligibility criteria: 34 described pharmacokinetic drug interactions and 30 described adverse events ascribed to a drug interaction. Clinically significant pharmacokinetic interactions between aprepitant/fosaprepitant and bosutinib PO, cabazitaxel IV, cyclophosphamide IV, dexamethasone PO, methylprednisolone IV, midazolam PO/IV, oxycodone PO and tolbutamide PO were identified, as were adverse events resulting from an interaction between aprepitant/fosaprepitant and alcohol, anthracyclines, ifosfamide, oxycodone, quetiapine, selective serotonin reuptake inhibitors/serotonin-norepinephrine reuptake inhibitors and warfarin. CONCLUSIONS: The potential for a drug interaction with aprepitant and fosaprepitant should be considered when selecting antiemetic therapy.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".