Aprepitant and Fosaprepitant: A 10-Year Review of Efficacy and Safety
Bibliographic record
Abstract
Chemotherapy-induced nausea and vomiting (CINV) is a common adverse event associated with anticancer treatment that can have a significant adverse impact on patient health-related quality of life and that can potentially undermine the effectiveness of chemotherapy. Traditional regimens to prevent CINV generally involved a combination of a corticosteroid plus a 5-hydroxytryptamine (5HT3) receptor antagonist (RA). In the past 10 years, antiemetic treatment has greatly advanced with the availability of the neurokinin-1 receptor antagonist (NK1 RA) aprepitant and its prodrug fosaprepitant. NK1 RAs have a different mechanism of action in CINV than corticosteroids and 5HT3 RAs, thus their use can complement traditional antiemetic drugs and can enhance control of CINV. This review examined accumulated data regarding the safety and efficacy of aprepitant and fosaprepitant over the decade since the first regulatory approval. Data from key studies of aprepitant and fosaprepitant in the prevention of CINV in patients receiving moderately and highly emetogenic chemotherapy were explored, as were recommendations in currently available guidelines for their use. In addition, their use as antiemetic therapy in special patient populations was highlighted. Future perspectives on potential uses of aprepitant and fosaprepitant for indications other than CINV are presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".