Recent developments in the clinical pharmacology of rolapitant: subanalyses in specific populations
Bibliographic record
Abstract
Knowledge of the involvement of the neurokinin substance P in emesis has led to the development of the neurokinin-1 receptor antagonists (NK-1 RAs) for control of chemotherapy-induced nausea and vomiting (CINV), in combination with serotonin type 3 receptor antagonists and corticosteroids. The NK-1 RA rolapitant, recently approved in oral formulation, has nanomolar affinity for the NK-1 receptor, as do the other commercially available NK-1 RAs, aprepitant and netupitant. Rolapitant is rapidly absorbed and has a long half-life in comparison to aprepitant and netupitant. All three NK-1 RAs undergo metabolism by cytochrome P450 (CYP) 3A4, necessitating caution with the concomitant use of CYP3A4 inhibitors, but in contrast to aprepitant and netupitant, rolapitant does not inhibit or induce CYP3A4. However, rolapitant is a moderate inhibitor of CYP2D6, and concomitant use with CYP2D6 substrates with narrow therapeutic indices should be avoided. Aprepitant, netupitant, and rolapitant have all demonstrated efficacy in the control of delayed CINV in patients receiving moderately and highly emetogenic chemotherapy in randomized controlled trials, including over multiple cycles of chemotherapy. We reviewed recent post hoc analyses of clinical trial data demonstrating that rolapitant is efficacious in the control of CINV in patient populations with specific tumor types, namely, breast cancers, gastrointestinal/colorectal cancers, and lung cancers. In addition, we show that rolapitant has efficacy in the control of CINV in specific age groups of patients receiving chemotherapy (<65 and ≥65 years of age). Overall, the safety profile of rolapitant in these specific patient populations was consistent with that observed in primary analyses of phase 3 trials.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".