Recent developments in the clinical pharmacology of rolapitant: subanalyses in specific populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it