Model‐based population pharmacokinetic and exposure response analyses for safety and efficacy of nivolumab as adjuvant treatment in subjects with resected oesophageal or gastroesophageal junction cancer
Bibliographic record
Abstract
AIMS: Nivolumab is approved as adjuvant treatment in subjects with resected oesophageal or gastroesophageal junction cancer (EC/GEJC) based on results from the pivotal CheckMate 577 trial. We present a model-based clinical pharmacology profiling and benefit-risk assessment of nivolumab as adjuvant treatment in subjects with resected EC/GEJC supporting a less frequent dosing regimen. METHODS: Population pharmacokinetic (popPK) analysis was conducted to characterize nivolumab pharmacokinetics (PK) using clinical data from 1493 subjects from seven monotherapy clinical studies across multiple solid tumours. The exposure-response (E-R) analyses included data from 756 patients from CheckMate 577. E-R relationships for efficacy and safety were characterized by evaluating the relationship between nivolumab exposure and disease-free survival (DFS) for efficacy; and time to first occurrence of Grade ≥2 immune-mediated adverse events (Gr2 + IMAEs) for safety. RESULTS: Nivolumab exposure was found to be associated with both DFS and risk of Gr2 + IMAEs. However, the hazard ratios (HRs) (95% confidence interval [CI]) at the 5th and 95th percentiles of nivolumab exposure were similar for DFS and Gr2 + IMAEs, indicating flat E-R relationships within the exposure range produced by the studied regimen. Model-predicted probability of DFS and Gr2 + IMAEs were similar between the two regimens of 240 mg every 2 weeks or 480 mg every 4 weeks for 16 weeks followed by 480 mg Q4W up to 1 year. CONCLUSIONS: The analyses demonstrated a flat E-R relationship over the range of exposures produced by the studied regimen and supported the approval of an alternative dosing regimen with less frequent dosing in patients with adjuvant EC/GEJC.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".