Model‐Informed Drug Development of Subcutaneous Nivolumab: Comparison of Pharmacokinetic Analysis Methodologies Using Clinical Trial Simulation
Bibliographic record
Abstract
ABSTRACT A subcutaneous formulation of nivolumab was evaluated in the phase III study CheckMate 67T (NCT04810078). The co‐primary pharmacokinetic exposure endpoints, time‐averaged serum concentration over the first 28 days ( C avgd28 ), and steady‐state trough concentration ( C minss ) were determined through population pharmacokinetic analysis as compared to conventional non‐compartmental analysis (NCA). We proposed a model‐based approach to determine subcutaneous and intravenous nivolumab exposures in CheckMate 67T, leveraging extensive prior pharmacokinetic data across various tumor types. The robustness of this model‐informed drug development (MIDD) approach was assessed via clinical trial simulations. Concentration–time profiles in randomly sampled patients with renal cell carcinoma receiving second‐line systemic therapy were simulated for subcutaneous/intravenous nivolumab. Population pharmacokinetic parameters, sampled from the joint parameter uncertainty distribution, were applied in simulations based on the CheckMate 67T design. Two population pharmacokinetic approaches—the PRIOR subroutine ($PRIOR) in NONMEM and a pooled analysis with historical nivolumab pharmacokinetic data—were used to analyze the simulated data. Results were compared to NCA using intensive and conventional sampling schemes. Analyses showed that model‐based analysis provided more accurate area under the curve estimates than NCA. Model‐predicted exposure measures, including C avgd28 , maximum serum concentration after the first dose, and minimum serum concentration at day 28, were also consistent across both population pharmacokinetic approaches, with minimal differences in geometric means. In conclusion, both the $PRIOR and pooled population pharmacokinetic methods yielded more accurate results compared to conventional NCA. The MIDD approach was validated as a robust and feasible method to support non‐inferiority assessment based on clinical trial simulations.
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.
How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".