Population pharmacokinetic modelling of tremelimumab in patients with advanced solid tumours and the impact of disease status on time‐varying clearance
Bibliographic record
Abstract
AIMS: Tremelimumab, a cytotoxic T-lymphocyte-associated protein 4 human monoclonal antibody of the immunoglobulin G2 κ isotype, has been studied in oncology clinical trials as both monotherapy and in combination with durvalumab. This study characterized the pharmacokinetics of tremelimumab as monotherapy and in combination with durvalumab and evaluated the impact of patient covariates on pharmacokinetics. METHODS: A pooled-analysis population pharmacokinetics model was built using NONMEM methodology. Pharmacokinetic data from 5 studies spanning different tumour types and therapy regimens were pooled for model development (956 patients). A dataset pooled from 4 additional studies was used for external validation (554 patients). Demographic and relevant clinical covariates were explored during model development. RESULTS: Tremelimumab exhibited linear pharmacokinetics, well described by a 2-compartment model, with time-varying clearance (0.276 L/day at baseline) associated primarily with therapy regimen and linked with changes in disease status. As monotherapy and combination therapy, tremelimumab clearance over 1 year increased by ~16% and decreased by ~17%, respectively. Pharmacokinetic behaviour was consistent across patient demographics and cancer subtypes. Patients with higher bodyweight and lower albumin levels at baseline had significantly higher clearance; however, no dosage adjustments are warranted. A flat dose (75 mg) was projected to provide comparable exposure to weight-based dosing (1 mg/kg) in adults. CONCLUSION: Tremelimumab exhibited linear pharmacokinetics but consistently opposite trends of time-varying clearance as monotherapy and in combination with durvalumab. Baseline bodyweight and albumin were significant covariates, but conversion from weight-based dosing at 1 mg/kg to flat dosing at 75 mg had no clinically relevant impact.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".