Bevacizumab dosing strategy in paediatric cancer patients based on population pharmacokinetic analysis with external validation
Bibliographic record
Abstract
AIM: The aim of the present study was to evaluate the pharmacokinetics of bevacizumab and various dosing strategies for this agent in paediatric patients. METHODS: Data were collected from 232 paediatric patients (1971 concentrations) in five studies, with a wide range of age (0.5-21 years), body weight (BWT; 5.9-125 kg), and regimens (5-15 mg kg(-1) biweekly or triweekly). Data from 152 patients (1427 concentrations) and 80 patients (544 concentrations) were used for model building and external validation, respectively. Steady-state exposure was simulated under BWT-based, body surface area (BSA)-based, ideal body weight (IBW)-based, and tier-based doses. NONMEM and R were used for analyses. RESULTS: Typical estimates of clearance, central volume of distribution (V1), and median half-life were 9.04 ml h(-1) , 2851 ml, and 19.6 days, respectively. Clearance decreased with increasing albumin. Clearance and V1 increased with BWT and were higher in male patients. Clearance and V1 were lower in children with primary central nervous system (CNS) tumours than in children with sarcomas, resulting in 49% higher trough (C min) and 29% higher peak (Cmax) concentrations. BWT-adjusted clearance and V1 remained unchanged across ages. Paediatric C min was similar to adult C min under all dosing strategies. Paediatric Cmax exceeded adult Cmax under tier-based doses. CONCLUSIONS: BWT-adjusted pharmacokinetic parameter estimates in paediatric patients were similar to those in adults, and similar across ages. Bevacizumab exposure was higher in children with primary CNS tumours than in children with sarcomas. BSA-based, IBW-based, and tier-based doses offered no substantial advantage over the BWT-based dose currently used in adults for bevacizumab. Given the similarity in pharmacokinetics among many monoclonal antibodies, this may help to develop practical paediatric dosing guidelines for other therapeutic antibodies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".