An Enzyme-Linked Immunosorbent Assay to Study Bevacizumab Pharmacokinetics
Bibliographic record
Abstract
INTRODUCTION: Bevacizumab is an antivascular endothelial growth factor humanized monoclonal antibody used to inhibit angiogenesis in cancer. It displays an important interindividual pharmacokinetic variability, which could explain part of the interindividual differences in clinical response. Therefore, an assay to measure bevacizumab serum concentrations is needed. METHODS: An enzyme-linked immunosorbent assay was developed using microtiter plates sensitised with vascular endothelial growth factor 165, a recombinant form of vascular endothelial growth factor. Lower and upper limits of quantitation as well as limit of detection were determined. Eight calibrators and three quality controls, with concentrations of 5 mg/L, 30 mg/L, and 75 mg/L, were tested on five occasions initially and on five subsequent occasions. Trough and peak serum concentrations of bevacizumab were measured in patients with metastatic colorectal cancer. Bevacizumab concentrations were described using a two-compartment population pharmacokinetic model with first-order constants. RESULTS: Imprecision and accuracy of calibrators and quality controls were 20% or less, except for the zero calibrator. The limit of detection was 0.033 mg/L. Lower and upper limits of quantitation were 5 and 75 mg/L, respectively. A total of 175 blood samples was available for analysis from 16 patients. Median (range) trough and peak concentrations during the treatment were 47.2 (9.6-106.9) mg/L and 159.3 (33.0-327.3) mg/L, respectively. CONCLUSION: This method is rapid, accurate, reproducible, and may be useful for pharmacokinetic and pharmacokinetic-pharmacodynamic studies as well as in therapeutic drug monitoring of bevacizumab.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".