Population pharmacokinetics of the humanised monoclonal antibody, HuHMFG1 (AS1402), derived from a phase I study on breast cancer
Bibliographic record
Abstract
BACKGROUND: HuHMFG1 (AS1402) is a humanised monoclonal antibody that has undergone a phase I trial in metastatic breast cancer. The aim of this study was to characterise the pharmacokinetics (PKs) of HuHMFG1 using a population PK model. METHOD: Data were derived from a phase I study of 26 patients receiving HuHMFG1 at doses ranging from 1 to 16 mg kg(-1). Data were analysed using NONMEM software and covariates were included. A limited sampling strategy (LSS) was developed using training and a validation data set. RESULTS: A linear two-compartment model was shown to be adequate to describe data. Covariate analysis indicated that weight was not related to clearance. An LSS was successfully developed on the basis of the model, in which one sample is collected immediately before the start of an infusion and the second is taken at the end of infusion. CONCLUSION: A two-compartment population PK model successfully describes HuHMFG1 behaviour. The model suggests using a fixed dose of HuHMFG1, which would simplify dosing. The model could be used to optimise dose level and dosing schedule if more data on the correlation between exposure and efficacy become available from future studies. The derived LSS could optimise further PK assessment of this antibody.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".