Population pharmacokinetic modeling of motesanib and its active metabolite, M4, in cancer patients
Bibliographic record
Abstract
Motesanib is a small molecule and potent multikinase inhibitor with antiangiogenic and antitumor activity. Population pharmacokinetic (POPPK) modeling of motesanib and M4, an active metabolite, was performed to assess sources of variability in cancer patients. The analysis included data collected from 451 patients from 8 clinical trials with oral doses of motesanib ranging from 25 to 175 mg, either once daily or twice daily. The POPPK analyses were performed using nonlinear mixed-effect models with a sequential approach. Covariate effects of demographics and other baseline characteristics were assessed with stepwise covariate modeling. A 2-compartment model with food effect on absorption parameters fitted the PK data of motesanib well. The effects albumin and sex on apparent clearance (CL/F) of motesanib were statistically significant. The albumin effect was more important but remained below a 25% difference. A 1-compartment model fitted PK data of M4 well. Effects of race (Asian vs non-Asian) and dosing frequency were identified as statistically significant covariates on the CL/F of M4. The maximum effect of albumin would result in less than 25% change in motesanib CL/F and as such would not warrant any dosing adjustment. However, faster elimination of M4 in Asian patients requires further investigation.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".