Confounding mitigation for the exposure-response relationship of bevacizumab in colorectal cancer patients
Bibliographic record
Abstract
Aims. The exposure-response relationship of bevacizumab may be confounded by various factors, i.e. baseline characteristics, time-dependent target engagement and recursive relationships between exposure and response. This work aimed at investigating the exposure-response relationships of bevacizumab in mCRC patients while mitigating potential sources of bias. Methods. Bevacizumab pharmacokinetics was described using target-mediated drug disposition (TMDD) modeling. The relationships between target kinetics, and progression-free (PFS) and overall (OS) survivals were assessed using joint pharmacokinetic and parametric hazard function models. Both potential biases due to prognostic-driven and response-driven of the concentration-effect relationship were mitigated. These models were used to evaluate the effect of increased antigen target levels and clearance, as well as intensified dosing regimen, on survival. Results. Estimated target-mediated pharmacokinetic parameters in 130 assessed patients were: baseline target levels (R0=8.4 nM), steady-state dissociation constant (KSS=10 nM) and antibody-target complexes elimination constant (kint=0.52 day-1). Distribution of R0 was significantly associated with an increased baseline CEA and circulating VEGF levels, and the presence of extra-hepatic metastases. Unbound target levels (R) significantly influenced both progression and death hazard functions. Increased R0 or CL values led to decreased bevacizumab unbound concentrations, increased R levels, and shortened PFS and OS, whereas increasing bevacizumab dose led to decreased R and longer survival. Conclusion. This study is the first to show the relationship between bevacizumab concentrations, target involvement and clinical efficacy by mitigating potential sources of bias. Most of target amount may be tumoral in mCRC. A more in-depth description of this relationship should be made in future studies.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".