Pharmacodynamic monitoring of BAY 43‐9006 (Sorafenib) in phase I clinical trials involving solid tumor and AML/MDS patients, using flow cytometry to monitor activation of the ERK pathway in peripheral blood cells
Bibliographic record
Abstract
BACKGROUND: We previously reported a flow cytometry technique to monitor pharmacodynamic effects of the raf kinase inhibitor BAY 43-9006 based on the ability of phorbol ester (PMA) to phosphorylate extracellular-regulated kinase (ERK) in peripheral blood (Chow et al., Cytometry 2001;46:72-78). In this article, we describe its application to phase I trials of BAY 43-9006 in solid tumor and AML/MDS patients. METHODS: The previously described whole blood lysis method was used to monitor BAY 43-9006 effects on peripheral T-cells of solid tumor patients. A modified whole blood fixation protocol was developed for the AML/MDS trial, using the c-kit ligand stem cell factor (SCF) to activate ERK as an alternative to PMA, and incorporating immunophenotypic markers to identify leukemic blasts. RESULTS: At all dose levels of BAY 43-9006 used to treat solid tumor patients, ERK could be activated by PMA in peripheral T-cells and we were not able to show inhibition of raf kinase. A similar effect was seen in the lymphocytes of AML/MDS patients during treatment with BAY 43-9006. However, we found strong inhibition when ERK was activated via c-kit using SCF. Furthermore, normal donor CD34+ve stem cells were much more sensitive to BAY 43-9006 when ERK was activated by SCF, compared to PMA. CONCLUSIONS: These findings support the further development of flow cytometry applications to monitor signal transduction inhibitors during early phase clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".