Pharmacodynamic Monitoring of Molecular-Targeted Agents in the Peripheral Blood of Leukemia Patients Using Flow Cytometry
Bibliographic record
Abstract
The introduction of specific, molecular-targeted drugs is radically changing cancer treatment. Pharmacodynamics, which measures drug effects on the host, is key during early-phase clinical trials of novel agents to determine the relations between drug dose and target inhibition as well as measure the downstream effects of target inhibition on the cancer. In this article, we describe the application of flow cytometry to the pharmacodynamic monitoring of molecular-targeted agents in leukemia patients. The methods are based on current clinical flow-cytometry applications, with the addition of phosphospecific antibodies to measure the activation states of intracellular signaling elements and the introduction of techniques that maintain drug-target equilibrium during sample preparation. Using this approach, we successfully showed dose-dependent inhibition of c-Kit during a phase I clinical trial treating acute leukemia patients with the novel agent sorafenib. Further refinements identify considerable interpatient variation in signaling activity within leukemic blast populations, suggesting that an individualized approach to treatment based on flow cytometric monitoring might be advantageous. Improvements in sample turnaround offer the potential to introduce real-time pharmacodynamic monitoring 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".