Development and validation of an LC-MS/MS method for ruxolitinib quantification: advancing personalized therapy in hematologic malignancies
Bibliographic record
Abstract
Background: Hematologic malignancies such as leukemia and lymphoma present treatment challenges due to their genetic and molecular heterogeneity. Ruxolitinib, a Janus kinase (JAK) inhibitor, has demonstrated efficacy in managing these cancers. However, optimal therapeutic outcomes are contingent upon maintaining drug levels within a therapeutic window, highlighting the necessity for precise drug monitoring. Methods: We developed a sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify ruxolitinib in human plasma, improving upon traditional methods in specificity, sensitivity, and efficiency. The process involved the use of advanced chromatographic techniques and robust mass spectrometric conditions to ensure high accuracy and minimal matrix effects. The study was conducted using samples from 20 patients undergoing treatment, with calibration standards ranging from 10 to 2000 ng/mL. Results: > 0.99) across the studied range and proved highly selective with no significant interference observed. The method's precision and accuracy met FDA guidelines, with recovery rates consistently exceeding 85%. Clinical application demonstrated significant variability in ruxolitinib plasma levels among patients, reinforcing the need for individualized dosing schedules. Conclusion: The validated LC-MS/MS method offers a reliable and efficient tool for the therapeutic drug monitoring of ruxolitinib, facilitating personalized treatment approaches in hematologic malignancies. This approach promises to enhance patient outcomes by optimizing dosing to reduce toxicity and improve efficacy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".