The Role of Therapeutic Drug Monitoring of Imatinib in Patients with Chronic Myeloid Leukemia and Metastatic or Unresectable Gastrointestinal Stromal Tumors
Bibliographic record
Abstract
Imatinib mesylate is a tyrosine kinase inhibitor used as first-line treatment in Philadelphia chromosome-positive chronic myeloid leukemia (Ph+ CML) and metastatic or unresectable gastrointestinal stromal tumors (GIST). Therapeutic drug monitoring (TDM) for imatinib has been suggested to improve efficacy, assess compliance, and evaluate drug-drug interactions. Imatinib has proven efficacy in improving treatment response and survival in patients with Ph+ CML and GIST. Several analytical methods are available to quantify total plasma imatinib concentrations. A good relationship exists between total imatinib plasma concentrations and pharmacologic response. Clinical evaluation of pharmacologic response to imatinib alone may be insufficient given the long duration of therapy before clinical response in patients with Ph+ CML and GIST. Thus, the authors have used a previously published 9-step decision-making algorithm to evaluate the utility of TDM for imatinib. The suggested trough concentrations for improved complete cytogenetic or major molecular response in patients with Ph+ CML and improved time to progression for patients with GIST are >1000 and >1100 ng/mL, respectively. Imatinib exhibits interindividual pharmacokinetic variability. Increased apparent clearance of imatinib has been observed in chronic phase chronic myeloid leukemia and increased body weight. Decreased apparent clearance has been observed in renal impairment and patients on concomitant medications with potent inhibition of cytochrome P450 3A4. Duration of therapy in patients with Ph+ CML and GIST is lifelong. Based on the available evidence, TDM for imatinib may provide additional information on efficacy, compliance, and safety than clinical evaluation alone. Patients with suboptimal response to treatment, treatment failure, rare adverse events, drug interactions, or suspected nonadherence will attain the greatest benefit from TDM.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".