Intratumoral and plasma concentrations of gefitinib in breast cancer patients: Preliminary results from a presurgical investigatory study (BCIRG 103)
Bibliographic record
Abstract
581 Background: Gefitinib (‘Iressa’, ZD1839) has a high volume of distribution in animals and humans, with a volume of 1400 L in cancer patients. Consistent with these pharmacokinetic data, gefitinib is extensively distributed into the tissues of rats and mice, with tissue:blood ratios of >10 in a number of organs. Concentrations of gefitinib in mouse subcutaneous tumour xenografts (LoVo: colorectal; A549: non-small-cell lung cancer) are considerably higher (10–15-fold) than plasma. Concentrations of gefitinib have been determined in plasma and breast cancer tissue as a secondary endpoint in a study designed primarily to identify molecular alterations in human breast cancer tissue after short-term exposure of patients to gefitinib. Preliminary concentration data are reported here. Methods: BCIRG 103 is a multicentre, open-label, noncomparative, presurgical study in women with invasive adenocarcinoma of the breast. Patients receive gefitinib (250 mg) once daily for at least 14 days and up to a maximum of 45 days prior to definitive surgery. Blood samples are taken immediately before and 24 h after the final dose of gefitinib. A sample of tumour tissue (100 mg) is taken and immediately snap-frozen during definitive surgery. Plasma and tumour samples were assayed for gefitinib and its major metabolites by high-performance liquid chromatography (HPLC) with mass spectrometric detection. Results: So far, 16 out of 40 patients have been enrolled and samples from 9 patients have been examined. Concentrations of gefitinib in tumour (2.3–25.8 μg/g) were much higher than plasma (0.10–0.42 μg/ml). There was no apparent correlation between tumour and plasma concentrations. Conclusions: The pronounced tumour:plasma ratio (mean 54-fold) was much higher than that from the mouse xenograft model. Gefitinib tumour concentrations will also be compared with the biomarker data as they become available. Data on a larger sample size will be presented. ‘Iressa’ is a trademark of the AstraZeneca group of companies Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration AstraZeneca AstraZeneca
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".