Phase II study of tariquidar, a selective P‐glycoprotein inhibitor, in patients with chemotherapy‐resistant, advanced breast carcinoma
Bibliographic record
Abstract
BACKGROUND: The primary objective of this study was to determine whether addition of the selective P-glycoprotein (P-gp) inhibitor tariquidar (XR9576) to chemotherapy could induce an objective tumor response in patients who previously were resistant to the same agents. The secondary objectives were to evaluate P-gp expression by immunohistochemistry (IHC), to determine functional activity of the P-gp transporter before and after administration of tariquidar with serial technetium-99m ((99m)Tc)-sestamibi scans, and to correlate those parameters with clinical response. METHODS: Seventeen women with Stage III-IV breast carcinoma were included in the study who progressed (n = 13 women) or had stable disease (n = 4 women) on doxorubicin-containing or taxane-containing chemotherapy regimens. During the study, the same chemotherapy was continued without dose modifications, but tariquidar (150 mg intravenously) was added to the treatment regimen. RESULTS: Thirty-six percent of patients had P-gp-positive tumors by IHC, and 5 patients (29%) experienced increases > or = 10% in sestamibi uptake (median increase, 40%; range, 10-63%) after the administration of tariquidar. There was one partial response in a patient who had the greatest increase in sestamibi uptake and who also showed inducible P-gp expression. There was one patient who experienced severe doxorubicin/docetaxel-related toxicity after tariquidar was added to her chemotherapy regimen. CONCLUSIONS: Tariquidar showed limited clinical activity to restore sensitivity to anthracycline or taxane chemotherapy. Functional imaging of the tumor with (99m)Tc-sestamibi scans before and after administration of multidrug-resistance inhibitor may be useful to identify the small subset of patients who could benefit from multidrug-resistance modulation in future 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".