An open-label study of sunitinib (SU) plus exemestane (E) in the first-line treatment of hormone receptor (HR)-positive metastatic breast cancer (MBC)
Bibliographic record
Abstract
e12019 Background: SU, an oral multitargeted tyrosine kinase inhibitor of VEGFRs, PDGFRs, KIT, FLT3, CSF-1R and RET has activity in heavily pretreated pts with MBC. The aromatase inhibitor (AI), E, has proven 1st-line activity that compares favorably with tamoxifen in pts with HR+ BC in the adjuvant setting (Jones et al. 2008). Combining agents that target different signaling pathways may have additive/synergistic activity; combining the AI letrozole with the anti-VEGF agent bevacizumab prolonged progression-free survival to >14 mos as 1st-line therapy for HR+ MBC (Dickler et al. 2008). An open-label, phase I, dose-finding study of first-line SU + E was conducted in HR+ MBC pts. Methods: Eligible pts (postmenopausal; female; ≥18 yrs) had an ECOG PS ≤1, LVEF ≥50% and locally recurrent (unresectable) or MBC. Exclusion criteria included HER2+ BC (unless pt had progressed after trastuzumab) and prior treatment in the metastatic setting. Pts received SU 37.5 mg/d + E 25 mg/d on a continuous daily dosing regimen; if dose-limiting toxicities (DLTs) were experienced by >1/6 pts in the first 8 wks then further pts would be enrolled at SU 25 mg/d + E 25 mg/d. Pharmacokinetic (PK) analyses were performed for each drug and the active SU metabolite SU12662. Results: As of December 2008, enrollment was completed (N=6; mean age 59 ± 11 yrs; 50% of pts had ≥3 metastatic sites). No DLTs were observed and no dose reductions were required throughout the treatment period. An overview of key data is shown below. One death occurred on study (non treatment-related Enterobacter sepsis). No treatment-related G4/5 AEs occurred. PK parameter values determined for SU and E suggested increases in the systemic exposure of both drugs when administered concurrently. Conclusions: These data indicate that SU + E was tolerable with manageable toxicities, with increases in PK parameters and a similar AE profile to that of either single agent alone. This combination should be considered in future clinical trials. [Table: see text] [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".