Phase I trial of the oral PARP inhibitor olaparib in combination with paclitaxel for first- or second-line treatment of patients with metastatic triple-negative breast cancer
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: This Phase I study evaluated the safety, tolerability and efficacy of olaparib, a potent oral poly(ADPribose) polymerase (PARP) inhibitor, in combination with paclitaxel in patients with metastatic triple-negative breast cancer (mTNBC). METHODS: Eligible patients who had received ≤1 prior cytotoxic regimen for mTNBC were treated with olaparib 200 mg bid continuously plus weekly paclitaxel 90 mg/m2 for three weeks per four-week cycle. Dose modifications in a large proportion of patients due to neutropenia resulted in enrollment of a second cohort of patients who, if they experienced grade ≥2 neutropenia in cycle 1, received granulocyte-colony stimulating factor, which was continued prophylactically in subsequent cycles. All patients had measurable disease; tumor responses were evaluated according to RECIST (version 1.0). RESULTS: Nineteen patients (cohort 1, n = 9; cohort 2, n = 10) received treatment; 15 had received prior taxane chemotherapy. The most frequent adverse events were diarrhea (n = 12, 63%), nausea (n = 11, 58%) and neutropenia (n = 11, 58%). Seven neutropenia events were reported in cohort 1 (four grade ≥3) and four in cohort 2 (two grade ≥3, including one event of febrile neutropenia). The median (range) dose intensity of paclitaxel was 57% (26 to 100%) in cohort 1 and 73% (29 to 100%) in cohort 2. Seven patients (37%) had a confirmed partial response; one patient remains on olaparib monotherapy without progression. CONCLUSIONS: The combination of olaparib and weekly paclitaxel was complicated by a significant clinical interaction, with higher-than-expected rates of neutropenia despite secondary prophylaxis. Given the encouraging response rate, alternative scheduling and dosing strategies should be considered (funded by AstraZeneca; ClinicalTrials.gov, NCT00707707).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it