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
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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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) | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".