Veliparib in Combination with Carboplatin and Etoposide in Patients with Treatment-Naïve Extensive-Stage Small Cell Lung Cancer: A Phase 2 Randomized Study
Bibliographic record
Abstract
PURPOSE: This study investigated the efficacy and safety of oral PARP inhibitor veliparib, plus carboplatin and etoposide in patients with treatment-naïve, extensive-stage small cell lung cancer (ED-SCLC). PATIENTS AND METHODS: Patients were randomized 1:1:1 to veliparib [240 mg twice daily (BID) for 14 days] plus chemotherapy followed by veliparib maintenance (400 mg BID; veliparib throughout), veliparib plus chemotherapy followed by placebo (veliparib combination only), or placebo plus chemotherapy followed by placebo (control). Patients received 4-6 cycles of combination therapy, then maintenance until unacceptable toxicity/progression. The primary endpoint was progression-free survival (PFS) with veliparib throughout versus control. RESULTS: = 0.059]; median PFS was 5.8 and 5.6 months, respectively. There was a trend toward improved PFS with veliparib throughout versus control in SLFN11-positive patients (HR, 0.6; 80% CI, 0.36-0.97). Median overall survival (OS) was 10.1 versus 12.4 months in the veliparib throughout and control arms, respectively (HR, 1.43; 80% CI, 1.09-1.88). Grade 3/4 adverse events were experienced by 82%, 88%, and 68% of patients in the veliparib throughout, veliparib combination-only and control arms, most commonly hematologic. CONCLUSIONS: Veliparib plus platinum chemotherapy followed by veliparib maintenance demonstrated improved PFS as first-line treatment for ED-SCLC with an acceptable safety profile, but there was no corresponding benefit in OS. Further investigation is warranted to define the role of biomarkers in this setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".