Data from 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
<div>AbstractPurpose:<p>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).</p>Patients and Methods:<p>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.</p>Results:<p>Overall (<i>N</i> = 181), PFS was improved with veliparib throughout versus control [hazard ratio (HR), 0.67; 80% confidence interval (CI), 0.50–0.88; <i>P</i> = 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.</p>Conclusions:<p>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.</p></div>
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".