A Phase I Dose-Escalation Study of Veliparib Combined with Carboplatin and Etoposide in Patients with Extensive-Stage Small Cell Lung Cancer and Other Solid Tumors
Bibliographic record
Abstract
PURPOSE: This study examined safety, pharmacokinetics, and efficacy of veliparib, a PARP inhibitor, combined with carboplatin and etoposide in patients with extensive-stage (ED) small cell lung cancer (SCLC) and other solid tumors. PATIENTS AND METHODS: on days 1-3) in 21-day cycles. Veliparib dose was explored from 80 to 240 mg b.i.d. on 7-day, 14-day, or continuous schedules. Patients without disease progression continued on maintenance monotherapy (veliparib 400 mg b.i.d.) until disease progression or unacceptable toxicity. RESULTS: Thirty-nine patients were enrolled to determine the recommended phase II dose of 240 mg veliparib for 14 days combined with carboplatin and etoposide based on long-term tolerability. Dose-limiting toxicity occurred in 1 patient (grade 2 toxic motor polyneuropathy) at veliparib 240 mg b.i.d. for 7 days. Most common adverse events related to veliparib were nausea (39%), fatigue (39%), and hematologic toxicities. Continuous dosing of veliparib 240 mg b.i.d. with carboplatin and etoposide resulted in excessive chemotherapy dose delays due to hematologic toxicity (grade 3/4 neutropenia/thrombocytopenia). Etoposide pharmacokinetics was not affected by veliparib. Confirmed responses occurred in 17 of 39 (44%) and 16 of 25 (64%) of all enrolled and ED SCLC patients, respectively. At the RP2D, confirmed responses occurred in 6 of 13 (46%) and 5 of 6 (83%) of all enrolled and ED SCLC patients, respectively. CONCLUSIONS: Veliparib (240 mg b.i.d. 14 days) plus carboplatin/etoposide can be safely combined. Phase II of this study is ongoing in first-line patients with ED SCLC.
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) | 0.001 | 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 | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".