Safety, pharmacokinetics, and efficacy of budigalimab with rovalpituzumab tesirine in patients with small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Agents targeting programmed cell death protein 1 (PD-1) have been approved as monotherapy for patients with small cell lung cancer (SCLC). In preclinical models, the combined targeting of PD-1 and delta-like protein 3 resulted in enhanced antitumor activity. Herein, we report results from the expansion arm of study NCT03000257 evaluating the combination of the anti-PD-1 antibody budigalimab and the targeted antibody-drug conjugate rovalpituzumab tesirine (Rova-T) in patients with previously treated SCLC. MATERIALS AND METHODS: This expansion arm of a multicenter, open-label, multi-arm, first-in-human phase 1 clinical trial enrolled adult patients with progressive SCLC. The primary objective was to assess safety and tolerability. Patients received budigalimab 375 mg via intravenous infusion every 3 weeks, and Rova-T was administered as a dose of 0.3 mg/kg intravenously, on day 1 of the first and third 3-week cycle. RESULTS: As of October 2019, 31 patients with SCLC were enrolled and treated with budigalimab plus Rova-T. The combination was tolerated, with the most common treatment-emergent adverse events (in >30%) being pleural effusion, fatigue, and cough. The overall response rate was 24.1%, with one confirmed complete response and six confirmed partial responses. The overall response rate in patients with high delta-like protein 3 expression was similar (21.1%). The median progression-free survival was 3.48 months. CONCLUSION: Combination therapy with budigalimab and Rova-T had promising efficacy and appeared to be tolerated in patients with SCLC. Although Rova-T development has been discontinued, development of budigalimab combined with other anticancer agents is ongoing. CLINICAL TRIAL REGISTRATION NUMBER: NCT03000257 Statement on originality of the work The manuscript represents original work and has not been submitted for publication elsewhere nor previously published. Statement of prior presentation Data from this study were previously presented at the European Society for Medical Oncology (ESMO) Congress 2019.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".