Effects of Rovalpituzumab Tesirine on Ventricular Repolarization in Patients With Small‐Cell Lung Cancer
Bibliographic record
Abstract
Small cell lung cancer (SCLC) is a leading cause of cancer death worldwide, with few treatment options. Rovalpituzumab tesirine (Rova-T) is an antibody-drug conjugate that targets delta-like 3 on SCLC cells to deliver a cytotoxic payload directly to tumor cells. In this study, the cardiac safety profile of Rova-T was assessed by evaluating changes in QT interval, electrocardiogram (ECG) waveform, heart rate, and proarrhythmic adverse events (AEs) after treatment with Rova-T in patients with previously treated extensive-stage SCLC. Patients underwent ECG monitoring for 2 weeks after each of 2 i.v. infusions of 0.3 mg/kg Rova-T over 30 minutes, administered 6 weeks apart. Forty-six patients received at least one dose of Rova-T. At the geometric mean Rova-T maximum serum concentration of 7,940 ng/mL, ECG monitoring showed no significant changes in the Fridericia-corrected QT (QTcF) interval; the upper limit of the 2-sided 90% confidence interval did not exceed 10 msec for any time point. There were no clinically significant changes in QRS or PR intervals, ECG waveforms, or heart rate after Rova-T administration. All patients experienced a treatment-emergent AE (TEAE); 78% had a grade ≥ 3 TEAE, 59% had a serious TEAE, and 41% had a cardiac-related TEAE. The TEAEs that might signal proarrhythmia tendencies were uncommon. Confirmed partial responses were observed in 24% of patients. Based on the evaluation of ECG data collected in this study from patients treated with Rova-T at 0.3 mg/kg i.v. administered every 6 weeks, a QTcF effect of clinical concern can be excluded.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".