Revisiting the ethics of phase 1 oncology trials in the era of precision medicine: A systematic review.
Bibliographic record
Abstract
e14060 Background: Phase 1 clinical trials are a crucial step in the evaluation of new cancer therapies. However, critics cite a low rate of response to therapy (5%), together with a not insignificant risk of death from associated toxicities (0.5%), suggesting a risk-benefit ratio that may limit the ethics of inviting patients to participate in Phase 1 trials. With the introduction of novel targeted therapies, a contemporary estimate of the risks and benefits is needed. Methods: A systematic review was conducted. Eligible for inclusion were phase 1 trials in both adult and pediatric populations published between Jan 2015 to July 2018 of targeted immunomodulators, molecularly targeted therapies, and antiangiogenic agents for solid and hematological malignancies. We systematically searched PubMed and Embase. Rates of objective response (complete and partial), stable disease, and Grades 3, 4 and 5 treatment-related adverse events were extracted and pooled. The protocol was prospectively registered in PROSPERO (CRD42018100386). Results: 116 trials (109 adult, 6 pediatric, 1 mixed) met inclusion criteria. The studies reported on nearly 4300 patients (52% male, 48% female), ages ranging from 1 to 90. Most trials reported on molecularly targeted therapies (58%), followed by immunomodulators (33%). The combined overall objective response rate was 6% (95% CI 4% to 8%), with a complete response rate of 0.3% (0.1% to 0.7%) and a partial response rate of 5% (3% to 7%). The rate of stable disease was 34% (30% to 38%). Of the three types of therapies, the objective response rate appeared highest in the molecularly targeted therapies, at 8% (5% to 11%). Overall, the rate of treatment-related deaths was 0.02% (0% to 0.2%). Conclusions: Our results suggest that response rates for single agent, targeted phase I anti-cancer therapies are not materially different from estimates derived in the conventional chemotherapy setting. In an era where providers tout the benefits of precision medicine, this is an important consideration when counselling patients considering participation in phase I trials. Treatment-related death rates were very low, weakening criticism that participation puts patients at great risk. Reporting was highly inconsistent across included studies, highlighting a need to improve the quality of reporting in phase I trials.
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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 | 0.364 | 0.649 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".