Early 3+3 Trial Dose-Escalation Phase I Clinical Trial Design and Suitability for Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Abstract Purpose: Despite the expansion of immune checkpoint inhibitor (ICI) indications, the relationship between ICI dose and toxicity or response is not well established. To understand this correlation, we performed a meta-analysis of ICI trials that used dose escalation. Experimental Design: We searched PubMed and abstracts presented at (inter)national meetings for trials using FDA-approved ICIs. The reported rates of grade 3–5 adverse events (G3–5 AE), immune-related adverse events (irAE), and response were correlated with doses within each ICI using marginal exact generalized linear models. Results: A total of 74 trials (7,469 patients) published between January 2010 and January 2017 were included. For ipilimumab, the incidence of G3–5 AEs was 34% with a significant 27% reduced risk in lower doses (P = 0.002). However, no relationship was observed between dose and irAEs or response. For nivolumab, the incidence of G3–5 AEs was 20.1% which was lower in non–small cell lung cancer (NSCLC) compared with renal cell carcinoma (RCC) or melanoma (P ≤ 0.05) with no dose-toxicity relationship. In melanoma and NSCLC, a dose–response association was observed, which was not observed in RCC. For pembrolizumab, the incidence of G3–5 AEs was 13.3%, which was lower in melanoma compared with NSCLC (P = 0.03) with no dose-toxicity relationship. In melanoma, lower dose levels correlated with decreased odds of response (P = 0.01), a relationship that was not observed in NSCLC. Conclusions: Our analysis shows a lack of consistent dose-toxicity or dose–response correlation with ICIs. Therefore, dose escalation is not an appropriate design to conduct ICI studies. Here we present an innovative trial design for immune-modulating agents.
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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 | 0.077 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".