Evolution of Clinical Trial Design in Early Drug Development: Systematic Review of Expansion Cohort Use in Single-Agent Phase I Cancer Trials
Bibliographic record
Abstract
PURPOSE: To evaluate the use and objectives of expansion cohorts in phase I cancer trials and to explore trial characteristics associated with their use. METHODS: We performed a systematic review of MEDLINE and EMBASE, limiting studies to single-agent phase I trials recruiting adults and published after 2006. Eligibility assessment and data extraction were performed by two reviewers. Data were assessed descriptively, and associations were tested by univariable and multivariable logistic regression. RESULTS: We identified 611 unique phase I cancer trials, of which 149 (24%) included an expansion cohort. The trials were significantly more likely to use an expansion cohort if they were published more recently, were multicenter, or evaluated a noncytotoxic agent. Objectives of the expansion cohort were reported in 74% of trials. In these trials, safety (80%), efficacy (45%), pharmacokinetics (28%), pharmacodynamics (23%), and patient enrichment (14%) were cited as objectives. Among expansion cohorts with safety objectives, the recommended phase II dose was modified in 13% and new toxicities were described in 54% of trials. Among trials aimed at assessing efficacy, only 11% demonstrated antitumor activity assessed by response criteria that was not previously observed during dose escalation. CONCLUSION: The utilization of expansion cohorts has increased with time. Safety and efficacy are common objectives, but 26% fail to report explicit aims. Expansion cohorts may provide useful supplementary data for phase I trials, particularly with regard to toxicity and definition of recommended dose for phase II studies.
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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 | 0.295 | 0.560 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".