Phase I Trial Design for Solid Tumor Studies of Targeted, Non-Cytotoxic Agents: Theory and Practice
Bibliographic record
Abstract
BACKGROUND: New targeted, non-cytotoxic anticancer agents, such as small-molecule kinase inhibitors, pose challenges to the current phase I paradigm of dose selection based on toxicity. Moreover, increasing the drug dose to toxicity may be unnecessary for drug effect, making the use of maximum tolerated dose as a surrogate of effective dose inappropriate in the phase I setting. Because little is known about the optimal methods of recommended phase II dose selection of targeted, non-cytotoxic therapies, we reviewed the strategies that were used in completed phase I studies of these drugs. METHODS: We retrieved 60 publications of phase I studies involving 31 single agents representative of the most common targets of interest in the oncology literature. For each publication, we abstracted data regarding patient population, starting dose, methods of dose escalation and determination of recommended phase II dose, and inclusion of correlative studies in study conduct. RESULTS: Of the 60 completed phase I studies, 36 used toxicity and eight used pharmacokinetic data as endpoints for selection of the recommended phase II dose. Nontraditional endpoints, such as measures of molecular drug effects in tumor or surrogate tissue or functional imaging studies, were not routinely incorporated into the study design and rarely formed the primary basis for dose selection. CONCLUSIONS: To date, phase I studies of targeted anticancer agents have generally used traditional endpoints for selection of the recommended phase II dose. More research is needed to define suitable molecular measures of drug effect and the means to incorporate them in the early drug development process.
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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 | 0.319 | 0.427 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".