Phase I Trial Design for Solid Tumor Studies of Targeted, Non-Cytotoxic Agents: Theory and Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.465 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it