Choice of Starting Dose for Molecularly Targeted Agents Evaluated in First-in-Human Phase I Cancer Clinical Trials
Bibliographic record
Abstract
PURPOSE: One tenth of the lethal dose to 10% of mice is one of the conventional parameters used to derive a safe starting dose in phase I trials of cytotoxic agents. There is no consensus on which preclinical models and parameters should define the starting dose for molecularly targeted agents. PATIENTS AND METHODS: Reports of 81 first-in-human phase I trials evaluating 60 different molecularly targeted agents administered as monotherapy were reviewed. The maximum-tolerated dose (MTD) was defined as the highest safe dose administered to patients, whereas the maximum-administered dose (MAD) was recorded if the MTD was not reached. RESULTS: Fifty-seven of the 81 trials specified the animal model used to determine the starting dose, with 29 (51%) of 57 based on rodent data and 28 (49%) of 57 based on non-rodent data. A wide range of toxicologic parameters was used to select the starting dose. The starting dose exceeded the human MTD in three (3.7%) of 81 trials, and in all three trials, nonhematologic toxicity was dose limiting. The median number of dose levels to reach MTD or MAD from starting dose was five (range, one to 14 dose levels), and the median ratio of MTD or MAD to starting dose was 12 (range, < 1 to 300). Hypothetical doubling of the starting dose appeared to be safe, whereas tripling of the starting dose was unsafe. CONCLUSION: The derivation of starting dose for first-in-human phase I trials of molecularly targeted agents in patients with cancer is safe but is based on diverse practices using a variety of preclinical toxicologic parameters.
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.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".