Determinants of the recommended phase 2 dose of molecular targeted agents
Bibliographic record
Abstract
BACKGROUND: The recommended phase 2 dose (RP2D) of anticancer agents is determined traditionally by dose-limiting toxicities. Nontoxicity or biological endpoints such as pharmacokinetics, pharmacodynamics, and efficacy can also be used to identify RP2D, which may be relevant to molecularly targeted agents (MTAs). METHODS: A systematic review identified all monotherapy phase 1 studies of MTAs in solid tumors published between 2001 and 2013. Dose, dosing schedule, and determinants of RP2D were collected from each study. A supplementary search of the US Food and Drug Administration (FDA) website identified the licensed dose for drugs with regulatory approval. Logistic regression was used to explore predictors for the RP2D being consistent with the final approved dose. RESULTS: The search identified 4175 records, of which 250 studies evaluating 181 individual MTAs were included. Of these MTAs, 161 (64%) determined an RP2D. Fifty-two trials (32%), used toxicity alone to specify an RP2D. The remaining trials used a nonclassical approach with either multiple endpoints that included toxicity (n = 87, 54%), multiple nontoxicity endpoints (n = 12, 7%), or a single nontoxicity endpoint (n = 10, 6%). Twenty-nine (16%) MTAs were approved by the FDA for solid tumor indications. The use of nonclassical definitions compared with toxicity alone was significantly associated with higher likelihood of FDA approval (odds ratio, 5.03; 95% confidence interval, 1.11-22.73; P = .036). CONCLUSIONS: In the past decade, there has been a dominance of a nonclassical approach using multiple endpoints with or without toxicity or single nontoxicity endpoints to define RPTD in MTA monotherapy phase 1 trials. Nonclassically defined RP2Ds for MTAs appear to be associated with a higher rate of FDA drug approval. Cancer 2017;123:1409-1415. © 2016 American Cancer Society.
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How this classification was reachedexpand
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 | 0.002 | 0.051 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".