The Design of Clinical Trials for New Molecularly Targeted Compounds: Progress and New Initiatives
Bibliographic record
Abstract
Investigators involved in the development of cancer therapeutics are testing new trial designs and endpoints in order to accommodate the perceived challenges in defining appropriate doses and schedules for further testing. Many new agents with specific molecular targets have entered clinical development or are being considered for development. While some of the agents have both toxicity and antitumour efficacy apparent at clinically achievable doses, thus the use of traditional algorithms is appropriate, others have significant clinical activity at doses considerably lower than the maximum tolerated dose. New initiatives in clinical trial design, both phase I and phase II may allow the development of appropriate plans for the development of these new molecularly targeted agents. Measures of target effect (tissue or imaging) are now commonly included in early trials of new targeted compounds, in an attempt to demonstrate proof of principle as well as guide dose selection. Phase II trial designs including novel correlative, imaging and clinical endpoints are being tested. Alternate endpoints such as progression or time to progression are being increasingly considered, and novel designs such as randomized discontinuation designs, multinomial designs and growth modulation indices are being prospectively tested. Progress in this area of early trial design are reviewed.
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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 | 0.052 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".