Design and conduct of early clinical studies of immunotherapy agent combinations: recommendations from the task force on Methodology for the Development of Innovative Cancer Therapies
Bibliographic record
Abstract
The Methodology for the Development of Innovative Cancer Therapies task force considered aspects of the design and conduct of early studies of combinations of immunotherapy agents during their 2018 meeting. The task force defined the relevant data to justify combination clinical trials, which includes a robust hypothesis for the combination, pre-clinical data with evidence of efficacy and an understanding of the pharmacodynamics effects of each agent, and ideally evidence of single agent activity. Evaluation of pharmacodynamic biomarkers is critical in early phase combination trials, and should be incorporated into trial objectives and go/no-go decisions. The task force also identified the need to develop assessment tools and end points that capture the unique patterns of tumour responses to immunotherapy, including pseudoprogression and hyperprogression. At least one additional tumour measurement before baseline and an early CT scan (at 4 weeks for example) would help define the incidence of hyperprogression, although a common definition is needed. Finally, the task force highlighted substantial redundancy and inefficiency in the combination immunotherapy space, and recommended the adoption of innovative trial designs.
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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 | 0.212 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".