Stakeholder Views on Participant Selection for First-in-Human Trials in Cancer Nanomedicine
Bibliographic record
Abstract
BACKGROUND: Participant selection for first-in-human (fih) trials involves complex decisions. The trial design makes it unlikely that participants will receive clinically relevant therapeutic benefit, but they are likely to experience risks of various magnitudes and types. The aim of the present paper was to describe and discuss the views of investigators and ethics committee members about the choice of trial participants for fih trials in cancer nanomedicine. METHODS: We drew insights from an exploratory qualitative study involving thematic analysis of 46 in-depth interviews with key stakeholders in Europe and North America involved in fih nanomedicine trials. The present work draws on subset of 21 interviews with investigators and ethics committee members who have either conducted or reviewed a fih cancer nanomedicine trial or are planning one. RESULTS: Investigators and ethics committee members are aware of the ethics standards for recruiting patients with end-stage cancer into fih trials, but they nonetheless question the practice and provide reasons against it. CONCLUSIONS: Although it is a standard and ethically accepted practice to enrol patients with end-stage cancer and no treatment options into fih trials of investigational chemotherapeutic molecules, doing so can threaten the validity and generalizability of the trials, thereby weakening translational research. Another possibility is to stratify and include patients with less advanced disease who demonstrate certain biomarkers or cancer genotypes and who have a disease profile similar to that tested in preclinical studies. The latter approach could be a step toward personalized medical research and targeted drug development. Such a patient selection approach requires multi-stakeholder discussion to reach scientific and ethics consensus.
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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 | 0.362 | 0.396 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".