An Overview of the Optimal Planning, Design, and Conduct of Phase I Studies of New Therapeutics
Bibliographic record
Abstract
Phase I clinical trials represent the first step in bringing promising new treatments from the laboratory to the clinic. Although the importance of phase I clinical trials is widely recognized, there is currently no consensus among the scientific, medical, and statistical communities on how best to do these studies in humans. With the advent of targeted therapies, it has become evident that we need to tailor the design of phase I studies for the particular drug class under investigation and any endpoints that are being defined. The National Cancer Institute (NCI) Investigational Drug Steering Committee (IDSC) provides broad external scientific and clinical input on the design and prioritization of early-phase clinical trials with agents for which the NCI Cancer Therapy Evaluation Program (CTEP) holds an Investigational New Drug (IND) application through the U.S. Food and Drug Administration (FDA). The IDSC has formed a number of task forces and working groups, including the Clinical Trial Design Task Force and the Biomarker Working Group, many with membership from within the IDSC as well as external experts, including participants from academia, the pharmaceutical industry, and regulatory authorities. The Clinical Trials Design Taskforce sponsored a Phase I Workshop with the primary goal being to develop consensus recommendations for the optimal design of phase I studies. The primary focus included (1) efficient trial designs, (2) phase I drug combinations, and (3) appropriate statistical and correlative endpoints. In this CCR Focus series, articles summarize key aspects and recommendations on phase I studies (including combination trials), such as design, use of biomarkers, the European Union and Japanese perspectives on design, requirements for first-in-human and other phase I studies, and ensuring regulatory and International Conference on Harmonization (ICH) compliance. A final article summarizes recommendations for the design and conduct of phase II studies.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.054 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".