Accelerating the Future of Oncology Drug Development: The Role of Consortia in the Delivery of Precision Oncology Early Phase Clinical Trials
Bibliographic record
Abstract
PURPOSE: Over the past 15 years, the landscape of early phase clinical trials (EPCTs) has undergone a remarkable expansion in both quantity and intricacy. The proliferation of sites, trials, sponsors, and contract research organizations has surged exponentially, marking a significant shift in research conduct. However, EPCT operations suffer from numerous inefficiencies, such as cumbersome start-up processes, which are particularly critical when drug safety and the recommended phase II dose need to be established in a timely manner. Networks and consortia may overcome some of these challenges of enrolling suitable patients and streamlining start-up, particularly when distance and disease trajectory come into play. DESIGN: In this article, we provide an overview of EPCT consortia in adult oncology across different continents assembled through systematic review of the literature and snowball sampling methodology. We illustrate their scope, structure, funding, and achievements. RESULTS: Fifteen EPCT consortia were identified including two in the United States, three in Europe, five in Asia-Pacific, two intercontinental consortia, and three within private oncology networks. These consortia vary in their scope, funding, and structure from government-funded models such as the National Cancer Institute Experimental Therapeutics Clinical Trials Networks through charitably funded and private research organizations. EPCT consortia play a role in collaborative research, molecular tumor boards to provide patient-centric biomarker-matched treatments, and streamlining trial conduct to improve timelines and cost efficiency. CONCLUSION: The growth in EPCT activity and complexity has resulted in expansion in the number of EPCT consortia globally. By actively engaging with regulatory bodies and pharmaceutical and contract research organization industries, consortia have an opportunity to address the evolving challenges faced in this field and to accelerate the translation of scientific discoveries into clinical practice.
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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 | 0.333 | 0.607 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.024 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".