Recent Lessons Learned From Prevention and Recent-Onset Type 1 Diabetes Immunotherapy Trials
Bibliographic record
Abstract
Type 1 diabetes (T1D) results from the immune system’s misguided attack on insulin-producing pancreatic β-cells, leading to lifelong insulin replacement therapy as well as to the risk for developing disease-associated complications (1–3). Over the past 2–3 decades, the field of clinical research in T1D has seen tremendous growth, including evaluation of a variety of promising immunotherapy approaches for the prevention or reversal of the disorder (4–6). In just the past 2 years, data from >10 trials have been reported, some revealing promising phase II results. However, phase III trials have failed to demonstrate efficacy. In light of these results, an anxiety-provoked question has arisen: Where does the field go from here? To this end, this article presents and elaborates on key emerging questions and recommendations for future immunotherapy trials in T1D. If implemented successfully, such strategies could accelerate the development of therapies with tangible clinical benefit in T1D because they perhaps more appropriately address the complex nature of the disease. Nearly 30 years after the first immunotherapy clinical trials in type 1 diabetes (T1D), progress has been realized. This progress includes advancements in scientific knowledge (e.g., immune markers, metabolic testing, pathogenesis), the breadth of agents under investigation (Fig. 1), and how clinical trials are increasingly performed as part of major collaborative networks with uniform protocols often bolstered with mechanistic assays. However, shortcomings remain in demonstrating a degree of therapeutic efficacy for recent-onset T1D immunotherapies that is sufficiently robust in terms of risk/benefit to satisfy the requirements for drug registration and approval by regulatory agencies (i.e., Food and Drug Administration, European Medicines Agency). In 2011 and early 2012 after a number of phase I and II recent-onset clinical trials, a series of phase IIB and III recent-onset T1D trials reported their outcomes (7–10). In advance …
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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 | 0.170 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".