TNF-α inhibitors for type 1 diabetes: exploring the path to a pivotal clinical trial
Bibliographic record
Abstract
Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of insulin-producing β-cells in the pancreas. This destruction leads to chronic hyperglycemia, necessitating lifelong insulin therapy to manage blood glucose levels. Typically diagnosed in children and young adults, T1D can, however, occur at any age. Ongoing research aims to uncover the precise mechanisms underlying T1D and to develop potential interventions. These include efforts to modulate the immune system, regenerate β-cells, and create advanced insulin delivery systems. Emerging therapies, such as closed-loop insulin pumps, stem cell-derived β-cell replacement and disease-modifying therapies (DMTs), offer hope for improving the quality of life for individuals with T1D and potentially moving towards a cure. Currently, there are no disease-modifying therapies approved for stage 3 T1D. Preserving β-cell function in stage 3 T1D is associated with better clinical outcomes, including lower HbA1c and decreased risk of hypoglycemia, neuropathy, and retinopathy. Tumor Necrosis Factor alpha (TNF-α) inhibitors have demonstrated efficacy at preserving β-cell function by measurement of C-peptide in two clinical trials in people with stage 3 T1D. However, TNF-α inhibitors have yet to be evaluated in a pivotal trial for T1D. To address the promising clinical findings of TNF-α inhibitors in T1D, Breakthrough T1D convened a panel of key opinion leaders (KOLs) in the field. The workshop aimed to outline an optimal clinical path for moving TNF-α inhibitors to a pivotal clinical trial in T1D. Here, we summarize the evidence for the beneficial use of TNF-α inhibitors in T1D and considerations for strategies collectively identified to advance TNF-α inhibitors beyond phase 2 clinical studies for stage 3 T1D.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".