Disease-modifying pharmacological treatments of type 1 diabetes: Molecular mechanisms, target checkpoints, and possible combinatorial treatments
Bibliographic record
Abstract
After a century of extensive scientific investigations, there is still no curative or disease-modifying treatment available that can provide long-lasting remission for patients diagnosed with type 1 diabetes (T1D). Although T1D has historically been regarded as a classic autoimmune disorder targeting and destroying pancreatic islet β-cells, significant research has recently demonstrated that β-cells themselves also play a substantial role in the disease's progression, which could explain some of the unfavorable clinical outcomes. We offer a thorough review of scientific and clinical insights pertaining to molecular mechanisms behind pathogenesis and the different therapeutic interventions in T1D covering over 20 possible pharmaceutical intervention treatments. The interventions are categorized as immune therapies, treatments targeting islet endocrine dysfunctions, medications with dual modes of action in immune and islet endocrine cells, and combination treatments with a broader spectrum of activity. We suggest that these collective findings can provide a valuable platform to discover new combinatorial synergies in search of the curative disease-modifying intervention for T1D. SIGNIFICANCE STATEMENT: This research delves into the underlying causes of T1D and identifies critical mechanisms governing β-cell function in both healthy and diseased states. Thus, we identify specific pathways that could be manipulated by existing or new pharmacological interventions. These interventions fall into several categories: (1) immunomodifying therapies individually targeting immune cell processes, (2) interventions targeting β-cells, (3) compounds that act simultaneously on both immune cell and β-cell pathways, and (4) combinations of compounds simultaneously targeting immune and β-cell pathways.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".