2003-LB: An mRNA Immunotherapy for Type 1 Diabetes
Bibliographic record
Abstract
Introduction and Objective: Antigen-specific immune tolerance therapy (ASIT) is a potentially curative approach to type 1 diabetes (T1D) but has not translated successfully to the clinic. We developed a tolerogenic lipid nanoparticle (tLNP) to co-deliver immunomodulators and mRNA-encoded beta-cell autoantigens as a novel ASIT strategy for T1D and tested it for diabetes prevention and reversal in NOD mice. Methods: To assess whether tLNPs prevent diabetes, we injected NOD females biweekly from 5 to 15 weeks with tLNPs, control LNPs (antigen-only or irrelevant antigen with/without immunomodulators), or buffer, and monitored blood glucose to 30 weeks (diabetes onset = 2 readings > 20 mM). We compared administration routes in the NOD diabetes prevention model and performed immune profiling in blood and spleens by flow cytometry. To test reversal of new-onset diabetes, tLNPs were administered only after 2 consecutive blood glucose readings >13 mM, with monitoring 2x weekly up to 10 weeks post-onset (diabetes progression = 2 readings > 15 mM). Results: Treatment with tLNPs reduced diabetes incidence vs. buffer-injected controls (p=0.002). Protection was antigen-dependent, as irrelevant antigen with immunomodulators in LNPs was ineffective, similar to buffer control. Likewise, antigen-only LNPs were ineffective. Intramuscular (IM) was the most effective administration route, with 3x IM injections conferring 100% protection to 30 weeks. IM tLNP injection expanded circulating polyclonal and antigen-specific regulatory T cells and increased dysfunction markers in splenic CD4+ and CD8+ T effectors. Remarkably, tLNPs reversed new-onset diabetes in 100% of mice, with treated mice remaining euglycemic without supplemental insulin for the 10-week study (p=0.007 vs. buffer). Conclusion: TLNPs delivering mRNA-encoded antigens induce robust immune tolerance, preventing and reversing diabetes in NOD mice. This novel technology enables mRNA as a promising ASIT modality and warrants further preclinical development for T1D. Disclosure H.C. Denroche: Other Relationship; Integrated Nanotherapeutics. L.P. Pallo: None. S. Chen: Other Relationship; Integrated Nanotherapeutics. V. Ng: Employee; Integrated Nanotherapeutics. J. Zaifman: Other Relationship; Integrated Nanotherapeutics. M. Ferrad: Employee; Integrated Nanotherapeutics. Y. Tam: Other Relationship; Integrated Nanotherapeutics. B. Verchere: Board Member; Integrated Nanotherapeutics. Stock/Shareholder; Integrated Nanotherapeutics. Funding Breakthrough T1D (3-IND-2024-1575-I-X); Breakthrough T1D Canada (3-COE-2022-1103-M-B)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".