Tolerogenic Immune Modifying Nanoparticles (TIMP) Encapsulating Multiple Diabetogenic Epitopes Prevent Onset of Type 1 Diabetes in NOD Mice
Bibliographic record
Abstract
Abstract Type 1 diabetes (T1D) is an autoimmune disease characterized by inflammatory lesions within islets (insulitis) and β cell loss which is associated T cell and B cell responses specific for pancreatic islet β cell proteins. Data show that treatment with poly(lactide-co-glycolide) (PLGA) nanoparticles (i.e. TIMP/COUR’s CNP) containing a single diabetogenic peptide blocks T1D induced by transfer of CD4+ BDC2.5 or CD8+ NY8.3 TCR transgenic T cells to NOD-scid mice. In contrast, Ag-specific tolerance approaches targeting single β-islet cell protein epitopes, e.g. GAD65 or Insulin, have failed to achieve efficacy in both wildtype NOD mice and clinical trials. The possibility exists that T1D initiation and progression is due to activation of T cell populations specific for multiple diabetogenic epitopes. To test this hypothesis, recombinant Chormogranin A, GAD65, and Insulin proteins were encapsulated within CNPs to assess Treg/Tr1 cell induction, inhibition of Ag-specific T cell responses, and blockade of T1D in NOD mice. While treatment of NOD mice with CNPs containing a single protein inhibited the corresponding Ag-specific T cell response, the inhibition of T1D development only occurred if all three diabetogenic proteins were included within the CNPs (CNP-T1D). CNP-T1D-induced blockade of T1D was characterized by Treg/Tr1 cell induction and a significant decrease in both peri-insulitis and immune cell infiltration into pancreatic islets. We have recently published that CNP treatment is both safe and induces Ag-specific tolerance in Phase I/IIa celiac disease clinical trials. Therefore, utilization of this technology including multiple diabetogenic proteins is a promising Ag-specific tolerance approach to T1D treatment.
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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 | 0.001 | 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) | 0.001 | 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; 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".