Preclinical studies to determine myelin peptide-specific nanoparticle tolerance mechanisms and clinical translation
Bibliographic record
Abstract
Abstract Infiltration of autoreactive T cells, subsequent tissue destruction, and the release of additional self-epitopes resulting in epitope spreading underly pathogenesis in many autoimmune diseases. Treatment with Ag-containing biodegradable poly(lactide-co-glycolide) nanoparticles, i.e. tolerogenic immune-modifying particles (TIMP/CNPs), has been shown to be safe and efficacious for the induction of Ag-specific tolerance in murine models of autoimmunity, and in a Phase Ib/IIa clinical trial for celiac disease. The present studies identified novel pathways required for Ag-specific TIMP-induced tolerance, the ability of treatment to modulate spread epitope-specific CD4+ T cell activation, and the functionality of TIMPs containing human myelin-derived peptide epitopes (CNP-102) in a pre-clinical mouse model of multiple sclerosis (MS). Following treatment, myeloid cells phagocytose TIMPs, undergo apoptosis, and Ag-specific tolerance therapy increases both FoxP3+ regulatory T cells and IL-10+ Tr1 cells via a STING/IFNAR-dependent pathway. TIMP treatment also modulates spread epitope-specific T cells associated with disease progression, but not encapsulated within the TIMP, i.e. tissue-specific tolerance. Therefore, treatment activates various Ag-specific regulatory T cell subsets capable of inhibiting disease-associated autoantigens not encapsulated within the TIMP via release of immunoregulatory cytokines, and CNP-102 has proven efficacious in preclinical mouse models of MS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".