Peptide-MHC-Based Nanomedicines for the Treatment of Autoimmunity: Engineering, Mechanisms, and Diseases
Bibliographic record
Abstract
The development of autoimmunity results from a breakdown of immunoregulation and involves cellularly complex immune responses against broad repertoires of epitope specificities. As a result, selective targeting of specific effector autoreactive T- or B-cells is not a realistic therapeutic option for most autoimmune diseases. Induction of autoantigen-specific regulatory T-cells capable of effecting bystander (dominant), yet tissue-specific, immunoregulation has thus emerged as a preferred therapeutic alternative. We have shown that peptide-major histocompatibility complex (pMHC)-based nanomedicines can re-program cognate autoantigen-experienced T-cells into disease-suppressing regulatory T-cells, which in turn elicit the formation of complex regulatory cell networks capable of comprehensively suppressing organ-specific autoimmunity without impairing normal immunity. Here, we summarize the various pMHC-based nanomedicines and disease models tested to date, the engineering principles underpinning the pharmacodynamic and therapeutic potency of these compounds, and the underlying mechanisms of action.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".