Nanoparticle‐Based Tolerogenic Vaccines: Next‐Generation Strategies for Autoimmune and Allergic Disease Therapies
Bibliographic record
Abstract
Abstract The concurrent rise of autoimmune diseases, which affect nearly 10% of the global population, along with allergic conditions such as asthma, food allergy, and atopic disease, together pose substantial health and economic burdens. Traditional therapies rely on systemic immunosuppression that temporarily mitigates symptoms but often compromises protective immunity, increases infection and malignancy risk, and fails to restore central or peripheral tolerance. These limitations underscore the need for antigen‐specific strategies capable of re‐establishing durable immune balance. Tolerogenic vaccines have emerged as a promising solution by retraining the immune system to restore antigen‐specific tolerance while preserving normal host defense, though challenges remain in efficiently targeting antigen‐presenting cells (APCs), avoiding their overactivation, and minimizing off‐target effects. Nanoparticles provide a versatile platform to address these hurdles, as their size, composition, and surface modifications can be tailored to direct biodistribution, enhance antigen delivery, and modulate immune signaling. By co‐delivering antigens and immunomodulators in programmable ways, nanoparticles offer a pathway to overcome key translational barriers and achieve precise immune reprogramming. This review explores how advances in nanomedicine are being applied to tolerogenic vaccines, focusing on three areas: (1) current nanoparticle platforms, (2) the role of biomaterial selection, and (3) multifunctional engineering strategies, while also considering the clinical outlook and translational challenges of bringing these therapies from bench to bedside.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".