Advancing Tolerogenic Immunotherapy: A Multi-Epitope Vaccine Design Targeting the CYP2D6 Autoantigen in Autoimmune Hepatitis Through Immuno-Informatics
Bibliographic record
Abstract
Abstract Juvenile autoimmune hepatitis (JAIH) is a rare autoimmune disorder affecting children, characterized by the immune system’s misguided attack on liver cells, primarily targeting the CYP2D6 autoantigen. This repeated attack leads to hepatic inflammation, fibrosis, and eventual liver failure. Current therapeutic strategies predominantly rely on immunosuppressive agents or whole B cell depletion antibodies, which render patients susceptible to infections and cancers. Hence, there is an urgent need for antigen-specific therapies to mitigate the severity of autoimmune hepatitis. Tolerogenic antigens represent a promising avenue in immunotherapy, capable of dampening autoimmunity. Here, we present a novel computationally designed multi-epitope tolerogenic vaccine tailored to target CYP2D6, aimed at inducing tolerogenic dendritic cells (DCs) and halting autoimmune progression in JAIH patients. To validate our approach, we have developed a similar vaccine for testing in mouse models of JAIH. The selected tolerogenic epitopes exhibit antigenicity without allergenicity or toxicity, and specifically induce IL-10 production (restricted to CD4+ T cell epitopes). In our vaccine design, tolerogenic poly-epitopes are linked with Toll-like receptor (TLR)-4-agonist, the 50S ribosomal unit, and IL-10, effectively programming DCs towards a tolerogenic state. Molecular docking and dynamic simulations have confirmed strong binding affinities and stable complexes between the vaccine structures, TLR4 and IL-10 receptor alpha (IL-10RA), indicating their potential for in vivo DC interaction and programming. Consequently, this innovative vaccine approach demands further exploration through wet lab experiments to assess its tolerogenicity, safety, and efficacy, thereby laying the groundwork for potential application in clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".