Pharmacotherapeutic strategies to promote regulatory T cell function in autoimmunity
Bibliographic record
Abstract
Autoimmune diseases arise when self-antigen-specific T and B cells escape central and peripheral mechanisms of tolerance. One such mechanism is control of autoreactivity by regulatory T cells (Tregs), which have an essential role in suppressing autoimmunity. Consequently, there is significant interest in developing ways to boost or restore the function of Tregs in order to prevent or treat autoimmunity, induce tolerance, and thus reduce the reliance on broadly immunosuppressive agents. Strategies include enhancing the numbers and/or function of Tregs directly in vivo or via adoptive cell therapy. Here, we review recent advances in our understanding of how pharmacologic approaches can be applied to enhance Treg function in vivo through repurposing of established drug therapies or application of new therapies. Specifically, we discuss the potential of Treg-promoting drugs, including interleukin-2 and its derivatives, and tumor necrosis factor receptor 2 agonists, as well as Treg-preserving tyrosine kinase 2 inhibitors. We discuss how co-stimulatory blockade with CTLA-4 immunoglobulin affects tolerogenic environments and consider whether lymphodepleting therapies, such as antithymocyte globulin and teplizumab, might be needed to condition the environment for better Treg-promoting effects. We focus on the potential application of Treg-promoting drugs in type 1 diabetes and draw on evidence from transplantation. With multiple pharmacotherapeutic strategies to optimize Tregs in vivo, there is significant promise for new approaches to effectively and durably induce autoimmune disease remission.
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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 | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".