SMAC mimetics and RIPK inhibitors as therapeutics for chronic inflammatory diseases
Bibliographic record
Abstract
New therapeutic approaches for chronic inflammatory diseases such as inflammatory bowel disease, rheumatoid arthritis, and psoriasis are needed because current treatments are often suboptimal in terms of both efficacy and the risks of serious adverse events. Inhibitor of apoptosis proteins (IAPs) are E3 ubiquitin ligases that inhibit cell death pathways and are themselves inhibited by second mitochondria-derived activator of caspases (SMAC). SMAC mimetics (SMs), small-molecule antagonists of IAPs, are being evaluated as cancer therapies in clinical trials. IAPs are also crucial regulators of inflammatory pathways because they influence both the activation of inflammatory genes and the induction of cell death through the receptor-interacting serine-threonine protein kinases (RIPKs), nuclear factor κB (NF-κB)-inducing kinase, and mitogen-activated protein kinases (MAPKs). Furthermore, there is an increasing interest in specifically targeting the substrates of IAP-mediated ubiquitylation, especially RIPK1, RIPK2, and RIPK3, as druggable nodes in inflammation control. Several studies have revealed an anti-inflammatory potential of RIPK inhibitors that either block inflammatory signaling or block the form of inflammatory cell death known as necroptosis. Expanding research on innate immune signaling through pattern recognition receptors that stimulate proinflammatory NF-κB and MAPK signaling may further contribute to uncovering the complex molecular roles used by IAPs and downstream RIPKs in inflammatory signaling. This may benefit and guide the development of SMs or selective RIPK inhibitors as anti-inflammatory therapeutics for various chronic inflammatory conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".