NOVEL 3-BASE OLIGONUCLEOTIDE THERAPEUTIC SOF-SKN™ ANTAGONISES TLR7/8 IN AUTOIMMUNE SKIN DISEASE
Bibliographic record
Abstract
PV063 / #180 Poster Topic: AS07 - Cutaneous Lupus Background/Purpose Toll-like receptors (TLRs) 7 and 8 are innate immune sensors that trigger inflammatory cytokine and Type I interferon (IFN) production in response to short RNA fragments. Several recent studies have established that aberrant TLR7 activation is definitively linked to human lupus. We have discovered that synthetic, RNA-like 3-base oligonucleotides can be designed to bind to TLR7 and TLR8 with low nanomolar potency to effectively block their activation by RNA and synthetic agonists. Methods Based on this discovery, we undertook medicinal chemistry studies on select 3-base 2′-OMe oligonucleotides to generate therapeutics that harness TLR7 and TLR8 antagonism, leading to the development of an oligonucleotide drug candidate with dual antagonistic activities on human TLR7 and TLR8 (named SOF-16). Results We have previously reported that topical pretreatment with our most potent murine TLR7 inhibitory oligonucleotide greatly ameliorated skin inflammation and reduced proinflammatory gene expression in the skin of mice treated with Aldara™ cream (containing the TLR7 agonist imiquimod). Using a multistep formulation development process beginning with excipient screening and followed by extensive characterization, including stability, in vitro permeation and release testing, and biological activity assays, we have now developed a proprietary topical formulation that uses Pharmacopoeia-compliant excipients to provide sustained and durable delivery of SOF-16 directly to TLR7/8-expressing immune cells in the dermis. SOF-16 formulated as SOF-SKN™ was able to blunt an inflammatory gene signature associated with Aldara™-driven TLR7 activation in mice. We have also generated crucial preclinical data around the pharmacokinetics, toxicity profile, and safety pharmacology of SOF-SKN™. Conclusions Our results establish that select 3-base oligonucleotides can be rationally designed to effectively antagonize TLR7/8 and dampen skin autoimmunity. SOF-16 formulated as SOF-SKN™ is currently in preclinical development as a first-line topical treatment for cutaneous lupus erythematosus (CLE), with a Phase I clinical trial – HERACLES ( H arnessing E ndogenous R egulators A gainst CLE Study) – slated to begin in Australia in 2025.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".