A Novel 2,3-Derivative of 4,5,6,7-Tetrahydrobenzothiophene RORγt Inverse Agonist to Target Th17 Rejection in a Sensitized Mouse Skin Allograft Model
Bibliographic record
Abstract
🧾 AbstractIntroduction: Th17 and IL17 play a critical role in acute and chronic antibody mediated allograft rejection and increased graft loss. The retinoic acid receptor-related orphan receptor gamma t (RORγt) is a nuclear receptor and a master regulator of Th17 and other IL17+ cells of the immune system. The modulators of RORγt have been clinically tested in autoimmune diseases; however, they showed unacceptable liver and lymphatic tissues toxicity. Lipophilic efficiency, low hepatic clearance, high lymphatic/plasma drug concentration are the major contributing factors. We have recently designed novel inverse agonists against RORγt. The inverse agonists demonstrated a potent in vitro activity against Il-17 expression in-vitro Th17 lymphocyte polarization assay. Further, we tested the compounds in liver cirrohsis model and they showed an acceptable liver toxicity profile. We wanted to determine if it would delay skin allograft rejection in a sensitized mouse model. Methods: C57BL/6 mice were sensitized by administration of 107 Balb/c splenocytes (IP) at day 0, 7 and 14 and transplanted with Balb/c skin grafts at day fifteen. Mice were injected daily with RORγt inhibitor TF-S14 (1mg/kg, IP), or tacrolimus (0.5mg/kg, IP) or combination. Modified ASEPSIS score for mice was used to assess wound inflammation. Graft survival was evaluated daily for rejection end point (100% necrosis) and were sampled at day five for histology. Results: TF-S14 prolonged median graft survival from 6 to 13.5 and from 7 to 23 days when combined with tacrolimus, Figure 1. It reduced wound score 4 folds either alone or combined with tacrolimus compared to vehicle treated or tacrolimus treated mice, P<0.05. Neutrophilic infiltration of grafts decreased in TF-S14, or combination therapy compared to vehicle or tacrolimus treated mice, Figure 2. Lymphocytic infiltration (CD3+) decreased in TF-S14 and was absent in combination treated mice. Conclusion: The novel RORγt inhibitor TF-S14 prolongs graft survival in sensitized mouse skin allograft model through the inhibition of Th17 cellular and antibody medicated responses. The novel agent brings a new hope to treat rejection in highly sensitized patients who stay on waiting lists for years to find a suitable donor match.📁 File DescriptionFouda_Abstract_FG_2023_P42.pdf — Scanned page (p. 30) from the printed Proc. Fraser N. Gurd Surg. Res. Forum 2023 booklet.📍 Conference and MetadataPresented at: 33rd Annual Fraser N. Gurd Surgical Research Forum 2023 Date: June 8, 2023 Location: Montreal, QC, CanadaAbstract Category: Laboratory Science Presentation Type: Oral PresentationAuthors: Ahmed Fouda, Sarita Negi, Steven Paraskevas, Jean Tchervenkov Affiliation: Department of Experimental Surgery, McGill University, Montreal, QC, Canada📚 Full CitationFouda, A.; Maallah, M. T.; Negi, S.; Paraskevas, S.; Tchervenkov, J. A Novel 2,3-Derivative of 4,5,6,7-Tetrahydrobenzothiophene RORγt Inverse Agonist to Target Th17 Rejection in a Sensitized Mouse Skin Allograft Model. In Proc. Fraser N. Gurd Surg. Res. Forum 2023; p. 30. 33rd Annual Fraser N. Gurd Surgical Research Forum, Montreal, QC, Canada, June 8, 2023. DOI: 10.6084/m9.figshare.30422527 © 2023, Ahmed Fouda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".