Identification of Novel Modulators of RORγt by Rational Drug Design and Molecular Docking
Bibliographic record
Abstract
🧾AbstractIntroduction: Retinoic acid receptor–related orphan receptor gamma t (RORγt) is the master regulator of T helper 17 (Th17) cells, which are implicated in many autoimmune diseases and in solid-organ graft rejection. Research from our laboratory and others has shown that Th17 cells contribute to antibody-mediated rejection through the promotion of tertiary lymphoid tissue formation and the production of donor-specific antibodies. Th17 cells secrete IL-17A, IL-17F, IL-21, IL-22, and other cytokines that promote both Th1- and Th2-type inflammation, B-cell proliferation, antibody production, macrophage recruitment, and related inflammatory pathways. There are currently no systemic RORγt inhibitors in clinical trials registered in the NIH database, as previous candidates were discontinued due to liver toxicity. Objectives: To identify new inhibitors of RORγt and evaluate their in-vitro effects on PBMCs from highly sensitized hemodialysis patients and other transplantation candidates. Methods: Using a rational drug-design approach employing 2D/3D structural similarity and scaffold hopping, we identified new chemical entities with high binding affinity for the RORγt ligand-binding pocket, confirmed using two molecular-docking programs against theoretical natural substrates and reference inhibitors. Pharmacokinetics, toxicity, and drug-likeness were predicted using the SwissADME web tool. Two analogues were tested on PBMCs from healthy volunteers and from highly sensitized and non-sensitized hemodialysis patients in an in-vitro Th17 polarization assay. Results: Compounds TF-S1 and TF-S2 both acted as inverse agonists, significantly reducing IL-17, IL-21, and IL-22–producing cells (p < 0.05, n = 9; 150 < IC₅₀ < 750 nM). Both compounds inhibited Th17-associated cytokine production in PBMCs from healthy volunteers, confirming RORγt inverse agonist activity in primary human cells. Conclusion: Small-molecule inhibitors of RORγt attenuate Th17 responses and could be therapeutically beneficial in preventing and treating allosensitization in solid-organ transplantation. Significance: These novel compounds may represent safer alternatives to earlier RORγt inhibitors and could contribute to refining in-silico drug-discovery tools, thereby reducing reliance on animal testing. 📁 File Description The initial submission published in the conference proceedings and the final author-reprint, posted online on 22 October 2025.📍 Conference and MetadataPresented at: IVth International Drug Discovery and Development Forum 2018 Presentation Date: October 24, 2018 Location: Montreal, QC, Canada Abstract Category: Drug Design and Synthesis Presentation Type: Oral and PosterAuthors: Ahmed Fouda, Sarita Negi, Steven Paraskevas, Jean Tchervenkov Affiliation: Department of Experimental Surgery, McGill University, Montreal, QC, Canada📚 Full CitationFouda, A.; Negi, S.; Paraskevas, S.; Tchervenkov, J. Identification of Novel Modulators of RORγt by Rational Drug Design and Molecular Docking. In Proc. Int. Drug Discov. Dev. Forum 2018; 4, 12 (Abstr. 13). IVth International Drug Discovery and Development Forum, Montreal, QC, Canada, October 22–24, 2018. DOI: 10.6084/m9.figshare.30418093 © 2018, Ahmed Fouda.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".