RORγt: A Drug Target in Organ Transplantation — Identification of Novel Modulators of RORγt by Rational Drug Design and Molecular Docking
Bibliographic record
Abstract
🖼️ Poster DescriptionThis poster was presented in the 4th International Drug Discovery and Development Forum 2018. It integrates computational and experimental drug discovery approaches aimed at identifying novel small-molecule modulators of the retinoic acid receptor-related orphan receptor gamma t (RORγt), a transcription factor that orchestrates T helper 17 (Th17) cell differentiation and cytokine secretion. Dysregulated Th17 responses are strongly implicated in antibody-mediated rejection (AMR) and chronic allograft dysfunction through the formation of tertiary lymphoid tissue and the secretion of pro-inflammatory cytokines such as IL-17A, IL-21, and IL-22. Targeting RORγt therefore represents a promising immunomodulatory strategy in transplantation. The project implements a rational drug design workflow combining multiple stages of in-silico and in-vitro screening. Over 90 million compounds from the PubChem chemical library and 152 RORγt bioassays are analyzed to identify potential RORγt inverse agonists. Computational steps include 2D/3D fingerprint similarity screening, molecular docking, property calculation, and virtual ADMET filtering to prioritize candidates with optimal predicted pharmacokinetics and safety profiles. Two 2,3-Derivatives of 4,5,6,7-Tetrahydrobenzothiophene compounds, TF-S1 and TF-S2, emerge as promising hit molecules with strong binding affinity for the RORγt ligand-binding domain and favorable drug-likeness characteristics. Their biological activity is validated using in-vitro Th17 polarization assays based on peripheral blood mononuclear cells (PBMCs) from highly sensitized (HSP) and nonsensitized (NSP) transplant patients, as well as healthy volunteers (HV). The cultures are stimulated with IL-1β, IL-6, IL-23, and TGF-β over 16 days, followed by intracellular cytokine staining and multicolor flow cytometry to quantify CD4⁺IL-17A⁺, CD4⁺IL-21⁺, and CD4⁺IL-22⁺ populations. Treatment with TF-S1 and TF-S2 significantly attenuates Th17 polarization and cytokine expression, with inhibitory effects comparable to those observed with cyclosporine A (CsA) and dexamethasone (DEX). The response is dose-dependent and consistent across patient groups, supporting the hypothesis that these compounds act as functional RORγt inverse agonists. Statistical analysis using two-way ANOVA with Tukey’s or paired t-tests confirms significance (p < 0.05). The findings demonstrate that selective inhibition of RORγt can effectively modulate Th17-mediated immune activation, offering a potential therapeutic approach for reducing allosensitization, AMR, and chronic graft inflammation. Beyond its immediate biological results, the poster emphasizes the power of integrating computational chemistry, virtual pharmacology, and cellular immunology to accelerate small-molecule discovery in transplantation research.📍 Conference and MetadataConference: IVth International Drug Discovery and Development Forum 2018 Presentation Date: 24 October 2018 Location: Montreal, QC, CanadaPoster Category: Drug Design and Synthesis Authors: 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. RORγt: A Drug Target in Organ Transplantation — Identification of Novel Modulators of RORγt by Rational Drug Design and Molecular Docking. Poster presented at the IVth International Drug Discovery and Development Forum 2018, Montreal, QC, Canada, October 22–24, 2018; Online publication date: October 22nd 2025. DOI: 10.6084/m9.figshare.30415822 © 2025, 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".