Dose–Response Evaluation of Novel RORγt Inhibitors by Low-Throughput TR-FRET and Phenotypic Assays
Bibliographic record
Abstract
🧾 AbstractBackground: Transplantation is considered the treatment of choice for end stage kidney disease, as it decreases mortality, morbidity and health-care costs compared to dialysis. However, more than 20% of kidney transplantation candidates are highly sensitized as defined by a high calculated panel reactive antibody (cPRA > 80%). These patients can develop an antibody mediated rejection (AMR) and less than 10% of them can get a successful transplantation. Growing evidence suggests the involvement of immunosuppressant resistant Th17 and Tc17 cells in orchestrating the rejection in highly sensitized patients and other cases of AMR. Differentiation of naïve T-cell into Th17 is regulated through the expression of retinoic acid receptor related orphan receptor gamma t (RORγt) nuclear receptor. Using rational drug design and virtual screening, we discovered a group of compounds that can inhibit RORγt and can decrease de novo generation of Th17 and Tc17 cells in PBMCs derived from highly sensitized patients. Objective: to determine the binding affinity and dose-response of novel modulators of RORγt using time resolved fluorescence energy transfer binding assay. Methods: we evaluated the binding of 23 compounds by time resolved fluorescence energy transfer of RORγt-LBD-biotinylated coactivator peptide complex. A known inhibitor of RORγt, GSK2981278, was used as a reference. Ten 2.3-fold dilutions per compound were tested and IC50 was determined for all the compounds. Additionally, we tested the effect of compounds on Th17 and Treg polarization phenotypic assays. Results: all the compounds tested inhibited RORγt-LBD coactivator binding at picomolar to nanomolar concentrations (IC50 < 100 nM). In Th17 polarization assay, two compounds were tested, and both showed a nanomolar inhibition of IL17A when used at 2, 15 nM concentration and reduced the percentage of Th17 cells by 20-30% (p < 0.05). Conclusion: we identified potent inhibitors of RORγt and can efficiently attenuate Th17 polarization. They can be used in-vivo to block T cells differentiation to Th17 and Tc17 and treat conditions associated with increased IL17 production.📁 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: Vth International Drug Discovery and Development Forum 2019 Presentation Date: October 2, 2019 Location: Montreal, QC, Canada Abstract Category: Mechanism of Drug Action Presentation Type: Oral and Poster 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. Dose–Response Evaluation of Novel RORγt Inhibitors by Low-Throughput TR-FRET and Phenotypic Assays. In Proc. Int. Drug Discov. Dev. Forum 2019; 5, 33–34 (Abstr. 23). Vth International Drug Discovery and Development Forum, Montreal, QC, Canada, October 2, 2019. DOI: 10.6084/m9.figshare.30421933 © 2019, 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".