Dose–Response Evaluation of Novel RORγt Inhibitors by Low-Throughput TR-FRET and Phenotypic Assays.
Bibliographic record
Abstract
This abstract was presented at the <b>Vth International Drug Discovery and Development Forum 2019</b>, held in Montreal, QC, Canada, from 30 September to 2 October 2019. The work focuses on the dose–response evaluation of novel RORγt inhibitors using low-throughput time-resolved fluorescence resonance energy transfer (TR-FRET) and phenotypic assays. The study describes a set of compounds identified through rational drug design and virtual screening that inhibit the nuclear receptor retinoic acid receptor–related orphan receptor gamma t (RORγt), a key regulator of Th17 differentiation in transplantation and autoimmune disorders. These compounds were evaluated for binding affinity, potency, and biological activity using PBMCs from highly sensitized renal-transplant candidates. <b>Correction notice: </b>In this reprint version, typographical and data-alignment issues from the original proceedings have been corrected. Clarifications were made in the Results section to specify the precise range of inhibitory concentrations (IC₅₀ < 100 nM) and to correct minor formatting inconsistencies. No scientific findings were altered. <b>Presented at:</b> Vth International Drug Discovery and Development Forum 2019 <b>Presented on: </b>October 2, 2019 <b>Location:</b> Montreal, QC, Canada <b>Category:</b> Mechanism of Drug Action <b>Presentation Type: </b>Oral and Poster Presentation <b>Authors:</b> Ahmed Fouda, Sarita Negi, Steven Paraskevas, Jean Tchervenkov <b>Affiliation:</b> Division of Surgical and Interventional Sciences (formerly Department of Experimental Surgery), Department of Surgery, McGill University, Montreal, QC, Canada <b>📚 Full Citation </b>Fouda, 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. McGill University. DOI: 10.6084/m9.figshare.30421933. © 2019, Ahmed Fouda.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.066 | 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 teacher head, 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".