Dose–Response Evaluation of Novel RORγt Inhibitors by Low-Throughput TR-FRET and Phenotypic Assays.
Bibliographic record
Abstract
This abstract was presented at the Vth International Drug Discovery and Development Forum 2019, 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. Correction notice: 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. Presented at: Vth International Drug Discovery and Development Forum 2019 Presented on: October 2, 2019 Location: Montreal, QC, Canada Category: Mechanism of Drug Action Presentation Type: Oral and Poster Presentation Authors: Ahmed Fouda, Sarita Negi, Steven Paraskevas, Jean Tchervenkov Affiliation: Division of Surgical and Interventional Sciences (formerly Department of Experimental Surgery), Department of Surgery, McGill University, Montreal, QC, Canada 📚 Full Citation 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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".