Abstract LB-087: Discovery and development of first-in-class orally bioavailable USP19 inhibitors
Bibliographic record
Abstract
Abstract Over the past decade, protein ubiquitination has emerged as an important post-translational modification with regulatory functions in all important cellular processes. Deubiquitinating enzymes (DUBs) including ubiquitin specific proteases (USPs) catalyse the de-ubiquitination of protein substrates, hence regulating their levels and/or function. As a result of their increasing implications in the aetiology of numerous pathological conditions including cancer, neurodegeneration and metabolic disorders, DUBs represent an attractive and promising target class for the development of innovative medicines with high therapeutic impact. However, despite 15 years of research DUBs have proved largely refractory to drug discovery efforts. As a result of genetic and other validation studies, USP19 has recently emerged as a potentially important target in muscular atrophy associated with various conditions including cancer, as well as in other disorders involving aberrant protein quality control. Herein, we describe the application of our Ubi-Plex™ drug discovery platform to the identification and optimisation of first in class USP19 inhibitors. Several series of novel, highly potent (e.g. IC50 < 5.0 nM) and reversible USP19 inhibitors have been identified. Further profiling has demonstrated excellent selectivity against a large panel of DUBs and other non-related enzymes (e.g. kinases, proteases). These inhibitors are cell-permeable and exhibit potent target engagement in both cancer and muscle cells with EC50 values < 30 nM. We will describe the development of lead molecules with drug-like properties which have allowed us to establish pharmacological target validation by demonstrating efficacy in a muscle wasting model in vivo. Recent developments in the programme leading to orally available USP19 inhibitors will also be presented. This work further exemplifies the tractability of the DUB target family and reports the discovery and detailed profiling of first-in-class inhibitors of USP19. These findings support the rationale to target USP19 for debilitating muscle wasting disorders associated with various conditions such as cancer, as well as for potentially other therapeutic indications, particularly those associated with aberrant protein quality control. Citation Format: Xavier Jacq, Gerald Gavory, Colin O'Dowd, Aaron Cranston, Oliver Baker, Christina Bell, Stephanie Burton, Eamon Cassidy, Joana Costa, Ashling Henderson, Matthew Helm, Peter Hewitt, Caroline Hughes, Mary McFarland, Hugues Miel, Lauren Proctor, Shane Roundtree, Rachel Church, Ewelina Rozycka, Mark Wappett, Steven Whitehead, Tim Harrison, Nathalie Bedard, Simon S. Wing. Discovery and development of first-in-class orally bioavailable USP19 inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-087.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".