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Abstract LB-087: Discovery and development of first-in-class orally bioavailable USP19 inhibitors

2019· article· en· W2954779234 on OpenAlexaff
Xavier Jacq, Gérald Gavory, C. O'Dowd, Aaron Cranston, O. K. Baker, Christina Bell, Stephanie G. Burton, Eamon Cassidy, Joana Costa, Ashling Henderson, Matthew Helm, Peter R. Hewitt, Caroline Hughes, Mary McFarland, Hugues Miel, Lauren Proctor, Shane Roundtree, Rachel J. Church, Ewelina Rozycka, Mark Wappett, Steven Whitehead, Tim J. Harrison, Nathalie Bédard, Simon S. Wing

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsDeubiquitinating enzymeDrug discoveryProteasesPharmacologyDrug developmentComputational biologyUbiquitinKinaseDruggabilityDrugBiologyBioinformaticsBiochemistryCancer researchMedicineEnzymeGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.044
GPT teacher head0.329
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations4
Published2019
Admission routes1
Has abstractyes

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