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Record W7132881131

Hijacking Cancer Signaling: First-in-class JPRK-targeting Ligands for Fibrolamellar Hepatocellular Carcinoma

2022· dissertation· W7132881131 on OpenAlexaff
Tudor B. Radu

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAllosteric regulationIn silicoFusion proteinLigand (biochemistry)KinaseCancerHepatocellular carcinomaMutantMutationDrug discovery
DOInot available

Abstract

fetched live from OpenAlex

Described herein is a drug screening project focused on an oncoprotein beginning with in silico fragment screening against an un-drugged target and concluding with a collaboration to evaluate single digit nanomolar potent and >500-fold selective drug candidates in patient derived xenografts. Fibrolamellar hepatocellular carcinoma is a rare un-treated liver cancer driven by a fusion kinase known as JPRK, a mutant of protein kinase A (PKA). Targeting this fusion kinase selectively is challenging due to the structural similarity of the catalytic domain to other kinases, most significantly, PKA, whose function regulates cardiac activity. In silico screening of the predicted binding of 10,000 fragments to an allosteric site on JPRK was used to advance 51 fragments for in vitro for potency and selectivity and led to a hit compound. A subsequent Structure Activity Relationship (SAR) guided by biophysical assays led to an 8 nM KD inhibitor with > 500-fold selectivity against PKA. Compounds were also developed which selectively kill oncogenic liver cells which bear the JPRK mutation over the same cell line without this mutation. Crystallography and NMR provided atomic-level resolution of how the ligand was binding in a unique allosteric site, leading to allosteric signal transmission through the protein, and the subsequent change in protein-conformation. Furthermore, alongside this change in conformation, key protein-protein interactions with onco-proteins, such asSTAT5B and HDAC6, were perturbed. Target engagement studies confirmed that the compounds were not only altering the protein-protein interaction with HDAC6, but also hampering its phosphorylation. To further probe how the compounds selectively trigger cell death, global proteomics were performed studies to examine the effects of the ligands, and phosphorylation events on key protein-protein interactions. The JPRK-specific drugs downregulated proteins involved in metabolism and the Warburg effect while upregulating critical epigenetic apoptosis drivers leading to cancer cell death. These compounds will be advanced for mouse safety, pharmacokinetics, and patient derived xenograft studies.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.335
Teacher spread0.315 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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