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Record W4409148488 · doi:10.1111/cbdd.70101

The Development of Selective Chemical Probes for Serine Arginine Protein Kinase 3

2025· article· en· W4409148488 on OpenAlexafffund
Danielle Hanke, Conall McCutcheon, Brent D. G. Page

Bibliographic record

VenueChemical Biology & Drug Design · 2025
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundKarolinska InstitutetNatural Sciences and Engineering Research Council of CanadaMichael Smith Health Research BCScience for Life LaboratoryVetenskapsrådet
KeywordsIn silicoKinaseSerineBiochemistryDocking (animal)ArginineDrug discoveryBreast cancerIC50ChemistryPhosphorylationBiologyComputational biologyCancerAmino acidIn vitroMedicineGeneticsGene

Abstract

fetched live from OpenAlex

ABSTRACT The serine arginine protein kinases (SRPKs) are a family of kinases whose irregular function is implicated in cancer and viral infections. While the roles of SRPK1 and SRPK2 in disease are well established, much less is known about SRPK3. There are several studies implicating SRPK3 in breast cancer, but the mechanism is still unknown. This work describes the first‐reported SRPK3 chemical probes that show excellent selectivity over the other SRPKs. 1‐(4‐cyanophenyl)‐3‐phenylurea was identified as an initial hit for SRPK3 through a kinase screen. Subsequent rounds of in silico docking, medicinal chemistry optimization, and biochemical assays were performed to increase its potency and selectivity for SRPK3. Six top compounds were identified that displayed single digit micromolar IC 50 values in SRPK3 activity assays and negligible inhibition of SRPK1 or SRPK2. These six compounds demonstrated impairment of breast cancer cell viability that correlated with their biochemical IC 50 values, suggesting that they can be used as tools to study the biological functions of SRPK3 in breast cancer. With an enhanced understanding of SRPK3's biological function, it may emerge as a meaningful drug target, wherein our top inhibitors could be further optimized to produce novel cancer therapeutics.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.016
GPT teacher head0.278
Teacher spread0.262 · 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 teacher head, 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

Citations1
Published2025
Admission routes2
Has abstractyes

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