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Abstract A152: Emerging indications for kinase-targeted therapies: screening for new targets

2018· article· en· W2885983462 on OpenAlexaff
Rick Li, Shenshen Lai, Allan K. Mah, Hong Zhang, Jun Yan, A. Marotta, Zaihui Zhang

Bibliographic record

VenueMolecular Cancer Therapeutics · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsSignalChem (Canada)
Fundersnot available
KeywordsKinomeKinaseVandetanibTyrosine kinaseReceptor tyrosine kinaseMedicinePharmacologyCabozantinibCancer researchSorafenibBiologyCancerBiochemistrySignal transductionInternal medicine

Abstract

fetched live from OpenAlex

Abstract Protein kinases play key roles in normal physiologic functions. Aberration of individual protein kinases and the pathways that these enzymes govern are centrally involved in the pathogenesis of a number of diseases including cancer, neurologic disorders, metabolic diseases, and autoimmune conditions. Targeting these enzymes using small-molecule approaches has been proven to be highly relevant in terms of treating cancer and, more recently, autoimmune conditions like rheumatoid arthritis. Evaluating the specificities of these compounds across the kinome and relating these effects with clinical manifestations may provide important insights into how these compounds can be used to treat other diseases. In this study, we evaluated the specificity of four compounds–saracatinib, vandetanib, crizotinib, and cabozantinib–across a panel of 48 kinases representing 7 major groups and other kinases. They were assessed at 1 and 10 µM using the “gold standard” radiometric assay. A 50% reduction in kinase activity at the single point concentration at 1 μM was deemed to be of relevance in this study as that would signify an IC50 of less than 1 µM. The study demonstrated that all four compounds inhibited greater than 30% of the kinases within this 48-kinase panel, albeit different kinase targets. Interestingly, none of the selected kinases within AGC, CAMK, and CGMC subgroups was inhibited more than 50% at 1 μM. Furthermore, all 4 inhibitors exhibited greatest potency against their intended kinase targets, mainly receptor and cytoplasmic tyrosine kinases, RTKs, and CTKs, respectively. Specifically, saracatanib inhibited PDGFR, KIT, EGFR, and BTK; vandetanib inhibited FGFR2 and KDR, an RTK that interacts with the ligand VEGF; crizotinib inhibited insulin receptor (InsR); and cabozantinb had effect on DDR2, FLT1, FLT3, KDR, MET, and TRKA. In conclusion, we have demonstrated the power of using unbiased protein kinase panels to uncover novel biochemical interactions with potential therapeutic values. Of the four inhibitors tested above, we have found several new targets for each, including an interaction between crizotinib and insulin receptor, an RTK involved in metabolic homeostasis. This opens up possible implications for crizotinib in regulating metabolic homeostasis. Citation Format: Rick Li, Shenshen Lai, Allan Mah, Hong Zhang, Jun Yan, Anthony Marotta, Zaihui Zhang. Emerging indications for kinase-targeted therapies: screening for new targets [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A152.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.062
GPT teacher head0.377
Teacher spread0.316 · 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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Citations0
Published2018
Admission routes1
Has abstractyes

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