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Discovery of Selective Tertiary Amide Inhibitors of Cyclin-Dependent Kinase 2 (CDK2)

2023· article· en· W4386132087 on OpenAlexaff
Mingshuo Zeng, Jessica M. Grandner, Marian C. Bryan, Vishal Verma, Robin Larouche‐Gauthier, Jean‐Philippe Leclerc, Liang Zhao, Pouyan Haghshenas, Samuel Aubert‐Nicol, Arun A. Yadav, Melissa Ashley, Jacob Z. Chen, Matthew R. Durk, Karen E. Samy, Marika Nespi, Elizabeth Levy, Karl A. Merrick, John G. Moffat, Jeremy Murray, Angela Oh, Christine Orr, Ehud Segal, Jessica Sims, Christopher J. Sneeringer, Madeleine Prangley, Steffan Vartanian, Steven Magnuson, Brendan T. Parr

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsParaza Pharma (Canada)
FundersNational Institute of General Medical SciencesGenentech
KeywordsCyclin-dependent kinaseCyclin-dependent kinase 2KinaseContext (archaeology)Small moleculeChemistryDrug discoveryCell growthCell cycleCombinatorial chemistryCancer researchBiochemistryBiologyCellProtein kinase A

Abstract

fetched live from OpenAlex

Cyclin-dependent kinases (CDKs) are key regulators of the cell cycle and are frequently altered in cancer cells, thereby leading to uncontrolled proliferation. In this context, CDK2 has emerged as an appealing target for anticancer drug development. Herein, we describe the discovery of a series of selective small molecule inhibitors of CDK2 beginning with historical compounds from our ERK2 program (e.g., compound 6 ). Structure-based drug design led to the potent and selective tool compound 32, where excellent selectivity against ERK2 and CDK4 was achieved by filling the lipophilic DFG-1 pocket and targeting interactions with CDK2-specific lower hinge binding residues, respectively. Compound 32 demonstrated 112% tumor growth inhibition in mice bearing OVCAR3 tumors with 50 mg/kg bis in die (BID) oral dosing.

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 score0.009
Threshold uncertainty score0.783

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 head0.005
GPT teacher head0.228
Teacher spread0.222 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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Same venueACS Medicinal Chemistry LettersSame topicMicrotubule and mitosis dynamicsFrench-language works237,207