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Design and Synthesis of Pyrazole-Based Macrocyclic Kinase Inhibitors Targeting BMPR2

2023· article· en· W4380050493 on OpenAlexfundno aff
Jennifer Alisa Amrhein, Guiqun Wang, Benedict‐Tilman Berger, Lena M. Berger, Amalia D. Kalampaliki, Andreas Krämer, Stefan Knapp, Thomas Hanke

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhosphodiesterase function and regulation
Canadian institutionsnot available
FundersGenentechJanssen BiotechInnovative Medicines InitiativeH2020 HealthNational and Kapodistrian University of AthensDeutschen Konsortium für Translationale KrebsforschungOntario Genomics InstituteTakeda Pharmaceutical CompanyEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAOntario GenomicsGenome CanadaBayerDeutsches KrebsforschungszentrumBristol-Myers SquibbPfizerMcGill UniversityBoehringer Ingelheim
KeywordsBMPR2ReceptorTyrosine kinaseKinaseChemistryActivin receptorG protein-coupled receptorCancer researchPhosphorylationBiochemistryPharmacologyCell biologyBiologyBone morphogenetic protein

Abstract

fetched live from OpenAlex

Bone morphogenetic protein (BMP) signaling is mediated by transmembrane protein kinases that form heterotetramers consisting of type-I and type-II receptors. Upon BMP binding, the constitutively active type-II receptors activate specific type-I receptors by transphosphorylation, resulting in the phosphorylation of SMAD effector proteins. Drug discovery in the receptor tyrosine kinase-like (TKL) family has largely focused on type-I receptors, with few inhibitors that have been published targeting type-II receptors. BMPR2 is involved in several diseases, most notably pulmonary arterial hypertension, but also contributes to Alzheimer’s disease and cancer. Here, we report that macrocyclization of the promiscuous inhibitor 1, based on a 3-amino-1 H -pyrazole hinge binding moiety, led to a selective and potent BMPR2 inhibitor 8a .

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 score0.006
Threshold uncertainty score0.549

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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Same venueACS Medicinal Chemistry LettersSame topicPhosphodiesterase function and regulationFrench-language works237,207