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Record W4399766048 · doi:10.32920/26052790

Synthesis of Diazaspirocycles for MRCK Inhibitors as Anti-cancer Agents

2024· preprint· en· W4399766048 on OpenAlexaff
Vanessa Ruscetta

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicSynthesis and Biological Evaluation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCancerPharmacologyChemistryCancer researchMedicineInternal medicine

Abstract

fetched live from OpenAlex

<p>In the quest for new therapeutics targeting the metastatic spread of cancer, the synthesis of novel inhibitors for underexplored kinases is at the forefront. Recently, potent and selective inhibitors for a new cancer therapy target in the myotonic dystrophy-related Cdc42-binding kinases (MRCK) have been discovered for the treatment of aggressive cancers. The inhibitors feature a diazaspirocycle that significantly influences potency. While spirocycles are privileged scaffolds in medicinal chemistry, synthetic methods for their construction are limited, and asymmetric methods are in demand to access single enantiomers for biological applications. This work highlights the design and synthesis of diazaspirocycles for MRCK inhibition, focusing on the development of efficient asymmetric methods. Substituted α-amino nitriles are employed as key building blocks to expand the parent heterocycle into the desired bicyclic structure through haloalkylation, reductive cyclization, or direct reductive amination. Chiral directing groups, including naturally occurring menthol and amino alcohols, are assessed for their ability to access the desired enantiomer necessary for MRCK inhibition. The synthetic pathway designed can be applied to a wide range of substrates to create novel spirocyclic inhibitors in the future. As a result, the asymmetric construction of innovative spirocycles for MRCK inhibition opens the door for the discovery of modern therapeutics targeting the spread of aggressive cancers.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.339
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.068
GPT teacher head0.346
Teacher spread0.278 · 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.

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
Published2024
Admission routes1
Has abstractyes

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