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Record W4392137584 · doi:10.1021/acs.jmedchem.3c01532

Toward a Template for Synthetic Actin-Targeting Macrolide Analogues That Inhibit Cancer Cell Invasiveness

2024· article· en· W4392137584 on OpenAlexafffund
Daria N. Trofimova, Madhu Aeluri, Kirana Devarahosahalli Veeranna, Yun Jiang, Rebecca L. Grange, Bhavin V. Pipaliya, Murugan Subaramanian, Andrew W. Craig, P. Andrew Evans, John S. Allingham

Bibliographic record

VenueJournal of Medicinal Chemistry · 2024
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChemistryExtracellular matrixA549 cellCancer cellActinCancerSubstituentExtracellularMotilityCellBiochemistryCancer researchCell biologyStereochemistryBiology

Abstract

fetched live from OpenAlex

Actin barbed end-binding macrolides have been shown to inhibit cancer cell motility and invasion of extracellular matrix (ECM), evoking their potential utility as therapies for metastatic cancers. Unfortunately, the direct use of these compounds in clinical settings is impeded by their limited natural abundance, challenging total synthesis, and detrimental effects on normal tissues. To develop potent analogues of these compounds that are simpler to synthesize and compatible with cell-specific targeting systems, such as antibodies, we designed over 20 analogues of the acyclic side chain (tail) of the macrolide Mycalolide B. These analogues probed the contributions of four distinct regions of the tail towards the inhibition of actin polymerization and ECM invasion by human lung cancer A549 cells. We observed that two of these regions tolerate considerable substituent variability, and we identified a specific combination of substituents that leads to the optimal inhibition of the ECM invasion activity of A549 cells.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.049
GPT teacher head0.321
Teacher spread0.272 · 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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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