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Record W2529828015 · doi:10.1021/acs.jmedchem.6b01264

Antitumor Activity of Lankacidin Group Antibiotics Is Due to Microtubule Stabilization via a Paclitaxel-like Mechanism

2016· article· en· W2529828015 on OpenAlexafffund
Ahmed T. Ayoub, Rabab M Abou El-Magd, Jack Xiao, Cody W. Lewis, Tatiana Martins Tilli, Kenji Arakawa, Yosi Nindita, Gordon K. Chan, Luxin Sun, Mark Glover, Mariusz Kłobukowski, Jack A. Tuszyński

Bibliographic record

VenueJournal of Medicinal Chemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCancer Research Society
KeywordsPaclitaxelChemistryMicrotubuleAntimicrobialMechanism of actionTubulinAntibioticsCombinatorial chemistryPeptideMode of actionStereochemistryBiochemistryBiophysicsChemotherapyCell biologyIn vitroOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Lankacidin group antibiotics show strong antimicrobial activity against various Gram-positive bacteria. In addition, they were shown to have considerable antitumor activity against certain cell line models. For decades, the antitumor activity of lankacidin was associated with the mechanism of its antimicrobial action, which is interference with peptide bond formation during protein synthesis. This, however, was never confirmed experimentally. Due to significant similarity to paclitaxel-like hits in a previous computational virtual screening study, we suggested that the cytotoxic effect of lankacidin is due to a paclitaxel-like action. In this study, we tested this hypothesis computationally and experimentally and confirmed that lankacidin is a microtubule stabilizer that enhances tubulin assembly and displaces taxoids from their binding site. This study serves as a starting point for optimization of lankacidin derivatives for better antitumor activities. It also highlights the power of computational predictions and their aid in guiding experiments and formulating rigorous hypotheses.

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 score0.086
Threshold uncertainty score0.456

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 head0.250
Teacher spread0.241 · 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

Citations32
Published2016
Admission routes2
Has abstractyes

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