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Review of Cytotoxic CA4 Analogues that Do Not Target Microtubules: Implications for CA4 Development

2016· review· en· W2409633283 on OpenAlexaff
Daniel Tarade, Siyaram Pandey, James McNulty

Bibliographic record

VenueMini-Reviews in Medicinal Chemistry · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsMcMaster UniversityUniversity of Windsor
Fundersnot available
KeywordsCytotoxic T cellMicrotubuleChemistryCell biologyBiologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

BACKGROUND: One of the most studied anti-cancer compounds of the last several decades is the microtubule targeting agent and cis-stilbene, combretastatin A4 (CA4). Despite promising results at the pre-clinical level, future clinical use of CA4 as a monotherapy is in question due to metabolic vulnerability and conformational instability. OBJECTIVE: Thus, medicinal chemists have focused on synthesizing derivatives with improved pharmokinetic profile. One common strategy has been the incorporation of the ethylene linker into a ring system, thus preventing the isomerization of CA4 into the virtually inactive trans-isomer. Although many structurally stable and potent analogues of CA4 have been designed and synthesized, several analogues have been discovered to possess anti-proliferative properties seemingly independent of microtubule targeting. The presence of such analogues suggests that CA4 may also possess nonmicrotubule targets, which reveals the necessity for future structure activity relationship studies and optimization of any non-microtubule targeting. Furthermore, analogues of CA4 not inhibiting microtubule polymerization can no longer be assumed to be inactive. CONCLUSION: Future clinical development of the CA4 pharmacophore requires that attention should be paid to abnormal CA4 analogues, which appear to retain cytotoxicity independent of canonical microtubule inhibition.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.349
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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