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Novel Colchicine Derivatives and their Anti-cancer Activity

2017· review· en· W2586156640 on OpenAlexaff
Lorelei Johnson, Ing Swie Goping, Aja M. Rieger, Jonathan Y. Mane, Torin Huzil, Asok Banerjee, Richard F. Ludueña, Bashar Hassani, Philip Winter, Jack A. Tuszyński

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2017
Typereview
Languageen
FieldChemistry
TopicSynthesis and biological activity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPharmacophoreColchicineTubulinCancerCancer cell linesCancer cellComputational biologyChemistryCancer researchBiologyBiochemistryPharmacologyMicrotubuleCell biologyGenetics

Abstract

fetched live from OpenAlex

In this paper we provide an overview of the status of various colchicine derivatives in preclinical development with special focus on their anti-cancer activity. We discuss several groups of compounds that have been designed to differentially bind with specific affinities for tubulin β isotypes, especially in regard to βIII, which is commonly over-expressed in cancer. Computational prediction, protein-based and cell-based assays are summarized as well as some animal tests conducted on these compounds. It is concluded that an untapped potential exists for exploiting the colchicine scaffold as a pharmacophore with the possibility of increasing its affinity for tubulin isotypes overexpressed in cancer and decreasing it for normal cells thereby widening the therapeutic window.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.231
GPT teacher head0.420
Teacher spread0.189 · 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 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

Citations33
Published2017
Admission routes1
Has abstractyes

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