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Record W2977275111 · doi:10.1101/793109

Phenylethynylbenzyl-Modified Biguanides Inhibit Pancreatic Cancer Tumor Growth

2019· preprint· en· W2977275111 on OpenAlexafffund
Audrey Hébert, Maxime Parisotto, Marie‐Camille Rowell, Alexandra Doré, Guillaume Lefrançois, Paloma Kalegari, Gerardo Ferbeyre, Andreea R. Schmitzer

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de MontréalFonds Québécois de la Recherche sur la Nature et les TechnologiesCanadian Institutes of Health ResearchCancer Research Society
KeywordsIn vivoMetforminPancreatic cancerIn vitroMechanism of actionMitochondrionGrowth inhibitionChemistryCancerNAD+ kinasePharmacologyCancer researchIC50BiochemistryInternal medicineBiologyEndocrinologyMedicineDiabetes mellitusEnzyme

Abstract

fetched live from OpenAlex

Abstract We present the design and synthesis of a small library of substituted biguanidium salts and their capacity to inhibit the growth/viability of pancreatic cancer cells. We first present their in vitro and membrane activity, before we address their mechanism of action in living cells and in vivo activity. We show that phenylethynyl biguanidium salts possess higher ability to cross hydrophobic barriers, improved mitochondrial accumulation and anticancer activity. Mechanistically, the most active compound 1b , like metformin, decreases the NAD + /NADH ratio and mitochondrial respiration, but at 800-fold lower concentration. In vivo studies with, the most active, compound 1b show a significant growth inhibition of pancreatic cancer xenografts in mice, while biguanides currently in clinical trials had no activity.

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 score0.005

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)0.0010.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicMetabolism, Diabetes, and Cancer→French-language works237,207→