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Record W2996367240 · doi:10.1039/c9md00442d

Targeting prenylation inhibition through the mevalonate pathway

2019· review· en· W2996367240 on OpenAlexafffund
Pimyupa Manaswiyoungkul, Elvin D. de Araujo, Patrick T. Gunning

Bibliographic record

VenueRSC Medicinal Chemistry · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsUniversity of TorontoCanadian Celiac AssociationToronto Public Health
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCanada Research ChairsCanada Foundation for InnovationOntario Research Foundation
KeywordsPrenylationMevalonate pathwayChemistryPharmacologyMedicineBiochemistryEnzymeBiosynthesis

Abstract

fetched live from OpenAlex

Protein prenylation is a critical mediator in several diseases including cancer and acquired immunodeficiency syndrome (AIDS). Therapeutic intervention has focused primarily on directly targeting the prenyltransferase enzymes, FTase and GGTase I and II. To date, several drugs have advanced to clinical trials and while promising, they have yet to gain approval in a medical setting due to off-target effects and compensatory mechanisms activated by the body which results in drug resistance. While the development of dual inhibitors has mitigated undesirable side effects, potency remains sub-optimal for clinical development. An alternative approach involves antagonizing the upstream mevalonate pathway enzymes, FPPS and GGPPS, which mediate prenylation as well as cholesterol synthesis. The development of these inhibitors presents novel opportunities for dual inhibition of cancer-driven prenylation as well as cholesterol accumulation. Herein, we highlight progress towards the development of inhibitors against the prenylation machinery.

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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.032
GPT teacher head0.288
Teacher spread0.255 · 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

Citations20
Published2019
Admission routes2
Has abstractyes

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