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Record W2987822484 · doi:10.3390/molecules24213961

Medically Useful Plant Terpenoids: Biosynthesis, Occurrence, and Mechanism of Action

2019· review· en· W2987822484 on OpenAlexafffund
Matthew E. Bergman, Benjamin B. Davis, Michael A. Phillips

Bibliographic record

VenueMolecules · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsTerpenoidMevalonate pathwayBiosynthesisArtemisininMechanism (biology)ChemistryMechanism of actionBiologyNatural productComputational biologyBiochemistryGeneIn vitroMalariaPlasmodium falciparum

Abstract

fetched live from OpenAlex

Specialized plant terpenoids have found fortuitous uses in medicine due to their evolutionary and biochemical selection for biological activity in animals. However, these highly functionalized natural products are produced through complex biosynthetic pathways for which we have a complete understanding in only a few cases. Here we review some of the most effective and promising plant terpenoids that are currently used in medicine and medical research and provide updates on their biosynthesis, natural occurrence, and mechanism of action in the body. This includes pharmacologically useful plastidic terpenoids such as p-menthane monoterpenoids, cannabinoids, paclitaxel (taxol®), and ingenol mebutate which are derived from the 2-C-methyl-d-erythritol-4-phosphate (MEP) pathway, as well as cytosolic terpenoids such as thapsigargin and artemisinin produced through the mevalonate (MVA) pathway. We further provide a review of the MEP and MVA precursor pathways which supply the carbon skeletons for the downstream transformations yielding these medically significant natural products.

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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.048
GPT teacher head0.295
Teacher spread0.247 · 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

Citations392
Published2019
Admission routes2
Has abstractyes

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