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Record W2109893892 · doi:10.2174/1568026033392499

Traditionally-Used Antimalarials from the Meliaceae

2003· review· en· W2109893892 on OpenAlexaff
Samia S. Omar, J. Zhang, Shawna L. MacKinnon, Danna J. Leaman, Tony Durst, Bernard J. R. Philogène, J. T. Arnason, Pablo Sánchez-Vindas, L. Poveda, Pamela Tamez, John M. Pezzuto

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhytochemical compounds biological activities
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMeliaceaeAzadirachtaPhytomedicineTraditional medicineLimonoidBiologyMelia azedarachMedicinal plantsPharmacologyBotanyMedicine

Abstract

fetched live from OpenAlex

A quantitative ethnobotanical approach to antimalarial drug discovery led to the identification of Lansium domesticum Corr. Ser. (Meliaceae) as an important antimalarial used by Kenyah Dyak healers in Indonesian Borneo. Triterpenoid lansiolides with antimalarial activity were isolated from the bark and shown to have activity in both in vitro bioassays with Plasmodium falciparum, and in mice infected with P. berghei. A survey of African and tropical American Meliaceae led to further development of the limonoid gedunin from the traditionally used medicinal plants, tropical cedar, Cedrela odorata L., and neem, Azadirachta indica A. Juss. Gedunin has significant in vitro activity but initially showed poor in vivo activity. In vivo activity was improved by (1) incorporation into an easy to absorb suspension, (2) preparation of a more stable compound, 7-methoxygedunin; and (3) synergism with dillapiol, a cytochrome P450 3A4 inhibitor. The results show the potential for both antimalarial drug and phytomedicine development from traditionally used plants.

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)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.086
GPT teacher head0.357
Teacher spread0.271 · 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

Citations70
Published2003
Admission routes1
Has abstractyes

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