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Design of novel artemisinin-like derivatives with cytotoxic and anti-angiogenic properties

2010· article· en· W1968570263 on OpenAlexaff
Shahid A. Soomro, Tobias Langenberg, Anne Mahringer, V. Badireenath Konkimalla, Cindy Horwedel, Pavlo Holenya, Almut Brand, Canan Çetin, Gert Fricker, Mieke Dewerchin, Peter Carmeliet, Edward M. Conway, Herwig Jansen, Thomas Efferth

Bibliographic record

VenueJournal of Cellular and Molecular Medicine · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversity of British Columbia
FundersVlaamse regeringUniversität Ulm
KeywordsArtesunateArtemisininIn vivoMalariaDihydroartemisininPharmacologyBiopharmaceuticalPlasmodium falciparumDrugDrug resistanceBiologyImmunologyBiotechnologyMicrobiology

Abstract

fetched live from OpenAlex

Artemisinins are plant products with a wide range of medicinal applications. Most prominently, artesunate is a well tolerated and effective drug for treating malaria, but is also active against several protozoal and schistosomal infections, and additionally exhibits anti-angiogenic, anti-tumorigenic and anti-viral properties. The array of activities of the artemisinins, and the recent emergence of malaria resistance to artesunate, prompted us to synthesize and evaluate several novel artemisinin-like derivatives. Sixteen distinct derivatives were therefore synthesized and the in vitro cytotoxic effects of each were tested with different cell lines. The in vivo anti-angiogenic properties were evaluated using a zebrafish embryo model. We herein report the identification of several novel artemisinin-like compounds that are easily synthesized, stable at room temperature, may overcome drug-resistance pathways and are more active in vitro and in vivo than the commonly used artesunate. These promising findings raise the hopes of identifying safer and more effective strategies to treat a range of infections and cancer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.202
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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 teacher head, 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

Citations54
Published2010
Admission routes1
Has abstractyes

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