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Record W7071051084

Repurposing Fumaric Acid Esters to Treat Conditions of Oxidative Stress and Inflammation : A Promising Emerging Approach with Broad Potential

2019· other· en· W7071051084 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDimethyl fumarateFumaric acidRepurposingOxidative stressPsoriasisInflammationDrugDrug repositioning
DOInot available

Abstract

fetched live from OpenAlex

The medicinal benefit of salts of fumaric acid and its esters (FAE), known as fumarates (mono and dimethyl fumarate), was realized many years ago. Early on, FAE were derived from plants and mushrooms (e.g., Fumaria officinalis, Boletus fomentarius var. pseudo-igniarius). The FAE containing formulation Fumaderm® was licensed in Germany for the treatment of psoriasis in 1994. Recently, a clinical formulation of dimethyl fumarate known as BG12 (Tecfidera) was approved for use in the United States, New Zealand, Australia, European Union, Switzerland, and Canada for the treatment of multiple sclerosis. Others and we have assessed the potential benefit of FAE in a number of disease conditions that are diverse with respect to etiology but unified with regard to the involvement of inflammation and oxidative stress. Hence, a FAE-based drug with robust anti-oxidative and anti-inflammatory effects that is already US-FDA approved is a perfect contender for repurposing and rapid clinical implementation for their management. There is a burgeoning literature on the use of FAE in the prevention and treatment of diseases, other than psoriasis and MS, in which oxidative stress and/or inflammation are prominent. This chapter highlights critical information gleaned from these studies, exposes lacunae of potential importance, and provides related perspectives.

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 categoriesMeta-epidemiology (narrow), Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0290.021
Science and technology studies0.0010.001
Scholarly communication0.0040.017
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.316
Teacher spread0.278 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBiblioBoard Library Catalog (Open Research Library)French-language works237,207