Repurposing Fumaric Acid Esters to Treat Conditions of Oxidative Stress and Inflammation: A Promising Emerging Approach with Broad Potential
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".