7-O-Esters of taxifolin with pronounced and overadditive effects in neuroprotection, anti-neuroinflammation, and amelioration of short-term memory impairment in vivo
Bibliographic record
Abstract
Alzheimer's disease (AD) is a multifactorial disease and the most common form of dementia. There are no treatments to cure, prevent or slow down the progression of the disease. Natural products hold considerable interest for the development of preventive neuroprotectants to treat neurodegenerative disorders like AD, due to their low toxicity and general beneficial effects on human health with their anti-inflammatory and antioxidant features. In this work we describe regioselective synthesis of 7-O-ester hybrids of the flavonoid taxifolin with the phenolic acids cinnamic and ferulic acid, namely 7-O-cinnamoyltaxifolin and 7-O-feruloyltaxifolin. The compounds show pronounced overadditive neuroprotective effects against oxytosis, ferroptosis and ATP depletion in the murine hippocampal neuron HT22 cell model. Furthermore, 7-O-cinnamoyltaxifolin and 7-O-feruloyltaxifolin reduced LPS-induced neuroinflammation in BV-2 microglia cells as assessed by effects on the levels of NO, IL6 and TNFα. In all in vitro assays the 7-O-esters of taxifolin and ferulic or cinnamic acid showed strong overadditive activity, significantly exceeding the effects of the individual components and the equimolar mixtures thereof, which were almost inactive in all of the assays at the tested concentrations. In vivo studies confirmed this overadditive effect. Treatment of an AD mouse model based on the injection of oligomerized Aβ25-35 peptide into the brain to cause neurotoxicity and subsequently memory deficits with 7-O-cinnamoyltaxifolin or 7-O-feruloyltaxifolin resulted in improved performance in an assay for short-term memory as compared to vehicle and mice treated with the respective equimolar mixtures. These results highlight the benefits of natural product hybrids as a novel compound class with potential use for drug discovery in neurodegenerative diseases due to their pharmacological profile that is distinct from the individual natural components.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".