General Approach to Identify, Assess, and Characterize Inhibitors of Lipid Peroxidation and Associated Cell Death
Bibliographic record
Abstract
Lipid peroxidation (LPO) is associated with a variety of pathologies and drives a form of regulated necrosis called ferroptosis. There is much interest in small-molecule inhibitors of LPO as potential leads for therapeutic development for neurodegeneration, stroke, and acute organ failure, but this has been hampered by the lack of a universal high-throughput assay that can identify and assess candidates. Herein, we describe the development and validation of such an approach. Phosphatidylcholine liposomes loaded with ∼10% phospholipid hydroperoxide and STY-BODIPY, a fluorescent signal carrier that co-autoxidizes with polyunsaturated phospholipids, are shown to autoxidize at convenient and constant rates when subjected to an optimized Fe 2+ -based initiation cocktail. The use of this initiation system enables the identification of each of the various classes of LPO inhibitors which have been shown to rescue from cell death in ferroptosis: radical-trapping antioxidants (RTAs), peroxidase mimics, and iron chelators. Furthermore, a limited dose–response profile of inhibitors enables the resolution of RTA and non-RTA inhibitors─thereby providing not only relative efficacy but mechanistic information in the same microplate-based experiment. Despite this versatility, the approach can still be used to estimate rate constants for the reaction of RTAs with chain-propagating peroxyl radicals, as demonstrated for a representative panel of RTAs. To illustrate the utility of this assay, we carried out a preliminary investigation of the ‘off-target’ activity of several ferroptosis suppressors that have been proposed to act independently of inhibition of LPO, including lipoxygenase inhibitors, cannabinoids, and necrostatins, the archetype inhibitors of necroptosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".