The Potency of Diarylamine Radical-Trapping Antioxidants as Inhibitors of Ferroptosis Underscores the Role of Autoxidation in the Mechanism of Cell Death
Bibliographic record
Abstract
Two aromatic amines (ferrostatin-1 and liproxstatin-1) were recently identified from high-throughput screening efforts to uncover potent inhibitors of ferroptosis, the necrotic-like cell death induced by inhibition of glutathione peroxidase 4 (GPX4), deletion of the corresponding gpx4 gene, or starvation of GPX4 of its reducing cosubstrate, glutathione (GSH). We have since demonstrated that these two aromatic amines are highly effective radical-trapping antioxidants (RTAs) in lipid bilayers, suggesting that they subvert ferroptosis by inhibiting lipid peroxidation (autoxidation) and, thus, that this process drives the execution of ferroptosis. Herein, we show that diarylamine RTAs used to protect petroleum-derived products from autoxidation can be potent inhibitors of ferroptosis. The diarylamines investigated include representative examples of additives to engine oils, greases and rubber (4,4′-dialkyldiphenylamines), core structures of dyes and pharmaceuticals (phenoxazines and phenothiazines), and aza-analogues of these three classes of compounds that we have recently shown can be modified to achieve much greater reactivity. We find that regardless of how ferroptosis is induced (GPX4 inhibition, gpx4 deletion or GSH depletion), compounds which possess good RTA activity in organic solution ( k inh > 10 5 M –1 s –1 ) and lipid bilayers ( k inh > 10 4 M –1 s –1 ) are generally potent inhibitors of ferroptosis (in mouse embryonic fibroblasts). Likewise, structural analogs that do not possess RTA activity are devoid of antiferroptotic activity. These results further support the argument that lipid peroxidation (autoxidation) plays a major role in the mechanism of cell death induced by either GPX4 inhibition, gpx4 deletion, or GSH depletion. Moreover, it offers clear direction that ongoing medicinal chemistry efforts on liproxstatin and ferrostatin derivatives, which have been proposed as lead compounds for the treatment and/or prevention of ischemia/reperfusion injury, renal failure, and neurodegeneration, can be widened to include other aminic RTAs. To aid in these efforts, some relevant structure–reactivity relationships are discussed.
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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".