Investigating the Mechanisms of Aromatic Amine-Induced Protein Free Radical Formation by Quantitative Structure−Activity Relationships: Implications for Drug-Induced Agranulocytosis
Bibliographic record
Abstract
Aromatic amine drugs have been associated with agranulocytosis (neutrophil depletion) for which the mechanism is unknown. We have previously shown that the metabolism of two aromatic amine drugs by human myeloperoxidase (MPO) results in phenyl radical metabolite formation and also in protein free radical formation on MPO. Because the concentration of drug required to produce a maximum signal for MPO protein free radical (MPO*) detection was different for each drug, this prompted us to consider that other aromatic amines may also show varying degrees of ability to induce MPO* formation. Immunoassay experiments using the immuno-spin-trapping technique were performed, which evaluated the potency of different aromatic amines containing the aniline substructure to generate the MPO*. Each reaction contained equal amounts of H(2)O(2), 5,5-dimethyl-1-pyrroline-N-oxide, MPO, and variable concentrations of aniline derivatives. Several physicochemical parameters for aniline derivatives were used to derive quantitative structure-activity relationship equations, which showed that the Hammett constant (sigma) best correlated with the MPO* formation for all aniline derivatives. More statistically robust equations were derived if the anilines were separated into mono- and disubstituted groups. However, some aniline derivatives did not induce MPO* formation. Using electron spin resonance spectroscopy, we evaluated the ability of all aniline derivatives tested to produce phenyl radical metabolites, as previously shown by spin trapping for the aromatic amine drugs. Interestingly, we found that only those aniline derivatives that produced a phenyl radical also formed MPO*. We propose that the phenyl radical is the reactive free radical metabolite responsible for generating the MPO*.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".