Investigation of Clozapine and Olanzapine Reactive Metabolite Formation and Protein Binding by Liquid Chromatography-Tandem Mass Spectrometry
Bibliographic record
Abstract
Drug-induced toxicity has, in many cases, been linked to oxidative metabolism resulting in the formation of reactive metabolites and subsequent covalent binding to biomolecules. Two structurally related antipsychotic drugs, clozapine (CLZ) and olanzapine (OLZ), are known to form similar nitrenium ion reactive metabolites. CLZ-derived reactive metabolites have been linked to agranulocytosis and hepatotoxicity. We have studied the oxidative metabolism of CLZ and OLZ as well as two known metabolites of CLZ, desmethyl-CLZ (DCLZ), and CLZ-N-oxide (CLZ-NO), using in vitro rat liver microsomal (RLM) incubations with glutathione (GSH) trapping of reactive metabolites and liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS). Reactive metabolite binding to selected standard peptides and recombinant purified human proteins was also evaluated. Bottom-up proteomics was performed using two complementary proteases, prefractionation of peptides followed by LC-HRMS/MS for elucidating modifications of target proteins. Induced RLM was selected to form reactive metabolites enzymatically to assess the complex profile of reactive metabolite structures and their binding potential to standard human proteins. Multiple oxidative metabolites and several different GSH adducts were found for CLZ and OLZ. Modification sites were characterized on human glutathione S-transferase (hGST) alpha 1 (OLZ-modified at Cys112), hGST mu 2 (OLZ at Cys115), and hGST pi (CLZ, DCLZ, CLZ-NO and OLZ at Cys170), human microsomal GST 1 (hMGST1, CLZ and OLZ at Cys50), and human serum albumin (hSA, CLZ at Cys34). Furthermore, two modified rat proteins, microsomal GST 1 (CLZ and OLZ at Cys50) and one CYP (OLZ-modified, multiple possible isoforms), from RLM background were also characterized. In addition, direct effects of the reactive metabolite modifications on proteins were observed, including differences in protease cleavage specificity, chromatographic behavior, and charge-state distributions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".