Autoantibodies Against CYP2D6 and Other Drug-Metabolizing Enzymes in Autoimmune Hepatitis Type 2
Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is a disease of unknown etiology, characterized by liver-related autoantibodies. Autoimmune hepatitis is subdivided into two major types: AIH type 1 is characterized by the detection of ANA, SMA, ANCA, anti-ASGP-R, and anti-SLA/LP. Autoimmune hepatitis type 2 is characterized to be mainly related with drug-metabolizing enzymes as autoantigens, such as anti-LKM (liver-kidney microsomal antigen)-1 against CYP2D6, anti-LKM-2 against CYP2C9-tienilic acid, anti-LKM-3 against UGT1A, and anti-LC1 (liver cytosol antigen)-1 and anti-APS (autoimmune polyglandular syndrome type-1) against CYP1A2, CYP2A6, and others. Anti-LKM-1 sera inhibited CYP2D6 activity in vitro but did not inhibit cellular drug metabolism in vivo. CYP2D6 is the major target autoantigen of LKM-1 and expressed on plasma membrane (PM) of hepatocytes, suggesting a pathogenic role for anti-LKM-1 in liver injury as a trigger. Anti-CYP1A2 was observed in dihydralazine-induced hepatitis, and radiolabeled CYP1A2 disappeared from the PM with a half-life of less than 30 min, whereas microsomal CYP1A2 was stably radiolabeled for several hours. Main antigenic epitopes on CYP2D6 are aa 193-212, aa 257-269, and aa 321-351; and D263 is essential. The third epitope is located on the surface of the protein CYP2D6 and displays a hydrophobic patch that is situated between an aromatic residue (W316) and histidine (H326). Some drugs such as anticonvulsants (phenobarbital, phenytoin, and carbamazepine) and halothane are suggested to induce hepatitis with anti-CYP3A and anti-CYP2E1, respectively. Autoantibodies against CYP11A1, CYP17, and/or CYP21 involved in the synthesis of steroid hormones are also detected in patients with adrenal failure, gonadal failure, and/or Addison disease.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".