Cytochrome P450 2D6.1 and cytochrome P450 2D6.10 differ in catalytic activity for multiple substrates
Bibliographic record
Abstract
CYP2D6 is involved in the metabolism of several classes of drugs, including tricyclic antidepressants, selective serotonin reuptake inhibitors and various amphetamines. CYP2D6*10 is an allelic variant, producing an enzyme with Pro34Ser and Ser486Thr amino acid substitutions. Approximately 75% of Asians possess the *10 allele. We sought to further characterize CYP2D6.10 catalytically in vitro in a baculovirus expression system using various substrates and inhibitors, in comparison to CYP2D6.1 (wild-type). Using dextromethorphan (DEX), P-methoxyamphetamine, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and (+/-)3,4-methylenedioxymethamphetamine (MDMA), the ratios of intrinsic clearance (Vmax/Km) of *1 to *10 were 50, 34, 22 and 123, respectively. The CYP2D6 substrates amitriptyline, and (+) and (-) methamphetamine (MAMP) are both p-hydroxylated and N-demethylated (NDM). The intrinsic clearance *1/*10 ratios were 42, 30 and 67 for the p-hydroxylation; and 60, 120 and 157 for the NDM, respectively, illustrating chemical pathway and enantiomeric selectivity for MAMP. It was apparent that (+) and (-) MAMP NDM and MDMA demethylenation were most significantly different in CYP2D6.10. Using DEX as the substrate, the ratios of Ki(*10)/Ki(*1) for inhibitors were: budipine (1.3), sparteine (1.6), debrisoquine (8.1), fluoxetine (16), norfluoxetine (30), paroxetine (14), MDMA (21) and MMDA-2 (7.1), indicating that CYP2D6.10 shows drug-specific altered susceptibility to inhibition. Taken together, these data suggest that CYP2D6*10/*10 individuals may be expected to require different drug doses; and show altered susceptibility to toxicity, interaction risk and, in the case of the amphetamines, drug dependence and toxicity compared to CYP2D6*1/*1 individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".