Stereoselective Formation of Mono- and Dihydroxylated Polychlorinated Biphenyls by Rat Cytochrome P450 2B1
Bibliographic record
Abstract
Changes in atropisomer composition of chiral polychlorinated biphenyls (PCBs) and their mono- and dihydroxylated metabolites (OH- and diOH-PCBs) via rat cytochrome P450 2B1 (CYP2B1) mediated biotransformation were investigated in vitro. Rat CYP2B1 could stereoselectively biotransform chiral PCBs to generate meta-OH-PCBs as the major metabolites after 60 min incubations. Nonracemic enantiomer fractions (EFs: concentration ratios of the (+)-atropisomer or the first-eluting atropisomer over the total concentrations of two atropisomers) of 5-OH-PCBs, were 0.17, 0.20, 0.85, 0.77, and 0.41 for incubations with PCBs 91, 95, 132, 136, and 149, respectively. CYP-mediated stereoselective formation of diOH-PCBs from OH-PCBs was observed for the first time. After 60 min stereoselective biotransformation, the EFs of both 4-OH-PCB 95 and 5-OH-PCB 95 changed from racemic (i.e., 0.50) to 0.62 and 0.46, respectively. These transformations generated statistically nonracemic 4,5-diOH-PCB 95, with EFs of 0.53 and 0.58 for 4-OH-PCB 95 and 5-OH-PCB 95 incubations, respectively. Biotransformation of PCBs 91 and 136 also generated 4,5-diOH-PCB 91 and 4,5-diOH-PCB 136, respectively. These in vitro results were consistent with that observed for stereoselective PCB biotransformation by rat liver microsomes and in vivo. Biotransformation interference between two atropisomers of PCB 136 was investigated for the first time in this study. The biotransformation process of (-)-PCB 136 was significantly disrupted by the presence of (+)-PCB 136 but not the other way around. Thus, stereoselective metabolism of chiral PCBs and OH-PCBs by CYPs is a major mechanism for atropisomer composition change of PCBs and their metabolites in the environment, with the degree of composition change dependent, at least in part, on stereoselective interference of atropisomers with each other at the enzyme level.
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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 | 0.001 |
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".