NFIB rs28379954 does not affect CYP2D6-mediated metabolism of prototypical substrates, tamoxifen or solanidine
Bibliographic record
Abstract
BACKGROUND: Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic drug-metabolizing enzyme involved in the metabolism of many clinically important medications. CYP2D6 is affected by genetic variation as well as drug interactions; however, this does not account for all the variability seen in CYP2D6. Previously, a single-nucleotide variant in the nuclear factor 1-B (NFIB), rs28379954 T>C, was linked to increased CYP2D6 activity and metabolism of CYP2D6 substrates. Thus, we investigated the effect of NFIB rs28379954 on the metabolism of CYP2D6 substrates, solanidine and tamoxifen. METHODS: Patients (N = 759) were genotyped for CYP2D6 genetic variants and NFIB rs28379954. Solanidine, tamoxifen, and their metabolites were measured with ultra-HPLC-tandem mass spectrometry. RESULTS: NFIB rs28379954 genotype (T/T versus T/C) was not associated with metabolism of solanidine to its CYP2D6-generated metabolites, 4-OH-solanidine or SSDA irrespective of CYP2D6 phenotype (poor metabolizer, intermediate metabolizer, or normal metabolizer; P > 0.05). Similarly, the ratio of endoxifen to tamoxifen was not affected by NFIB rs28379954 genotype in any CYP2D6 phenotypic group (P > 0.05). Multivariable linear regression modeling demonstrated that CYP2D6 phenotypes were associated with solanidine metabolic ratios as well endoxifen to tamoxifen ratios. However, the addition of NFIB genotype to the model did not significantly improve the predictability of solanidine or tamoxifen metabolites in plasma. CONCLUSION: In conclusion, we did not observe any significant impact of NFIB rs28379954 genetic variation on CYP2D6 activity in vivo, when assessed using tamoxifen or solanidine metabolites as prototypical CYP2D6 substrates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".