Solanidine Metabolites as Diet‐Derived Biomarkers of <scp>CYP2D6</scp>‐Mediated Tamoxifen Metabolism in Breast Cancer Patients
Bibliographic record
Abstract
Tamoxifen is an important antiestrogen for the treatment of hormone receptor-positive breast cancer and undergoes bioactivation by CYP2D6 to its active metabolite endoxifen. Genetic variation in CYP2D6 has been linked to endoxifen levels during tamoxifen therapy. Recent studies have suggested solanidine, a glycoalkaloid phytochemical in potatoes, undergoes CYP2D6-mediated metabolism to 4-OH-solanidine (m/z 414) and 3,4-seco-solanidine-3,4-dioic acid (SSDA; m/z 444). Using a retrospective cohort of 1,032 breast cancer patients on tamoxifen therapy, we examined the association of solanidine metabolites with CYP2D6 activity and its correlation with tamoxifen metabolism. Solanidine, 4-OH-solanidine, or SSDA was detected in 99.7% (N = 1,029) of plasma samples. Decreased solanidine metabolite ratios were found in CYP2D6 intermediate and poor metabolizers (P < 0.0001). Patients on CYP2D6 strong inhibitors had a 77.6% and 94.2% decrease in 4-OH-solandine/solanidine (P < 0.0001) and SSDA/solanidine (P < 0.0001), respectively. The ratio of endoxifen to tamoxifen was highly correlated with both 4-OH-solandine/solanidine (ρ = 0.3207, P < 0.0001) and SSDA/solanidine (ρ = 0.5022, P < 0.0001) ratios. Logistic regression modeling was used to determine that 4-OH-solanidine/solanidine and SSDA/solanidine ratios below 2.1 and 0.8, respectively, predicted endoxifen concentrations of <16 nM. In conclusion, solanidine, 4-OH-solanidine, and SSDA are diet-derived biomarkers of CYP2D6 activity. Moreover, in patients on tamoxifen therapy, 4-OH-solanidine/solanidine and SSDA/solanidine predicted endoxifen levels including the inhibitory effects of concomitantly prescribed CYP2D6-interacting medications. Accordingly, 4-OH-solanidine/solanidine or SSDA/solanidine ratio has the potential to be particularly useful prior to initiation of tamoxifen or for determining the impact of CYP2D6 drug interactions, as well as prior to switching from an aromatase inhibitor to tamoxifen.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".