Mechanism of Off-Target Interactions and Toxicity of Tamoxifen and Its Metabolites
Bibliographic record
Abstract
Tamoxifen is an estrogen modulator that acts to competitively inhibit the binding of endogenous estrogens. It is widely used for treatment of breast cancer; however, analogous with many antineoplastic agents, tamoxifen is associated with numerous adverse effects, most prominently nausea. We have identified several off-target receptors of tamoxifen and 22 of its metabolites that include histamine H1 and H3, and muscarinic M1, M4, and M5 subtypes, and dopamine D2 receptor. We have shown how they are associated with tamoxifen and its metabolites' toxicity through a comprehensive computational analysis of their interaction modes, which were also compared to that of the related endogenous substrates of each receptor. The results were further evaluated using available in vivo and in vitro data. The presented work provides foundational knowledge toward the determination of the precise mechanism of nausea induction, and in particular, interactions of tamoxifen and its metabolites with the receptors involved in that biomolecular pathway. This study can assist in predicting the potential undesired effects of the chemicals with common pharmacophores or similar fragments to that of tamoxifen and its metabolites and serve drug discovery research in developing more effective and tolerable tamoxifen analogues or chemotherapeutic agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".