The Antiangiogenic Effects of Tamoxifen might be Attributed to Receptor Binding Capacity of its Solvent Dimethylsulfoxide in Breast Cancer: A Molecular Docking Study
Bibliographic record
Abstract
Aim: Tamoxifen, a Dimethyl sulfoxide (DMSO)-soluble chemotherapeutic, is widely used in the treatment of breast cancer. Tamoxifen has an anti-angiogenic effect especially on breast tumor cells by blocking VEGF(Vascular Endothelial Growth Factor) production. On the other hand, according to our previously studies, we demonstrated that DMSO could mimics the cytotoxic effects of Thalidomide in 4T1 mouse breast cancer cells and is related with the anti-angiogenic response of HeLa cells. At this point of view, the aim of this study was to determine the possible binding effects of DMSO on certain cell surface receptors. Methods: The in silico studies were implemented using the Docking Server. X-ray structures of receptor proteins’ PDB files were obtained from Protein Data Bank [Human Progesteron Receptor (PDB ID: 1A28, Human Tumor Necrosis Factor Receptor-1 (PDB ID: 1EXT), Human Interferon Gamma Receptor-1 (PDB ID: 1FG9), Human Epidermal Growth Factor Receptor (PDB ID: 1IVO)]. Docking simulations were performed using the Lamarckian genetic algorithm (LGA) and the Solis & Wets local search method. Results: According to the molecular docking results of this study, DMSO, the general solvent of Tamoxifen, has a 90% binding capacity to the Interferon Gamma (IFNɣ) receptor and 100% to Tumor Necrosis Factor alfa (TNFα) receptor, respectively. Conclusion: These receptors have significant effects on the proliferation and angiogenesis of cancer cells which leads to the metastasis of breast cancer. In conclusion, the antiangiogenic effects of Tamoxifen might be reduced due to its solvent DMSO's angiogenic effects.
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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 | 0.007 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".