Molecular Simulations Identify Target Receptor Kinases Bound by Astaxanthin to Induce Breast Cancer Cell Apoptosis
Bibliographic record
Abstract
Background: We investigated molecular mechanisms behind astaxanthinmediated induction of apoptosis in breast cancer cell lines toward combination therapy against cancer drug resistance. Methods: Breast cancer cell lines were treated with serial concentrations of astaxanthin to determine its IC50. We used drug-design software to predict interactions between astaxanthin and receptor tyrosine kinases or other key gene products involved in intracellular signaling pathways. Changes in gene expression were examined using RT-PCR. The effect of astaxanthin-nanocarbons combinations on cancer cells was also evaluated. Results: Astaxanthin induced cell death in all three breast cancer cell lines was examined so that its IC50 in two HER2-amplifying lines SKBR3 and BT-474 stood, respectively, at 36 and 37 ?M; however, this figure for MCF-7 was significantly lowered to 23 ?M (P<0.05). Astaxanthin-treated SKBR3 cells showed apoptotic death upon co-staining. Our in silico examinations showed that some growth-promoting molecules are strongly bound by astaxanthin via their specific amino acid residues with their binding energy standing below -6 KCa/Mol. Next, astaxanthin was combined with either graphene oxide or carboxylated multi-walled carbon nanotube, with the latter affecting SKBR cell survival more extensively than the former (P<0.05). Finally, astaxanthin coinduced tumor suppressors p53 and PTEN but downregulated the expression of growth-inducing genes in treated cells. Conclusion: These findings indicate astaxanthin carries' multitarget antitumorigenic capacities and introduce the compound as a suitable candidate for combination therapy regimens against cancer growth and drug resistance. Development of animal models to elucidate interactions between the compound and tumor microenvironment could be a major step forward towards the inclusion of astaxanthin in cancer therapy trials.
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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 | 0.000 | 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 | 0.000 |
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".