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 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".