Modulation of Butyrate Anticancer Activity by Solid Lipid Nanoparticle Delivery: An in Vitro Investigation on Human Breast Cancer and Leukemia Cell Lines
Bibliographic record
Abstract
Purpose. Histone modification has emerged as a promising approach to cancer therapy. The short-chain fatty acid, butyric acid, a histone deacetylase (HD) inhibitor, has shown anticancer activity. Butyrate transcriptional activation is indeed able to withdraw cancer cells from the cell cycle, leading to programmed cell death. Since butyrate’s clinical use is hampered by unfavorable pharmacokinetic and pharmacodynamic properties, delivery systems, such as solid lipid nanoparticles (SLN), have been developed to overcome these constraints. Methods. In order to outline the influence of butyrate delivery on its anticancer activity, the effects of butyrate as a free (sodium butyrate, NB) or nanoparticle (cholesteryl butyrate solid lipid nanoparticles, CBSLN) formulation on the growth of different human cancer cell lines, such as the promyelocytic leukemia, HL-60, and the breast cancer, MCF-7 was investigated. A detailed investigation into the mechanism of the induced cytotoxicity was also carried out, with a special focus on the modulation of HD and cyclin-dependent kinase (CDK) mRNA gene expression by real time PCR analysis. Results. In HL-60 cells, CBSLN induced a higher and prolonged expression level of the butyrate target genes at lower concentrations than NB. This led to a significant decrease in cell proliferation, along with considerable apoptosis, cell cycle block in the G0/G1 phase, significant inhibition of total HD activity and overexpression of the p21 protein. Conversely, in MCF-7 cells, CBSLN did not enhance the level of expression of the butyrate target genes, leading to the same anticancer activity as that of NB. Conclusions. Solid lipid nanoparticles were able to improve butyrate anticancer activity in HL-60, but not in MCF-7 cells. This is consistent with difference in properties of the cells under study, such as expression of the TP53 tumor suppressor, or the transporter for short-chain fatty acids, SLC5A8. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.Purpose. Histone modification has emerged as a promising approach to cancer therapy. The short-chain fatty acid, butyric acid, a histone deacetylase (HD) inhibitor, has shown anticancer activity. Butyrate transcriptional activation is indeed able to withdraw cancer cells from the cell cycle, leading to programmed cell death. Since butyrate’s clinical use is hampered by unfavorable pharmacological pharmacokinetic and pharmacodynamicproperties, delivery systems, such as solid lipid nanoparticles (SLN), have been developed to overcome these constraints. Methods. In order to outline the influence of butyrate delivery on its anticancer activity, the effects of butyrate as a free (sodium butyrate, NB) or nanoparticle (cholesteryl butyrate solid lipid nanoparticles, CBSLN) formulation was investigated on the growth of different human cancer cell lines, such as the promyelocytic leukemia, HL-60, and the breast cancer, MCF-7 was investigated. A detailed investigation into the mechanism of the induced cytotoxicity was also carried out, with a special focus on the modulation of HD and cyclin-dependent kinase (CDK) mRNA gene expression by real time PCR analysis. Results. In HL-60 cells, CBSLN induced a higher and prolonged expression level of the butyrate target genes at lower concentrations than NB. This led to a significant decrease in cell proliferation, along with considerable apoptosis, cell cycle block in the G0/G1 phase, significant inhibition of total HD activity and overexpression of the p21 protein. Conversely, in MCF-7 cells, CBSLN did not enhance the level of expression of the butyrate target genes, leading to the same anticancer activity as that of NB. Conclusions. Solid lipid nanoparticles were able to improve butyrate anticancer activity in HL-60, but not in MCF-7 cells. This is consistent with the difference in cells’ properties of the cells under study, such as expression of the TP53 tumor suppressor, or the transporter for short-chain fatty acids, SLC5A8. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.
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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) | 0.000 | 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 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".