Alteration of Genomic Responses to Doxorubicin and Prevention of MDR in Breast Cancer Cells by a Polymer Excipient: Pluronic P85
Bibliographic record
Abstract
Polymer therapeutics has emerged as a new clinical option for the treatment of human diseases. However, little is known about pharmacogenetic responses to drugs formulated with polymers. In this study, we demonstrate that a formulation containing the block copolymer Pluronic P85 and antineoplastic drug doxorubicin (Dox) prevents the development of multidrug resistance in the human breast carcinoma cell line, MCF7. Specifically, MCF7 cells cultured in the presence of Pluronic were unable to stably grow in concentrations of Dox that exceeded 10 ng of Dox/mL of culture medium. In sharp contrast, MCF7 cells cultured in the absence of the block copolymer resulted in the selection and stable growth of cells that tolerated a 1000 times higher concentration of the drug (10 000 ng of Dox/mL of culture medium). Detailed characterization of the isolated sublines demonstrated that those cells selected in the polymer-drug formulation did not show amplification of the MDR1 gene, likely resulting in their high sensitivity to the drug. Conversely, cells selected with Dox alone showed an elevated level in the expression of the MDR1 gene along with a corresponding increase in the expression level of the drug efflux transporter, Pgp, and likely contributing to the high resistance of the cells to Dox. Global analysis of the expression profiles of 20K genes by DNA microarray revealed that the use of Pluronic in combination with Dox drastically changed the direction and magnitude of the genetic response of the tumor cells to Dox and may potentially enhance therapeutic outcomes. Overall, this study reinforces the need for a thorough assessment of pharmacogenomic effects of polymer therapeutics.
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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".