Cationic polymer‐mediated small interfering RNA delivery for P‐glycoprotein down‐regulation in tumor cells
Bibliographic record
Abstract
BACKGROUND: Among the treatment options that have been developed for cancer, chemotherapy remains 1 of the leading clinical approaches. Chemotherapy can usually control tumor growth at the onset of disease, but its effectiveness becomes limited by the overexpression of transporter proteins responsible for drug efflux, leading to multidrug resistance (MDR). To overcome this obstacle, the authors explored the feasibility of down-regulating the main drug transporter, P-glycoprotein (P-gp), by using nonviral small interfering RNA (siRNA) delivery as means to enhance the accumulation of chemotherapeutic agents in drug-resistant cancer cells. METHODS: Several cationic carriers capable of siRNA complexation were investigated for P-gp down-regulation in the MDA435/LCC6 cell line and, consequently, increased cellular uptake of the chemotherapeutic agents doxorubicin and paclitaxel. RESULTS: Efficient siRNA delivery into tumor cells was demonstrated particularly using a palmitic-acid substituted poly(L-lysine), with no apparent differences in siRNA delivery between the wild type (WT)-expressing and P-gp-expressing phenotype (MDR1) of the cells. Efficient siRNA delivery led to approximately 40% to 50% P-gp suppression (based on the average expression level of the protein), an approximately 3-fold increased DOX uptake, and increased cytotoxicity in MDR1 cells. CONCLUSIONS: The authors concluded that effective siRNA delivery with nonviral carriers can reduce the level of P-gp on cell surfaces and enhance the efficiency of chemotherapeutic agents in vitro.
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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) | 0.000 | 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".