Traceable Multifunctional Micellar Nanocarriers for Cancer-Targeted Co-delivery of MDR-1 siRNA and Doxorubicin
Bibliographic record
Abstract
In this article we report on the development of polymeric micelles that can integrate multiple functions in one system, including the capability to accommodate a combination of therapeutic entities with different physicochemical properties (i.e., siRNA and doxorubicin; DOX), passive and active cancer targeting, cell membrane translocation, and pH-triggered drug release. A micellar system was constructed from degradable poly(ethylene oxide)-block-poly(ε-caprolactone) (PEO-b-PCL) block copolymers with functional groups on both blocks. The functional group on the PCL block was used to incorporate short polyamines for complexation with siRNA or to chemically conjugate DOX via a pH-sensitive hydrazone linkage. A virus mimetic shell was conferred by attaching two ligands, i.e., the integrin αvβ3-specific ligand (RGD4C) for active cancer targeting and the cell-penetrating peptide TAT for membrane activity. This system was used to improve the efficacy of DOX in multidrug-resistant MDA-MB-435 human tumor models that overexpress P-glycoprotein (P-gp), by simultaneous intracellular delivery of DOX and siRNA against P-gp expression. The carrier was tagged with near-infrared fluorescent imaging probes to provide a means to follow the fate of the system in vivo upon intravenous administration. Dy677-labeled siRNA was also used to assess the in vivo stability of the siRNA carrier. This multifunctional polymeric micellar system was shown to be capable of DOX and siRNA delivery to their intracellular targets, leading to the inhibition of P-gp-mediated DOX resistance in vitro and targeting of αvβ3-positive tumors in vivo.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".