Blended Nanoparticle System Based on Miscible Structurally Similar Polymers: A Safe, Simple, Targeted, and Surprisingly High Efficiency Vehicle for Cancer Therapy
Bibliographic record
Abstract
A novel blended nanoparticle (NP) system for the delivery of anticancer drugs and its surprisingly high efficacy for cancer chemotherapy by blending a targeting polymer folic acid-poly(ethylene glycol)-b-poly(lactide-co-glycolide) (FA-PEG-b-PLGA) and a miscible structurally similar polymer D-α-tocopheryl polyethylene glycol 1000 succinate-poly(lactide-co-glycolide) (TPGS-PLGA) is reported. This blended NP system can be achieved through a simple and effective nanoprecipitation technique, and possesses unique properties: i) improved long-term compatibility brought by PEG-based polymers; ii) reduced multidrug resistance mediated by P-glycoprotein (P-gp) in tumor cells and increased bioavailability of anticancer drugs by incorporation of TPGS; iii) the regulation of controlled release through polymer ratios and active targeting by FA. Both in vitro cell experiments and in vivo antitumor assays demonstrated the reported blended NP system can achieve the best therapeutic efficiency in an extremely safe, simple and highly efficient process for cancer therapy. Moreover, this NP system is highly efficient in forming NPs with multiple functions, without repeated chemical modification of polymers, which is sometimes complex, inefficient and high cost. Therefore, the development of this novel blended NP concept is extremely meaningful for the application of pharmaceutical nanotechnology in recent studies.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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 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".