Design of nanocarriers for nanoscale drug delivery to enhance cancer treatment using hybrid polymer and lipid building blocks
Bibliographic record
Abstract
Polymer-lipid hybrid nanoparticles (PLN) are an emerging nanocarrier platform made from building blocks of polymers and lipids. PLN integrate the advantages of biomimetic lipid-based nanoparticles (i.e. solid lipid nanoparticles and liposomes) and biocompatible polymeric nanoparticles. PLN are constructed from diverse polymers and lipids and their numerous combinations, which imparts PLN with great versatility for delivering drugs of various properties to their nanoscale targets. PLN can be classified into two types based on their hybrid nanoscopic structure and assembly methods: Type-I monolithic matrix and Type-II core-shell systems. This article reviews the history of PLN development, types of PLN, lipid and polymer candidates, fabrication methods, and unique properties of PLN. The applications of PLN in delivery of therapeutic or imaging agents alone or in combination for cancer treatment are summarized and illustrated with examples. Important considerations for the rational design of PLN for advanced nanoscale drug delivery are discussed, including selection of excipients, synthesis processes governing formulation parameters, optimization of nanoparticle properties, improvement of particle surface functionality to overcome macroscopic, microscopic and cellular biological barriers. Future directions and potential clinical translation of PLN are also suggested.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".