An update on strategies for optimizing polymer-lipid hybrid nanoparticle-mediated drug delivery: exploiting transformability and bioactivity of PLN and harnessing intracellular lipid transport mechanism
Bibliographic record
Abstract
INTRODUCTION: Polymer-lipid hybrid nanoparticle (PLN) is an emerging nanoplatform with distinct properties and functionalities from other nanocarrier systems. PLN can be optimized to overcome various levels of drug delivery barriers to achieve desired therapeutic outcomes via rational selection of polymer and lipid combinations based on a thorough understanding of their properties and interactions with therapeutic agents and biological systems. AREAS COVERED: This review provides an overview of PLN including the motive and history of PLN development, types of PLN, preparation methods, attestations of their versatility, and design strategies to circumvent various barriers for increasing drug delivery accuracy and efficiency. It also highlights recent advances in PLN design including: rationale selection of polymer and lipid components to achieve spatiotemporal drug targeting and multi-targeted cascade drug delivery; utilizing the intracellular lipid transport mechanism for active targeting to desired organelles; and harnessing bioreactive lipids and polymers to magnify therapeutic effects. EXPERT OPINION: A thorough understanding of properties of PLN components and their biofate is important for enhancing disease site targeting, deep tumor tissue penetration, cellular uptake, and intracellular trafficking of PLN. For futuristic PLN development, active lipid transport and dual functions of lipids and polymers as both nanocarrier material and pharmacological agents can be further explored.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".