Drug-Loaded Nanocarriers: Passive Targeting and Crossing of Biological Barriers
Bibliographic record
Abstract
Poor bioavailability and poor pharmacokinetic characteristics are some of the leading causes of drug development failure. Therefore, poorly-soluble drugs, fragile proteins or nucleic acid products may benefit from their encapsulation in nanosized vehicles, providing enhanced solubilization, protection against degradation, and increased access to pathological compartments. A key element for the success of drug-loaded nanocarriers is their ability to either cross biological barriers themselves, or allow loaded drugs to traverse them to achieve optimal pharmacological action at pathological sites. Depending on the mode of administration, nanocarriers may have to cross different physiological barriers in their journey towards their target. In this review, the crossing of biological barriers by passive targeting strategies will be presented for intravenous delivery (vascular endothelial lining, particularly for tumor vasculature and blood brain barrier targeting), oral administration (gastrointestinal lining), and upper airway administration (pulmonary epithelium). For each specific barrier, background information will be provided on the structure and biology of the tissues involved as well as available pathways for nano-objects or loaded drugs (diffusion and convection through fenestration, transcytosis, tight junction crossing, etc.). The determinants of passive targeting - size, shape, surface chemistry, surface patterning of nanovectors - will be discussed in light of current results. Perspectives on each mode of administration will be presented. The focus will be on polymeric nanoparticles and dendrimers, although advances in liposome technology will be also reported as they represent the largest body in the drug delivery literature.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".