Solid Lipid Nanoparticles for Pulmonary Delivery of Biopharmaceuticals: A Review of Opportunities, Challenges, and Delivery Applications
Bibliographic record
Abstract
Biopharmaceuticals such as nucleic acids, proteins, and peptides constitute a new array of treatment modalities for chronic ailments. Invasive routes remain the mainstay of administering biopharmaceuticals due to their labile nature in the biological environment. However, it is not preferred for long-term therapy due to the lack of patient adherence and clinical suitability. Therefore, alternative routes of administration are sought to utilize novel biopharmaceutical therapies to their utmost potential. Nanoparticle-mediated pulmonary delivery of biologics can facilitate both local and systemic disorders. Solid lipid nanoparticles (SLNs) afford many opportunities as pulmonary carriers due to their physicochemical stability and ability to incorporate both hydrophilic and hydrophobic moieties, thus allowing novel combinatorial drug/gene therapies. These applications include pulmonary infections, lung cancer, and cystic fibrosis, while systemic delivery of biomolecules, like insulin, is also attractive for the treatment of chronic ailments. This Review explores physiological and particle-associated factors affecting pulmonary delivery of biopharmaceuticals. It compares the advantages and limitations of SLNs as pulmonary nanocarriers along with design improvements underway to overcome these limitations. Current research illustrating various SLN designs to deliver proteins, peptides, plasmids, oligonucleotides, siRNA, and mRNA is also summarized.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".