Adverse Immunological Responses Against Non-viral Nanoparticle (NP) Delivery Systems in the Lung
Bibliographic record
Abstract
There is a large, unmet medical need to treat chronic obstructive pulmonary disease, asthma, idiopathic pulmonary fibrosis and other respiratory diseases. New modalities are being developed, including gene therapy which treats the disease at the DNA/RNA level. Despite recent innovations in non-viral gene therapy delivery for chronic respiratory diseases, unwanted or adverse interactions with immune cells, particularly macrophages, can limit drug efficacy. This review will examine the relationship between the design and fabrication of non-viral nucleic acid nanoparticle (NP) delivery systems and their ability to trigger unwanted immunogenic responses in lung tissues. NP formulated with peptides, lipids, synthetic and natural polymers provide a robust means of delivering the genetic cargos to the desired cells. However NP, or their components, may trigger local responses such as cell damage, edema, inflammation and complement activation. These effects may be acute short-term reactions or chronic long-term effects like fibrosis, increased susceptibility to diseases, autoimmune disorders and even cancer. This review examines the relationship between physicochemical properties, i.e., shape, charge, hydrophobicity, composition and stiffness, and interactions of NP with pulmonary immune cells. Inhalation is the ideal route of administration for direct delivery but inhaled NP encounter innate immune cells, such as alveolar macrophages (AM) and dendritic cells (DC), that perceive them as harmful foreign material, interfere with gene delivery to target cells, and can induce undesirable side effects. Recommendations for fabrication and formulation of gene therapies to avoid adverse immunological responses are given. These include fine tuning physicochemical properties, functionalization of the surface of NP to actively target diseased pulmonary cells and employing biomimetics to increase immunotolerance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".