Cytotoxicity assessment, inflammatory properties, and cellular uptake of Neutraplex lipid-based nanoparticles in THP-1 monocyte-derived macrophages
Bibliographic record
Abstract
Current antiretroviral drugs used to prevent or treat human immunodeficiency virus type 1 (HIV-1) infection are not able to eliminate the virus within tissues or cells where HIV establishes reservoirs. Hence, there is an urgent need to develop targeted delivery systems to enhance drug concentrations in these viral sanctuary sites. Macrophages are key players in HIV infection and contribute significantly to the cellular reservoirs of HIV because the virus can survive for prolonged periods in these cells. In the present work, we investigated the potential of the lipid-based Neutraplex nanosystem to deliver anti-HIV therapeutics in human macrophages using the human monocyte/macrophage cell line THP-1. Neutraplex nanoparticles as well as cationic and anionic Neutraplex nanolipoplexes (Neutraplex/small interfering RNA) were prepared and characterized by dynamic light scattering. Neutraplex nanoparticles showed low cytotoxicity in CellTiter-Blue reduction and lactate dehydrogenase release assays and were not found to have pro-inflammatory effects. In addition, confocal studies showed that the Neutraplex nanoparticles and nanolipoplexes are rapidly internalized into THP-1 macrophages and that they can escape the late endosome/lysosome compartment allowing the delivery of small interfering RNAs in the cytoplasm. Furthermore, HIV replication was inhibited in the in vitro TZM-bl infectivity assay when small interfering RNAs targeting CXCR4 co-receptor was delivered by Neutraplex nanoparticles compared to a random small interfering RNA sequence. This study demonstrates that the Neutraplex nanosystem has potential for further development as a delivery strategy to efficiently and safely enhance the transport of therapeutic molecules into human monocyte-derived macrophages in the aim of targeting HIV-1 in this cellular reservoir.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".