Novel intravaginal nanomedicine for the targeted delivery of saquinavir to CD4+ immune cells
Bibliographic record
Abstract
The goal of this study was to develop and characterize an intravaginal nanomedicine for the active targeted delivery of saquinavir (SQV) to CD4(+) immune cells as a potential strategy to prevent or reduce HIV infection. The nanomedicine was formulated into a vaginal gel to provide ease in self-administration and to enhance retention within the vaginal tract. SQV-encapsulated nanoparticles (SQV-NPs) were prepared from poly(lactic-co-glycolic acid) (PLGA) and conjugated to antihuman anti-CD4 antibody. Antibody-conjugated SQV-NPs (Ab-SQV-NPs) had an encapsulation efficiency (EE%) of 74.4% + 3.7% and an antibody conjugation efficiency (ACE%) of 80.95% + 1.10%. Over 50% of total loaded SQV was released from NPs over 3 days. NPs were rapidly taken up by Sup-T1 cells, with more than a twofold increase in the intracellular levels of SQV when delivered by Ab-SQV-NPs in comparison to controls 1 hour post-treatment. No cytotoxicity was observed when vaginal epithelial cells were treated for 24 hours with drug-free Ab-NPs (1,000 μg/mL), 1% HEC placebo gel (200 mg/mL), or 1% HEC gel loaded with drug-free Ab-NPs (5 mg NPs/g gel, 200 mg/mL of gel mixture). Overall, we described an intravaginal nanomedicine that is nontoxic and can specifically deliver SQV into CD4(+) immune cells. This platform may demonstrate potential utility in its application as postexposure prophylaxis for the treatment or reduction of HIV infection, but further studies are required.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".