Systematic Approach for the Formulation and Optimization of Solid Lipid Nanoparticles of Efavirenz by High Pressure Homogenization Using Design of Experiments for Brain Targeting and Enhanced Bioavailability
Bibliographic record
Abstract
The nonnucleoside reverse transcriptase inhibitors, used for the treatment of HIV infections, are reported to have low bioavailability pertaining to high first-pass metabolism, high protein binding, and enzymatic metabolism. They also show low permeability across blood brain barrier. The CNS is reported to be the most important HIV reservoir site. In the present study, solid lipid nanoparticles of efavirenz were prepared with the objective of providing increased permeability and protection of drug due to biocompatible lipidic content and nanoscale size and thus developing formulation having potential for enhanced bioavailability and brain targeting. Solid lipid nanoparticles were prepared by high pressure homogenization technique using a systematic approach of design of experiments (DoE) and evaluated for particle size, polydispersity index, zeta potential, and entrapment efficiency. Particles of average size 108.5 nm having PDI of 0.172 with 64.9% entrapment efficiency were produced. Zeta potential was found to be −21.2 mV and the formulation was found stable. The in-vivo pharmacokinetic studies revealed increased concentration of the drug in brain, as desired, when administered through intranasal route indicating its potential for an attempt towards complete eradication of HIV and cure of HIV-infected patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".