Highly bioavailable silibinin nanoparticles inhibit HCV infection
Bibliographic record
Abstract
OBJECTIVE: Silibinin is a flavonolignan that is well established for its robust antiviral activity against HCV infection and has undergone several clinical trials for the management of hepatitis C. Despite its potency, silibinin suffers from poor solubility and bioavailability, restricting its clinical use. To overcome this limitation, we developed highly bioavailable silibinin nanoparticles (SB-NPs) and evaluated their efficiency against HCV infection. DESIGN: SB-NPs were prepared using a nanoemulsification technique and were physicochemically characterised. Infectious HCV culture systems were used to evaluate the influence of SB-NP on the virus life cycle and examine their antioxidant activity against HCV-induced oxidative stress. The safety profiles of SB-NP, in vivo pharmacokinetic studies and antiviral activity against infection of primary human hepatocytes were also assessed. RESULTS: SB-NP consisted of nanoscale spherical particles (<200 nm) encapsulating amorphous silibinin at >97% efficiency and increasing the compound's solubility by >75%. Treatment with SB-NP efficiently restricted HCV cell-to-cell transmission, suggesting that they retained silibinin's robust anti-HCV activity. In addition, SB-NP exerted an antioxidant effect via their free radical scavenging function. Oral administration of SB-NP in rodents produced no apparent in vivo toxicity, and pharmacokinetic studies revealed an enhanced serum level and superior biodistribution to the liver compared with non-modified silibinin. Finally, SB-NP efficiently reduced HCV infection of primary human hepatocytes. CONCLUSIONS: Due to SB-NP's enhanced bioavailability, effective anti-HCV activity and an overall hepatoprotective effect, we suggest that SB-NP may be a cost-effective anti-HCV agent that merits further evaluation for the treatment of hepatitis C.
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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".