Recent Advances in Asialoglycoprotein Receptor and Glycyrrhetinic Acid Receptor-Mediated and/or pH-Responsive Hepatocellular Carcinoma- Targeted Drug Delivery
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) seriously affects human health, especially, it easily develops multi-drug resistance (MDR) which results in treatment failure. There is an urgent need to develop highly effective and low-toxicity therapeutic agents to treat HCC and to overcome its MDR. Targeted drug delivery systems (DDS) for cancer therapy, including nanoparticles, lipids, micelles and liposomes, have been studied for decades. Recently, more attention has been paid to multifunctional DDS containing various ligands such as polymer moieties, targeting moieties, and acid-labile linkages. The polymer moieties such as poly(ethylene glycol) (PEG), chitosan (CTS), hyaluronic acid, pullulan, poly(ethylene oxide) (PEO), poly(propylene oxide) (PPO) protect DDS from degradation. Asialoglycoprotein receptor (ASGPR) and glycyrrhetinic acid receptor (GAR) are most often used as the targeting moieties, which are overexpressed on hepatocytes. Acid-labile linkage, catering for the pH difference between tumor cells and normal tissue, has been utilized to release drugs at tumor tissue. OBJECTIVES: This review provides a summary of the recent progress in ASGPR and GAR-mediated and/or pH-responsive HCC-targeted drug delivery. CONCLUSION: The multifunctional DDS may prolong systemic circulation, continuously release drugs, increase the accumulation of drugs at the targeted site, enhance the anticancer effect, and reduce side effects both in vitro and in vivo. But it is rarely used to investigate MDR of HCC; therefore, it needs to be further studied before going into clinical trials.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".