Targeted Delivery of Macromolecular Drugs: Asialoglycoprotein Receptor (ASGPR) Expression by Selected Hepatoma Cell Lines used in Antiviral Drug Development
Bibliographic record
Abstract
The asialoglycoprotein receptor (ASGPR), an endocytotic cell surface receptor expressed by hepatocytes, is triggered by triantennary binding to galactose residues of macromolecules such as asialoorosomucoid (ASOR). The capacity of this receptor to import large molecules across the cellular plasma membrane makes it an enticing target for receptor-mediated drug delivery to hepatocytes and hepatoma cells via ASGPR-mediated endocytosis. This study describes the preparation and characterization of (125)I-ASOR, and its utility in the assessment of ASGPR expression by HepG2, HepAD38 and Huh5-2 human hepatoma cell lines. ASOR was prepared from human orosomucoid, using acid hydrolysis to remove sialic acid residues, then radioiodinated using iodogen. (125)I-ASOR was purified by gel column chromatography and characterized by SDS-PAGE electrophoresis. The ASOR yield by acid hydrolysis was 75%, with approximately 87 % of the sialic acid residues removed. Electrophoresis and gel chromatography demonstrated substantial differences in (125)I-ASOR quality depending on the method of radioiodination. ASGPR densities per cell were estimated at 76,000 (HepG2), 17,000 (HepAD38) and 3,000 (Huh-5-2). (125)I-ASOR binding to ASGPR on HepG2 cells was confirmed through galactose- and EDTA- challenge studies. It is concluded that (125)I-ASOR is a facilely-prepared, stable assay reagent for ASGPR expression if appropriately prepared, and that HepG2 cells, but not HepAD38 or Huh-5-2 cells, are suitable for studies exploiting the endocytotic ASGPR.
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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".