Antibody Conjugation Methods for Active Targeting of Liposomes
Bibliographic record
Abstract
Liposomes are useful drug delivery vehicles since they may protect encapsulated drugs from enzymatic degradation and rapid clearance in vivo, or alter biodistribution, potentially leading to reduced toxicities (1,2). A major limitation to the development of many specialized applications is the problem of directing liposomes to tissues where they would not normally accumulate. Consequently, a great deal of effort has been made over the years to develop liposomes that have targeting vectors attached to the bilayer surface. These vectors have included ligands such as oligosaccharides (3,4), peptides (5,6), proteins (7,8) and vitamins (9). Most studies have focused on antibody conjugates since procedures for producing highly specific monoclonal antibodies (MAbs) are well established. In principle it should be possible to deliver liposomes to any cell type as long as the cells are accessible to the carrier. In practice it is usually not this simple since access to tissue, competition, and rapid clearance are formidable obstacles. It has also been shown that antibodies become immunogenic when coupled to liposomes (10,11), although in similar experiments with ovalbumin we have demonstrated that immunogenicity can be suppressed by formulating the liposomes with the cytotoxic drug doxorubi-cin (12). Such issues as these suggest that the development of antibody-targeted liposomes for in vivo applications will present difficult challenges.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".