Learning from biology: synthetic lipoproteins for drug delivery
Bibliographic record
Abstract
Synthetic lipoproteins represent a relevant tool for targeted delivery of biological/chemical agents (chemotherapeutics, siRNAs, photosensitizers, and imaging contrast agents) into various cell types. These nanoparticles offer a number of advantages for drugs delivery over their native counterparts while retaining their natural characteristics and biological functions. Their ultra-small size (<30 nm), high biocompatibility, favorable circulation half-life, and natural ability to bind specific lipoprotein receptors, i.e., low-density lipoprotein receptor (LDLR) and Scavenger receptor class B member 1 (SRB1) that are found in a number of pathological conditions (e.g., cancer, atherosclerosis), make them superior delivery strategies when compared with other nanoparticle systems. We review the various approaches that have been developed for the generation of synthetic lipoproteins and their respective applications in vitro and in vivo. More specifically, we summarize the approaches employed to address the limitation on use of reconstituted lipoproteins by means of natural or recombinant apolipoproteins, as well as apolipoprotein mimetic molecules. Finally, we provide an overview of the advantages and disadvantages of these approaches and discuss future perspectives for clinical translation of these nanoparticles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".