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. WIREs Nanomed Nanobiotechnol 2015, 7:298–314. doi: 10.1002/wnan.1308 This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Biology-Inspired Nanomaterials > Lipid-Based Structures Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".