Lipoprotein-Inspired Nanoparticles for Cancer Theranostics
Bibliographic record
Abstract
Over hundreds of millions of years, animals have evolved endogenous lipoprotein nanoparticles for shuttling hydrophobic molecules to different parts of the body. In the last 70 years, scientists have developed an understanding of lipoprotein function, often in relationship to lipid transport and heart disease. Such biocompatible, lipid-protein complexes are also ideal for loading and delivering cancer therapeutic and diagnostic agents, which means that lipoprotein and lipoprotein-inspired nanoparticles also offer opportunities for cancer theranostics. By mimicking the endogenous shape and structure of lipoproteins, the nanocarrier can remain in circulation for an extended period of time, while largely evading the reticuloendothelial cells in the body's defenses. The small size (less than 30 nm) of the low-density (LDL) and high-density (HDL) classes of lipoproteins allows them to maneuver deeply into tumors. Furthermore, lipoproteins can be targeted to their endogenous receptors, when those are implicated in cancer, or to other cancer receptors. In this Account, we review the field of lipoprotein-inspired nanoparticles related to the delivery of cancer imaging and therapy agents. LDL has innate cancer targeting potential and has been used to incorporate diverse hydrophobic molecules and deliver them to tumors. Nature's method of rerouting LDL in atherosclerosis provides a strategy to extend the cancer targeting potential of lipoproteins beyond its narrow purview. Although LDL has shown promise as a drug nanocarrier for cancer imaging and therapy, increasing evidence indicates that HDL, the smallest lipoprotein, may also be of use for drug targeting and uptake into cancer cells. We also discuss how synthetic HDL-like nanoparticles, which do not include human or recombinant proteins, can deliver molecules directly to the cytoplasm of certain cancer cells, effectively bypassing the endosomal compartment. This strategy could allow HDL-like nanoparticles to be used to deliver drugs that have increased activity in the cytoplasm. Lipoprotein nanoparticles have evolved to be ideal delivery vehicles, and because of that specialized function, they have the potential to improve cancer theranostics.
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 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".