A Systematic Review of Advances in Plant-Based Phospholipid Liposomes in Breast Cancer Therapy: Characterization, Innovations, Clinical Applications, and Future Directions
Bibliographic record
Abstract
Introduction: Plant-based phospholipid (PP) liposomes are sustainable, biocompatible, and biodegradable carriers with advantages over synthetic and animal-derived lipids, including lower immunogenic risk and abundant availability from sources such as soy, sunflower, and canola. This systematic review examines their characteristics, innovations, and applications in breast cancer (BCA) therapy. Methods: A total of 43 studies published between 2010 and June 2025 were identified from MEDLINE, Scopus, and Web of Science, focusing on PP composition, drug delivery mechanisms, and therapeutic efficacy in in vitro, in vivo, and preclinical BCA models. Results: Advances include nanotechnology and ligand-targeted systems that improve stability, control drug release, and enhance tumor-specific uptake. PP liposomes co-loaded with chemotherapeutics showed synergistic anticancer effects, increased tumor accumulation, and reduced systemic toxicity. Personalized targeting strategies further improved therapeutic precision and minimized off-target effects. Conclusions: PP liposomes offer an innovative and environmentally sustainable approach for BCA treatment with demonstrated preclinical benefits in efficacy and safety. Translation to clinical practice requires standardized characterization, scalable production, and well-designed trials to confirm safety, dosing, and long-term effectiveness.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".