Malaria Biomimetic for Tumor Targeted Drug Delivery
Bibliographic record
Abstract
Malaria infected erythrocytes utilize the parasite protein VAR2CSA to bind to a unique presentation of chondroitin sulfate (CS) for their placenta specific tropism. Interestingly, many cancers express a similar form of CS, thereby termed oncofetal CS (ofCS). The distinctive tropism of malaria infected erythrocytes and the identification of oncofetal CS, therefore, represent potentially potent tools for cancer targeting. Here we describe an intriguing drug delivery platform that effectively mimics infected erythrocytes and their specificity for ofCS. We used a lipid catcher-tag conjugation system for the functionalization of erythrocyte membrane-coated drug carriers with recombinant VAR2CSA (rVAR2). We show that these malaria mimicking erythrocyte nanoparticles (MMENPs) loaded with docetaxel (DTX) specifically target and kill melanoma cells in vitro . We further demonstrate effective targeting and therapeutic efficacy in a xenografted melanoma model. These data thus provide a proof of concept for the use of a malaria biomimetic for tumor targeted drug delivery. Given the broad presentation of ofCS found across various types of malignancies, this biomimetic may therefore show potential as a broadly targeted cancer therapy against multiple tumor indications.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".