Nanoparticle delivery of a pH-sensitive prodrug of doxorubicin and a mitochondrial targeting VES-H8R8 synergistically kill multi-drug resistant breast cancer cells
Bibliographic record
Abstract
Abstract Multi-drug resistance (MDR) remains a major obstacle in cancer treatment while being heavily dependent on mitochondrial activity and drug efflux. We previously demonstrated that cationic lipids, such as the vitamin E succinate modified octahistidine-octaarginine (VES-H8R8) conjugate, target mitochondria, resulting in depolarized mitochondria and inhibited drug efflux in MDR breast cancer cells. We hypothesized that the effective cell uptake, efflux inhibition, and mitochondrial depolarization properties of VES-H8R8 would synergistically enhance the toxicity of a pH-sensitive prodrug of doxorubicin (pDox) when co-encapsulated in nanoparticles (NPs). pDox was successfully synthesized and validated for pH-sensitive release from NPs under lysosome-mimicking, acidic conditions. The synergistic effect of VES-H8R8 and pDox was confirmed against MDR breast cancer cells in vitro. Importantly, synergism was only observed when VES-H8R8 and pDox were co-encapsulated in a single nanoparticulate system. The synergistic mechanism was investigated, confirming superior pDox uptake and retention, Pgp efflux inhibition, mitochondrial depolarization, and enhanced induction of ROS, and apoptosis. This work demonstrates the translational potential of doubly-loaded NPs co-encapsulating pDox with VES-H8R8 to synergistically kill MDR breast cancer cells.
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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 | 0.000 | 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".