Coordinating Biointeraction and Bioreaction of a Nanocarrier Material and an Anticancer Drug to Overcome Membrane Rigidity and Target Mitochondria in Multidrug‐Resistant Cancer Cells
Bibliographic record
Abstract
Abstract Multidrug resistance (MDR) is a main cause of chemotherapy failure in cancer treatment. It is associated with complex cellular and molecular mechanisms including overexpression of drug efflux transporters, increased membrane rigidity, and impaired apoptosis. Numerous efforts have been made to overcome efflux transporter‐mediated MDR using nanotechnology‐based approaches. However, these approaches fail to surmount plasma membrane rigidity that attenuates drug penetration and nanoparticle endocytosis. Here, a “one‐two punch” nanoparticle approach is proposed to coordinate intracellular biointeraction and bioreaction of a nanocarrier material docosahexaenoic acid (DHA) and an anticancer prodrug mitomycin C (MMC) to enhance mitochondrion‐targeted toxicity. Incorporation of DHA in solid polymer‐lipid nanoparticles first reduces the membrane rigidity in live cancer cells thereby increasing nanoparticle cellular uptake and MMC accumulation. Subsequent intracellular MMC bioreduction produces free radicals that in turn react with adjacent DHA inducing significantly elevated mitochondrial lipid peroxidation, leading to irreversible damage to mitochondria. Preferential tumor accumulation of the nanoparticles and the synergistic anticancer cytotoxicity remarkably inhibit tumor growth and prolonged host survival without any systemic toxicity in an orthotopic MDR breast tumor model. This work suggests that combinatorial use of biophysical and biochemical properties of nanocarrier materials with bioreactive prodrugs is a powerful approach to overcoming multifactorial MDR in cancer.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".