Mechanistic studies of Gemcitabine-loaded nanoplatforms in resistant pancreatic cancer cells
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer remains the deadliest of all cancers, with a mortality rate of 91%. Gemcitabine is considered the gold chemotherapeutic standard, but only marginally improves life-span due to its chemical instability and low cell penetrance. A new paradigm to improve Gemcitabine's therapeutic index is to administer it in nanoparticles, which favour its delivery to cells when under 500 nm in diameter. Although promising, this approach still suffers from major limitations, as the choice of nanovector used as well as its effects on Gemcitabine intracellular trafficking inside pancreatic cancer cells remain unknown. A proper elucidation of these mechanisms would allow for the elaboration of better strategies to engineer more potent Gemcitabine nanotherapeutics against pancreatic cancer. METHODS: Gemcitabine was encapsulated in two types of commonly used nanovectors, namely poly(lactic-co-glycolic acid) (PLGA) and cholesterol-based liposomes, and their physico-chemical parameters assessed in vitro. Their mechanisms of action in human pancreatic cells were compared with those of the free drug, and with each others, using cytotoxity, apoptosis and ultrastructural analyses. RESULTS: Physico-chemical analyses of both drugs showed high loading efficiencies and sizes of less than 200 nm, as assessed by dynamic light scattering (DLS) and transmission electron microscopy (TEM), with a drug release profile of at least one week. These profiles translated to significant cytotoxicity and apoptosis, as well as distinct intracellular trafficking mechanisms, which were most pronounced in the case of PLGem showing significant mitochondrial, cytosolic and endoplasmic reticulum stresses. CONCLUSIONS: Our study demonstrates how the choice of nanovector affects the mechanisms of drug action and is a crucial determinant of Gemcitabine intracellular trafficking and potency in pancreatic cancer settings.
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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 | 0.001 | 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 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".