Lipid Nanoparticle Mediated Delivery of Docetaxel Prodrug for Exploiting Full Potential of Gold Radiosensitizers in the Treatment of Pancreatic Cancer.
Bibliographic record
Abstract
Abstract Background Pancreatic cancer is one of the leading causes of cancer deaths worldwide. Current chemoradiation therapy suffers from normal tissue toxicity. To overcome this problem, we are proposing incorporating nanoparticles as radiosensitizers and as drug delivery vehicles into current chemoradiation regimes. Gold nanoparticles (GNPs) and Docetaxel (DTX) have shown very promising synergetic radiosensitization effects despite DTX toxicity to normal tissues. Here, we explored the effect of DTX prodrug encapsulated in lipid nanoparticles (LNPDTX−P) on GNP uptake in pancreatic cancer models in vitro and in vivo. Methods For the in vitro experiment, pancreatic cancer cell line, MIA PaCa-2, were cultured and dosed with 7.5 µg/mL of GNPs (of ~ 11 nm in diameter functionalized with PEG and RGD peptide) and with 45 nM of free DTX or equivalent dose of LNPDTX−P. For the in vivo experiment, MIA PaCa-2 were implanted subcutaneously in NRG mice and the mice were dosed with 2 mg/kg of GNPs and 6 mg/kg of DTX or equivalent dose of LNPDTX−P. GNPs content in mice and in cells were measured using Inductively Coupled Plasma–Mass Spectrometry (ICP–MS). Results The results show that LNPDTX−P treated tumour samples have no less than 191% increase in GNPs uptake compared to control samples in both in vitro and in vivo. The results also show that LNPDTX−P treated tumour samples have retained over 160% of GNPs compared to control samples in both in vitro and in vivo. No significant difference was found in GNP uptake or retention between free DTX and LNPDTX−P in tumour treated samples in vivo. Conclusions The results are very promising as LNPDTX−P have superior targeting of tumour tissues compared to free DTX due to their nano size and their ability to be functionalized. Because of their minimal toxicity to normal tissues, both GNPs and LNPDTX−P can be ideal radiosensitization candidates in radiotherapy and would produce very promising synergistic therapeutic outcome. Current radiotherapy treatments can hugely benefit from the effects of these two radiosensitizers boosting the radiation dose to the tumour while reducing the normal tissue dose. This in return will improve patient outcome and quality of life.
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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) | 0.000 | 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 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".