PO-016 Inhibition of cell proliferation by anti-epidermal growth factor receptor (EGFR) aptamer conjugated chitosan/siRNA nanoparticles
Bibliographic record
Abstract
Introduction Gene silencing mediated by small interfering RNA (siRNA) has been widely investigated as a potential therapeutic approach. Its use, however, is hampered by its rapid degradation and poor cellular uptake into cells. Therefore, the success will depend on the design of effective systems able to selectively and efficiently deliver siRNA to target cells/organs. Our strategy relies on the use of a biocompatible biopolymer (chitosan) as carrier of siRNA coupled with specific anti-EGFR aptamers for cell targeting which is overexpressed in various cancer cells. Finally, poly (ethylene glycol) (PEG) and diethylaminoethyl (DEAE) will be covalently-linked with chitosan, in order to improve blood residency and transfection efficiency. The selected siRNA will be directed to silence receptor activator of nuclear factor-kB ligand (RANKL). Its levels are elevated in numerous cancers. Blockage of EGFR by aptamer and knockdown of RANKL by siRNA inhibit cancer cell proliferation in vitro. Material and methods Synthesise and characterise chitosan conjugates: poly (ethylene glycol) (PEG), diethylaminoethyl (DEAE), and anti-EGFR-aptamer are covalently-linked with chitosan. Synthetize, purify and characterise DEAE/PEG/anti-EGFR-aptamer-chitosan/siRNA nanoparticles Optimise the nanoformulations (adjusting polymeric and charge ratios) through tests in vitro including transfection efficacy and cell proliferation assays in different cancer cell lines. Results and discussions Nanoparticles were produced on the basis of our previous results. Particle size and zeta potential were measured by Zetasizer Nano ZS90 (Malvern Instruments Ltd., Malvern, UK). The sizes of synthesised nanoparticles were around 259±3 nm for Chitosan-DEAE15/siRNA with a zeta potential of +28.3±0.8 mV. The average cell viability of free siRNA or nanoparticle-treated cells was 89%–97% compared to nonrelated cells. The results showed that anti-EGFR-aptamer-chitosan/siRNA nanoparticles had a dose-dependent inhibition of cell proliferation. These nanoparticles had a significant inhibition effect of RANKL mRNA expression (RT-PCR assay. Conclusion Conventional cancer treatments such as chemotherapy have severe side toxicity on both tumour and host cells. Biological targeted agents such as monoclonal antibodies are available, but costly. EGFR and RANKL are two of major targets for drug development for cancer treatment. Non-viral gene therapy involved aptamer-EGFR and siRNA-RANKL is a promising therapy strategy. The supportIng grant was from MESI-Quebec (PSR-SIIRI, 2017–2020).
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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 | 0.000 |
| 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 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".