Design and Evaluation of pH Sensitive PEG-Protamine Nanocomplex of Doxorubicin for Treatment of Breast Cancer.
Ikhlaque AhmadMuhammad Farhan Ali KhanAbbas RahdarSaddam HussainFahad Khan TareenMuhammad Waqas SalimNarges AjalliMuhammad Imran AmirzadaAhmad KhanPublished in: Polymers (2022)
Cancer is the most common cause of mortality worldwide. There is dire need of modern strategies-such as surface modification of nanocarriers-to combat this global illness. Incorporation of active targeting ligands has arisen as a novel platform for specific tumor targeting. The aim of the current study was to formulate PEG-protamine complex (PPC) of doxorubicin (DOX) for treatment of breast cancer (BC). DOX coupling with PEG can enhance cell-penetrating ability: combating resistance in MDA-MB 231 breast cancer cells. Ionic gelation method was adopted to fabricate a pH sensitive nanocomplex. The optimized nanoformulation was characterized for its particle diameter, zeta potential, surface morphology, entrapment efficiency, crystallinity, and molecular interaction. In vitro assay was executed to gauge the release potential of nanoformulation. The mean particle size, zeta potential, and polydispersity index (PDI) of the optimized nanoparticles were observed to be 212 nm, 15.2 mV, and 0.264, respectively. Crystallinity studies and Fourier transform infrared (FTIR) analysis revealed no molecular interaction and confirmed the amorphous nature of drug within nanoparticles. The in vitro release data indicate sustained drug release at pH 4.8, which is intracellular pH of breast cancer cells, as compared to the drug solution. PPC loaded with doxorubicin can be utilized as an alternative and effective approach for specific targeting of breast cancer.
Keyphrases
- drug delivery
- cancer therapy
- drug release
- breast cancer cells
- high throughput
- single cell
- combination therapy
- risk assessment
- human health
- risk factors
- cardiovascular disease
- room temperature
- stem cells
- cell proliferation
- cell death
- cardiovascular events
- big data
- papillary thyroid
- adverse drug
- machine learning
- childhood cancer
- data analysis
- reactive oxygen species
- solid state
- ultrasound guided
- walled carbon nanotubes