Evaluation of EGCG Loading Capacity in DMPC Membranes.
Filipa PiresVananélia P N GeraldoBárbara RodriguesAntónio de Granada-FlorRodrigo F M de AlmeidaOsvaldo N OliveiraBruno L VictorMiguel MachuqueiroMaria RaposoPublished in: Langmuir : the ACS journal of surfaces and colloids (2019)
Catechins are molecules with potential use in different pathologies such as diabetes and cancer, but their pharmaceutical applications are often hindered by their instability in the bloodstream. This issue can be circumvented using liposomes as their nanocarriers for in vivo delivery. In this work, we studied the molecular details of (-)-epigallocatechin-3-gallate (EGCG) interacting with 1,2-dimyristoyl- sn-glycero-3-phosphocholine (DMPC) monolayer/bilayer systems to understand the catechin loading ability and liposome stability, using experimental and computational techniques. The molecular dynamics simulations show the EGCG molecules deep inside the lipid bilayer, positioned below the lipid ester groups, generating a concentration-dependent lipid condensation. This effect was also inferred from the surface pressure isotherms of DMPC monolayers. In the polarization-modulated infrared reflection absorption spectra assays, the predominant effect at higher concentrations of EGCG (e.g., 20 mol %) was an increase in lipid tail disorder. The steady-state fluorescence data confirmed this disordered state, indicating that the catechin-induced liposome aggregation outweighs the condensation effects. Therefore, by adding more than 10 mol % EGCG to the liposomes, a destabilization of the vesicles occurs with the ensuing release of entrapped catechins. The loading capacity for DMPC seems to be limited by its disordered lipid arrangements, typical of a fluid phase. To further increase the clinical usefulness of liposomes, lipid bilayers with more stable and organized assemblies should be employed to avoid aggregation at large concentrations of catechin.
Keyphrases
- molecular dynamics simulations
- drug delivery
- fatty acid
- drug release
- cardiovascular disease
- type diabetes
- squamous cell carcinoma
- risk assessment
- papillary thyroid
- big data
- escherichia coli
- artificial intelligence
- glycemic control
- adipose tissue
- electronic health record
- drug induced
- skeletal muscle
- machine learning
- young adults
- data analysis
- childhood cancer
- klebsiella pneumoniae
- protein kinase