Preparation of maltodextrin nanoparticles and encapsulation of bovine serum albumin - Influence of formulation parameters.

Maltodextrin, which is obtained by partial hydrolysis of starch, is water soluble and could serve as hydrophilic carrier for the encapsulation of protein-based active pharmaceutical ingredients. We investigated three different commercial maltodextrins (Dextrose Equivalents (DE) 4.0-7.0, DE 13.0-17.0 and DE 16.5-19.5) with focus on their ability to form nanoparticles by inverse precipitation. Successful particle formation was observed for DE 13.0-17.0 and DE 16.5-19.5 but not for DE 4.0-7.0. The process was investigated using acetone as anti-solvent and poloxamer 407 as stabilizer. A tunable size between 170 nm and 450 nm was achieved by varying the type of maltodextrin and the stabilizer concentration. Particles were spherical in shape and were stable over a time period of 14 days. Maltodextrin nanoparticles (MD NPs) were tested on A549 cells and did not show any cytotoxic effects. This underlines the potential of maltodextrin as material for drug delivery systems. Bovine serum albumin (BSA) as a model protein was successfully encapsulated into MD NPs with encapsulation efficiencies of approx. 70% and loading rates of up to 20%.

Integrative network-based approach identifies key genetic elements in breast invasive carcinoma.

BACKGROUND: Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. RESULTS: Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods. CONCLUSION: The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.