Rationally designed dendritic silica nanoparticles for oral delivery of exenatide

Type 2 diabetes makes up approximately 85% of all diabetic cases and it is linked to approximately one-third of all hospitalisations. Newer therapies with long-acting biologics such as glucagon-like peptide-1 (GLP-1) analogues have been promising in managing the disease, but they cannot reverse the pathology of the disease. Additionally, their parenteral administration is often associated with high healthcare costs, risk of infections, and poor patient adherence associated with phobia of needles. Oral delivery of these compounds would significantly improve patient compliance; however, poor enzymatic stability and low permeability across the gastrointestinal tract makes this task challenging. In the present work, large pore dendritic silica nanoparticles (DSNPs) with a pore size of ~10 nm were prepared, functionalized, and optimized in order to achieve high peptide loading and improve intestinal permeation of exenatide, a GLP-1 analogue. Compared to the loading capacity of the most popular, Mobil Composition of Matter No. 41 (MCM-41) with small pores, DSNPs showed significantly high loading owing to their large and dendritic pore structure. Among the tested DSNPs, pristine and phosphonate-modified DSNPs (PDSNPs) displayed remarkable loading of 40 and 35% w/w, respectively. Furthermore, particles successfully coated with positively charged chitosan reduced the burst release of exenatide at both pH 1.2 and 6.8. Compared with free exenatide, both chitosan-coated and uncoated PDSNPs enhanced exenatide transport through the Caco-2 monolayer by 1.7 fold. Interestingly, when a triple co-culture model of intestinal permeation was used, chitosan-coated PDSNPs performed better compared to both PDSNPs and free exenatide, which corroborated our hypothesis behind using chitosan to interact with mucus and improve permeation. These results indicate the emerging role of large pore silica nanoparticles as promising platforms for oral delivery of biologics such as exenatide.We thank the National Health and Medical Research Council’s Project Grant GNT1107836 and Early Career Fellowship and Career Development Fellowship to A.P. We also thank NHMRC for EC Fellowship to T.K. We would also like to thank the Centre of Microscopy and Microanalysis at The University of Queensland for providing facilities to conduct TEM. This article was, in part, a result of the project NORTE-01-0145-FEDER-000012, supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was financed by FEDER—Fundo Europeu de Desenvolvimento Regional funds—through the COMPETE 2020–Operational Programme for Competitiveness and Internationalization (POCI), Portugal 2020, and by Portuguese funds through FCT—Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Ensino Superior in the framework of the project “Institute for Research and Innovation in Health Sciences” (POCI-01-0145-FEDER-007274)

Simulation vs. Reality: A Comparison of In Silico Distance Predictions with DEER and FRET Measurements

Site specific incorporation of molecular probes such as fluorescent- and nitroxide spin-labels into biomolecules, and subsequent analysis by Förster resonance energy transfer (FRET) and double electron-electron resonance (DEER) can elucidate the distance and distance-changes between the probes. However, the probes have an intrinsic conformational flexibility due to the linker by which they are conjugated to the biomolecule. This property minimizes the influence of the label side chain on the structure of the target molecule, but complicates the direct correlation of the experimental inter-label distances with the macromolecular structure or changes thereof. Simulation methods that account for the conformational flexibility and orientation of the probe(s) can be helpful in overcoming this problem. We performed distance measurements using FRET and DEER and explored different simulation techniques to predict inter-label distances using the Rpo4/7 stalk module of the M. jannaschii RNA polymerase. This is a suitable model system because it is rigid and a high-resolution X-ray structure is available. The conformations of the fluorescent labels and nitroxide spin labels on Rpo4/7 were modeled using in vacuo molecular dynamics simulations (MD) and a stochastic Monte Carlo sampling approach. For the nitroxide probes we also performed MD simulations with explicit water and carried out a rotamer library analysis. Our results show that the Monte Carlo simulations are in better agreement with experiments than the MD simulations and the rotamer library approach results in plausible distance predictions. Because the latter is the least computationally demanding of the methods we have explored, and is readily available to many researchers, it prevails as the method of choice for the interpretation of DEER distance distributions