Microstructural insight into inhalation powder blends through correlative multi-scale X-ray computed tomography.

Dry powder inhalers (DPI) are important for topical drug delivery to the lungs, but characterising the pre-aerosolised powder microstructure is a key initial step in understanding the post-aerosolised blend performance. In this work, we characterise the pre-aerosolised 3D microstructure of an inhalation blend using correlative multi-scale X-ray Computed Tomography (XCT), identifying lactose and drug-rich phases at multiple length scales on the same sample. The drug-rich phase distribution across the sample is shown to be homogeneous on a bulk scale but heterogeneous on a particulate scale, with individual clusters containing different amounts of drug-rich phase, and different parts of a carrier particle coated with different amounts of drug-rich phase. Simple scalings of the drug-rich phase thickness with carrier particle size are used to derive the drug-proportion to carrier particle size relationship. This work opens new doors to micro-structural assessment of inhalation powders that could be invaluable for bioequivalence assessment of dry powder inhalers.

Nanoformulation-by-design: an experimental and molecular dynamics study for polymer coated drug nanoparticles.

The formulation of drug compounds into nanoparticles has many potential advantages in enhancing bioavailability and improving therapeutic efficacy. However, few drug molecules will assemble into stable, well-defined nanoparticulate structures. Amphiphilic polymer coatings are able to stabilise nanoparticles, imparting defined surface properties for many possible drug delivery applications. In the present article we explore, both experimentally and in silico, a potential methodology to coat drug nanoparticles with an amphiphilic co-polymer. Monomethoxy polyethylene glycol-polycaprolactone (mPEG-b-PCL) diblock copolymers with different mPEG lengths (M w 350, 550, 750 and 2000), designed to give different levels of colloidal stability, were used to coat the surface of indomethacin nanoparticles. Polymer coating was achieved by a flow nanoprecipitation method that demonstrated excellent batch-to-batch reproducibility and resulted in nanoparticles with high drug loadings (up to 78%). At the same time, in order to understand this modified nanoprecipitation method at an atomistic level, large-scale all-atom molecular dynamics simulations were performed in parallel using the GROMOS53a6 forcefield parameters. It was observed that the mPEG-b-PCL chains act synergistically with the acetone molecules to dissolve the indomethacin nanoparticle while after the removal of the acetone molecules (mimicking the evaporation of the organic solvent) a polymer-drug nanoparticle was formed (yield 99%). This work could facilitate the development of more efficient methodologies for producing nanoparticles of hydrophobic drugs coated with amphiphilic polymers. The atomistic insight from the MD simulations in tandem with the data from the drug encapsulation experiments thus leads the way to a nanoformulation-by-design approach for therapeutic nanoparticles.

CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space.

CoCo ("complementary coordinates") is a method for ensemble enrichment based on principal component analysis (PCA) that was developed originally for the investigation of NMR data. Here we investigate the potential of the CoCo method, in combination with molecular dynamics simulations (CoCo-MD), to be used more generally for the enhanced sampling of conformational space. Using the alanine penta-peptide as a model system, we find that an iterative workflow, interleaving short multiple-walker MD simulations with long-range jumps through conformational space informed by CoCo analysis, can increase the rate of sampling of conformational space up to 10 times for the same computational effort (total number of MD timesteps). Combined with the reservoir-REMD method, free energies can be readily calculated. An alternative, approximate but fast and practically useful, alternative approach to unbiasing CoCo-MD generated data is also described. Applied to cyclosporine A, we can achieve far greater conformational sampling than has been reported previously, using a fraction of the computational resource. Simulations of the maltose binding protein, begun from the "open" state, effectively sample the "closed" conformation associated with ligand binding. The PCA-based approach means that optimal collective variables to enhance sampling need not be defined in advance by the user but are identified automatically and are adaptive, responding to the characteristics of the developing ensemble. In addition, the approach does not require any adaptations to the associated MD code and is compatible with any conventional MD package.