System-agnostic prediction of pharmaceutical excipient miscibility via computing-as-a-service and experimental validation.

We applied computing-as-a-service to the unattended system-agnostic miscibility prediction of the pharmaceutical surfactants, Vitamin E TPGS and Tween 80, with Copovidone VA64 polymer at temperature relevant for the pharmaceutical hot melt extrusion process. The computations were performed in lieu of running exhaustive hot melt extrusion experiments to identify surfactant-polymer miscibility limits. The computing scheme involved a massively parallelized architecture for molecular dynamics and free energy perturbation from which binodal, spinodal, and mechanical mixture critical points were detected on molar Gibbs free energy profiles at 180 °C.

Direct determination of amorphous number density from the reduced pair distribution function.

The inference of amorphous bulk density, while straightforward for nonporous, soluble materials, may present a formidable challenge in some of the most important classes of industrial applications, involving melts, porous solids, and non-soluble organic pharmaceuticals, with varied implications depending on the material's level of technological interest. Within nanotechnology and the life sciences in particular, accurate determination of amorphous true density is a frequent requirement and a regular puzzle, when, e.g.

Bona-fide method for the determination of short range order and transport properties in a ferro-aluminosilicate slag.

The thermodynamics, structural and transport properties (density, melting point, heat capacity, thermal expansion coefficient, viscosity and electrical conductivity) of a ferro-aluminosilicate slag have been studied in the solid and liquid state (1273-2273 K) using molecular dynamics. The simulations were based on a Buckingham-type potential, which was extended here, to account for the presence of Cr and Cu. The potential was optimized by fitting pair distribution function partials to values determined by Reverse Monte Carlo modelling of X-ray and neutron diffraction experiments.