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. We established tight agreement between the computed stability (miscibility) limits of 9.0 and 10.0 wt% vs. the experimental 7 and 9 wt% for the Vitamin E TPGS and Tween 80 systems, respectively, and identified different destabilizing mechanisms applicable to each system. This paradigm supports that computational stability prediction may serve as a physically meaningful, resource-efficient, and operationally sensible digital twin to experimental screening tests of pharmaceutical systems. This approach is also relevant to amorphous solid dispersion drug delivery systems, as it can identify critical stability points of active pharmaceutical ingredient/excipient mixtures.

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., neither the Archimedean principle nor gas pycnometry may be applied, the former being only applicable to insoluble compounds, while the latter yielding skeletal density - an overestimate of true density to the extent of blind pores - and its efficiency is affected by the choice of the gas medium. In these cases, it is feasible to infer amorphous density from diffraction experiments through the use of the reduced Pair Distribution Function (PDF). Although an estimate of crystalline density has been known to be possible via the PDF shape, here we outline a new method extending this facility to include the estimation of amorphous density. •Amorphous density may be inferred from the position of a local minimum of the reduced PDF profile, the latter extracted via a Fourier transformation of collected diffraction intensity.•The PDF minimum is located within the PDF range bounded by rmin = 2π/Qmax and the position of the first coordination peak, where Qmax is the maximum length of the scattering vector achieved in the diffraction experiment.•Amorphous density is calculated as the ratio of the value of the reduced PDF at the local minimum, divided by the term 4πr, where r is the real space coordinate of the local minimum.

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. The resulting short range order features and ring statistics were in tight agreement with experimental data and created consensus for the accurate prediction of transport properties. Accordingly, calculations yielded rational values both for the average heat capacity, equal to 1668.58 J/(kg·K), and for the viscosity, in the range of 4.09-87.64 cP. The potential was consistent in predicting accurate values for mass density (i.e. 2961.50 kg/m(3) vs. an experimental value of 2940 kg/m(3)) and for electrical conductivity (5.3-233 S/m within a temperature range of 1273.15-2273.15 K).