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Recently, the U.S. Food and Drug Administration (FDA) approved the first oral antiviral drug to treat mild to moderate cases of coronavirus disease. The combination of nirmatrelvir with an already used protease inhibitor class drug, ritonavir, has led to Paxlovid®. Several studies considered drug repositioning as the first trial for new drugs. The precise identification and quantification of polymorphs in raw materials and finished products are important to researchers involved in pharmaceutical development and quality control processes. In this work, we study the solid-state behavior of the antiretroviral drugs ritonavir and lopinavir in raw materials and in milled compositions. The results indicate that mixtures of ritonavir Forms I and II are found in different batches of raw materials from the same manufacturer; besides three equal crystalline samples, an amorphous batch was found in lopinavir. Furthermore, the milling process of the already amorphous lopinavir seems to facilitate the amorphization of ritonavir as well as the production of some unexpected crystalline forms of ritonavir. A phase transition of ritonavir Form I to Form II is only observed when co-milling with amorphous lopinavir. These findings reveal significant variations in phase purity of raw materials that affect the processing and solid-state properties, representing risks for the product quality.

Here we explore the use of scanning electron diffraction coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr$_{65}$Cu$_{17.5}$Ni$_{10}$Al$_{7.5}$ bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don't require energy filters on the instrument. We spatially map different quantities of interest (QoI's), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI's. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.