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.

Genome-scale RNAi profiling of cell division in human tissue culture cells.

Cell division is fundamental for all organisms. Here we report a genome-scale RNA-mediated interference screen in HeLa cells designed to identify human genes that are important for cell division. We have used a library of endoribonuclease-prepared short interfering RNAs for gene silencing and have used DNA content analysis to identify genes that induced cell cycle arrest or altered ploidy on silencing. Validation and secondary assays were performed to generate a nine-parameter loss-of-function phenoprint for each of the genes. These phenotypic signatures allowed the assignment of genes to specific functional classes by combining hierarchical clustering, cross-species analysis and proteomic data mining. We highlight the richness of our dataset by ascribing novel functions to genes in mitosis and cytokinesis. In particular, we identify two evolutionarily conserved transcriptional regulatory networks that govern cytokinesis. Our work provides an experimental framework from which the systematic analysis of novel genes necessary for cell division in human cells can begin.