Abstract: In a method for facilitating selection of chromatography parameters for manufacturing a therapeutic protein, one or more process parameter values associated with a hypothetical chromatography process, and one or more molecular descriptors descriptive of the therapeutic protein, are received. The method also includes predicting a performance indicator for the hypothetical chromatography process at least by analyzing the one or more process parameters and the one or more molecular descriptors using a machine learning model. The machine learning model is a regression tree model, an extreme gradient boost model, or an elastic net model. The method also includes causing the predicted performance indicator, and/or an indication of whether the predicted performance indicator satisfies one or more acceptability criteria, to be presented to a user via a user interface.

Abstract: A biologics manufacturing process that connects the drug substance and drug product processes into an integrated, continuous process.

Abstract: Compositions and methods are provided for the cell-free synthesis of active reprogramming factor polypeptides. The reprogramming factors may be synthesized as fusion proteins comprising a permeant domain, such as polyarginme. The cell free-synthesis may be conducted at about 25 C in a bacterial cell extract from genetically alterd cells having decreased endogenous protease activity Further, the proteins may comprise a fusion partner which enhances solubility and may be refolded on a column.