Developing a toolkit towards prediction of protein aggregation
Presented By: Wolfgang Friess, Ph.D., Department of Pharmacy, Ludwig-Maximilians-Universitaet Muenchen
Presented Live: October 15, 2019
PIPPI, which stands for Protein-excipient Interactions and Protein-Protein Interactions, is a European academic-industrial consortium addressing the challenges in formulation of protein-based drugs. Its overall objective is to develop methodologies, tools and databases to guide the rational formulation of robust protein-based therapeutics. In this webinar we will present much of the effort carried out under the PIPPI program.
Machine learning: Due to the overwhelming amount of data available from high-throughput techniques such as plate-based DLS, there is a staggering increase in opportunities for finding patterns in data. Such patterns are helpful to predict long-term stability of therapeutic proteins. The application developed to identify those patterns by artificial neural network is shown.
Self-association: An in-depth case study regarding the nature of the native self-association of a full-length IgG1, as well as the corresponding Fab and Fc fragments, is presented. The case study includes comprehensive investigations by SEC-MALS, DLS, SLS, AUC, SAXS, AF4-MALS and intrinsic fluorescence.
RP-MALS: Finally, the benefits of coupling ultra-high-pressure reverse-phase liquid chromatography (UHPLC-RP) with a low-dispersion MALS detector, for the characterization of intact monoclonal antibody (mAbs) and their fragments, are shown.
