Developing a toolkit towards prediction of protein aggregation  
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October 15, 2019
11 am to 12 pm EDT



Webinar Description:

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.

Key Learning Objectives:

  • Gain exposure to research into machine learning for prediction of aggregation
  • Understand the benefits of MALS detection for reverse phase chromatography
  • Become acquainted with extensive formulation screening methodology developed in the context of the PIPPI program
  • Gain understanding of native reversible self-association and its analytical challenges

Who Should Attend:

  • Product Managers for protein-based biotherapeutics
  • Formulation scientists
  • Lab managers responsible for protein formulation analytics and/or data mining


Prof. Dr. Wolfgang Frieß image

Prof. Dr. Wolfgang Frieß
Department of Pharmacy; Pharmaceutical Technology and Biopharmaceutics
Ludwig-Maximilians-Universitaet Muenchen

Wolfgang Frieß holds a position as Professor for Pharmaceutical Technology and Biopharmaceutics at the LMU Munich since 2001. He received his PhD in Pharmaceutical Technology in 1993 and his Pharmacy degree in 1989 from the University of Erlangen. He has worked for several years in academia both in Germany and the US. His primary research goals are protein formulation, drug delivery and biomaterials, in particular new analytical tools for protein formulations, freeze-drying of proteins and different local delivery routes. He is co-editor of the European Journal of Pharmaceutics and Biopharmaceutics and has published over 150 research papers, patents and book chapters.


Cost: No Cost!