Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization

Lorenzo Gentiluomo, Hristo L Svilenov, Dillen Augustijn, Inas El Bialy, Maria Laura Greco, Alina Vitaliyivna Kulakova, Sowmya Indrakumar, Sujata Mahapatra, Marcello Martinez Morales, Christin Pohl, Aisling Roche, Andreas Tosstorff, Robin Curtis, Jeremy P Derrick, Allan Nørgaard, Tarik A Khan, Günther H.J. Peters, Alain Pluen, Åsmund Rinnan, Werner W StreicherChristopher F van der Walle, Shahid Uddin, Gerhard Winter, Dierk Roessner, Pernille Harris*, Wolfgang Frieß

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.
Original languageEnglish
JournalMolecular Pharmaceutics
Volume17
Issue number2
Pages (from-to)426-440
ISSN1543-8384
DOIs
Publication statusPublished - 2020

Cite this