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Prediction and Characterization of Therapeutic Protein Aggregation

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Prediction and Characterization of Therapeutic Protein Aggregation Dissertation zur Erlangung des Doktorgrades der Fakultät für Chemie und Pharmazie der Ludwigs-Maximilians-Universität München ___________________________________________________________________________________ Prediction and Characterization of Therapeutic Protein Aggregation ____________________________________________________________________________________ Lorenzo Gentiluomo aus Rom, Italien 2020 ERKLÄRUNG Diese Dissertation wurde im Sinne von §7 der Promotionsordnung vom 18. Juni 2016 von Herrn Prof. Dr. Wolfgang Frieß betreut. EIDESSTATTLICHE VERSICHERUNG Diese Dissertation wurde eigenständig und ohne unerlaubte Hilfe erarbeitet. München, 27.01. 2020 ___________________________ Lorenzo Gentiluomo Datum der Einreichung: 31.01.2020 Dissertation eingereicht am: 1. Gutachter: Prof. Dr. Wolfgang Frieß 2. Gutachter: Prof. Dr. Gerhard Winter Mündliche Prüfung am: 27.04.2020 Tutto quello che sono lo devo ai miei genitori, a mio fratello, e a mia moglie. A loro dedico questo lavoro. Acknowledgements Most of all, I want to express my deepest gratitude to my supervisors Prof. Dr. Wolfgang Frieß and Dr. Dierk Roessner. I highly appreciate their valuable advice, guidance, and inspiring discussions. Thanks to Dr. Dierk Roessner for providing the greatest working environment. I am deeply grateful for numerous opportunities to express myself and develop in most interesting scientific projects and collaborations. Thanks to Prof. Dr. Wolfgang Frieß for tutoring me throughout the entire PhD. I started this project coming from a completely different field, and I have highly appreciated all the effort he spent to make sure I had the best scientific support. Thanks to Prof. Dr. Gerhard Winter for all the inspiring discussions. He has always reserved for me wise words and encouragement. Thanks to all the Wyatt technology colleagues for the many contributions to this thesis and for the nice time in Dernbach. I was delighted from all the support I have received. I know I have been extremely lucky in my PhD to work without any downtime and always in the best working environment. Thanks to Dr. Roger Scherrers and his team to always provide me with all the instruments and support I needed. A special mention goes to Thomas Davis who taught me all the technicality of the Wyatt toolbox. Thanks to his Stakhanovism I always had a solution to my issues with an instrument. Thanks to Christoph Johann for all the inspiring conversation on FFF and all the friendly interactions. I have enjoyed all the time together with his family and the time spent in his house. Thanks to Felix Gloge for all the inspiring conversations on DLS and CG-MALS. His suggestions were always of the highest value. I have especially enjoyed all the nice friendly time we spent together. Thanks to all the colleagues in Santa Barbara and from around the world that put their trust in me. A special thanks goes to Michelle Chen, Daniel Some and Steve Trainoff for the inspiring conversations. Least but not last, thanks to the Wyatt family, Philip, Geoffrey and Clifford, who have created and managed such an amazing company. Thanks to all my colleagues at the Ludwig Maximilian University of Munich for the many contributions to this thesis and for all the nice time we spent together. I have always felt home in Munich thanks to them. Special thanks go to my PIPPI colleagues from Munich, Hristo Svilenov, Inas El Bialy and Andres Tosstorff, for all the scientific input and all the friendly time together; you all have been for me a source of never ending inspiration. Thanks to all my PIPPI colleagues around Europe, Marcello Morales, Maria Laura Greco, Matja Zalar, Aisling Roche, Christin Pohl, Dillen Augustijn, Marco Polimeni, Sujata Mahapatra, Sowmya Indrakumar, Alina Kulakova, Stefan Hansen, for the many contributions to this thesis and for all the crazy time together. During much of the PhD I was a wandering student. At each institution and company I visited I widened the cirle of people whom I am indebted for suggestion and comments. Thus, I would like to thank all the PIPPI consortium members and companies for the great experience and the support they provided throughout the project. As too many contributions and names would need to be acknowledging, I would acknowledge instead the head of this consortium, Pernille Harris, for creating such amazing international team. Thanks to Åsmund Rinnan and Dillen Augustijn to introduce and support me in the world of data science. Their support has been of paramount importance for the success of my work. Thanks to Werner Streicher for the nice time together at Novozymes and for all the support and training I received for AUC experiments and data analysis. Thanks to Vanessa Schneider for the excellent work on the RP-MALS development and for all the nice time together in Dernbach. My greatest thanks go to Valentina, my wife. I am exceedingly grateful for your never ending encouragement, for exceptionally motivating and for supporting me. We have grown up together. We have been facing life together. Without you I would not be the man I am today. Finally, thanks from the deepest of my heart to my mother, father and brother. No words will be ever enough to express the love we reserve for each other. Table of contents Table of contents CHAPTER I: Introduction ............................................................................................................................ 1 1 A general overview on protein formulation development .................................................................. 1 2 Proteins´ physical stability in solution ............................................................................................... 2 2.1 Effect of chemical stability on physical stability ......................................................................... 3 2.2 Assessment of protein solution behavior in early stages .......................................................... 4 3 Protein aggregation ........................................................................................................................... 4 3.1 Protein aggregation pathways ................................................................................................... 5 3.2 External factors affecting protein aggregation .......................................................................... 7 4 Brief overview on data mining, multivariate data analysis and machine learning ........................... 12 4.1 The problem of inferring proteins behavior in solution ............................................................ 14 4.2 Artificial neural networks ......................................................................................................... 15 5 Light scattering techniques and their application to protein characterization ................................. 17 5.1 Recent applications of light scattering for protein characterization ......................................... 18 6 References ...................................................................................................................................... 19 AIM AND OUTLINE OF THE THESIS ........................................................................................................ 53 CHAPTER II: Advancing therapeutic protein discovery and development through comprehensive computational and biophysical characterization .................................................................................. 55 Abstract ................................................................................................................................................... 56 1 Introduction ...................................................................................................................................... 57 i Table of contents 2 Material and methods ...................................................................................................................... 58 2.1 Sample preparation ................................................................................................................. 58 2.2 In silico modeling of monoclonal antibodies and estimation of molecular descriptors ............ 59 2.3 Dynamic light scattering (DLS) ................................................................................................ 60 2.4 High throughput fluorimetric analysis of thermal protein unfolding with nanoDSF® ............... 60 2.5 Differential scanning fluorimetry (DSF) ................................................................................... 61 2.6 Isothermal chemical denaturation (ICD) ................................................................................. 61 2.7 PEG-assay .............................................................................................................................. 61 2.8 Electrophoretic mobility and zeta potential ............................................................................. 62 2.9 Capillary isoelectric focusing (cIEF) ........................................................................................ 62 2.10 Size exclusion chromatography coupled to multi-angle light scattering (SEC-MALS) ........... 63 2.11 Stress study ............................................................................................................................. 64 2.12 Response surface methodology (RSM) .................................................................................. 64 2.13 Tests for statistical significance of linear correlations ............................................................
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