Predictive QSAR tools to aid in early process development of monoclonal antibodies John Micael Andreas Karlberg Published work submitted to Newcastle University for the degree of Doctor of Philosophy in the School of Engineering November 2019 Abstract Monoclonal antibodies (mAbs) have become one of the fastest growing markets for diagnostic and therapeutic treatments over the last 30 years with a global sales revenue around $89 billion reported in 2017. A popular framework widely used in pharmaceutical industries for designing manufacturing processes for mAbs is Quality by Design (QbD) due to providing a structured and systematic approach in investigation and screening process parameters that might influence the product quality. However, due to the large number of product quality attributes (CQAs) and process parameters that exist in an mAb process platform, extensive investigation is needed to characterise their impact on the product quality which makes the process development costly and time consuming. There is thus an urgent need for methods and tools that can be used for early risk-based selection of critical product properties and process factors to reduce the number of potential factors that have to be investigated, thereby aiding in speeding up the process development and reduce costs. In this study, a framework for predictive model development based on Quantitative Structure- Activity Relationship (QSAR) modelling was developed to link structural features and properties of mAbs to Hydrophobic Interaction Chromatography (HIC) retention times and expressed mAb yield from HEK cells. Model development was based on a structured approach for incremental model refinement and evaluation that aided in increasing model performance until becoming acceptable in accordance to the OECD guidelines for QSAR models. The resulting models showed that it was possible to predict HIC retention times of mAbs based on their inherent structure. Further improvements of the models are suggested due to performance being adequate but not sufficient for implementation as a risk assessment tool in QbD. However, the described methodology and workflow has been proven to work for retention time prediction in a HIC column and is therefore likely to be applicable to other purification columns. i ii List of publications resulting from this research Karlberg, M., von Stosch, M. and Glassey, J., 2018. “Exploiting mAb structure characteristics for a directed QbD implementation in early process development.” Critical reviews in biotechnology, 38(6), pp.957-970 Kizhedath, A., Karlberg, M. and Glassey, J., 2019. “Cross interaction chromatography based QSAR model for early stage screening to facilitate enhanced developability of monoclonal antibody therapeutics”. Biotechnology journal, Accepted Karlberg, M., Kizhedath, A., Glassey, J., 2019. “QSAR model for Hydrophobic Interaction Chromatography behaviour from primary sequence of monoclonal antibodies”. Biotechnology Journal, Submitted Karlberg, M., de Souza, J., Kizhedath, A., Bronowska, A, Glassey, J., 2019. “QSAR model for Hydrophobic Interaction Chromatography behaviour from 3D structure of monoclonal antibodies”. Biotechnology Journal, Submitted List of conference contributions Karlberg, M, von Stosch, M, McCreath, G, Glassey, J. “Early Bioprocess development using PAT approaches”, ESBES, Sep 2016, Dublin, Ireland Karlberg, M, “Exploiting mAb structure characteristics for a directed QbD implementation in process development”, XIII BioProcess UK”, Nov 2016, Newcastle, UK Karlberg, M, Kizhedath, A., von Stosch, M, Wilkinson, S, Glassey J. “Exploiting mAb structure characteristics for rapid screening and a directed QbD implementation in process development”, ACS Biot 253rd meeting, Apr 2017, San Francisco, USA Karlberg, M, von Stosch, M, Glassey, J. “Exploiting mAb structure characteristics for a directed QbD implementation in process development”, WCCE ECAB, Oct 2017, Barcelona, Spain Karlberg, M and Glassey, J. “Exploiting MAB structure characteristics for a directed QbD implementation in process development”, ESBES, Sep 2018, Lisbon, Portugal iii iv Acknowledgements Many people have supported me during the past three years, and I have greatly enjoyed working with all of them. Especially, I would like to thank my main supervisor and friend, Jarka Glassey for pushing me to always do better and for providing encouragement to keep on going. Without this, I would not have learned as much as I now know today for which I am truly grateful. I would like to thank secondary supervisor Agnieszka Bronowska for showing me the cool world of Molecular Dynamics and the endless potential it holds for use in process development of biologics. I also want to thank Joao Victor de Souza Cunha for taking the time in teaching me the theory and application of Molecular Dynamics which added great value to the project. I also want to thank my former supervisor Moritz von Stosch for all the fruitful discussion on multivariate data analysis. In the city of Newcastle and at Newcastle University, I would like to thank my friends Arathi, Andre, Ana, Sylvester and Joao, who made every day an adventure. I truly enjoyed coming to university every day. From Fujifilm Diosynth in Billingham, UK, I would also like to thank the people who supported and helped me during my secondment. I truly appreciate the industrial insight and fruitful discussion that they provided as this clarified the problem statement of this project and encouraged me to continue research on this topic. I would also like to thank my family and friends in Sweden for their continued support and encouragement when writing up this thesis. During the three years of the project I had the pleasure and honour of becoming godfather to my beloved nephew Gabriel and niece Maja for which I will definitely tell tall tales and stories about my great adventure across the seas. This project received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No 643056 (Biorapid project). v vi Table of Contents Abstract ...................................................................................................................................... i List of publications resulting from this research .................................................................. iii List of conference contributions ............................................................................................. iii Acknowledgements ................................................................................................................... v List of Figures ......................................................................................................................... xv List of Tables ......................................................................................................................... xxv Abbreviations ....................................................................................................................... xxix Nomenclature ..................................................................................................................... xxxiii Introduction .............................................................................................................................. 1 Thesis structure ....................................................................................................................... 2 Chapter 1: Literature Review .............................................................................................. 2 Chapter 2: Modelling Development and Assessment ......................................................... 2 Chapter 3: Primary Sequence-based Descriptors ................................................................ 2 Chapter 4: Impact of mAb isotypes and species origins on primary sequence-based descriptors ........................................................................................................................... 3 Chapter 5: QSAR model development: Primary sequence-based descriptors .................... 3 Chapter 6: 3D Structure Descriptors ................................................................................... 3 Chapter 7: QSAR model development: 3D Structure Descriptors ...................................... 3 Chapter 8: Conclusion and Future Perspectives .................................................................. 3 Chapter 1 Literature review .................................................................................................... 5 1.1 Antibody Market ............................................................................................................... 5 1.2 State of the Art in mAb manufacturing............................................................................. 7 1.2.1 Implementation of QbD ............................................................................................. 8 1.2.2 Challenges in QbD implementation ......................................................................... 14 1.2.3 Current Focus and Improvements in Process Development .................................... 15 1.3 Quantitative Structure-Activity Relationship ................................................................. 19 vii 1.3.1 Descriptor generation ............................................................................................... 19 1.3.2 QSAR for protein behaviour prediction .................................................................. 22 1.4 Towards