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Signature Redacted Signature of a Uthor A computational framework for predicting CHO cell culture performance in bioreactors by Martin Circamo Behrens M.Eng., Bioprocess Engineering, Pontificia Universidad Cat'lica at Chile, 2013 Submitted to the MIT Department of Biological Engineering and the Sloan School of Management in Partial Fulfillment of the Requirements for the Degr of MASSACHUSETTS INSTITUTE OF TECHNOLOGY Master of Science in Biological Engineering F and JUL 03 2019 Master of Business Administration LIBRARIES In conjunction with the Leaders for Global Operations Program ARCHIVES at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2019 2019 Martin Circamo Behrens. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly copies of this thesis document in whole or in part in any medium now known or heregfter. Signature redacted Signature of A uthor ............................................................................................ ...... ......... -...... De rtment of Biological Engineering MIT Sloan School of Management Ma -10), 2 019 Certified by ............................................................................ S ig n a tu re re d a cte d . Douglas Lauffenburger, Ford Professor of Biological Engineering, Chemical Engineering, and Biology Head, Department of Biological Engineering IThesis $upervisor Certified by ........................................................................................... S ig n a tu re re d a c te d I Roy Welsch Eastman Kodak Leaders for Global Operations I Professor of Statistics and Management Science, MIT Sloan School of Managpenpnt ' Thepis Supfrvisor A ccepted by.................................................................................................... S ig n a tu re re d a c te d Forest White Professor of Biological Engineering Chair of Graduate Program, Department ofBqiological Engineering Accepted by............................................................................... S ig n a tu re re d a cte d ............. Maura Herson, Assistant Dean, MBA Program MIT Sloan School of Management This page has been intentionally left blank. 2 A computational framework for predicting CHO cell culture performance in bioreactors by Martin Carcamo Behrens Submitted to the MIT Department of Biological Engineering and the MIT Sloan School of Management on May 10, 2019 in Partial Fulfillment of the Requirements for the Degrees of Master of Science in Biological Engineering and Master of Business Administration. Abstract Breaking the trade-off between speed and productivity is a key milestone across industries. In particular, in the biopharmaceutical industry this trade-off is exacerbated by a highly regulated environment, which hinders continuous improvement and fixes future manufacturing costs. Given the complexity of living organisms and the improvement in quality of life offered by the product - which demand agile development - the industry has traditionally taken phenomenological approaches to process development, generally sacrificing costs. Nonetheless, technological developments and lower entry barriers make the biopharmaceutical industry far more competitive than in its origins, demanding efficient and reliable processes. Developing efficient manufacturing processes for new products while being agile to market is a key differentiating capability of Amgen's process development organization. In collaboration with the process development team at Amgen, a computational framework for in- silico upstream bioprocess development has been developed, allowing for faster, more robust, and more optimal process development. Specifically, a mechanistic model of a bioreactor has been designed, implemented, and applied to an Amgen product. The project was divided into three major components: The first was a survey of internal Amgen capabilities and the state of the art in external industrial and academic models to identify the algorithms and design the signal flow required to support the range of expected process engineering applications. The second consisted of implementing a modular, extensible software platform with the architecture and interfaces dictated by the first component. The third part consisted of applying the software to an actual product development problem capturing the primary process variables. A constraint-based model of a metabolic network consisting of 35 reactions of the main carbon- nitrogen metabolism relevant in energy and redox balance was adapted from literature (Nolan & Lee, 2011). The metabolic network was coupled with glucose, glutamine and asparagine kinetics with temperature, dissolved oxygen, pH and osmolarity dependence. Stress induced by temperature shifts was modeled as a first-order step response coupled to a non-growth associated ATP of maintenance. The cellular model was coupled with a well-mixed bioreactor model consisting of mass balance equations. We solved the model using dynamic Flux Balance Analysis (dFBA). We first calibrated the model with experimental process characterization data for a product in development. We used a Non-dominated Sorted Genetic Algorithm (NSGA-II) to solve the calibration problem, minimizing the error in metabolite concentrations to yield estimates of 13 strain-specific parameters. We then assessed the calibrated model's predictions of biomass growth and metabolite concentrations against a second experiment run with different process settings. Finally, I developed a graphical user interface for subject-matter-experts to simulate experiments and test hypotheses using the model. We applied the tool to three process-relevant case studies, and analyzed the in-silico results. The calibrated model can predict biomass and titer from process settings, potentially reducing experimental time from 20 days to 30 seconds, in addition to reducing the experimental cost. This page has been intentionally left blank 4 Acknowledgment I would first like to acknowledge my MIT academic advisors, Douglas Lauffenburger and Roy Welsch, for their guidance and support in shaping the direction of this project. I would also like to thank Amgen Inc., for trusting in me and giving me the opportunity to work in such a challenging and beautiful project. I would especially like to thank my supervisor, Will Johnson, for the unconditional support and excellent mentorship; it was an absolute honor working with him. I would also like to thank everyone in the Digital Integration and Predictive Technologies (DIPT) group, for their support, teachings and making this such a fun and growing experience. And in general, to all the Amgen family, working with such talented, passionate and human people has been by far my favorite professional experience so far. I will treasure all the learnings and connections always. Special thanks to Dollie Grajczak, the LGO alumni community and my fellow off-cycle Amgen interns. Additionally, I would like to acknowledge the Leaders for Global Operations (LGO) program staff and my fellow LGO classmates for their guidance in making the internship and thesis writing process progress go so smoothly. This has been an absolute life-changing experience, and I can't express how much gratitude I feel for trusting me and allowing me to grow so much. Special thanks to Thomas Roemer, Patty Eames, Ted Equi and Anna Voronova. I would also like to thank my parents for their support and for encouraging me to always challenge myself. Special thanks to my grandma, 'la Wely', who would have been so proud of this accomplishment, and for her infinite teachings and wisdom. Finally, I would like to thank my amazing wife, Fer Awad, for embarking on this two-year LGO journey with me and for her unconditional support and most importantly, being the best friend, mother and wife- without her none of this would have been possible. I would also like to thank my daughter, Julia, for allowing me to disconnect and see the world from a playful and pure lens and for her infinite love. Also, I would like to thank Elena, who will be joining our family, and we can't wait to meet! 5 This page has been intentionally left blank 6 Table of Contents A BSTRA CT ........................................................................................................................................... 3 A CK N OW LED G M EN T ....................................................................................................................... 5 TA BLE O F C O NTEN TS ....................................................................................................................... 7 LIST OF FIGU RES ............................................................................................................................... 9 LIST O F TABLES.............................................................................................. ....... 10 1. INTRODUCTION ........................................................ 11 1.1. DEFINITIONS AND M ARKET........................................................................................................11 1.2. CURRENT CHALLENGES.............................................................................................................12 1.3. H OW TO SOLVE THESE PROBLEM S........................................................................................ 13 1.4. CHALLENGES IN BIOPHARMACEUTICAL PROCESS DEVELOPMENT .......................................... 13 1.5. PROCESS INTENSIFICATION IN BIOTECHNOLOGY ..................................................................... 15 1.6. Q UALITY
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