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- •Contents
- •Foreword
- •Preface
- •About the Editors
- •Contributors
- •References
- •2.3.4 Barriers to Automation Adoption
- •2.4 Core Ingredients for Successful Digital Transformation
- •2.1 Introduction
- •2.3.1 Operational Challenges
- •2.3.2 Cultural Challenges
- •2.4.2 Cloud Computing
- •2.5 Case Studies of Successful Digital Transformation
- •2.6 Conclusion
- •References
- •3. Computational Protein Design Strategies for Optimization of Antigen Generation to Drive Antibody Discovery
- •3.1 Introduction
- •3.3 Antigen Generation Strategies
- •3.4 Computational Methods
- •3.4.2 Computational Protein Structure Prediction
- •References
- •4. Bioinformatic Analyses of Antibody Repertoires and Their Roles in Modern Antibody Drug Discovery
- •4.1 Introduction
- •4.6 Summary and Future Directions
- •Acknowledgments
- •References
- •5.1 Introduction
- •5.2 Databases
- •5.2.1 Databases in Machine Learning Approaches
- •5.2.2 Database Types
- •5.3 Applications of Machine Learning in Antibody Discovery and Development
- •5.3.1 Structure Prediction with Deep Learning
- •5.3.3 Developability
- •5.4 Antibody Generation and Design by Language Models
- •5.4.1 Antibody Representations
- •5.4.2 Representation Learning
- •5.4.3 Language Models
- •References
- •6.1 Introduction
- •6.2 Antibody Generation through Deep Generative Models
- •6.3.1 Sampling and Scoring
- •6.5 Conclusions and Perspectives
- •Acknowledgments
- •References
- •7.1 Introduction
- •7.2.3 Computational Approaches to Predict Antibody–Antigen Interaction
- •7.3 Conclusion
- •Competing Interests
- •Acknowledgments
- •References
- •8.2 Common Types of Molecular Simulations for Biomolecules
- •8.2.1 Molecular Dynamics (MD) Simulations
- •8.2.2 Monte Carlo (MC) Simulations
- •8.2.3 Challenges of Molecular Simulations
- •8.3.1 Periodic Boundary Conditions
- •8.4 Uses of Molecular Simulation in Antibody Drug Development
- •8.5 Conclusion
- •References
- •9. Considerations of Developability During the Early Stages of Antibody Drug Discovery and Design
- •9.1 Introduction
- •9.2 Historical Perspective
- •9.3 Clinical Antibody Data Set
- •9.5 Control Antibodies
- •9.7 Assessment of Chemical Liabilities
- •9.8 Conclusions and Future Perspectives
- •Acknowledgments
- •References
- •Abbreviations
- •10.1 Introduction
- •10.4.1 Conclusions and Outlook
- •Acknowledgments
- •References
- •11.8 Conclusions and Future Directions
- •References
- •12.1 Introduction to PK/PD and QSP Modeling
- •12.1.1 PK/PD Modeling
- •12.1.2 QSP Modeling
- •12.2.1 Monoclonal Antibodies (mAbs)
- •12.2.3 Cell Therapies
- •12.2.4 Gene Therapies
- •12.2.5 Vaccines
- •12.2.6 mRNA/siRNA/Oligonucleotide Therapeutics
- •12.4 Case Studies
- •12.5 Conclusions and Future Perspectives
- •References
- •13.1 Introduction
- •13.2 AI/ML: A Game Changer for Antibody Design
- •13.3 Multispecific Antibody Design
- •13.4 Adapting AI to the Design of Multispecific Antibodies
- •13.4.1 Structure Prediction and Modeling
- •13.4.2 Developability Prediction and Optimization
- •13.4.4 In Silico Modeling and Simulation
- •13.5 The Future: Beyond Optimization
- •13.5.1 Market Trends and Commercialization
- •13.5.2 Logic Gates, Biosensors, and De Novo Design
- •13.5.3 Challenges and Opportunities
- •13.6 Conclusion
- •Acknowledgments
- •References
- •Index

11 • Systems Biology Approaches 301
Ang, Joo Ern, Rupinder Pandher, Joo Chew Ang, Yasmin J. Asad, Alan T. Henley, Melanie
Valenti, Gary Box, etal. 2016. “Plasma Metabolomic Changes Following PI3K Inhibition
as Pharmacodynamic Biomarkers: Preclinical Discovery to Phase I Trial Evaluation.”
Molecular Cancer Therapeutics 15 (6): 1412–24. doi:10.1158/1535–7163.MCT‑15–0815.
Arigoni‑Affolter, Ilaria, Ernesto Scibona, Chia‑Wei Lin, David Brühlmann, Jonathan Souquet,
Hervé Broly, and Markus Aebi. 2019. “Mechanistic Reconstruction of Glycoprotein
Secretion through Monitoring of Intracellular N‑Glycan Processing.” Science Advances 5
(11). doi:10.1126/sciadv.aax8930.
Barzadd, Mona Moradi, Magnus Lundqvist, Claire Harris, Magdalena Malm, Anna Luisa Volk,
Niklas Thalén, Veronique Chotteau, et al. 2022. “Autophagy and Intracellular Product
Degradation Genes Identied by Systems Biology Analysis Reduce Aggregation of
Bispecic Antibody in CHO Cells.” New Biotechnology 68 (May): 68–76. doi:10.1016/j.
nbt.2022.01.010.
Baxter, Joseph S., Olivia C. Leavy, Nicola H. Dryden, Sarah Maguire, Nichola Johnson, Vita
Fedele, Nikiana Simigdala, etal. 2018. “Capture Hi‑C Identies Putative Target Genes at 33
Breast Cancer Risk Loci.” Nature Communications 9 (1). doi:10.1038/s41467‑018‑03411‑9.
Blondeel, Eric J.M., Raymond Ho, Steffen Schulze, Stanislav Sokolenko, Simon R. Guillemette,
Igor Slivac, Yves Durocher, etal. 2016. “An Omics Approach to Rational Feed: Enhancing
Growth in CHO Cultures with NMR Metabolomics and 2D‑DIGE Proteomics.” Journal of
Biotechnology 234: 127–38. doi:10.1016/j.jbiotec.2016.07.027.
Bonabeau, Eric.2002. “Agent‑Based Modeling: Methods and Techniques for Simulating Human
Systems.” Proceedings of the National Academy of Sciences of the United States of America
99 (SUPPL. 3): 7280–87. doi:10.1073/pnas.082080899.
Bruggeman, Frank J., and Hans V. Westerhoff. 2007. “The Nature of Systems Biology.” Trends in
Microbiology 15 (1): 45–50. doi:10.1016/j.tim.2006.11.003.
Bryan, Laura, Michael Henry, Ronan M. Kelly, Christopher C. Frye, Matthew D. Osborne, Martin
Clynes, and Paula Meleady. 2021. “Mapping the Molecular Basis for Growth Related
Phenotypes in Industrial Producer CHO Cell Lines Using Differential Proteomic Analysis.”
BMC Biotechnology 21 (1): 1–21. doi:10.1186/s12896‑021‑00704‑8.
Butcher, Eugene C., Ellen L. Berg, and Eric J. Kunkel. 2004. “Systems Biology in Drug Discovery.”
Nature Biotechnology. doi:10.1038/nbt1017.
Chandra Mulukutla, Bhanu, Jaitashree Kale, Taylor Kalomeris, Michaela Jacobs, and Gregory
W. Hiller. 2017. “Identication and Control of Novel Growth Inhibitors in Fed‑Batch
Cultures of Chinese Hamster Ovary Cells.” Biotechnol. Bioeng 114: 1779–90. https://doi.
org/10.1002/bit.26313
Chang, Michelle M., Leonid Gaidukov, Giyoung Jung, Wen Allen Tseng, John J. Scarcelli,
Richard Cornell, Jeffrey K. Marshall, etal. 2019. “Small‑Molecule Control of Antibody
N‑Glycosylation in Engineered Mammalian Cells.” Nature Chemical Biology 15 (7): 730–
36. doi:10.1038/s41589‑019‑0288‑4.
Chen, Yiqun, Brian O. McConnell, Venkata Gayatri Dhara, Harnish Mukesh Naik, Chien Ting
Li, Maciek R. Antoniewicz, and Michael J. Betenbaugh. 2019. “An Unconventional
Uptake Rate Objective Function Approach Enhances Applicability of Genome‑Scale
Models for Mammalian Cells.” Npj Systems Biology and Applications 5 (1). doi:10.1038/
s41540‑019‑0103‑6.
Dhara, Venkata Gayatri, Harnish Mukesh Naik, Natalia I. Majewska, and Michael J. Betenbaugh.
2018. “Recombinant Antibody Production in CHO and NS0 Cells: Differences and
Similarities.” BioDrugs 32 (6): 571–84. doi:10.1007/s40259‑018‑0319‑9.
Ekins, Sean, Ana C. Puhl, Kimberley M. Zorn, Thomas R. Lane, Daniel P. Russo, Jennifer J. Klein,
Anthony J. Hickey, and Alex M. Clark. 2019. “Exploiting Machine Learning for End‑to‑End
Drug Discovery and Development.” Nature Materials. doi:10.1038/s41563‑019‑0338‑z.

302 Biopharmaceutical Informatics
Fouladiha, Hamideh, Sayed Amir Marashi, Fatemeh Torkashvand, Fereidoun Mahboudi, Nathan
E. Lewis, and Behrouz Vaziri. 2020. “A Metabolic Network‑Based Approach for Developing
Feeding Strategies for CHO Cells to Increase Monoclonal Antibody Production.” Bioprocess
and Biosystems Engineering 43 (8): 1381–89. doi:10.1007/s00449‑020‑02332‑6.
Goldstein, Rebecca L., Shao Ning Yang, Tony Taldone, Betty Chang, John Gerecitano, Kojo
Elenitoba‑Johnson, Rita Shaknovich, et al. 2015. “Pharmacoproteomics Identies
Combinatorial Therapy Targets for Diffuse Large B Cell Lymphoma.” Journal of Clinical
Investigation 125 (12): 4559–71. doi:10.1172/JCI80714.
Guerra, André C., and Jarka Glassey. 2018. “Machine Learning in Biopharmaceutical
Manufacturing.” European Pharmaceutical Review 23 (4): 62–65.
Hasin, Yehudit, Marcus Seldin, and Aldons Lusis. 2017. “Multi‑Omics Approaches to Disease.”
Genome Biology. doi:10.1186/s13059‑017‑1215‑1.
Hay, Michael, David W Thomas, John L Craighead, Celia Economides, and Jesse Rosenthal. 2014.
“Clinical Development Success Rates for Investigational Drugs.” Nature Biotechnology 32
(1): 40–51. doi:10.1038/nbt.2786.
Hiller, Gregory W, Ana Maria Ovalle, Matthew P Gagnon, Meredith L Curran, and Wenge Wang.
2017. “Cell‑Controlled Hybrid Perfusion Fed‑Batch CHO Cell Process Provides Signicant
Productivity Improvement Over Conventional Fed‑Batch Cultures.” Biotechnology and
Bioengineering 114: 1438–47. https://doi.org/10.1002/bit.26259
Hoang, Duc, Bingyu Kuang, George Liang, Zhao Wang, and Seongkyu Yoon. 2022. “Modulation
of Nutrient Precursors for Controlling Metabolic Inhibitors by Genome‑Scale Flux Balance
Analysis.” Biotechnology Progress. doi:10.1002/btpr.3313.
Hofherr, Alexis, Julie Williams, Li Ming Gan, Magnus Söderberg, Pernille B.L. Hansen, and Kevin
J. Woollard.2022. “Targeting Inammation for the Treatment of Diabetic Kidney Disease:
A Five‑Compartment Mechanistic Model.” BMC Nephrology 23 (1): 1–17. doi:10.1186/
s12882‑022‑02794‑8.
Hu, Wei‑Shou. 2020. Cell Culture Bioprocess Engineering, Second edition, edited by Wei‑Shou
Hu. Boca Raton : CRC Press. doi:10.1201/9780429162770.
Huang, Zhuangrong, Jianlin Xu, Andrew Yongky, Caitlin S. Morris, Ashli L. Polanco,
Michael Reily, Michael C. Borys, Zheng Jian Li, and Seongkyu Yoon. 2020. “CHO Cell
Productivity Improvement by Genome‑Scale Modeling and Pathway Analysis: Application
to Feed Supplements.” Biochemical Engineering Journal 160 (April). doi:10.1016/j.
bej.2020.107638.
Jerby, Livnat, and Eytan Ruppin. 2012. “Predicting Drug Targets and Biomarkers of Cancer via
Genome‑Scale Metabolic Modeling.” Clinical Cancer Research. doi:10.1158/1078–0432.
CCR‑12–1856.
Ji, Zhiwei, Jing Su, Dan Wu, Huiming Peng, Weiling Zhao, Brian Nlong Zhao, and Xiaobo Zhou.
2017. “Predicting the Impact of Combined Therapies on Myeloma Cell Growth Using a
Hybrid Multi‑Scale Agent‑Based Model.” Oncotarget 8 (5): 7647–65. doi:10.18632/
oncotarget.13831.
Joslyn, Louis R., Jennifer J. Linderman, and Denise E. Kirschner. 2022. “A Virtual Host Model
of Mycobacterium Tuberculosis Infection Identies Early Immune Events as Predictive
of Infection Outcomes.” Journal of Theoretical Biology 539: 111042. doi:10.1016/j.
jtbi.2022.111042.
Kildegaard, Helene Faustrup, Deniz Baycin‑Hizal, Nathan E. Lewis, and Michael J. Betenbaugh.
2013. “The Emerging CHO Systems Biology Era: Harnessing the ’Omics Revolution
for Biotechnology.” Current Opinion in Biotechnology 24 (6): 1102–7. doi:10.1016/j.
copbio.2013.02.007.

11 • Systems Biology Approaches 303
Kim, Yangjin, Gibin Powathil, Hyunji Kang, Dumitru Trucu, Hyeongi Kim, Sean Lawler, and
Mark Chaplain. 2015. “Strategies of Eradicating Glioma Cells: A Multi‑Scale Mathematical
Model with MiR‑451‑AMPK‑MTOR Control.” PLoS One 10 (1): 1–30. doi:10.1371/jour‑
nal.pone.0114370.
Kitano, Hiroaki. 2002. “Systems Biology: A Brief Overview.” Science295 (5560): 1662–64.
doi:10.1126/science.1069492.
Krambeck, Frederick J., Sandra V. Bennun, Mikael R. Andersen, and Michael J. Betenbaugh.
2017. “Model‑Based Analysis of N‑Glycosylation in Chinese Hamster Ovary Cells.” PLoS
One 12 (5): 1–30. doi:10.1371/journal.pone.0175376.
Krambeck, Frederick J., Sandra V. Bennun, Someet Narang, Sean Choi, Kevin J. Yarema, and
Michael J. Betenbaugh. 2009. “A Mathematical Model to Derive N‑Glycan Structures and
Cellular Enzyme Activities from Mass Spectrometric Data.” Glycobiology 19 (11): 1163–
75. doi:10.1093/glycob/cwp081.
Kreitmaier, Peter, Georgia Katsoula, and Eleftheria Zeggini. 2023. “Insights from Multi‑Omics
Integration in Complex Disease Primary Tissues.” Trends in Genetics. doi:10.1016/j.
tig.2022.08.005.
Kremling, Andreas. 2013. Systems Biology: Mathematical Modeling and Model Analysis. CRC
Press. https://books.google.com/books?hl=en&lr=&id=FXf6AQAAQBAJ&oi=fnd&pg=P
P1&dq=systems+biology+mathematical+modeling+and+model+analysis&ots=PJmEm3G
FRC&sig=7TPiMDS7x0XIiABc9l‑Sewbr2gk#v=onepage&q&f=false.
Layton, Anita T., and Harold E. Layton. 2019. “A Computational Model of Epithelial Solute and
Water Transport along a Human Nephron.” PLoS Computational Biology 15 (2): 1–23.
doi:10.1371/journal.pcbi.1006108.
Lee, Alison P., Yee Jiun Kok, Meiyappan Lakshmanan, Dawn Leong, Lu Zheng, Hsueh Lee
Lim, Shuwen Chen, etal. 2021. “Multi‑Omics Proling of a CHO Cell Culture System
Unravels the Effect of Culture PH on Cell Growth, Antibody Titer, and Product Quality.”
Biotechnology and Bioengineering 118 (11): 4305–16. doi:10.1002/bit.27899.
Lewis, Amanda M., William D. Croughan, Nelly Aranibar, Alison G. Lee, Bethanne Warrack,
Nicholas R. Abu‑Absi, Rutva Patel, etal. 2016. “Understanding and Controlling Sialylation
in a CHO Fc‑Fusion Process.” PLoS One 11 (6). doi:10.1371/journal.pone.0157111.
Li, Haining, Austin W.T. Chiang, and Nathan E. Lewis. 2022. “Articial Intelligence in the Analysis
of Glycosylation Data.” Biotechnology Advances. doi:10.1016/j.biotechadv.2022.108008.
Li, Ju Yueh, Chia Jung Li, Li Te Lin, and Kuan Hao Tsui. 2020. “Multi‑Omics Analysis
Identifying Key Biomarkers in Ovarian Cancer.” Cancer Control 27 (1): 1–10.
doi:10.1177/1073274820976671.
Lövfors, William, Christian Simonsson, Ali M. Komai, Elin Nyman, Charlotta S. Olofsson, and
Gunnar Cedersund. 2021. “A Systems Biology Analysis of Adrenergically Stimulated
Adiponectin Exocytosis in White Adipocytes.” Journal of Biological Chemistry 297 (5):
101221. doi:10.1016/j.jbc.2021.101221.
Macdonald, Gareth John. 2022. “Digital Twins and AI Reshape Biopharmaceutical Manufacturing.”
Genetic Engineering and Biotechnology News 42 (8): 44–46. doi:10.1089/gen.42.08.13.
Makowski, Emily K., Patrick C. Kinnunen, Jie Huang, Lina Wu, Matthew D. Smith, Tiexin
Wang, Alec A. Desai, etal. 2022. “Co‑Optimization of Therapeutic Antibody Afnity and
Specicity Using Machine Learning Models That Generalize to Novel Mutational Space.”
Nature Communications 13 (1). doi:10.1038/s41467‑022‑31457‑3.
Menezes, Bruna, Cornelius Cilliers, Timothy Wessler, Greg M. Thurber, and Jennifer J. Linderman.
2020. “An Agent‑Based Systems Pharmacology Model of the Antibody‑Drug Conjugate
Kadcyla to Predict Efcacy of Different Dosing Regimens.” AAPS Journal 22 (2): 1–13.
doi:10.1208/s12248‑019‑0391‑1.

304 Biopharmaceutical Informatics
Milo, R., S. Shen‑Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. 2002. “Network
Motifs: Simple Building Blocks of Complex Networks.” Science 298 (5594): 824–27.
doi:10.1126/science.298.5594.824.
Mulukutla, Bhanu Chandra, Jeffrey Mitchell, Pauline Geoffroy, Cameron Harrington, Manisha
Krishnan, Taylor Kalomeris, Caitlin Morris, Lin Zhang, Pamela Pegman, and Gregory W.
Hiller. 2019. “Metabolic Engineering of Chinese Hamster Ovary Cells towards Reduced
Biosynthesis and Accumulation of Novel Growth Inhibitors in Fed‑Batch Cultures.”
Metabolic Engineering 54 (July): 54–68. doi:10.1016/j.ymben.2019.03.001.
Orth, Jeffrey D., Ines Thiele, and Bernhard O. Palsson. 2010. “What Is Flux Balance Analysis?”
Nature Biotechnology 28 (3): 245–48. doi:10.1038/nbt.1614.
Paananen, Jussi, and Vittorio Fortino. 2020. “An Omics Perspective on Drug Target Discovery
Platforms.” Briengs in Bioinformatics 21 (6): 1937–53. doi:10.1093/bib/bbz122.
Pang, Junling, Xianmei Qi, Ya Luo, Xiaona Li, Ting Shu, Baicun Li, Meiyue Song, etal. 2021.
“Multi‑Omics Study of Silicosis Reveals the Potential Therapeutic Targets PGD2 and
TXA2.” Theranostics 11 (5): 2381–94. doi:10.7150/thno.47627.
Park, Jihwan, Rojesh Shrestha, Chengxiang Qiu, Ayano Kondo, Shizheng Huang, Max Werth,
Mingyao Li, Jonathan Barasch, and Katalin Suszták. 2018. “Single‑Cell Transcriptomics
of the Mouse Kidney Reveals Potential Cellular Targets of Kidney Disease.” Science360
(6390): 758–63. doi:10.1126/science.aar2131.
Park, Seo Young, Cheol Hwan Park, Dong Hyuk Choi, Jong Kwang Hong, and Dong Yup Lee. 2021.
“Bioprocess Digital Twins of Mammalian Cell Culture for Advanced Biomanufacturing.”
Current Opinion in Chemical Engineering 33: 100702. doi:10.1016/j.coche.2021.100702.
Pereira, Sara, Helene Faustrup Kildegaard, and Mikael Rørdam Andersen. 2018. “Impact of CHO
Metabolism on Cell Growth and Protein Production: An Overview of Toxic and Inhibiting
Metabolites and Nutrients.” Biotechnology Journal. doi:10.1002/biot.201700499.
Piña, Benjamin, Demetrio Raldúa, Carlos Barata, José Portugal, Laia Navarro‑Martín, Rubén
Martínez, Inmaculada Fuertes, and Marta Casado. 2018. “Functional Data Analysis: Omics
for Environmental Risk Assessment.” Comprehensive Analytical Chemistry 82: 583–611.
doi:10.1016/bs.coac.2018.07.007.
Ramos, Pablo Ivan Pereira, Darío Fernández Do Porto, Esteban Lanzarotti, Ezequiel J. Sosa,
Germán Burguener, Agustín M. Pardo, Cecilia C. Klein, et al. 2018. “An Integrative,
Multi‑Omics Approach towards the Prioritization of Klebsiella Pneumoniae Drug Targets.”
Scientic Reports 8 (1): 1–19. doi:10.1038/s41598‑018‑28916‑7.
Rapaport, Franck, Raya Khanin, Yupu Liang, Azra Krek, Paul Zumbo, Christopher E Mason,
Nicholas D Socci, and Doron Betel. 2013. “Comprehensive Evaluation of Differential Gene
Expression Analysis Methods for RNA‑Seq Data.” Genome Biology 14: 1–14. https://doi.
org/10.1186/gb‑2013‑14‑9‑r95.
Rathore, Anurag S., Saxena Nikita, Garima Thakur, and Somesh Mishra. 2022. “Articial
Intelligence and Machine Learning Applications in Biopharmaceutical Manufacturing.”
Trends in Biotechnology 41(4): 1–14. doi:10.1016/j.tibtech.2022.08.007.
Rathore, Anurag S., and Helen Winkle. 2009. “Quality by Design for Biopharmaceuticals.” Nature
Biotechnology 27 (1): 26–34. doi:10.1038/nbt0109–26.
Reis, Ruy Freitas, Alexandre Bittencourt Pigozzo, Carla Rezende Barbosa Bonin, Barbara de
Melo Quintela, Lara Turetta Pompei, Ana Carolina Vieira, Larissa de Lima e. Silva, Maicom
Peters Xavier, Rodrigo Weber dos Santos, and Marcelo Lobosco. 2021. “A Validated
Mathematical Model of the Cytokine Release Syndrome in Severe COVID‑19.” Frontiers
in Molecular Biosciences 8 (July): 1–13. doi:10.3389/fmolb.2021.639423.
Ritacco, Frank V., Yongqi Wu, and Anurag Khetan. 2018. “Cell Culture Media for Recombinant
Protein Expression in Chinese Hamster Ovary (CHO) Cells: History, Key Components, and
Optimization Strategies.” Biotechnology Progress. doi:10.1002/btpr.2706.

11 • Systems Biology Approaches 305
Santos, Guido, Xin Lai, Martin Eberhardt, and Julio Vera. 2018. “Bacterial Adherence and
Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus Pneumoniae
Identied in Multi‑Level Mathematical Modeling.” Frontiers in Cellular and Infection
Microbiology 8 (MAY): 1–19. doi:10.3389/fcimb.2018.00159.
Savizi, Iman Shahidi Pour, Ehsan Motamedian, Nathan E. Lewis, Ioscani Jimenez del Val, and
Seyed Abbas Shojaosadati. 2021. “An Integrated Modular Framework for Modeling the
Effect of Ammonium on the Sialylation Process of Monoclonal Antibodies Produced by
CHO Cells.” Biotechnology Journal 16 (8). doi:10.1002/biot.202100019.
Schinn, Song Min, Carly Morrison, Wei Wei, Lin Zhang, and Nathan E. Lewis. 2021. “Systematic
Evaluation of Parameters for Genome‑Scale Metabolic Models of Cultured Mammalian
Cells.” Metabolic Engineering 66 (July): 21–30. doi:10.1016/j.ymben.2021.03.013.
Seyhan, Attila A. 2019. “Lost in Translation: The Valley of Death across Preclinical and Clinical
Divide–Identication of Problems and Overcoming Obstacles.” Translational Medicine
Communications 4 (1). doi:10.1186/s41231‑019‑0050–7.
Sha, Sha, Cyrus Agarabi, Kurt Brorson, Dong Yup Lee, and Seongkyu Yoon. 2016. “N‑Glycosylation
Design and Control of Therapeutic Monoclonal Antibodies.” Trends in Biotechnology 34
(10): 835–46. doi:10.1016/j.tibtech.2016.02.013.
Shamsi, Milad, Mohsen Saghaan, Morteza Dejam, and Amir Sanati‑Nezhad.2018. “Mathematical
Modeling of the Function of Warburg Effect in Tumor Microenvironment.” Scientic
Reports 8 (1). doi:10.1038/s41598‑018‑27303‑6.
Singhania, Akul, Robert J. Wilkinson, Marc Rodrigue, Pranabashis Haldar, and Anne O’Garra.
2018. “The Value of Transcriptomics in Advancing Knowledge of the Immune Response
and Diagnosis in Tuberculosis.” Nature Immunology 19 (11): 1159–68. doi:10.1038/
s41590‑018‑0225–9.
Stach, Christopher S., Meghan G. McCann, Conor M. O’Brien, Tung S. Le, Nikunj Somia, Xinning
Chen, Kyoungho Lee, etal. 2019. “Model‑Driven Engineering of N‑Linked Glycosylation
in Chinese Hamster Ovary Cells.” ACS Synthetic Biology 8 (11): 2524–35. doi:10.1021/
acssynbio.9b00215.
Strasser, Lisa, Amy Farrell, Jenny T.C. Ho, Kai Schefer, Ken Cook, Patrick Pankert, Peter Mowlds,
Rosa Viner, Barry L. Karger, and Jonathan Bones. 2021. “Proteomic Proling of IgG1
Producing CHO Cells Using LC/LC‑SPS‑MS3: The Effects of Bioprocessing Conditions
on Productivity and Product Quality.” Frontiers in Bioengineering and Biotechnology 9
(April). doi:10.3389/fbioe.2021.569045.
Subramanian, Aravind, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert,
Michael A Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis:
A Knowledge‑Based Approach for Interpreting Genome‑Wide Expression Proles.”
doi:10.1073/pnas.0506580102.
Sumit, Madhuresh, Sepideh Dolatshahi, An Hsiang Adam Chu, Kaffa Cote, John J. Scarcelli,
Jeffrey K. Marshall, Richard J. Cornell, etal. 2019a. “Dissecting N‑Glycosylation Dynamics
in Chinese Hamster Ovary Cells Fed‑Batch Cultures Using Time Course Omics Analyses.”
IScience12: 102–20. doi:10.1016/j.isci.2019.01.006.
Sumit, Madhuresh, Andreja Jovic, Richard R. Neubig, Shuichi Takayama, and Jennifer J.
Linderman. 2019b. “A Two‑Pulse Cellular Stimulation Test Elucidates Variability and
Mechanisms in Signaling Pathways.” Biophysical Journal 116 (5): 962–73. doi:10.1016/j.
bpj.2019.01.022.
Swinney, David C., and Jason Anthony. 2011. “How Were New Medicines Discovered?” Nature
Reviews Drug Discovery 10 (7): 507–19. doi:10.1038/nrd3480.
Templeton, Neil, Jason Dean, Pranhitha Reddy, and Jamey D. Young. 2013. “Peak Antibody
Production Is Associated with Increased Oxidative Metabolism in an Industrially Relevant
Fed‑Batch CHO Cell Culture.” Biotechnology and Bioengineering 110 (7): 2013–24.
doi:10.1002/bit.24858.

306 Biopharmaceutical Informatics
Tomazou, Marios, Marilena M. Bourdakou, George Minadakis, Margarita Zachariou, Anastasis
Oulas, Evangelos Karatzas, Eleni M. Loizidou, etal. 2021. “Multi‑Omics Data Integration
and Network‑Based Analysis Drives a Multiplex Drug Repurposing Approach to a
Shortlist of Candidate Drugs against COVID‑19.” Briengs in Bioinformatics 22 (6): 1–24.
doi:10.1093/bib/bbab114.
Turanli, Beste, Kubra Karagoz, Gholamreza Bidkhori, Raghu Sinha, Michael L. Gatza, Mathias
Uhlen, Adil Mardinoglu, and Kazim Yalcin Arga. 2019. “Multi‑Omic Data Interpretation to
Repurpose Subtype Specic Drug Candidates for Breast Cancer.” Frontiers in Genetics 10
(MAY): 1–12. doi:10.3389/fgene.2019.00420.
Uenaka, Takeshi, Wataru Satake, Pei Chieng Cha, Hideki Hayakawa, Kousuke Baba, Shiying
Jiang, Kazuhiro Kobayashi, etal. 2018. “In Silico Drug Screening by Using Genome‑Wide
Association Study Data Repurposed Dabrafenib, an Anti‑Melanoma Drug, for Parkinson’s
Disease.” Human Molecular Genetics 27 (22): 3974–85. doi:10.1093/hmg/ddy279.
Vavourakis, Vasileios, Triantafyllos Stylianopoulos, and Peter A. Wijeratne. 2018. “In‑Silico
Dynamic Analysis of Cytotoxic Drug Administration to Solid Tumours: Effect of Binding
Afnity and Vessel Permeability.” PLoS Computational Biology 14 (10). doi:10.1371/jour‑
nal.pcbi.1006460.
Vincent, Fabien, Arsenio Nueda, Jonathan Lee, Monica Schenone, Marco Prunotto, and Mark
Mercola. 2022. “Phenotypic Drug Discovery: Recent Successes, Lessons Learned and New
Directions.” Nature Reviews Drug Discovery. doi:10.1038/s41573‑022‑00472‑w.
Wang, Shuai, Hui Yong, and Xiao‑Dong He. 2021. “Multi‑Omics: Opportunities for Research
on Mechanism of Type 2 Diabetes Mellitus.” World Journal of Diabetes 12 (7): 1070–80.
doi:10.4239/wjd.v12.i7.1070.
Wild, Sophia A., Ian G. Cannell, Ashley Nicholls, Katarzyna Kania, Dario Bressan, Gregory J.
Hannon, and Kirsty Sawicka. 2022. “Clonal Transcriptomics Identies Mechanisms of
Chemoresistance and Empowers Rational Design of Combination Therapies.” ELife 11:
1–36. doi:10.7554/eLife.80981.
Young, Daniel L., and Seth Michelson. 2011. Systems Biology in Drug Discovery and Development.
Wiley.

Recent Advances
in PK/PD and
12
Quantitative
Systems Pharmacology
(QSP) Models for
Biopharmaceuticals
Hardik Mody, Venkata Krishna Kowthavarapu,
and Alison Betts
12.1 INTRODUCTION TO PK/PD AND QSP MODELING
Pharmacokinetic/pharmacodynamic (PK/PD) modeling and quantitative systems
pharmacology (QSP) modeling are mathematical techniques which can be used to
answer quantitative questions and enable decision‑making across the drug discov‑
ery and development continuum. These approaches have sufcient exibility and
tractability to answer critical questions arising from early drug discovery to clinical
development. As such, they are useful tools to increase efciency and effectiveness in
research and development (R&D), which can play an important role in offsetting the
high cost and attrition of drug research. A recent publication by the Food and Drug
307

308 Biopharmaceutical Informatics
Administration (FDA) states that quantitative systems pharmacology (QSP) modeling
and simulation are seen as critical tools for accelerating drug development and assist‑
ing in regulatory decisions [1].
The type of modeling approach required depends upon the granularity of the ques‑
tion asked, the data available, and the time required for decision‑making (Table12.1).
For example, PK/PD models are useful for ‘top‑down’ tting of data, more empirically
grounded, quicker and easier to develop and use, and good at extrapolating within a
limited eld of vision across different doses and subpopulations. QSP models use ‘bot‑
tom‑up’ approaches to describe the dynamic interactions between drugs and complex
biological systems. They are more mechanistic in nature, combining data and knowl‑
edge from various sources to construct a mathematical framework for the entire system.
As such, they are useful for more complex hypothesis‑driven questions. PK/PD model‑
ing and QSP modeling are compared in Table12.1 and discussed in more depth below.
12.1.1 PK/PD Modeling
The basic principles of pharmacokinetics (PK), pharmacodynamics (PD), and physiol‑
ogy form the foundation of PK/PD modeling. PK encompasses the factors affecting the
time course of drug concentrations in relevant biological uids and tissues after vari‑
ous routes of administration and represents the driving force for pharmacological and
most toxicological effects [2]. PD is the study of a drug’s molecular, biochemical, and
physiological effects or actions. Through years of implementation in drug development,
PK/PD modeling has demonstrated tremendous value in elucidating the relationship
between the PK of a therapeutic intervention and the resulting PD effect [3].
In PK/PD analysis, relatively simple models can be used to understand the time‑con‑
centration‑effect relationship through tting of data. This ‘top‑down’ approach enables
TABLE12.1 Comparison of PK/PD and QSP models
PK/PD QSP
Level Organism Scale of interest, e.g., cellular,
tissue/organ
Granularity Low granularity
Low in assumptions
Fitting of experimental data with
estimation of parameters
Data driven
Time scale/
impact
Use Understanding of the
Examples Emax, indirect response,
Faster questions/drug level Slower questions/modality level
time-concentration-effect
relationship
Dose predictions and TI
Optimizing the design and
interpretation of in vivo studies
transduction models, TGI, etc.
Higher granularity–contains more
mechanistic information.
Assumption rich–needs
experimental data to calibrate
Data integrative
Understanding target pharmacology
Focus on setting project targets
Enabler for biomarker selection and
translational strategy
Mechanistically informed PK, dose,
and regimen predictions
PBPK, DILIsym, and disease-scale
platform models

12 • Recent Advances in PK/PD 309
estimation of parameters describing potency (e.g., EC
E
), which dene the relationship between drug effect and concentration in plasma
max
) and capacity or efcacy (e.g.,
50
or in a specic tissue. PK/PD modeling can account for delays in drug response due to
biodistribution to distal sites of action or transduction of biological signals. PK/PD can
also be used to characterize physiological turnover and homeostasis, and stimulation or
inhibition of these processes by therapeutic intervention [2]. In addition, PK/PD analy‑
sis routinely incorporates population variability and uncertainty, which can be useful in
understanding variability in responses. This is called non‑linear mixed‑effects (NLME)
modeling as it incorporates both xed and random effects in order to understand inter‑
and intra‑individual variabilities. PK/PD modeling is a helpful tool for optimizing the
design and interpretation of in vivo studies. This may include predicting the outcome
of a dose yet to be tested or determining the optimal time points for PK or response
measures. In the translational space, the PK/PD parameters derived from relevant pre‑
clinical studies can be adjusted for the clinical scenario and used to provide prospective
simulations to guide clinical dose level and frequency.
Potential drawbacks of PK/PD modeling are that it tends to focus on specic PD
endpoints and so does not capture the behavior of the underlying system. As such, it may
miss interactions between bio‑signals or only capture behavior under a particular set of
conditions. As such, PK/PD models may have limited capacity to extrapolate beyond
collected datasets [4]. A major advantage of PK/PD models is that they don’t require
a lot of resources, including data or computational power to implement. Therefore, the
results from PK/PD modeling can be realized in a short time frame, which makes them
particularly suitable for inuencing decision‑making in the early stages of research.
Over the years, simpler PK/PD modeling approaches have evolved to incorporate more
mechanistic components, in so‑called mechanism‑based PK/PD models, to facilitate
translation across species and/or between different patient populations. As such, the dif‑
ference between PK/PD models and QSP is more of a continuum of increased complex‑
ity depending on the question to be answered.
12.1.2 QSP Modeling
With advances in computational power, access to greater biological knowledge, increased
data availability, and a simultaneous explosion in the complexity of therapeutic modali‑
ties, more mechanistic questions are being asked within the drug discovery and devel‑
opment process. These questions require a more holistic quantitative description of the
mechanism of action, and a different type of modeling approach is required. QSP models
combine computational modeling and experimental data to examine the relationships
between a drug, the biological system, and the disease process [5,6]. They are designed
to investigate the effects of drug action on emergent behaviors of the underlying system,
such as pathway, cell, tissue, organ, or multi‑organ/whole‑body process. To do so, QSP
models integrate datasets from diverse studies, contexts, and spatio‑temporal scales into
a mathematical framework that reects our knowledge of the system [7].
A key feature of these models is their explicit distinction between ‘drug’ and ‘sys‑
tem’ parameters. System‑specic parameters typically include organ/tissue blood ow
rates, receptor expression, internalization rates and turnover rates, cell lifespans, and
homeostatic feedback mechanisms. Ideally, these parameters should be available from

310 Biopharmaceutical Informatics
the literature or from prior experiments. Drug‑specic parameters typically include PK
parameters, such as clearance and volume of distribution, and pharmacological param‑
eters, such as in vivo target afnity and intrinsic efcacy of compounds, and are usually
estimated from PK/PD data gathered for the drug [8]. The strength of QSP models is
that they only need to be as complex as the question they need to answer [9]. For exam‑
ple, QSP models can be used early in the drug discovery process to evaluate potential
targets and to inform drug design for optimal efcacy and therapeutic index (TI). At
this stage, system parameters can be extracted from the literature and hypothetical drug
parameters can be used to perform exploratory simulations.
For more complex questions, disease‑scale platform models are a type of QSP
model which describes the interplay of multiple drug targets, pathways, and tissues in
disease. These models will often characterize the untreated disease state and a broad
range of disease phenotypes. They will enable comparisons, and hence differentiation,
of a range of drugs and support multiple applications, including evaluation of the impact
on efcacy and safety of monotherapies and combinations of drugs at different dosing
regimens. These models give the added value that they can be reused, adapted, and
repurposed for new treatments, questions, and indications [7].
Physiologically based pharmacokinetic (PBPK) modeling may be considered as
a type of QSP modeling for characterizing and predicting the disposition of drugs.
Traditionally, the plasma PK of drugs has been used to infer its tissue concentrations
and interpret its PD or toxicodynamic effects. While this may be relevant for small‑mol‑
ecule drugs, it is not suitable for other modalities, such as biotherapeutics, where the
plasma concentration may not accurately reect the concentration at the site of action.
As such, PBPK models have gained traction as a more mechanistic and realistic mod‑
eling approach to describe drug disposition [10]. These models are highly complex in
nature and integrate drug‑specific parameters, like intrinsic clearance and tissue parti‑
tion coefficients, with a drug‑independent structural model consisting of anatomical
compartments (e.g., organs and tissues) connected via physiological processes, e.g.,
blood flow and lymph flow. The physiological nature of these models makes them rela‑
tively easy to scale between species, and the mechanistic nature makes them exible
enough to adapt to changes in pathological conditions.
Due to the mechanistic detail and larger scale, QSP model development requires
more time and biological information compared with PK/PD model development.
However, they offer a signicant return on investment with respect to the delity of
questions answered.
12.1.3 Why Are PK/PD Modeling and QSP
Modeling Important for Biotherapeutics?
PK/PD and QSP models are useful tools to answer quantitative questions for all drug
modalities. However, they are specically useful for biotherapeutics as these drugs have
some unique challenges compared to small‑molecule drugs. First, the PK and PD of
biotherapeutics are intrinsically linked. For most small molecules, the concentrations at
which they are administered generally greatly exceed the concentration of the receptors
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