Cambridge Healthtech Institute’s 4th Annual

Bioprocess Data Management and Analysis

Advancing Bioprocessing through Instrumentation, Characterization, Control, and Analytics

January 20-21, 2020

Part of the Process Technologies & Purification pipeline

The biopharmaceutical industry is meeting increasing demands and costs for biotherapeutics through process optimization. Data from advanced instrumentation through sampling techniques, new sensor technologies, and analyzers have emerged to monitor both upstream and downstream processes. When well-prepared and analyzed, this data leads to process knowledge, process control, and continuous improvement, resulting in greater speed, quality, and economy. Cambridge Healthtech Institute’s 4th Annual Bioprocess Data Management and Analysis conference addresses statistical analysis strategies allowing for optimized and informed control of bioprocessing. 

Final Agenda


4:00 - 6:00 pm Pre-Conference Registration (Sapphire West Foyer)


7:00 am Registration (Sapphire West Foyer) and Morning Coffee (Sapphire West & Aqua West Foyer)

Data Defines Process Development
Sapphire 411

9:00 Organizer’s Welcome Remarks

Mary Ann Brown, Executive Director, Conferences & Team Lead, PepTalk, Cambridge Healthtech Institute

9:05 Chairperson’s Opening Remarks

Anne Richelle, PhD, Senior Specialist - Metabolic Modeling, Global Vaccines, Technical R&D, GlaxoSmithKline Vaccines



9:10 Intelligent Data Management and Analytics towards Advanced Biopharmaceutical Process Development and Manufacturing

Cenk Undey, PhD, Executive Director, Process Development, Amgen

We generate a significant amount of data during development and manufacturing of biopharmaceutical therapeutic proteins. Managing data right from the start and throughout the product development lifecycle into manufacturing is critical for data flow, as well as optimizing and accelerating the development activities. We will review how we have been advancing the intelligent capture, management and leveraging of development data to optimize manufacturing using machine learning and other advanced data analytical methods.

9:50 Digital Twins as Product Life Cycle Companions

Zahel_ThomasThomas Zahel, PhD, Head of Innovation, Exputec GmbH

Digital Twins are in silico representations of entire manufacturing processes. Due to the linkage of models of multiple unit operations, it is possible to predict the impact of any process parameter or material attribute onto final product quality. Thereby, multiple benefits for manufacturers can be achieved such as setting feasible acceptance limits as well as a model-based control strategy, both leading to lowered number of failed batches and increased patient safety.

10:20 Networking Coffee Break (Sapphire West & Aqua West Foyer)

10:45 Importance of Upstream Analytical Assays and DOE Studies to Guide Early Process Development

Mott_JonathanJonathan Mott, MS, Scientist, Upstream Process Sciences, Nektar Therapeutics

For some fast-paced programs, there is a temptation to rush the upstream process development and move forward with a functional but poorly characterized process. Here I present two case studies demonstrating how upstream analytical assays and DOE studies early in upstream process development are crucial to successful scale-up and commercialization.

11:15 Digital Bioprocessing: The Impact of Instrument and Software Integration

Wang_SpinSpin Wang, MS, Co-Founder and CEO, TetraScience

Scientists and informatics teams place a heavy reliance on the manual collection, transfer, manipulation, storage, and reporting of their instrument data. This lack of automation slows processes, inhibits scalability, and can jeopardize data integrity. We have designed and developed a new approach to collect and manage data from a bioprocess workflow. Our hope is for this to serve as a blueprint to those pursuing a digital bioprocessing strategy.

11:45 How Can Systems Biology Tools Facilitate a Cost-Effective Upstream Process Development in the Biopharmaceutical Industry?

Richelle_AnneAnne Richelle, PhD, Senior Specialist - Metabolic Modeling, Global Vaccines, Technical R&D, GlaxoSmithKline Vaccines

In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools. While pharmaceutical companies are already investing substantially in computational approaches to guide drug discovery and cell design, model-based methods can also be applied for upstream process development to improve process understanding, lower the experimental effort and increase the process robustness.

GE 12:15 pm Digitalization of Process Development – Current State and Future Outlook

Knapp_HarlanHarlan Knapp, Business Development, Enterprise Solutions, Enterprise, GE Healthcare Life Sciences

Digitalization is a global trend across industries. Although the pharma industry is a late adopter, a shift is on the horizon. In this presentation we will highlight process development and the critical role of control strategy for implementing Industry 4.0 in pharma. We will assess the current state of our industry and provide a vision of the desired future state. Then, we will discuss key challenges and technological breakthroughs to address them.

12:45 Session Break

12:55 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

Bioreactors and Continuous Processing

2:00 Chairperson’s Remarks

Bryan Jones, PhD, Research Fellow, BioTechnology Discovery Research, Eli Lilly and Company

2:05 Separation of Recombinant Protein in Perfusion Bioreactor Bleed Material Using Acoustic Wave Separator

Hong_Jin_SungJin Sung Hong, PhD, ORISE Research Fellow, Center for Drug Evaluation and Research, FDA

In this study, we assessed separation of recombinant protein from a perfusion WAVE bioreactor bleed material using acoustic wave separator (AWS) for continuous upstream bioprocessing approach. We integrated a perfusion WAVE 25 bioreactor for perfusion cell culture to a Cadence AWS, thus providing continuous cell clarification of bleed material. Our data indicate AWS can provide effective cell clarification/filtration, and product recovery and quality.

2:35 SELECTED POSTER PRESENTATION: Automatically Merging On-Line Bioprocess Data with Off-Line Analytics

Igor Drobnak, PhD, Senior Scientist, Analytical Development, Lek DD

3:05 Find Your Table and Meet Your BuzZ Session Moderator

3:15 BuzZ Sessions with Refreshments

Join your peers and colleagues for interactive roundtable discussions.

Click here for more details

High-Throughput Platforms: Data Management and Modeling

4:30 E2E Biologics Platform – From Discovery to Development

Lin_YuanYuan Lin, Biologics Solution Lead, Pfizer, Inc.

For the past 5 years, Pfizer has developed an E2E Biologics informatics Platform. The platform is a cohesive and authoritative data repository for Pfizer Biologics-oriented therapeutic projects across R&D. It streamlines sample registration, workflow and inventory management, assay data capture, and biomolecule analysis. In addition, it provides comprehensive data search, navigation, and report functionalities, and enables machine learning for better designing and developing biologics products

5:00 Development of Higher-Throughput Assays for Antibody Discovery that Are Predictive of Developability Properties

Jones_BryanBryan Jones, PhD, Research Fellow, BioTechnology Discovery Research, Eli Lilly and Company

Therapeutic antibodies must possess suitable biophysical & developability properties to allow for their manufacture and ultimate delivery to the patient. Unfortunately, many of the common difficulties that arise during development of antibodies often only manifest under specific conditions (e.g., high concentration) that are impossible to “screen” for during antibody discovery. Therefore, we have focused on developing assays that exhibit predictability of downstream behavior (e.g., solubility), that are useful earlier in antibody discovery.

5:30 Platformization of Multi-Specific Protein Engineering: Learning from High-Throughput Screening Data

Furtmann_NorbertNorbert Furtmann, PhD, Head of Data Lab, High Throughput Biologics, Sanofi-Aventis Deutschland GmbH

Our novel, automated high-throughput engineering platform enables the fast generation of large panels of multi-specific variants (up to 10.000) giving rise to large data sets (more than 100.000 data points). Here we report on our visualization and data analysis workflows to improve the understanding of our complex molecules and guide the engineering process.

6:00 - 7:15 Welcome Reception in the Exhibit Hall with Poster Viewing (Sapphire Ballroom)

7:15 Close of Day


8:15 am Registration (Sapphire West Foyer) and Morning Coffee (Sapphire West & Aqua West Foyer)

CHO Cell Bioanalytical and Biological Process Development
Sapphire 410

8:45 Chairperson’s Remarks

Nathan Lewis, PhD, Associate Professor, Department of Pediatrics, University of California, San Diego

8:50 Modeling Chinese Hamster Ovary Cell Metabolism: A Systematic Look at Model Parameters and Risk of Overfitting

Schinn_MatthewMatthew Schinn, PhD, Postdoctoral Researcher, Department of Pediatrics, University of California, San Diego

Metabolic network models provide mechanistic understandings of cell metabolism, and therefore could guide the rational design of cell lines and culture processes. However, such models are liable to overfit due to their high degrees of freedom. Here we systematically evaluate a wide range of model parameters important to describing CHO fedbatch culture performance.

9:20 Model-Driven Process Development for Enhanced Bioprocessing

Lakshmanan_MeiyappanMeiyappan Lakshmanan, PhD, Research Scientist & Group Leader, Systems Biology, Bioprocessing Technology Institute, A*STAR

Chinese hamster ovary (CHO) cells are the preferred choice for biotherapeutic protein production. However, ensuring consistent high product quality remains a major challenge. The availability of the CHO genome sequence has enabled the development of genome-scale models (GEMs) to examine the metabolic signatures of CHO cells upon varying bioprocess conditions. This talk will show how the genome-scale models can help process development by characterizing key bottlenecks in media formulations and propose targets for media/feed optimization.

9:50 Coffee Break in the Exhibit Hall with Poster Viewing (Sapphire Ballroom)


11:00 Chairperson’s Remarks

Khandaker Siddiquee, PhD, Principal Scientist, Abbott Diagnostic Division, Abbott Laboratories

11:00 Use of Statistics in Early Phase Bioprocess Development

Li_RuojiaRuojia Li, PhD, Principal Scientist, Statistics Team Lead, Biologics Development, Bristol-Myers Squibb

Statistical analyses play a critical role in bioprocess development. Typical applications include power and sample size calculations, determination of proper threshold for comparison, design of experiments (DOE), and predictive modeling for process optimization. This talk will provide an overview and some case studies on how statistical analyses can be applied to advance early phase biologic process development.

11:30 Quality by Design Revealed that Oxidation of a Recombinant Fab Is Driven by CHO Cell Growth Conditions, Physiology, and Overexpression of Oxidative Stress Genes

Khandaker Siddiquee, PhD, Principal Scientist, Abbott Diagnostic Division, Abbott Laboratories

Quality by Design (QbD) and Design of Experiment (DOE) tools were utilized to optimize a bioprocess for production of a CHO recombinant antigen binding fragment (rFab) in small-scale bioreactors. The study also revealed the mechanism and pathway for oxidation of the rFab molecule during cell culture bioprocess optimization. The study further demonstrated the importance of integrating cell culture, analytical chemistry, and gene expression data to optimize the cell culture bioprocess prior to scaling up into the large-scale production bioreactor.

12:00 pm Sponsored Presentation (Opportunity Available)

12:30 Session Break

12:40 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:10 Close of Bioprocess Data Management and Analysis Conference