Bioreactor processes are the production platforms for many formulated products industries such as biopharmaceuticals (e.g. monoclonal antibodies, vaccines), food and beverages (e.g. yoghurt, beer) and speciality chemicals (e.g. penicillin). R&D, engineering, and manufacturing teams all face significant challenges due to the complexity of biological expression systems, large multi-dimensional decision space and constricted timelines.

Science-based data-calibrated digital twins based on mechanistic models use process understanding combined with targeted experimentation to describe and predict the quantitative behaviour of processes for process design and optimisation, risk assessment, design space exploration and scale up/tech transfer. The formulated products industries are increasingly using bioreactor digital twins to address challenges described above and achieve these predictive goals, thus bringing value to their businesses across R&D, engineering, and manufacturing functions.

Examples of common challenges and questions we aim to address through using bioreactor digital twins include:

  • I am working on scale-up and optimisation. How can modelling speed up process development whilst providing a more fundamental understanding of the behaviour of a process?
  • My organization is interested in continuous production to increase productivity – how can modelling help to switch from batch/fed-batch to perfusion?
  • How can digital twins be deployed online to bring value in manufacturing?
  • My organization has no/very little prior experience in mechanistic modelling. How can we harness the benefits of science-based digital twins without prior experience?

In this webinar, we will discuss Siemens PSE’s vision for how model-based solutions can bring value addressing industrial challenges in bioreactor process development and operation. We will then focus on case studies where model-based solutions have provided value to industry. In these case studies, we will examine the methodologies used to deliver value, examples of quantification of this value, and the considerations made to select the model appropriate solution to solve the challenges presented.

What this webinar covers

  • An overview of industrial challenges and SPSE’s vision on how model-based solutions can support addressing them.
  • Case studies showing where model-based solutions for bioreactors and the methodologies employed provide value to industry, including:
    • Enabling R&D efficiency in bioreactor process design and optimisation
    • De-risking moving to continuous processes, technology transfer and scale-up of bioreactor processes
  • PSE’s vision on how bioreactor model-based solutions can bring value in manufacturing via the deployment of digital twin solutions.

Who should attend?

Those with an interest in bioreactor processes from any industry, including:

  • Non-modellers through to expert modellers in technical roles in R&D, engineering and operations.
  • Decision-makers with an interest in learning about how bioreactor digital twins can bring value to their businesses across R&D, engineering, and manufacturing functions.

When

22 September 2021, 14:00 BST

Duration

45 mins plus Q&A

Presenter(s)

Dr Edward Close
Dr Edward Close, Siemens Process Systems Engineering (SPSE)

Dr Edward Close (Edd) is the Practice Director for Bioprocessing based in the Formulated Products business unit at SPSE. He obtained his undergraduate degree in chemical engineering in 2009 and his EngD in biochemical engineering in 2013, both at University College London. His EngD was in collaboration with Pfizer, and focused on developing mechanistic models of industrial purification processes to assist in process design and operation for the robust production of therapeutic proteins. Edd joined SPSE in 2013 and focuses on developing and applying SPSE’s solutions for upstream and downstream bioprocessing to deliver value to users across the Formulated Products industries.

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Workshop

28 September; 14, 20 October

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Biogen Production Bioreactor Digital Twin

Siemens Process Systems Engineering new bioreactor module in gPROMS FormulatedProducts has the potential to change the way bioreactor digital twins are developed and maintained in the industry. Experiments were replicated from Biogen’s mAb development in simulation of cell growth, death, and lysis. The consumption and secretion of numerous amino acids was modeled. An optimized feeding schedule to maximize growth with constraints was performed. The optimized experiment was performed, and the results showed the need for iteration in model development and experimentation and the need to question assumptions in the model. Future work will attempt to incorporate model learnings from the experiment.

What this webinar covers

  • Use case of Bioreactor Module in gPROMS
  • Optimization of Titer

When

29 September 2021, 09:00 EDT/ 14:00 BST

Duration

45 minutes plus Q&A

Presenter(s)

Jamison Hauser, Biogen

Jamison Hauser has a B.S. in Biomedical Engineering from the University of North Carolina at Chapel Hill. Jamison is an Engineer I in the Cell Culture Development group at Biogen experienced in upstream process development, media optimization, and process modeling.

Kaschif Ahmed, Biogen

Dr. Kaschif Ahmed has a Ph.D. in Chemical Engineering specializing in Advanced Process Control from the Illinois Institute of Technology. Kaschif has experience in modeling, optimizing, and controlling different systems such as fuel cell cars, coal gasifier, hybrid electric cars, bioreactors, and chromatography. Kaschif is currently a Senior APC engineer in the Advanced Data Analytics group in Biogen developing models and advanced controls for biologics upstream and downstream.

Sarwat Khattak, Biogen

Dr. Sarwat Khattak has a Ph.D in Chemical Engineering specializing in upstream process development, cell culture scale up, harvest development, and technology transfer.

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GSK Kinetic Modeling of a Fed-Batch Mammalian Cell Culture Production Process

Development of cell culture processes for production of biopharmaceuticals such as monoclonal antibodies (mAbs) requires extensive experimental work. For instance, media and feed composition, feeding strategy, and an array of bioreactor set-points (pH, temperature, agitation, etc.) must be optimized to sustain viable cultures and maximize production of high-quality product. To reduce this experimental burden, one attractive strategy is to employ mechanistic models that capture key process dynamics to complement experimental design and hasten acquisition of process knowledge. Cell culture process modelling has not been widely adopted in the biopharmaceutical industry, though, in part due to a lack of accessible modelling tools for non-experts and a perception that cell culture processes are too complex to be amenable to first-principles modelling approaches. In this work, we developed a kinetic model of a Chinese Hamster Ovary (CHO) fed-batch process that was successful in capturing the dynamics of several key cell culture parameters including nutrient/metabolite concentrations, viable cell density, and product titer. The model was subsequently used to improve understanding of how feeding strategy variations can impact end-of-run titer and to help optimize the fed-batch production process.

What this webinar covers

  • Development and deployment of a kinetic model for a bioreactor production process

When

6 October 2021, 09:00 EDT/ 14:00 BST

Duration

45 minutes plus Q&A

Presenter(s)

Steve Provencher, GSK

Steve Provencher is a Senior Scientist within the Microbial and Cell Culture Development (MCCD) group. Steve joined GSK after completing his undergraduate degree in Chemical Engineering at the University of Pittsburgh in 2018. Since joining GSK, Steve has served as a lead user for the ambr high-throughput bioreactor systems. He also leads informatics initiatives within MCCD including instrument connectivity, data pipelining, and dashboarding in addition to improving the data modeling capabilities within MCCD.

Gregory Nierode, GSK

Greg Nierode is an Investigator in the Microbial & Cell Culture Development (MCCD) department. Greg received a B.S. in chemical and biological engineering from the University of Colorado – Boulder and a doctorate in chemical and biological engineering from Rensselaer Polytechnic Institute (RPI) with a research focus on development and application of phenotypic high-throughput screening systems. Since joining GSK in 2018, Greg has been involved in a variety of technology development initiatives within MCCD – including development of next generation cell culture production processes and process modeling.

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More Information

WEBINAR 1 - Bioreactor digital twins – From upstream development to online model-based decision support systems

When
22 September 2021, 14:00 BST

Duration
45 minutes plus Q&A

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WEBINAR 2 - Biogen Production Bioreactor Digital Twin

When
29 September 2021, 09:00 EDT/ 14:00 BST

Duration
45 minutes plus Q&A

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WEBINAR 3 - GSK Kinetic Modeling of a Fed-Batch Mammalian Cell Culture Production Process

When
6 October 2021, 09:00 EDT/ 14:00 BST

Duration
45 minutes plus Q&A

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