Tuesday 22nd September: Pre-Conference DayMonday, 22 September 2025 - ET (Eastern Time, GMT-05:00)

Intensified and continuous bioprocessing place fundamentally different demands on production cell lines than traditional fed-batch manufacturing. High-density perfusion cultures require cell lines engineered for long-term stability, robustness to shear and nutrient gradients, and controlled growth at extreme cell concentrations, rather than short-term peak productivity alone. This presentation will explore what defines a “perfusion-ready” cell line, highlighting engineering strategies to improve metabolic efficiency, reduce waste accumulation, and sustain consistent product quality over extended culture durations. The interplay between cell line behavior and cell retention technologies such as ATF and TFF will be discussed, along with the implications for process robustness and scalability. Finally, the talk will address how cell line development workflows must evolve for continuous manufacturing, emphasizing early exposure to perfusion conditions and selection criteria aligned with long-term intensified process performance.

• What are the scale up risks: Cell retention, fouling, oxygen transfer, media logistics, sampling/sterility strategy.
• Practical approaches to managing large media volumes/ logistics in continuous operations.
• How do ATF and TFF systems improve cell retention and productivity

TBC

• What problems AI is actually good at solving?
• Pattern recognition versus mechanistic understanding
• Why AI does not replace process understanding
• The three types of AI: Descriptive, Predictive, Prescriptive – Where does bioprocessing mostly sit today?
• What AI can realistically deliver in the next 12-24 months? (where it is working, where it is still experimental, where expectations need resetting)

TBC

• Introduction to Biopharmaceutical Life Cycle.
• Explain what upstream bioprocessing involves: the early stages of production, including cell culture and fermentation.
• Outline the key objectives: generating the desired biological product through cell growth and expression.
• Discuss the selection of cell lines (e.g., CHO cells, microbial cells).
• Introduce bioreactors and their role in providing a controlled environment for cell growth.
• Discuss different types of bioreactors (e.g., stirred-tank, wave, single-use) and their applications.
• Explain the fermentation process and its parameters (e.g., pH, temperature, oxygen levels).
• Explain the importance of culture media in supporting cell growth and productivity.
• Describe the process of scaling up from lab-scale to commercial-scale production.
• Highlight current trends in upstream bioprocessing (e.g., single-use technologies, continuous processing).
• Discuss future directions and innovations in the field.

• Explain what downstream bioprocessing involves: the purification and formulation of the biological product after cell culture and fermentation.
• Outline the key objectives: ensuring product purity, quality, and stability.
• Describe the process of harvesting cells or extracellular products from the bioreactor.
• Explain the methods used for cell separation (e.g., centrifugation, filtration).
• Introduce the main purification methods: chromatography, filtration, and precipitation.
• Describe different types of chromatography (e.g., affinity, ion-exchange, size-exclusion) and their applications.
• Explain the principles and applications of ultrafiltration and diafiltration.

• Exploring the potential for process intensification through continuous membrane steps.
• How can continuous processes be scaled effectively for GMP manufacturing?
• What are the risks and limitations of adopting continuous chromatography?

TBC

• What AI needs to work – data volume versus relevance, why garbage in is dangerous?, Why AI magnifies process misunderstandings?
• How to diagnose data readiness
• What “good enough” looks like
• A practical checklist for AI readiness
• How to spot red flags in your own datasets
• When not to start an AI project

• Why pure AI fails in bioprocessing?
• What mechanistic modelling actually is?
• Where mechanistic models outperform AI?

• Growing importance of digitalization, AI, and machine learning in the biopharma industry.
• Key pillars of digital transformation in biopharma.

Key Areas of Digitalization

• Data Management and Integration (from Development to Manufacturing).
• Automation and Robotics in bioprocess workflows.
• Real-time Monitoring and Advanced Analytics for process optimization

Applications in Bioprocessing

• Use of digital twins and AI to optimize upstream and downstream unit operations.
• Role of ML/AI-driven tools for Advanced Therapy Medicinal Products (ATMP) manufacturing.AI-driven real-time monitoring, predictive maintenance, and anomaly detection in production lines.
• AI-driven real-time monitoring, predictive maintenance, and anomaly detection in production lines.
• Simulation-based process development for rapid scale-up.

Challenges and Considerations

• Overcoming data silos and ensuring system interoperability.
• Addressing regulatory requirements for AI and digital tools in GMP environments.
• Ensuring data quality, integrity, and security in digitalized workflows.
• Bridging talent gaps and fostering a digitally skilled workforce.

Case Studies

• Real-world examples of digital transformation in bioprocessing.
• Lessons learned from integrating AI-driven tools in ATMP production.

Future Trends and Directions

• Adoption of Industry 4.0 principles in biopharma manufacturing.Emerging technologies such as edge computing and IoT for bioprocessing.
• Emerging technologies such as edge computing and IoT for bioprocessing.
• Sustainability and digitalization: How to?

• Online biomass, metabolites, product titre
• Inline chromatography analytics
• Control strategies for steady-state operations
• Data integrity and batch definition in continuous processes

We demonstrate the first end-to-end concept downstream continuous processing train for AAV vectors at pilot 50 L scale, addressing key bottlenecks in traditional batch processing. The process utilizes novel enabling technologies for each unit operation, including multi-column affinity capture with fast-flow loading, dual-tank low pH viral-inactivation, rapid-cycling anion exchange chromatography using weak partitioning AEX for full-empty separations, and countercurrent hollow fiber filtration for single-pass UFDF. The continuous train was run for 72 hours of continuous operation and reduced resin volumes by >90% while matching or exceeding batch process CQAs. The continuous purification process as demonstrated can be linearly scaled up from 50 L to be compatible with 500-2,000 L scale staggered batch harvests as well as upstream perfusion systems.

TBC

• Combining first-principles understanding with machine learning
• How mechanistic and AI models are combined in practice?
• What hybrid architectures look like?

• Maintaining, retraining and governing ML models in GMP environments.
• What happens after the model is deployed in a real GMP environment?
• What drift actually looks like in QC and analytical models, PAT and process models, predictive stability models? Early signs? How often does drift occur?
• What happens when a model is wrong once?
• Why users stop trusting models?
• Who owns the model after deployment?
  
  

TBC

• Understanding emerging therapies: distinctions between cell therapy, gene therapy, etc.
  • Therapeutic potential and current clinical landscape of different emerging therapies, unique challenges and opportunities presented.
  • Differences and similarities from ‘traditional’ biologics:
  • What learnings can we take from traditional modalities to approach novel modalities?
• Understanding the Cell Therapy and Gene Therapy manufacturing processes.
  • Best practices when entering/transitioning into the advanced therapy industry.
  • Leveraging experiences from your background into industry.
• Strategies and approaches to best utilise available technologies in the development & production of emerging therapies.
  • Moving and translating research from academia, to start up, industry, and beyond.
  • Understanding the difference between these, how to transition, pros and cons.

Lessons and experiences from our panellists.

• The evolution of biopharmaceutical modality
• Analytical methods and their purpose in biopharmaceutical development and manufacturing
• Analytical method development and validation
• Product physicochemical characterization - high-performance liquid chromatography (HPLC), capillary electrophoresis (CE), spectroscopy, imaging, and post-translational modification (PTM)
• Product biological assays - cell-based assays (CBA), enzyme-linked immunosorbent assays (ELISA), and potency assays
• Microbiological contaminants - sterility testing, endotoxin testing, and microbial limits testing
• Process impurity testing - host cell DNA, host cell proteins, chromatography ligand
• Role of quality control (QC) and quality assurance (QA) in biopharma
• Case studies and industry examples
• Latest and future advancements in analytical methods and quality assurance
  
  

An honest assessment of continuous processing and integrated processes – what works, what doesn’t and what CDMOs must consider before investing.

• Realistic cost-benefit analysis: footprint, CapEx, development time
• Is an end-to-end continuous process feasible for a CDMO customer mix

With pressure to reduce COGs and time-to-market, process intensification has re-emerged as a strategic priority.

• Practical strategies to intensify processes without increasing risk
• Understanding where intensification truly delivers value
• Insight into future manufacturing models for biologics

• If you can’t explain it, you can’t use it.
• Making AI transparent for quality, manufacturing and regulators.
• How to document models
• Managing model updates
• When retraining becomes re-validation
• When to freeze a model versus evolve it?

• What surprised you most after deploying AI or models?
• Where should companies stop experimenting and start standardizing?
• What decisions are we still not ready to trust to models?
• What gaps are holding teams back more than technology?

• The evolution of biopharmaceutical modality
• Analytical methods and their purpose in biopharmaceutical development and manufacturing
• Analytical method development and validation
• Product physicochemical characterization - high-performance liquid chromatography (HPLC), capillary electrophoresis (CE), spectroscopy, imaging, and post-translational modification (PTM)
• Product biological assays - cell-based assays (CBA), enzyme-linked immunosorbent assays (ELISA), and potency assays
• Microbiological contaminants - sterility testing, endotoxin testing, and microbial limits testing
• Process impurity testing - host cell DNA, host cell proteins, chromatography ligand
• Role of quality control (QC) and quality assurance (QA) in biopharma
• Case studies and industry examples
• Latest and future advancements in analytical methods and quality assurance

Final panel with all presenters for one last Q & A opportunity!