How it will work is we have 3 Round Tables each covering a different challenge of Upstream & Cell Culture in Bioprocessing. Each will be led by an expert. The expert will kick off and guide the discussion which will last for 30 minutes. At the end of the 30 minutes the leader will report their main conclusions to the room for a maximum of 10 minutes.

Roundtable Discussions

Cell Culture Optimization

• Cell Line Development: Selecting and developing an optimal cell line is crucial for achieving high product yields and quality. Identifying a cell line with desirable characteristics, such as growth rate, productivity, and stability, can be a time-consuming and complex process.
• Cultivation Conditions: Maintaining optimal conditions for cell growth and productivity is challenging. Factors like temperature, pH, nutrient availability, and gas exchange need to be carefully controlled to ensure optimal performance. Achieving consistent and reproducible results across large-scale bioreactors adds another layer of complexity

Bioreactor & Upstream Scale-Up:

• Transition from Lab Scale to Production Scale: Scaling up bioprocesses from small laboratory bioreactors to large-scale production facilities can lead to challenges in maintaining consistent conditions. Factors like mixing, mass transfer, and heat dissipation become more complex as the scale increases. Achieving uniform distribution of nutrients and gases becomes crucial for maintaining cell health and product quality. Take into account Perfusion versus Fed-Batch.
• Bioreactor Design: The design of bioreactors for large-scale production must consider factors such as hydrodynamics, shear stress, and heat transfer to ensure optimal cell growth and product formation. Choosing the right type of bioreactor and ensuring scalability without compromising performance is a significant challenge

Process Monitoring and Control:

• Real-time Monitoring: Continuous monitoring of various parameters, such as cell viability, metabolite concentrations, and product titer, is essential for process control and optimization. Implementing reliable real-time monitoring techniques can be challenging, particularly for complex bioprocesses.
• Process Control: Maintaining tight control over the bioprocess is crucial to achieve consistent product quality. Controlling variables such as pH, dissolved oxygen, and nutrient concentrations requires advanced control strategies. Deviations from optimal conditions can negatively impact cell growth, viability, and product quality

• What do we see as standards and regulatory science needs to increase adoption of advanced technologies?
• How can AI be incorporated into smart and advanced manufacturing?
• How can regulatory agencies and industry work together to make safe and smart manufacturing a reality?

Dark Horse Consulting Representative

In the slowly evolving landscape of bioprocess development and manufacturing, digital bioprocess-twins have emerged as potential accelerators. While advanced algorithms are at the heart of this endeavor, they are just one piece of the puzzle. The talk delves into key discussion points that are integral to this paradigm shift. The foundation of accelerated process development and automated process control starts with a clever experimental design, in-time data accessibility combined with powerful modeling algorithms. The talk will highlight the advantages of using hybrid modeling, while emphasizing the other critical aspects of his journey. Several industrial relevant upstream showcases for microbial and mammalian cell lines will be highlighted. Thereby, concepts to save experimental effort by up to 70% will be elaborated, and the modeling structure created in the late-stage development will be reused for real-time monitoring and control in the later stages. Additionally, a downstream optimization showcase for UF/DF/SPTFF will be highlighted.

Continuous improvement methodology has been employed for many years to improve manufacturing where effectiveness depends on team experience. Advanced modeling technology for biomanufacturing promises to make continuous improvement methodology more widespread and allow teams to achieve expert results with far less experience. This presentation will illustrate model based continuous improvement across a drug substance biomanufacturing process using an industrial case study.

• How do we define AI/ML?
• Which industries have already implemented AI/ML? How can the Biopharma industry learn from this?
• Is Biopharma prepared for this transition?
• Early adopter’s vs late adopters: Will there be any impact?
• How can small, mid-size, and large companies strategize for ML/AI implementation?
• Can it be beneficial for all areas of biopharmaceutical development (discovery, development, clinical and manufacturing)
• Cost reduction and accelerated timelines for project