Scaling up monoclonal antibody (mAb) production from bench-scale to pilot-scale bioreactors is a complex process that requires optimizing operating parameters to maintain consistent hydrodynamic and mass-transport conditions. This talk highlights process characterization at bench-scale bioreactors and scale-up strategies to 40L or larger GMP production, focusing on ensuring regulatory compliance and product quality within a government laboratory setting.

Tangential flow filtration (TFF) enables scalable CHO cell retention for perfusion culture; however, resulting filter fouling reduces product recovery. We present a first-principles model combining cell kinetics, fluid mechanics, and fouling dynamics to predict membrane performance and product-sieving profiles across scales and projects. Our predictive framework demonstrates how operational parameters and filter geometry influence product sieving while providing insights into fouling mechanisms, membrane efficiency, and flux distributions.

This presentation covers the development and scale-up of intensified fed-batch processes, highlighting key strategies for increasing productivity and process consistency across manufacturing scales.

Midstream bioprocessing critically determines the recovery and purity of gene therapy products. This presentation examines how midstream decisions (harvest timing, depth filtration, tangential-flow filtration) set the stage for successful purification. We discuss the impact of midstream processing on chromatographic operations and the resulting quality attributes of adeno-associated virus, lentivirus, and adenovirus. Leveraging process optimization frameworks, we demonstrate how midstream excellence enables robust purification workflows that ensure therapeutic efficacy and safety.

This study evaluates alternative nucleases/methods for the harvest of AAV vectors after upstream production. By analyzing the impact of various nucleases/methods under diverse conditions, we assessed process performance, product quality, and downstream Cost of Goods (CoG). Our findings provide critical insights for optimizing the harvest process, balancing robust process with economic efficiency in gene therapy manufacturing.

In Sanofi’s AAV manufacturing platform, the harvest process typically involves a flocculation step, where mixing plays a critical role. Developing a robust mixing scale‑down model is essential. Floc formation dynamics were characterized in real time using EasyViewer technology. The developed scale‑down model demonstrated excellent predictive performance. Implementation of the optimized flocculation conditions resulted in a two‑fold increase in depth filter throughput.

Consistency and robustness in cell-culture processes are critical for the reliable production of biologics. This presentation will showcase studies describing strategies applied to identify potential sources of variability—and strategies to mitigate them. To this end, we use a proactive risk-assessment approach to identify potential raw-material variability and apply data-driven mitigation strategies. Additionally, we use omics tools to unravel complex molecular interactions.

This presentation introduces mechanistic modeling frameworks developed to predict the complex dynamics of multispecific co-culture systems, providing a quantitative foundation for understanding and monitoring process behavior across varying conditions.

Recent advances in cell culture have increased cell culture densities and productivities but also elevated submicron particles, challenging the clarification step. To address this, an optimized harvest process was developed. Consequently, process characterization was required for this harvest operation to ensure process robustness and consistent product quality. This presentation describes the risk-based approach used to identify potential critical parameters and details the study performed to evaluate their impact on harvest performance and product quality.

Digital twins are valuable for efficiently transferring complex processes from the laboratory to manufacturing scale and ensuring consistent results at different manufacturing sites. Here, we discuss case studies in the application of cell culture digital twins to tech transfer programs and demonstrate how modeling has allowed us to meet aggressive timelines and better serve the patients who need our products.

This talk will present the application of AI/ML to enhance mAb production in CHO cells. This AI tool automates rapid extraction of data from native file formats into structured templates powered by LLMs and performs in silico simulations to recommend optimal conditions for user-defined targets. By enabling intuitive and efficient exploration of complex datasets, the platform democratizes data access, accelerates insight generation, and supports data-driven decision-making.

This presentation explores how digital twins, combining mechanistic process understanding, AI, and process data, enable smarter, faster bioprocess development and control. Six industrial use cases demonstrate the value of digital twins: accelerated biosimilar development using PAT and glycan modeling; reduced experimental effort in viral vector process design; media optimization through time-resolved nutrient uptake prediction; UF/DF development guided by digital membrane and recovery modeling; scale-up informed by CFD-based reactor behavior; and fully integrated digital control of continuous bioprocesses sustained for over 30 days. Together, these examples show how digital twins streamline experimentation, enhance decision-making, and de-risk scale-up—unlocking end-to-end process insight from early development to production.

The future of bioprocessing is autonomous. I will show how a CHO digital twin fuses genome-scale metabolic modeling with PAT-driven AI to forecast VCD and titer in real time. By introducing an XAI-guided, BO–enabled adaptive control framework, we move to closed-loop decision-making by updating recipes and feeding towards desired setpoints trajectories. The result is interpretable, high-performance control that enables transparent, end-to-end bioprocess optimization.

• The Engine: Autonomous DT coupling with PAT and soft sensors
• The Intelligence: CHO GEM and AI forecasting for real-time cellular state prediction
• The Execution: An XAI-guided control framework bridging the gap between machine learning and operational trust