Biologics process development landscape is getting increasingly adoptive to Digital Transformation and Autonomous bioprocess technologies. Which creates opportunities to elevate the analytical testing and development operational model. This talk features a clear vision, roadmap, and case studies of lab automation and digital transformation efforts measured against main KPI’s such as speed and productivity for in-process analytics for bioprocess development and manufacturing.

This presentation will demonstrate how data-driven regulatory intelligence can revolutionize bioprocessing by automating compliance workflows, predicting process deviations, and accelerating scale-up decisions. Through real-world case studies and simplified AI workflows, attendees will see how raw manufacturing and quality data become actionable insights—transforming weeks-long regulatory tasks into minutes.

Digital and AI transformation in bioprocessing often underperforms not due to technology, but because workforce systems are misaligned with process complexity. This talk introduces a quantitative framework for engineering workforce performance in regulated bioprocessing environments. It shows how instability, leadership dependency, and mis-sequenced retain-retrain-recruit-automate decisions create execution risk, and how organisations can intervene earlier to sustain throughput, compliance, and digital adoption.

We present an automated, data-rich platform for nanoparticle manufacturing that enables rapid, material-efficient identification of critical process parameters while ensuring reproducibility and regulatory relevance. The system integrates an impinging jet mixer with real-time, spatially resolved dynamic light scattering, coordinated through advanced control software, database management, and a user-friendly interface. Future integration of Bayesian optimization and automated Design of Experiments will further accelerate process development, demonstrated using model drug delivery systems.

Nanobody® molecules are an emerging class of biologics whose multivalent formats pose unique purification challenges due to structural flexibility and complex interactions. We developed the first high-fidelity mechanistic chromatography model for a Nanobody molecule, capturing complex elution behavior and all critical quality attributes to support late-stage process development. The validated model enables robust design space definition, scale-up across, and a predictive, digitally driven filing alternative to traditional empirical workflows.

Achieving consistent yield and quality in modern commercial biologics manufacturing requires strong process understanding, integrated data systems, and predictive modeling. A structured AI/ML framework was applied using multi year manufacturing data to develop hybrid and machine learning models that accurately predict key drivers of yield and product quality. The program incorporated automated data pipelines, parameter contextualization, and governance through routine review forums. Deployment resulted in higher yields, tighter quality profile, and increased overall process robustness.

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 digitial 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