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Bioprocess development is increasingly becoming data-rich, multidimensional and predictive. Z2 combines biological and process-development understanding with an expanding capability in AI, machine learning, multivariate analysis and advanced data analytics.
The objective is to move development beyond simply "running more experiments" towards designing smarter experiments, extracting more knowledge from existing data and making better development decisions earlier – reducing unnecessary experimentation, development time and technical risk.
Where data and AI add value
Media optimisation
Data-driven and model-guided exploration of media and feed composition.
Process understanding
Multivariate analysis to reveal how parameters, metabolism and outcomes interact.
Predictive modelling
Mechanistic, statistical and hybrid AI/ML models to anticipate performance and scale effects.
Critical process relationships
Identifying the relationships that matter, to focus experimentation and control strategy.
Tools and approaches
- Design of Experiments (DoE) and response-surface methods
- Multivariate data analysis (MVDA)
- Process analytical technology, including Raman spectroscopy
- Mechanistic and hybrid (mechanistic + machine learning) models
- Machine learning on historical and experimental bioprocess data
Recent research illustrates the potential: an explainable AI-driven flux balance analysis of CHO cells during prolonged passaging linked a ~35% fall in peak antibody titre to underlying metabolic changes.1
Where Z2 helps
Biological and process understanding first, then the right analytical or AI tool – so models answer real development questions and data informs decisions earlier.
Sources
- Exploring CHO cell stability during prolonged passaging via explainable AI-driven flux balance analysis. PMC12992790.
