Hybrid Modelling: From Process Understanding to Intelligent Manufacturing

In the realm of biopharma and biotech, the integration of data and science is paramount to addressing the multifaceted challenges presented by modern processes. These industries are awash with data from various sources, including experimental results, patient records, and sensor data. Traditional methods of data analysis, whether purely mechanistic or purely data-driven, can fall short in capturing the full complexity of these datasets. By integrating data and scientific principles, hybrid modelling offers a robust framework that leverages the strengths of both approaches to provide deeper insights and more accurate predictions

Mechanistic models, grounded in the fundamental laws of physics, chemistry, and biology, offer a structured way to describe processes based on known scientific principles. These models are invaluable for their ability to explain the underlying mechanisms of a system. However, they often require extensive domain knowledge and may not capture all the variability present in real-world data. On the other hand, data-driven models, such as those powered by machine learning, excel at identifying patterns and correlations within large datasets without requiring explicit knowledge of the underlying processes. The synergy of these two approaches in hybrid modelling allows for the construction of models that are both explanatory and predictive. 

 

The integration of data and science through hybrid modelling is particularly crucial in the context of biopharma and biotech industries, where the stakes are high, and the timelines for developing new treatments and technologies are often compressed. By combining the explanatory power of mechanistic models with the predictive prowess of data-driven models, hybrid modelling can accelerate the development process, optimize resource allocation, and improve decision-making. This integrated approach not only enhances the accuracy and robustness of models but also provides a scalable solution to manage the increasing complexity of biopharma and biotech processes. 

Why Traditional Approaches Fall Short

Mechanistic models offer interpretability and scientific rigor but often require simplifying assumptions and extensive parameter estimation. On the other hand, data-driven models excel at capturing nonlinear patterns but may lack extrapolation capability and scientific explainability—especially when data is limited or operating conditions change. 

In real-world bioprocessing, neither approach alone is sufficient. 

Hybrid modelling addresses this gap by embedding scientific understanding into data-driven frameworks, ensuring models remain both predictive and physically meaningful.

Key Benefits of Hybrid Modelling in Bioprocessing

• Improved Predictability Across Scales 

Hybrid models perform reliably across lab, pilot, and manufacturing scales, reducing surprises during scale-up and tech transfer. 

• Faster Process Development

By leveraging both data and science, teams can reduce experimental iterations, saving time and cost.

• Better Decision-Making

Hybrid models provide actionable insights while retaining interpretability—critical for regulatory confidence and internal alignment. 

• Robust Performance Under Variability

They handle process disturbances, raw material variability, and operating changes more effectively than standalone models.

The Process Challenges Driving the Need for Hybrid Modelling 

Despite advances in automation and analytics, manufacturers across these industries continue to face recurring challenges, including: 

• • Unreliable scale-up and scale-down performance
  • Technology transfer failures between sites or scales
  • Batch-to-batch variability and inconsistent product quality
  • Limited process understanding and incomplete mechanistic knowledge
  • Hard-to-measure or unmeasured critical process states and CQAs
  • Strong nonlinearities and multivariable process interactions
  • Suboptimal process control and reactive decision-making
  • High experimental cost and long development timelines
  • Difficulty in demonstrating process robustness for regulatory submissions 

Hybrid modelling is specifically designed to address these issues in a systematic and technically rigorous manner. 

How Hybrid Modelling Addresses These Issues

1. Scale-Up and Technology Transfer 

Traditional scale-up approaches often rely on empirical rules or simplified assumptions that break down at larger scales. Hybrid modelling mitigates this by: 

• • • • Capturing scale-relevant physics (mixing, mass transfer, heat transfer) through mechanistic equations
      • Learning scale-dependent deviations and nonlinearities from pilot and manufacturing data
      • Reducing trial-and-error experimentation during scale-up and site transfer

This leads to improved predictive reliability across laboratory, pilot, and commercial scales. 

2. Process Variability and Nonlinear Dynamics 

Variability is a defining characteristic of biologics and specialty chemical processes. Common sources include raw material variability, biological heterogeneity, and time-varying kinetics. Hybrid modelling enables: 

• • • • Physically constrained learning of residual dynamics
      • Quantitative identification of variability drivers
      • Early detection of deviations and proactive mitigation strategies 

As a result, manufacturers achieve more consistent yields and quality attributes. 

3. Limited Observability and Soft Sensor Development 

Many critical states and quality attributes cannot be measured in real time due to technical or economic constraints. Hybrid modelling supports: 

• • • • Development of high-fidelity hybrid soft sensors
      • Real-time estimation of unmeasured states and CQAs
      • Enhanced monitoring, fault detection, and decision support 

This significantly improves process transparency and operational confidence. 

4. Process Optimization and Advanced Process Control 

Pharma, biotech, and chemical processes are typically multivariable, constrained, and strongly nonlinear, limiting the effectiveness of classical control strategies. Hybrid models, when deployed within digital twin and model predictive control (MPC) frameworks, enable: 

• • • • Predictive, constraint-aware control 
      • Real-time optimization of operating conditions 
      • Continuous model adaptation using plant data 

This leads to sustained improvements in productivity, yield, and energy efficiency. 

5. Regulatory Compliance and Quality-by-Design (QbD)

Regulatory frameworks increasingly emphasize mechanistic understanding and scientifically justified control strategies. Hybrid modelling directly supports QbD and PAT initiatives by: 

• • • • Maintaining mechanistic traceability and interpretability 
      • Quantitatively linking CPPs to CQAs
      • Supporting design space definition and model-informed regulatory submissions

This reduces regulatory risk while strengthening process understanding. 

Hybrid Modelling as the Backbone of Digital Twins

Hybrid modelling forms the computational core of industrial digital twins, enabling: 

• • Scenario analysis and what-if simulations
  • Predictive monitoring and lifecycle optimization
  • Transition from reactive to predictive and adaptive manufacturing 

Organizations adopting hybrid modelling consistently realize: 

• • Higher model fidelity with fewer experiments
  • Robust extrapolation beyond historical operating regions
  • Faster development timelines and reduced scale-up risk 
  • Improved regulatory confidence and operational robustness 

Hybrid modelling represents a shift from empirical, trial-and-error optimization toward science-guided, data-augmented intelligence across the entire manufacturing lifecycle. 

Hybrid Modelling as the Backbone of Digital Twins

Hybrid modelling is not just a modelling technique - it’s a step toward intelligent, adaptive processing. When integrated with real-time data and advanced analytics, hybrid models enable proactive control, predictive monitoring, and continuous improvement. 

Organizations that adopt this approach early gain a competitive edge through faster development, higher robustness, and smarter decision-making. 

To gain a deeper, application-focused understanding of how hybrid modelling is implemented in real industrial environments, we invite you to our 2-part technical webinar series on Hybrid Modelling.