A science‑driven path to process excellence across the lifecycle of chemicals and pharmaceuticals
Pharmaceutical, biopharma, and chemical manufacturers are under unprecedented pressure. Processes are becoming more complex, product portfolios more diverse, and regulatory expectations more stringent. Yet many organisations still rely on empirical optimisation, with incremental tweaks, trial‑and‑error experiments, and historical intuition, even when their plants generate terabytes of data.
Data is abundant, but actionable process understanding is scarce. Mechanistic models alone cannot capture the full behaviour of modern processes, while purely data‑driven models struggle with limited datasets, extrapolation, and regulatory acceptance. Hybrid modelling offers a way out of this trap.
Why traditional approaches fall short
Mechanistic models
Mechanistic or first‑principles models provide scientific interpretability with data on—mass balances, energy balances, reaction kinetics, fluid dynamics. But they require simplifying assumptions, and building them for complex biological or multiphase systems is time‑consuming and often incomplete.
Data‑driven models
Machine learning models excel at pattern recognition and nonlinear relationships. However, they depend on large, representative datasets and often fail when conditions shift, such as the introduction of new equipment, new raw materials, new scales. They also lack the mechanistic traceability regulators expect.
The result
Manufacturers face recurring pain points:
⚠️ Unreliable scale‑up and scale‑down
⚠️ Tech transfer failures between sites
⚠️ High batch‑to‑batch variability
⚠️ Limited visibility into CQAs and “unmeasurable” states
⚠️ Long development timelines and heavy experimental burden
Hybrid modelling: The best of both worlds
Hybrid modelling integrates first‑principles science with machine learning, creating models that are:
✔️ Mechanistically grounded: Every prediction is tied to physical reality
✔️ Highly predictive: ML captures nonlinearities and interactions
✔️ Robust to change: Models extrapolate more reliably across scales and equipment
✔️ Regulator‑friendly: Mechanistic traceability supports QbD and PAT
When deployed as digital twins, these hybrid models become real‑time decision engines. They reconstruct critical parameters that cannot be measured directly, such as mixing intensity, shear stress, mass transfer, local gradients, and provide early warnings when processes drift out of their ideal operating envelope.
Case studies across the value chain
1. Biopharma: Mammalian cell culture tech transfer
A biopharma manufacturer struggled to transfer a mammalian cell culture process between sites using different commercial bioreactors. Variations in vessel geometry and agitation systems caused inconsistent mixing and oxygen transfer.
A physics‑based digital twin was used to characterise hydrodynamics, shear, and mass transfer across vessels. By integrating plant data with mechanistic models, engineers:
✔️ Identified the root causes of variability
✔️ Matched mixing environments across scales
✔️ Reduced the number of physical optimisation runs
✔️ Improved viable cell density and titer consistency
This approach de‑risked tech transfer and accelerated time to performance.
2. Drug product: Protein dilution and shear control
During final formulation, a pharmaceutical company observed protein aggregation caused by high shear during dilution.
Using CFD‑based digital twins, engineers mapped shear stress and mixing profiles across operating conditions. The model revealed:
✔️ Agitation speeds that minimised shear
✔️ Addition strategies that maintained blend uniformity
✔️ Operating windows that protected sensitive proteins
The optimised process reduced protein damage and delivered savings of ~$75,000 per batch.
3. Biopharma: Bioreactor process health monitoring
Process drift, caused by subtle, cumulative deviations in bioreactor conditions, is a major cause of performance loss. It often goes undetected because key biological states cannot be measured directly.
A hybrid digital twin combined historical data with biological and mechanistic models to reconstruct these “unmeasurable” states in real time. This enabled:
✔️ Early detection of abnormal behaviour
✔️ Improved upstream robustness
✔️ Higher cell viability
✔️ Reduced batch variability
Regulatory impact: A stronger scientific foundation**
Regulators increasingly expect mechanistic understanding, data‑driven justification, and transparent control strategies. Hybrid modelling directly supports:
Quality by Design (QbD): Hybrid models quantify how CPPs influence CQAs, enabling robust design spaces.
Process Analytical Technology (PAT): Digital twins act as soft sensors, providing real‑time visibility into states that cannot be measured directly.
Model‑informed regulatory submissions: Mechanistic traceability ensures that predictions are scientifically defensible, which is a key requirement for global regulatory agencies.
By combining scientific rigour with data‑driven adaptability, hybrid modelling reduces regulatory risk and strengthens process understanding across the global supply chain.
Why this matters now
Hybrid modelling and digital twins deliver:
✔️ Faster development
✔️ More reliable scale‑up
✔️ Fewer failed batches
✔️ Stronger tech transfer
✔️ Lower cost of experimentation
✔️ Greater confidence in regulatory submissions
This is not just a modelling upgrade — it is a shift toward science‑driven, data‑empowered manufacturing. Hybrid modelling is becoming the backbone of modern process development and commercial manufacturing. For organisations seeking to reduce variability, accelerate innovation, and build resilient global supply chains, hybrid digital twins offer a clear, proven path forward.
