In biomanufacturing, digital twins are gaining traction to help manufacturers simulate, monitor, and optimize processes. And as regulators signal increasing openness to digital innovation, digital twins are poised to become central to both operational excellence and regulatory confidence.
What Is a Digital Twin?
A digital twin is a dynamic virtual model that mirrors a physical system—such as a bioreactor or an entire production line—updated continuously with live data. Unlike traditional models, digital twins evolve in real time, enabling predictive insights, scenario simulations, and faster, evidence-based decision-making.
The Status Quo: Progress with Caution
While the technology is maturing, adoption in pharma and biotech remains uneven. Many implementations are still in pilot stages, limited by:
- Fragmented data sources,
- Concerns over model validation, and
- Regulatory uncertainty about novel technologies.
Yet the momentum is clear. Forward-thinking manufacturers are integrating digital twins in biomanufacturing, where the rewards—greater consistency, higher yield, and faster release—are especially impactful.
Crucially, regulatory bodies are beginning to embrace the concept. The FDA, EMA, and PIC/S have all issued guidance encouraging digitalization, real-time monitoring, and science- and risk-based approaches to quality.
The FDA’s Emerging Technology Program has actively engaged with sponsors exploring digital twins for process control, seeing them as potential enablers of real-time release and greater product assurance. Also EMA’s digital transformation initiatives and ISPE’s Pharma 4.0 model both reference digital twins as part of a modern, data-integrated quality ecosystem.
In short, regulators are not blocking the path—they’re helping clear it.
Practical Use Cases in Biomanufacturing
- Bioreactor Simulation and Control
Digital twins model live cell cultures to optimize parameters like pH, temperature, and nutrient flow. This enhances process control, reduces batch failure, and supports real-time release in alignment with PAT and QbD principles.
- Process Scale-Up and Tech Transfer
Twins simulate transitions from small-scale to commercial production, helping manufacturers understand variability and validate parameters—supporting regulatory filings with richer data and lower risk.
- Predictive Maintenance
Using real-time equipment data, digital twins forecast failures before they occur, minimizing downtime and avoiding deviations that could trigger regulatory non-compliance or CAPAs.
- Real-Time Quality Assurance
Digital twins enable proactive quality oversight by:
- Predicting OOS risks,
- Modeling deviation impacts,
- Simulating CAPA outcomes, and
- Supporting continuous verification approaches accepted under ICH Q8/Q10.
What’s Next?
The future of digital twins in QA and biomanufacturing will focus on:
- Model validation frameworks accepted by regulators for use in GMP decision-making
- Cross-system integration—from QMS to MES to ERP—for holistic oversight,
- Digital twins of quality systems themselves (e.g., risk-based audit planning, supplier risk simulation)
- Automated regulatory reporting that draws directly from validated digital models.
Conclusion
Digital twins are not just operational tools. By enhancing transparency, traceability, and control, they offer manufacturers a powerful way to mitigate risk and uphold compliance in real time.
As regulators push toward performance-based oversight and as processes grow in complexity, the applications of digital twins are expected to increase.
Do you want to learn more about data-driven oversight in Quality, and how to implement it? Listen to the talk of Juan Torres, formed Chief Quality Officer at Biogen. He talks about the essential elements to move from compliance-centric to a patient-centric oversight and presents best practices from industry leaders.