Using Digital Twins to Optimize Pharmaceutical Production Lines for Enhanced cGMP Compliance

NASA developed the first digital twins in the 1960s, making copies of its spacecraft to perform simulations and train astronauts.

These digital twins played a key role in saving the lives of the astronauts on the problem-plagued Apollo 13 mission.

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Today, its uses are well-characterized in the manufacturing and engineering sectors.

Talking specifically about Digital Twin in Pharma, manufacturers are increasingly interested in the use of this technology in production processes, according to technology advisory firm ABI Research.

It projects spending by pharmaceutical manufacturers on data analytics tools — including the digital twin — to grow by 27% over the next seven years, to reach $1.2 billion in 2030.

But What Exactly is a Digital Twin in Pharma?

Simply put, a Digital Twin is a real-time digital counterpart of a physical entity.

It allows manufacturers to simulate, monitor, and adjust processes as if they were occurring in the real world, without the risks or costs associated with actual experimentation.

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A well-implemented Digital Twin not only reflects what’s happening on the production line but also predicts future behavior, enabling preemptive actions that improve product quality and compliance with cGMP standards.

McKinsey notes that the type of analytics Digital Twins provides, in conjunction with other Industry 4.0 technologies like robotics and automation, typically boosts productivity for pharmaceutical manufacturers by between 50 and 100%.

A Quick Overview of Digital Twin Framework

A Digital Twin consists of three key elements: a physical component, a virtual component, and a system for automated data exchange between them.

Digital twins in pharmaceutical manufacturing | Encyclopedia MDPI

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The physical component includes all data sources, such as sensors and network devices (e.g., routers, workstations).

The virtual component is a detailed digital version of the physical one, built using prior knowledge, historical data, and real-time data from the physical component. This allows for continuous improvement of predictions and accuracy.

The data management platform handles databases, data transmission, operations, and models, and should support data visualization, prediction, real-time analysis, and optimization.

Real-world Examples of Digital Twin in Pharma

Problem

Pfizer faced the challenge of rapidly scaling up biologic antibody production in response to emerging viruses. Traditional microbioreactor experiments were time-consuming and complex, especially given the need to reproduce fluid dynamics at larger scales.

Solution

Pfizer utilized M-Star CFD, a GPU-enabled computational fluid dynamics software, to create digital twins of bioreactors. This allowed them to simulate fluid flow and gas transfer, reducing the need for extensive physical experiments and streamlining the scale-up process.

Result

The digital twin approach reduced the number of required experiments, accelerated the manufacturing process, and provided a roadmap for scaling up bioreactors, enabling Pfizer to bring drugs to market faster.

Problem

Current transdermal drug delivery systems lack personalization, leading to ineffective or unsafe dosing due to individual differences in metabolism and needs.

Solution

Implement a virtual twin that simulates drug release and patient response. This digital model adjusts drug dosage in real-time based on feedback and biomarkers from wearable sensors.

Results

Age-Based Adjustments: Personalized dosing for fentanyl, showing older patients need less frequent patch changes compared to younger patients.

Enhanced Therapy: Improved pain relief duration and safety by optimizing dosage based on real-time feedback.

Broader Potential: Demonstrated success with fentanyl suggests future applications for other drugs.

Background

Sanofi is addressing the lengthy and uncertain nature of drug development, where nearly 90% of new candidates fail in clinical trials.

To improve this process, Sanofi has integrated AI to create digital twins — virtual patient populations that enhance research efficiency.

Implementation

Digital twinning utilizes quantitative systems pharmacology (QSP) modeling to simulate real patients.

By compiling data on disease biology and treatment pathways, researchers can accurately predict how new drugs will perform.

Example: Virtual Asthma Patients

Sanofi tested a novel asthma compound using virtual patients.

The model successfully predicted outcomes from a Phase 1b trial, demonstrating its reliability in assessing clinical efficacy against existing treatments.

Benefits

This approach accelerates R&D timelines, transforming processes from weeks to hours and reducing the need for real patient involvement.

Challenges and Considerations of Digital Twin in Pharma

Managing and processing large amounts of real-time data from sensors, which is crucial for maintaining accurate Digital Twins.

Ensuring real-time data updates with minimal latency between the physical production line and the Digital Twin for accurate monitoring.

Balancing the need for highly accurate models of complex pharmaceutical processes without compromising computational efficiency.

Difficulty in connecting older manufacturing equipment and control systems to modern Digital Twin platforms due to compatibility issues.

As production scales, the Digital Twin system must scale too, requiring more computational power to handle increased data and more complex simulations.

Ensuring consistency in data across distributed production sites and Digital Twins to maintain uniform compliance and operational standards.

Developing reliable predictive models for complex, often non-linear, pharmaceutical processes, which requires ongoing model updates and tuning.