Digital Twins in Biomanufacturing
Learn what digital twins are, follow a practical 12-month implementation roadmap, understand scaling-up versus scaling-out strategies, and discover how to validate your models for regulatory compliance.
SYNOPSIS
Digital twins are revolutionizing biomanufacturing by enabling companies to optimize production processes in a virtual environment before implementing changes on the factory floor. This comprehensive guide explores how Contract Development and Manufacturing Organizations (CDMOs) can leverage digital twin technology to enhance efficiency, reduce risk, and maintain competitive advantage.
Imagine a NASCAR team gearing up for race day. Before the car hits the track, the pit crew uses a high-tech simulator to practice every move—tire changes, fuel stops, and adjustments—under all kinds of conditions. They can test how a tweak to the suspension handles on a tight turn or predict when a part might wear out, perfecting their strategy without damaging tires or risking a crash. Now, apply that same idea to biomanufacturing. What if you could refine your production process, test new approaches, and troubleshoot issues without risking a single batch? That’s the magic of a digital twin, and it’s transforming biomanufacturing—especially for Contract Development and Manufacturing Organizations (CDMOs).
At Kymanox, we are passionate about helping life science companies harness this technology to boost efficiency, quality, and innovation. Let’s dive into what a digital twin is, why it’s a game-changer for biomanufacturing, and how it can elevate your operations.
What is a Digital Twin?
A digital twin is a virtual mirror of your physical manufacturing system. It is not a static snapshot; it is a living, “breathing” model that updates in real time with data from your actual process. By pulling in information from sensors and equipment, a digital twin lets you simulate scenarios, predict outcomes, and optimize performance—all without touching the real-world setup.
In biomanufacturing, where precision is non-negotiable, this capability allows you to experiment and improve processes in a risk-free digital sandbox.
Why Digital Twins Are a Perfect Fit for Biomanufacturing
Biomanufacturing is an intricate dance of biology and technology. From cell culture to purification, every step demands exacting control to meet quality standards and regulatory requirements. For CDMOs, the pressure is amplified: you are delivering for multiple clients, balancing tight deadlines requiring impeccable results, in a highly competitive environment with constant pressure on your gross margins.
Here is how a digital twin steps in:
- Optimize Without Downtime: Simulate process adjustments—like tweaking pH or flow rates—to see their impact on yield or purity before committing to changes.
- Predict and Prevent Issues: Leverage real-time data to spot potential equipment failures or deviations, addressing them proactively.
- Scale Efficiently: Test scale-up strategies virtually, ensuring a smooth transition to larger production volumes.
One of the largest obstacles organizations have to implementing the holy grail that is continuous manufacturing, is the inability to consistently match the output from one operation to the input demand from the next operation in the overall process. The use of a digital twin to virtually model the entirety of the process is a perfect application to solve this problem.
Real-World Success Stories: Digital Twins in Action
Digital twins are not just a concept—they are delivering results. Here are two compelling examples:
Reducing Variability in Cell Culture
GlaxoSmithKline (GSK) harnessed a digital twin to optimize their vaccine manufacturing process. By creating a virtual model of their production workflow—incorporating real-time data from bioreactors and purification stages—they simulated various conditions to identify inefficiencies and fine-tune critical parameters like cell density and nutrient levels. This approach reduced production variability, enhanced yield consistency, and accelerated scale-up timelines, paving the way for broader application across GSK’s vaccine pipeline. (Source: Fierce Pharma, “After triumphant pilot, GSK eyes ‘digital twins’ to fine-tune vaccine production, development,” published June 23, 2021, accessible at fiercepharma.com)
Streamlining Purification Processes
A biopharma company, highlighted in a Siemens white paper, used a digital twin to optimize their downstream purification. Virtual simulations of operating conditions reduced cycle times and enhanced product purity, driving efficiency and cost savings. (Source: Siemens white paper, available at https://www.siemens.com/global/en/products/automation/topic-areas/process-industries/software-for-process-industries/bioprocess-digital-twins-webinar-merck.html
These wins show how digital twins can turn challenges into opportunities—and they’re just the beginning.
The Basics of Building a Digital Twin: What You Need to Know
Getting started with a digital twin is simpler than it sounds. At its core, you will need three key software components, and here are some examples to bring it to life:
- Data Integration: Tools to collect real-time data from your equipment via sensors and IoT devices—the lifeblood of your digital twin. For example, Rockwell Automation’s FactoryTalk captures shop-floor data seamlessly.
- Simulation Software: Platforms to build a virtual model of your process, allowing you to test and tweak scenarios digitally. Aspen BioProcess Designer is tailored for modeling biologics processes like cell culture, while SuperPro Designer offers robust simulation for biopharma workflows.
- Analytics Platforms: Software to analyze data, monitor performance, and provide actionable insights for decision-making. Siemens Xcelerator integrates analytics with digital twin capabilities, and GE Digital’s Proficy delivers real-time insights to optimize operations.
While the technology might seem intricate, the right expertise makes it seamless and that is where Kymanox with our preferred partner network shines.
Roadmap to a Digital Twin
The persistent, challenging economic environment, client pressure to accelerate timelines amid resource constraints, and the regulatory complexity that comes along with evolving standards, especially for novel therapies, have left you looking for a way to gain a competitive advantage. You have made the decision to get a leg up on the competition and move forward with the implementation of a Digital Twin, as part of your overall risk management and future proofing strategies.
So what is your next step? Where do you start in developing a digital twin?
Below is the roadmap we, at Kymanox, have crafted that blends our life science expertise with real-world insights:
- Concept & Alignment (1-2 Months)
Start by kicking off and defining goals. Whether your goal is slashing scale-up time by 20% by reducing the time and cost from bench top to full commercial scale, reducing process deviations, or optimizing overall plant efficiency, we’ll drive alignment across your organization. - Current State & Technology Partner Selection (2-4 Months)
Based on your short and long-term goals and your manufacturing processes (biologics vs. ATMPs vs. Fill/Finish), we will carry out an evaluation and pair you with top vendors (e.g., Siemens, Dassault, Cytiva, and Twin Health) for developing your Digital Twin. - Data & Pilot Design (3-6 Months)
Once the goals are established and partners are selected, begin deploying any sensor or measurements upgrades that are necessary. We will plan a Digital Twin pilot study to demonstrate the effectiveness of the Digital Twin before full deployment. For instance, this pilot study could be a near term goal of optimizing transfection efficiency for an autologous cell therapy. Of course, data integrity and security are absolutely vital, and we will oversee and ensure a secure and General Data Protection Regulation (GDPR) compliant foundation. - Pilot Launch (6-9 Months)
We will use the validated pilot to demonstrate proof of concept. We will determine Key Performance Indicators (KPIs) and measure them. The KPIs, for example, could be fewer deviations or yield percent improvement. Regardless of KPIs selected, we will align with your team and refine the KPIs as needed. - Full Rollout (9-12 Months)
The next step is to scale plant-wide with the goal of optimizing everything from monoclonal antibody yields, to batch success rates, to cost per batch, to process development times. We will train your staff and ultimately, impress your clients, as the process optimizations become a reality, saving on time and costs. - Ongoing Innovation (Forever)
Keep it cutting-edge with updates and Artificial Intelligence (AI)-driven insights. We will track Return on Investment (ROI) and keep regulators happy by ensuring processes remain in compliance with current regulations.
Scaling-Up vs Scaling-Out Decisions
To understand how digital twins assist in scaling, we first need to define the two primary strategies:
Scaling Up
This approach increases the capacity of a single system. For example, a manufacturer might replace a 1,000 L bioreactor with a 5,000 L one to produce more product per batch.
Why choose scaling-up? It is often favored when efficiency is the priority. A larger system can reduce labor, streamline workflows, and lower per-unit costs—ideal when the process is well-characterized and scales predictably. However, challenges like uneven mixing or heat transfer can arise in larger setups.
Scaling Out
This strategy adds more units of the same size, such as operating five 1,000 L bioreactors instead of one larger bioreactor.
Why choose scaling-out? It shines in scenarios requiring flexibility or resilience. Multiple units provide redundancy (if one fails, others continue), adaptability to variable demand, and better control over processes that don’t scale linearly—like those in personalized ATMPs. However, the trade-off is increased complexity in managing multiple systems.
The decision is not always straightforward. Scaling-up might promise efficiency but falter if the process behaves differently at a larger scale. Scaling-out offers agility but can strain coordination efforts. So, how do you pick the right path? Digital twins provide the clarity needed to decide.
Why Choose One Over the Other?
The choice between scaling-up and scaling-out hinges on several factors:
- Process Characteristics:
- Scaling-Up: Best for stable, predictable processes (e.g., monoclonal antibody production) where larger volumes don’t compromise quality. It is less ideal for highly variable processes, like cell therapies, where conditions must be tightly controlled.
- Scaling-Out: Suited for processes with inherent variability or small-batch needs (e.g., patient-specific ATMPs). It allows parallel production, reducing risk if one unit underperforms.
- Operational Goals:
- Scaling-Up: Chosen to minimize operational complexity and centralize production, appealing to facilities aiming for high throughput with fewer resources.
- Scaling-Out: Preferred when flexibility trumps simplicity—such as adapting to fluctuating demand or testing new products without disrupting existing lines.
- Risk and Cost:
- Scaling-Up: A single large system can be a single point of failure; if it goes down, production halts. Upfront costs are high, but long-term savings may offset this risk.
- Scaling-Out: Multiple units spread risk but increase maintenance and coordination costs. It is a hedge against uncertainty, though it may not match scaling-up’s economies of scale.
These trade-offs make scaling a high-stakes puzzle. Fortunately, digital twins offer a way to test solutions virtually before committing resources.
How Digital Twins Enhance Scaling Decisions
A digital twin is a virtual model of a physical system, fed by real-time data and advanced simulations. In scaling decisions, it acts as a crystal ball, revealing outcomes without the expense of real-world trials. Here is how it helps:
- Simulating Scenarios:
- For scaling-up, a digital twin can predict how a larger bioreactor will perform. Will a 5,000 L system maintain cell viability, or will oxygen distribution falter? The twin flags potential issues—like poor nutrient gradients—before they occur.
- For scaling-out, it models multiple units operating together. Are yields consistent across five 1,000 L bioreactors? Does resource sharing create bottlenecks? The twin optimizes layouts and workflows virtually.
- Cost and Risk Analysis: Consider a biologics manufacturer weighing a single 5,000 L bioreactor against five 1,000 L units. A digital twin can compare:
- Yield and Quality: Does scaling-up sacrifice uniformity? Does scaling-out ensure batch-to-batch consistency?
- Costs: Energy, maintenance, and labor for each setup.
- Risks: Impact of a failure in one large system versus one of many smaller ones. This data empowers confident, evidence-based choices.
- Testing Hybrid Solutions: Digital twins are not limited to binary options. They can explore combinations—like two 2,500 L bioreactors—balancing efficiency and flexibility. For biologics and ATMPs, where needs vary widely, this adaptability is a game-changer.
- Tailoring to Biologics and ATMPs: These fields face unique challenges: donor variability, tight timelines, and strict quality standards. Digital twins can:
- Simulate how scaling-up affects cell therapy viability in a larger vessel.
- Assess whether scaling-out can deliver multiple patient-specific batches on schedule.
By factoring in these nuances, twins ensure that scaling supports both product and patient outcomes.
The Bigger Picture
Digital twins align with Quality-by-Design (QbD), embedding scalability into process development. They reduce reliance on costly physical pilots and accelerate timelines—a boon in an industry where speed and precision are paramount. Yes, building a twin requires robust data and expertise, but the benefits—optimized decisions, minimized risks, and enhanced scalability—far outweigh the investment.
How to Validate Your Digital Twin
Imagine you have built a digital twin to optimize the expression phase of cell culture—the pivotal moment when cells churn out therapeutic proteins. But how do you prove it is more than just a fancy simulation? Validation is the key to turning your virtual model into a trusted, FDA-ready tool. In this fourth installment of our series, we will walk you through a clear, actionable approach to validate your digital twin, ensuring it delivers reliable predictions for protein yields, cell viability, and more. Let’s get started.
Why Validation is Non-Negotiable
A digital twin is only as good as the trust you place in it. Validation bridges the gap between theory and reality, confirming your model can guide critical decisions—like tweaking feeding strategies or scaling up production—without costly missteps. For the expression phase, where precision drives batch success, a validated model is your ticket to efficiency and quality.
Step 1: Nail Down the Purpose
First things first: what is your digital twin supposed to do? Be specific. Is it predicting protein titers under different nutrient conditions? Optimizing cell growth rates? Write it down—this purpose shapes everything that follows.
Step 2: Size Up the Risks
Not every digital twin needs the same level of scrutiny. Conduct a risk assessment to decide how deep to dive:
- What happens if the model fails? (Think batch losses or quality issues.)
- How vital is the expression phase to your final product?
High-stakes uses, such as those associated with guiding Good Manufacturing Practices (GMP) processes, demand bulletproof validation. Lower-risk scenarios can lean lighter.
- Example: A model steering nutrient feeds could adversely impact protein expression if off-target. That’s high risk, so validate thoroughly.
Step 3: Verify the Basics
Before testing predictions, ensure your model’s groundwork is solid:
- Code Check: Confirm the software runs the algorithms as intended.
- Math Check: Verify equations (like those modeling cell metabolism) are coded correctly.
This step is like double-checking your blueprints before construction begins.
Step 4: Test Against Reality
Now, prove your digital twin mirrors the real world. Here is how:
- Collect data: Pull data from past batches, lab runs, or pilot studies.
- Simulate process: Run your model with the same inputs (e.g., pH, temperature) as your physical process.
- Compare outputs: Measure predictions against actual results—think protein yields, cell counts, or glucose levels.
Use statistical methods such as Root Mean Square Error (RMSE) to quantify the fit.
- Case Study: Your model predicts 3 g/L protein yield, but the lab gets 2.7 g/L. Dig into why—Data gaps? Faulty assumptions? Biology’s unpredictability?
Step 5: Tweak and Repeat
Validation is not a one-off. Use your findings to refine:
- Update assumptions (e.g., growth kinetics).
- Add varied data (e.g., new cell lines).
- Retest across conditions to ensure versatility.
This cycle sharpens your model’s edge.
Step 6: Document Like a Pro
Regulators—and your team—need a clear paper trail. Build a validation report with:
- Purpose and scope.
- Risk assessment outcomes.
- Verification and validation details.
- Data sources and stats.
- Limits and caveats.
This document is your model’s proof of credibility.
Step 7: Loop in the FDA Early
Do not blindside regulators. Schedule a pre-submission chat to:
- Share your validation plan.
- Get input on what evidence they want.
- Align on their expectations.
Proactive dialogue smooths the path to approval.
- Heads-Up: The FDA’s FDT-BioTech program is exploring digital twins—mention it to show you are in sync with their thinking.
Winning Over the FDA: Three Tactics
Validation gets you data; persuasion gets you buy-in. Try these approaches:
- Visualize Success: Show prediction-vs-actual charts and solid stats.
- Own the Flaws: Admit limitations and explain your safeguards.
- Sell the Upside: Link the model to better process control and quality.
Conclusion
The path to implementing a digital twin in biomanufacturing is not a one-size-fits-all approach. Throughout this series, we’ve explored why digital twins matter, how to develop a roadmap, the challenges of scaling, and finally, how to validate and implement these solutions.
Successfully integrating a digital twin requires the right expertise, technology partnerships, and a deep understanding of regulatory and operational complexities. That’s where Kymanox comes in.
With our proven experience in life sciences, process development, and regulatory compliance, we guide you through each step:
- Navigating the digital twin landscape
- Assessing the right scaling strategy for your needs
- Deciding on the best-fit technology and implementation plan
- Validating data integrity, security, and compliance
- Installing and optimizing a digital twin for long-term success
The right trusted partner makes all the difference. Let’s future-proof your biomanufacturing strategy together.
