Transforming Pharmaceutical Manufacturing with COSYNE

What is COSYNE?

Think of COSYNE as a digital counterpart to your manufacturing line. It’s a tool that uses artificial intelligence to create digital twins—accurate, data-driven simulations of your processes. Instead of waiting for months to gather enough data for analysis, COSYNE lets you model outcomes with just 10% of real-world information, helping you make decisions faster and more confidently.

Unlike conventional systems, COSYNE doesn’t rely on sheer volume. Instead, it blends real and synthetic data to predict outcomes, simulate batches, and identify issues before they occur. It’s not about replacing your processes but enhancing them with insights you can act on.

COSYNE excels at understanding intricate relationships between variables. In manufacturing processes, sensor readings—like temperature, pressure, and glucose levels—are deeply interdependent. COSYNE’s ability to model these correlations is unmatched, as evidenced by its correlation heatmaps. In side-by-side comparisons, COSYNE’s generated data aligns more closely with real-world data than its competitors, accurately capturing subtle relationships and dependencies.

When trained on just 10% of available real-world data, COSYNE achieves predictive accuracy that matches or surpasses models requiring eight times the data. This capability drastically reduces the time needed to deploy analytics systems, offering valuable insights much earlier in the production lifecycle.

One of COSYNE’s most significant contributions is its ability to shorten the path from data collection to actionable insights. In analytics-heavy use cases, such as yield optimisation or batch failure prediction, COSYNE enables businesses to act quickly, often saving weeks or even months.

COSYNE combines advanced AI with a clever approach to problem-solving:

• Understanding Your Inputs: COSYNE starts with raw materials and initial batch conditions. These act as the foundation for its predictions.

• Simulating Processes: Using algorithms like Generative Adversarial Networks (GANs), COSYNE generates realistic data that mimics how your processes behave in real life.

• Predicting Outcomes: By comparing real data with synthetic simulations, COSYNE can highlight potential inefficiencies or risks, allowing you to adjust before problems escalate.

This isn’t abstract technology—it’s practical, reliable, and tailored to the unique challenges of pharmaceutical manufacturing.

To put it simply, COSYNE is smart, efficient, and flexible. When tested against other technologies like TimeGAN, COSYNE consistently outperformed the competition.

• Accuracy: In trials using data from a 5,000-litre bioreactor, COSYNE’s simulations matched real-world results with impressive precision. Its Frechet Inception Distance (FID)—a measure of how close synthetic data is to the real thing—was 1.15, nearly twice as good as TimeGAN’s 2.27.

• Adaptability: COSYNE achieved an Alpha Precision score of 0.78 (vs. TimeGAN’s 0.20), proving it can handle complex, diverse manufacturing scenarios.

• Superior Predictive Utility: COSYNE’s simulations doubled the performance (R² of 0.4) of predictive models for yield estimation compared to real data alone (R² of 0.2)

For businesses in Europe, where regulatory compliance and cost efficiency are crucial, these numbers matter.

Pharmaceutical manufacturing in the EU comes with its own set of challenges. COSYNE addresses these with solutions that feel tailor-made:

By generating high-fidelity simulations, COSYNE ensures your processes align with strict standards without compromising speed or quality.

Running full-scale tests is expensive and time-consuming. COSYNE allows you to simulate scenarios before committing resources, saving both time and money.

With its ability to reduce waste and optimise yield, COSYNE supports greener manufacturing practices—a growing priority in Europe.

In one trial, COSYNE was tested on data from 180 batches of a 5,000-litre bioreactor. The aim was to predict the end-of-run titer—a critical measure of process success. COSYNE’s results were remarkable:

• When used to augment real data, COSYNE doubled the performance of machine learning models predicting outcomes.

• With 3,000 synthetic samples, it improved predictive accuracy (R²) from 0.2 (real data only) to 0.4, outperforming both data duplication techniques and TimeGAN.

These aren’t just numbers—they represent time saved, resources preserved, and better outcomes for patients relying on these medicines.

COSYNE is already proving its worth, but its journey is just beginning. Future updates could include:

Transformer Models: By leveraging transformer architectures, COSYNE could achieve even better sequence modelling, capturing long-term dependencies more effectively.

Diffusion Models: These could enhance COSYNE’s ability to learn complex data distributions, producing even higher-fidelity synthetic data.

For now, what COSYNE offers is a smarter, faster way to manage your manufacturing processes. It’s a tool designed for the real world, built to adapt and evolve alongside the challenges you face.

The Human Side of COSYNE

At its core, COSYNE isn’t about replacing people. It’s about helping them make better decisions. Whether you’re a technician on the floor or a manager balancing budgets, COSYNE gives you the tools to work smarter.

If you’ve ever wished for a way to get ahead of problems, to optimise processes without the endless trial and error, COSiYNE might be the answer.

Sometimes, the right technology isn’t about revolution—it’s about making your day a little easier. At Panda Intelligence, we specialise in connecting life sciences organisations with the experts who make technologies like COSYNE a reality. Whether you're looking to build a team capable of implementing innovative solutions or need guidance navigating the latest advancements in pharmaceutical manufacturing, our network of specialists is here to help. Visit Our Contact Page to discover how we can support your journey towards smarter, more efficient operations.

Reference:

Chandra, S., Duvinage, M., Prakash, P. K. S., Davis, P., & Timmer, S. (2024). Improving process yield through manufacturing digital twin using conditional synthetic data engine (COSYNE). In U. Endriss et al. (Eds.), Frontiers in Artificial Intelligence and Applications, 392 (pp. 4673–4680). IOS Press. https://doi.org/10.3233/FAIA241063