Bioprocessing is Still Largely Driven by Trial and Error.
Too many experiments.
Too many unknown interactions.
Too many surprises that cost time and money.
But bioprocess complexity cannot be reduced by running more experiments.
It must be reduced by running the right ones.
Data-based. Predictive. End-to-End.
Our approach integrates three tightly connected elements:
PRIOR KNOWLEDGE
We build and leverage proprietary bioprocess datasets.
DATA MODELING
We use simulation to guide experimentation.
IN-HOUSE EXECUTION
We test in the lab only what matters.
Each element reinforces the others.
Prior knowledge defines relevant boundaries. Models guide experiments. Experimental data refine models.
We run the right experiments
Up to 70% reduced experimental workload.
Faster development timelines & lower operational costs.
Reduced risk & deeper process understanding.
Across viral vectors, biologics and advanced modalities, we have built proprietary bioprocess datasets and an internal knowledge base. These databases capture known parameter interactions, realistic operating ranges and platform-specific constraints.
Our internal expertise is combined with LLM-supported experimental planning, which integrates insights from scientific literature and broader process knowledge.
We narrow the experimental search space early in development.
Instead of screening all possible parameter combinations, we identify the most informative regions of the design space and focus experiments there.
This enables us to:
Replace large factorial screening studies with knowledge-guided experiment sets
Exclude unrealistic or low-value parameter combinations early
Focus experiments on biologically meaningful interactions
Identify critical process drivers faster
The result is a smaller but more informative set of experiments that generates the same or greater process insight than traditional screening approaches.
Biological systems are nonlinear and interdependent. Single-factor optimization cannot capture real system behavior.
We use hybrid modeling approaches that combine mechanistic bioprocess understanding, statistical modeling and machine learning.
This integration links biological understanding with data-driven pattern recognition.
The models form the basis for digital twins – dynamic in-silico representations of the process that evolve with experimental data.
Within this environment we can:
Simulate process trajectories before running experiments
Analyze multivariate parameter interactions
Test different operating scenarios
Optimize multiple objectives simultaneously (e.g. yield vs. impurity levels)
Identify critical process drivers early
We simulate, test selectively, then refine. This iterative loop converts data into actionable process decisions.
Our in-house laboratory execution ensures tight integration between modeling and experimentation.
Experimental results directly feed back into model refinement, improving predictive accuracy and robustness.
This enables:
Rapid iteration cycles
Controlled expansion of design space
Validation of scale-relevant parameters
Early identification of process risks
Bioprocess complexity does not require more experiments. It requires better ones.
In a lytic viral vector process, our integrated workflow reduced experimental effort by 64% while maintaining predictive reliability.
From 22 experiments to 8.
12 weeks faster development
€40k saved in experimental costs
Same predictive accuracy
→ From Theory to Results:
Explore our Viral Vector Case Study
