Novasign’s technologies provide scientists and engineers with tools for accelerated process development, comprehensive simulations, reduced experimental efforts, and economic analysis. This approach enhances decision-making, mitigates risks, and ensures economic viability, allowing customers to achieve highly efficient bioprocesses
Smart experimental design enhances traditional DoE by introducing a novel concept – intensified Design of Experiments (iDoE), wherein process parameters are adjusted systematically within a single bioprocess. Designing complex iDoE becomes straightforward with Novasign’s experimental planner.
iDoE can reduce the labor required for process characterization by up to 70%, and Novasign’s experimental planner offers an easy-to-use interface to design the required experiments.
Employing Novasign’s smart experimental designs alongside hybrid modeling drastically accelerates the development timeline for new biopharmaceuticals.
Hybrid modeling combines engineering knowledge with process data and machine learning, providing a versatile framework that precisely captures bioprocess dynamics. Novasign’s hybrid models are created within our software in an easy-to-use way.
These models have proven to be more accurate in describing complex bioprocesses, and require less experiments, when compared to alternative modeling techniques. Additionally, hybrid modeling can forecast conditions not previously encountered, serving as a crucial component for model-based monitoring and control strategies.
At the heart of Novasign’s software, hybrid modeling presents scientists and engineers with a user-friendly tool that accelerates process development, reduces experimental needs, and shortens timelines.
The digital twin virtually simulates entire bioprocesses, enabling the investigation of process performance and product quality. Novasign’s digital twin is integrated into our software solution, providing a straightforward workflow.
Ultimately, this allows scientists and engineers to test various process scenarios, reduce experimental efforts, and gain greater confidence in process assessments.
Users benefit from enhanced process understanding, reduced time and resource requirements, and increased trust in the decision-making process.
The Economic Process Predictor enhances process modeling by adding cost calculations for materials and process times based on predicted performance. It enables cost analysis for scale-up, new modalities, or continuous processes before real-world execution.
This tool provides detailed cost insights and economic feasibility, relying on predictions instead of mere assumptions for accurate revenue forecasting and economic scenario evaluation.
Engineers gain the ability to make informed, data-supported economic decisions, reducing design risk and fostering strategic, financially viable planning.
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