AnalysisMode offers SimCell, an AI-driven bioprocess modeling solution that enables scientists to conduct virtual cell culture experiments, significantly reducing the need for wet-lab trials by up to 80%. This technology accelerates process optimization and scale-up, enhancing efficiency and lowering costs in biopharmaceutical development.
Funding
Funding not disclosed
Founders
Product
Problem
Biopharmaceutical companies face challenges in optimizing and scaling up bioprocesses, often relying on extensive and costly wet-lab experiments. Traditional methods lack the speed and predictive power needed to efficiently navigate the complex interplay of cell culture parameters, leading to delays and increased costs in bringing therapies to market.
Solution
AnalysisMode offers SimCell, an AI-driven bioprocess modeling solution that enables scientists to conduct virtual cell culture experiments, significantly reducing the need for wet-lab trials. SimCell leverages AI-driven Design of Experiments (DoE) to accelerate early-stage process optimization and provides predictive bioprocess scale-up capabilities, ensuring consistent and efficient operations across different scales. The platform also features digital twin-based monitoring for real-time simulation, analysis, and optimization, as well as AI-enabled process troubleshooting for quick resolution of process deviations. By integrating AI into experimentation and modeling, SimCell empowers users to optimize critical parameters and gain predictive insights before physical trials, improving decision-making speed and accuracy.
Target Audience
The primary target audience includes biopharmaceutical companies, process development scientists, and biomanufacturing teams seeking to accelerate process optimization, reduce experimental costs, and improve the efficiency of bioprocess scale-up.
Features
- AI-driven Design of Experiments (DoE) for rapid process optimization
- Predictive bioprocess scale-up from bench to production
- Digital twin-based monitoring for real-time simulation and optimization
- AI-enabled process troubleshooting for root cause analysis and performance enhancement
- Virtual bioreactor for in-silico experimentation
- Parameter Interaction Reports for detailed analysis of process dynamics
- Integration with Electronic Lab Notebooks (ELNs) and Laboratory Information Management Systems (LIMS)
- Neuroevolutionary AI for unsupervised learning and automatic feature engineering