DataHow’s
AI Approach
A new era for bioprocess data analytics and development
Bioprocess data analytics hasn’t significantly changed since the introduction of Design of Experiment (DoE) in the 1960s. The application of AI technologies is changing this. Purely statistical models remain inadequate to understand the complexities of bioprocesses; hybrid AI models that combine hardwired and AI technology are able to provide bioprocess scientists with the tools to generate unparalleled process insights.
Traditional DoE-based process development is based on a systematic and structured approach to experimentation. However, when applied to complex processes (high-dimensional design spaces), the number of experiments required becomes impractical, costly, and time-consuming.
Additionally, the linear models used for analysis fail to capture the complex non-linear dynamics within bioprocesses. DoE in bioprocess development is rigorous but often yields limited insights despite extensive experimental effort, time, and costs.
Hybrid models enable more accurate, faster learning of bioprocesses
Iterative experimental design and targeted, efficient exploration
These technologies and methods are embedded within DataHowLab, allowing process development teams to independently benefit from this approach and take one step closer to pharma 4.0.
Technology with Impact
Hybrid AI process models provide insight into complex bioprocess systems where scientific knowledge is inadequate. Take control of your process with reliable process insights.
Increase process yields for manufacturing and make an impact on manufacturing margins. Significant yield improvements can be made with less experimentation.
lower risk
The complex correlations between process parameters and product quality require rigorous understanding and control. Ensure final product quality with technologies that reliably understand your process.
Extensive experimental plans are replaced by fewer targeted experiments toward an objective. By combining transfer learning and hybrid AI process models, we have seen that experiments can be reduced by up to 80%.
With fewer experiments needed to achieve the process goals, process development timelines can be shortened.
As AI also supports drug discovery, more candidates are entering the pipeline, increasing the pressure on development resources. Increasing development efficiency and effectiveness provides more capacity to handle the pipeline.
Large-scale experiments are costly, so eliminating even one experiment has a material impact on your budget. In silico experimentation run with reliable process models provides reliable insight without any wet lab expense.
technical risk
With a greater understanding of the complex interrelationships within a bioprocess and an ability to run in silico simulations, engineers have greater confidence in their process and know how to optimize performance.
Process scientists can develop processes that minimize waste and undesirable materials while delivering a high-performing process. Develop a process for manufacturing that meets sustainability targets.
Meet with one of our bioprocess development experts for a demo of DataHowLab and see how holistic digital modeling, data analysis, and data management can transform your operation.