03 July 2020 | News
Insilico combines both biochemical network models and statistical methods facilitating analysis, diagnosis and curation of systematic errors
Photo Credit: Freepik
Insilico Biotechnology AG launches new data connectivity solution for coping with increased demand for predictive solutions in biologics manufacturing and process development.
An increasing amount of data from manufacturing and development in the biopharma industry is made available due to the advancement of data analysis and data collection tools. While capitalizing on the value of the data across projects offers a huge potential for reducing time and costs in manufacturing and development, data is often associated with systematic errors. These errors pose a high risk of false predictions, thus wrong decisions are made.
This is where Insilico’s solution comes into play by combining both biochemical network models and statistical methods. This new combination makes analysis, diagnosis and curation of systematic errors possible. Additionally, it allows merging of dynamic measuring streams of variable data density and frequency. The resulting curated data streams are then readily usable in applications that demand high data quality, such as the Machine-Learning workflows for Digital Twins. Consequently, these curated data streams allow a high predictive quality of Digital Twins in manufacturing and process development.
Klaus Mauch, CEO of Insilico, says “Although there has been a rapid growth in data production in the biopharma industry recently, errors in these data can lead to wrong decisions. Automatic error diagnosis is now possible using our solution. The Insilico Data Cleaner provides the biopharmaceutical industry with unique error diagnosis features leading to the most accurate predictions that these data can offer”.