Evaluation of data in downstream processing by statistics

Statistics are procedures that combine, organize, and summarize data to extract information. While the collected data are initially unorganized and ‘raw’, statistics allows the organization of the raw data into a condensed and more meaningful structure. This enables the examination and quantification of relationships among variables to show trends and investigate whether a theory or hypothesis is supported by the data. Statistics reaches from simple calculation of mean values and standard deviations to the complex fields of variable selection in high-dimensional data sets and hybrid modelling to integrate many different sources of information into a knowledge base. Biopharmaceutical process development is commonly very empirically driven and needs a large number of experiments delivering corresponding data from diverse analytical tools and monitoring processes. Statistical evaluation of such data to determine precision, accuracy, reproducibility, and robustness of measurements are daily business for all scientists. High throughput methods for screening, process development and analytics were set up and are current routine in many companies. Both lead to an exponential increase of included variables and parameters, and consequently, to huge expansion of the amounts of data. Furthermore, the release of the PAT guidance by the FDA in 2004 as the enabling aspect of Quality-by-Design in biopharmaceutical production changed the perspective on data interpretation including the need for modelling strategies, for predictability and evaluation of data in real time or close to. To address the above-mentioned challenges and needs a collaboration project shall be started between the Process Science Department of BI RCV and BOKU Vienna. It will include a postdoctoral position under the joint supervision of 2 key researchers one with a strong statistics background and the other one with established experience in bioengineering and downstream processing