The modern biopharmaceutical/biotechnology manufacturing facility contains many sophisticated control, data logging and data archiving systems. Massive amounts of data are collected from sources such as raw materials analysis, process outputs and final quality assessments, which are stored in data warehouses.
The sheer volume of data contained in these warehouses makes it a near impossible task to extract the information using simple charting and univariate methods of analysis. Such complex data requires methods of analysis that can cope with multiple variables simultaneously that not only reveal influential variables, but also reveal the relationship such variables have with each other. This is where Multivariate Analysis (MVA) is finding a much greater role in the analysis of complex bioprocess data.
With much more effort being put into the discovery and development of biotherapies and personalised medicines, biopharmaceutical and biotechnology companies are looking for ways to accelerate drug discovery, and through initiatives such as Quality by Design (QbD) and Data Driven Knowledge Discovery, reduce the regulatory approval time and be first to market. This means that data collected throughout the entire product lifecycle must be analysed and interpreted in order to gain extensive product and process understanding. This, in turn, leads to improved quality, greater confidence in the market for a company’s products and ultimately market capitalisation.