Multivariate Data Analysis for Biotechnology and Bio-Processing - Pharmaceutical Technology
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Multivariate Data Analysis for Biotechnology and Bio-Processing

Multivariate data analysis (MVA) and design of experiments (DoE) are advanced analysis techniques which enable biotech organisations to improve their data analysis and optimise operations across the product lifecycle. MVA and DoE are used in applications such as raw material assessment, analysis of clinical trial results, understanding and controlling fermentation processes, and improving quality control.

Given the large number and complexity of variables in biological systems, multivariate analysis has significant advantages over traditional statistical analysis tools. The powerful data mining capabilities allow researchers, scientists and engineers to cut through complex data sets to discover underlying patterns, while advanced regression methods can be used to make more robust predictions about a system's behaviour.

Today's biotech companies are increasingly looking to accelerate development, reduce process-related costs and improve time to market. Unlocking the value in their data with tools such as multivariate analysis and design of experiments is a major source of potential gains in these areas.

This white paper covers applications of MVA and DoE across the product lifecycle, including examples of data analysed for candidate therapy discovery, product formulation, clinical trials and fermentation batch process monitoring. It illustrates how these powerful analytical tools can be integrated with different systems throughout a biotechnology operation.

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