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Research project: Data-driven tools for accelarating pharmaceutical development
The objective is to show the versatile applicability of data-driven tools at different stages of the pharmaceutical development. The advantages of their implementation shall be proven starting from designing optimal screening experiments to multivariate model-based process control and scale-up.
Keywords: Bioprocesses, Multivariate Data Analysis, Engineering Statistics, Decision Theory
In the recent years, therapeutical proteins emerged to the leading 'biologics' with an annual turnover over 100 bln US $. In order ensure the required product quality, the US Food and Drug Administration motivated the Quality by Design approach for the pharmaceutical process development. Critical quality attributes as well as critical process parameters shall be identified within the hundreds of potentially influential factors. Often the existing expertise and the experimental accessibility are limited so that the application of statistical analysis on the collected data can reveal very important, hidden patterns and correlations. Therefore, data-driven knowledge discovery is becoming an attractive tool in the pharmaceutical manufacturing, which can be applied at various stages in the drug lifecycle supporting the decision taking in the development team. Hence, both, development costs and time to market can be considerably reduced.
In the recent years, therapeutical proteins emerged to the leading 'biologics' with an annual turnover over 100 bln US $. In order ensure the required product quality, the US Food and Drug Administration motivated the Quality by Design approach for the pharmaceutical process development. Critical quality attributes as well as critical process parameters shall be identified within the hundreds of potentially influential factors. Often the existing expertise and the experimental accessibility are limited so that the application of statistical analysis on the collected data can reveal very important, hidden patterns and correlations. Therefore, data-driven knowledge discovery is becoming an attractive tool in the pharmaceutical manufacturing, which can be applied at various stages in the drug lifecycle supporting the decision taking in the development team. Hence, both, development costs and time to market can be considerably reduced.
Simplify process complexity by combination of statistical tools, designed experiments and deterministic models
Simplify process complexity by combination of statistical tools, designed experiments and deterministic models