Optimization of the medium for the growth ofNitrobacter winogradskyiby statistical method

Design of experiments and design space approaches in the pharmaceutical bioprocess optimization

The optimization of pharmaceutical bioprocesses suffers from several challenges like complexity, upscaling costs, regulatory approval, leading to the risk of delivering substandard drugs to patients. Bioprocess is very complex and requires the evaluation of multiple components that need to be monitored and controlled in order to attain the desired state when the process ends. Statistical design of experiments (DoE) is a powerful tool for optimizing bioprocesses because it plays a critical role in the quality by design strategy as it is useful in exploring the experimental domain and providing statistics of interest that enable scientists to understand the impact of critical process parameters on the critical quality attributes. This review summarizes selected publications in which DoE methodology was used to optimize bioprocess. The main objective of the critical review was to clearly demonstrate potential benefits of using the DoE and design space methodologies in bioprocess optimization.

Development of new media formulations for cell culture operations based on regression models

The paper discusses modelling and optimization of multi-component cell culture medium. The specific productivity (Qp) was considered a function of the medium components and possible interactions described by linear factors, two-way interactions and squared terms that results in a high dimensional problem where the number of variables p (represented by the medium components and their interactions) is much larger than the number of observations n. Principal Components Regression (PCR), Partial Least Squares (PLS), Lasso and Elastic Net regressions were compared as modelling tools to deal with a high dimensional [Formula: see text] problem. PCR and PLS regression models resulted in better prediction results and were used for robust optimization of the medium composition by a nonlinear optimization. The case studies show that it is possible to formulate new media that result in higher Qp than the ones provided by the initial media experiments available. Also, the multivariate statistical approach permitted us to select media that is most informative about the optimum thus permitting modelling and optimization with a reduced set of initial experiments.