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Your search keyword '"Anne-Luise Tscheliessnig"' showing total 2 results
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1. Solubilization of inclusion bodies: insights from explainable machine learning approaches

Author

Cornelia Walther, Michael C. Martinetz, Anja Friedrich, Anne-Luise Tscheließnig, Martin Voigtmann, Alexander Jung, Cécile Brocard, Erich Bluhmki, and Jens Smiatek

Subjects
inclusion bodies, refolding, solubilization, SHAP (shapley additive explanation), explainable machine learning, Technology, Chemical technology, TP1-1185
Abstract

We present explainable machine learning approaches for gaining deeper insights into the solubilization processes of inclusion bodies. The machine learning model with the highest prediction accuracy for the protein yield is further evaluated with regard to Shapley additive explanation (SHAP) values in terms of feature importance studies. Our results highlight an inverse fractional relationship between the protein yield and total protein concentration. Further correlations can also be observed for the dominant influences of the urea concentration and the underlying pH values. All findings are used to develop an analytical expression that is in reasonable agreement with experimental data. The resulting master curve highlights the benefits of explainable machine learning approaches for the detailed understanding of certain biopharmaceutical manufacturing steps.

Published
2023
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2. Strategy to equivalence testing for development and scale up of biopharmaceutical downstream processes

Author

Cécile Brocard, Friedrich Leisch, Theresa Scharl, Astrid Dürauer, Anne-Luise Tscheliessnig, and Christina Yassouridis

Subjects
Applied Mathematics, General Chemical Engineering, Reference data (financial markets), Comparability, 02 engineering and technology, General Chemistry, Variance (accounting), 021001 nanoscience & nanotechnology, Industrial and Manufacturing Engineering, Reliability engineering, 020401 chemical engineering, Acceptance testing, Sample size determination, Outlier, SCALE-UP, 0204 chemical engineering, 0210 nano-technology, Equivalence (measure theory), Mathematics
Abstract

Efficient development for multistep downstream processing of biopharmaceuticals depends on fast and robust transfer from bench to manufacturing-scale. Equivalence tests are used to assess the impact of changes in product quality and quantity during process development, optimization and production. This article presents the results of a simulation study comparing the impact of sample sizes, data distributions, variances and equivalence acceptance criteria (EAC) on the performance of equivalence tests. Our results show that a sample size of at least four in the reference group was the minimum recommended size to obtain an acceptable overall performance. Reference data should be evaluated for outliers prior to the equivalence tests and reliable test decisions are only taken with a low variance in the data of the reference group using the EAC proposed by Limentani. Considering these recommendations we obtained a reliable product yield comparability after chromatographic purification of a biopharmaceutical in bench and manufacturing-scale.

Published
2021
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