TY - JOUR A1 - Bernau, C. R. A1 - Knödler, Matthias A1 - Emonts, Jessica A1 - Jäpel, Ronald Colin A1 - Buyel, Johannes Felix T1 - The use of predictive models to develop chromatography-based purification processes JF - Frontiers in Bioengineering and Biotechnology N2 - Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory. KW - biopharmaceutical production process KW - Data-driven models KW - downstream processing design KW - experiment quality KW - hybrid model validation Y1 - 2022 U6 - https://doi.org/10.3389/fbioe.2022.1009102 SN - 2296-4185 (online-ressource) IS - 10 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Emonts, Jessica A1 - Buyel, Johannes Felix T1 - An overview of descriptors to capture protein properties – Tools and perspectives in the context of QSAR modeling JF - Computational and Structural Biotechnology Journal N2 - Proteins are important ingredients in food and feed, they are the active components of many pharmaceutical products, and they are necessary, in the form of enzymes, for the success of many technical processes. However, production can be challenging, especially when using heterologous host cells such as bacteria to express and assemble recombinant mammalian proteins. The manufacturability of proteins can be hindered by low solubility, a tendency to aggregate, or inefficient purification. Tools such as in silico protein engineering and models that predict separation criteria can overcome these issues but usually require the complex shape and surface properties of proteins to be represented by a small number of quantitative numeric values known as descriptors, as similarly used to capture the features of small molecules. Here, we review the current status of protein descriptors, especially for application in quantitative structure activity relationship (QSAR) models. First, we describe the complexity of proteins and the properties that descriptors must accommodate. Then we introduce descriptors of shape and surface properties that quantify the global and local features of proteins. Finally, we highlight the current limitations of protein descriptors and propose strategies for the derivation of novel protein descriptors that are more informative. KW - Prediction of molecular features KW - Protein structure complexity KW - Quantitative structure activity relationship KW - Scalar parameters KW - Shape and surface properties Y1 - 2023 U6 - https://doi.org/10.1016/j.csbj.2023.05.022 SN - 2001-0370 (online-ressource) IS - 21 SP - 3234 EP - 3247 PB - Research Network of Computational and Structural Biotechnology CY - Gotenburg ER - TY - THES A1 - Emonts, Jessica T1 - Searching for many defective edges in hypergraphs Y1 - 2013 N1 - Ist auch als Online-Ausgabe erschienen: Emonts, Jessica: Searching for many defective edges in hypergraphs PB - Rheinisch-Westfälischen Technischen Hochschule Aachen CY - Aachen ER -