@article{GebhardtSchmidtHoetteretal.2010,
  author    = {Gebhardt, Andreas and Schmidt, Frank-Michael and H{\"o}tter, Jan-Steffen and Sokalla, Wolfgang and Sokalla, Patrick},
  title     = {Additive manufacturing by selective laser melting the realizer desktop machine and its application for the dental industry},
  series = {Physics Procedia},
  volume    = {5 B},
  journal   = {Physics Procedia},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1875-3892},
  doi       = {10.1016/j.phpro.2010.08.082},
  pages     = {543 -- 549},
  year      = {2010},
  abstract  = {Additive Manufacturing of metal parts by Selective Laser Melting has become a powerful tool for the direct manufacturing of complex parts mainly for the aerospace and medical industry. With the introduction of its desktop machine, Realizer targeted the dental market. The contribution describes the special features of the machine, discusses details of the process and shows manufacturing results focused on metal dental devices.},
  language  = {en}
}
@article{EmontsBuyel2023,
  author    = {Emonts, Jessica and Buyel, Johannes F.},
  title     = {An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling},
  series = {Computational and Structural Biotechnology Journal},
  journal   = {Computational and Structural Biotechnology Journal},
  number    = {21},
  publisher = {Research Network of Computational and Structural Biotechnology},
  address   = {Gotenburg},
  issn      = {2001-0370 (Online-Ressource)},
  doi       = {10.1016/j.csbj.2023.05.022},
  pages     = {3234 -- 3247},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{BernauKnoedlerEmontsetal.2022,
  author    = {Bernau, C. R. and Kn{\"o}dler, Matthias and Emonts, Jessica and J{\"a}pel, Ronald Colin and Buyel, Johannes Felix},
  title     = {The use of predictive models to develop chromatography-based purification processes},
  series = {Frontiers in Bioengineering and Biotechnology},
  journal   = {Frontiers in Bioengineering and Biotechnology},
  number    = {10},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2296-4185},
  doi       = {10.3389/fbioe.2022.1009102},
  pages     = {25 Seiten},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}