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      Model-Based Methods in the Biopharmaceutical Process Lifecycle

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          Abstract

          Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

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          Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions

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            • Record: found
            • Abstract: not found
            • Article: not found

            On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

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              • Abstract: found
              • Article: not found

              Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

              Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                +43 1 58801 166400 , christoph.herwig@tuwien.ac.at
                Journal
                Pharm Res
                Pharm. Res
                Pharmaceutical Research
                Springer US (New York )
                0724-8741
                1573-904X
                22 November 2017
                22 November 2017
                2017
                : 34
                : 12
                : 2596-2613
                Affiliations
                [1 ]ISNI 0000 0001 2348 4034, GRID grid.5329.d, Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, , Vienna University of Technology, ; Gumpendorfer Straße 1a – 166/4, A-1060 Vienna, Austria
                [2 ]ISNI 0000 0001 2348 4034, GRID grid.5329.d, Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, , TU Wien, ; Vienna, Austria
                Author information
                http://orcid.org/0000-0003-2314-1458
                Article
                2308
                10.1007/s11095-017-2308-y
                5736780
                29168076
                15b4173d-aedf-480d-b4a0-dadccaa58a53
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 5 July 2017
                : 21 September 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100006012, Christian Doppler Forschungsgesellschaft;
                Award ID: 171
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004955, Österreichische Forschungsförderungsgesellschaft;
                Award ID: 843546
                Award Recipient :
                Categories
                Expert Review
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2017

                Pharmacology & Pharmaceutical medicine
                bioprocess,data mining,modelling,monitoring,optimization
                Pharmacology & Pharmaceutical medicine
                bioprocess, data mining, modelling, monitoring, optimization

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