Model-based tools for optimal experiments in bioprocess engineering

Currently applied methods for characterization and optimization of bio-pharmaceutical processes are still strongly empirical. This often involves Design of Experiment (DoE) methods that require a large number of time-consuming experiments and can hardly fulfill the requirements of ‘Quality by Design’ (QbD). Linking mathematical models with experimental methods is seen as an efficient strategy that significantly reduces development time and costs. Furthermore, if combined with advanced Process Analytical Technology (PAT), higher automation and more efficient workflows can be established along the product lifecycle. This contribution presents the state of the art of model-based tools for experimental design and gives an outlook on future trends in the field of bio-process engineering.

[1]  Volker C. Hass,et al.  Advanced Process and Control Strategies for Bioreactors , 2017 .

[2]  Sebastian Sager,et al.  A Feedback Optimal Control Algorithm with Optimal Measurement Time Points , 2017 .

[3]  Michael Raven,et al.  Consistent development of bioprocesses from microliter cultures to the industrial scale , 2013 .

[4]  Robert Huber,et al.  Microbial Cell Factories Robo-lector – a Novel Platform for Automated High-throughput Cultivations in Microtiter Plates with High Information Content , 2022 .

[5]  Xiangzhong Xie,et al.  The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design , 2018 .

[6]  John Kane,et al.  Development of small scale cell culture models for screening poloxamer 188 lot‐to‐lot variation , 2014, Biotechnology progress.

[7]  Christoph Herwig,et al.  Soft sensor for monitoring biomass subpopulations in mammalian cell culture processes , 2017, Biotechnology Letters.

[8]  Roland Eils,et al.  Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model , 2009, PLoS Comput. Biol..

[9]  Ashraf Amanullah,et al.  Novel micro‐bioreactor high throughput technology for cell culture process development: Reproducibility and scalability assessment of fed‐batch CHO cultures , 2010, Biotechnology and bioengineering.

[10]  Byran J. Smucker,et al.  Optimal experimental design , 2018, Nature Methods.

[11]  Tom Lahmer,et al.  Optimal experimental design for nonlinear ill-posed problems applied to gravity dams , 2011 .

[12]  J. D. Stigter,et al.  Network inference via adaptive optimal design , 2012, BMC Research Notes.

[13]  Peter Neubauer,et al.  Design of experiments‐based high‐throughput strategy for development and optimization of efficient cell disruption protocols , 2017, Engineering in life sciences.

[14]  D. M. Titterington,et al.  Recent advances in nonlinear experiment design , 1989 .

[15]  W. Duetz,et al.  Microtiter plates as mini-bioreactors: miniaturization of fermentation methods. , 2007, Trends in microbiology.

[16]  J. Timmer,et al.  Systems biology: experimental design , 2009, The FEBS journal.

[17]  Seongkyu Yoon,et al.  Mechanistic modeling and applications for CHO cell culture development and production , 2018, Current Opinion in Chemical Engineering.

[18]  José Carlos Pinto,et al.  Experimental design for the joint model discrimination and precise parameter estimation through information measures , 2011 .

[19]  Johannes P. Schlöder,et al.  A nonlinear preconditioner for experimental design problems , 2011, ArXiv.

[20]  A Kiparissides,et al.  On the model‐based optimization of secreting mammalian cell (GS‐NS0) cultures , 2015, Biotechnology and bioengineering.

[21]  Arvind K. Saibaba,et al.  Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems , 2018, Inverse Problems.

[22]  B. Kelley,et al.  High-throughput screening of chromatographic separations: I. Method development and column modeling. , 2008, Biotechnology and bioengineering.

[23]  E. Haber,et al.  Numerical methods for experimental design of large-scale linear ill-posed inverse problems , 2008 .

[24]  Oliver Spadiut,et al.  Dynamic process conditions in bioprocess development , 2013 .

[25]  Lorenz M Mayr,et al.  Novel trends in high-throughput screening. , 2009, Current opinion in pharmacology.

[26]  Jan Van Impe,et al.  Uncertainty in optimal experiment design: comparing an online versus offline approaches , 2018 .

[27]  Biao Huang,et al.  Constrained receding‐horizon experiment design and parameter estimation in the presence of poor initial conditions , 2011 .

[28]  Jürgen Hubbuch,et al.  High Throughput Screening for the Design and Optimization of Chromatographic Processes - Miniaturization, Automation and Parallelization of Breakthrough and Elution Studies , 2008 .

[29]  Sandro Macchietto,et al.  The optimal design of dynamic experiments , 1989 .

[30]  André Bardow,et al.  Optimal experimental design of ill-posed problems: The METER approach , 2008, Comput. Chem. Eng..

[31]  J. Fekete,et al.  Establishing column batch repeatability according to Quality by Design (QbD) principles using modeling software. , 2015, Journal of pharmaceutical and biomedical analysis.

[32]  Massimiliano Barolo,et al.  Online model-based redesign of experiments with erratic models: A disturbance estimation approach , 2012, Comput. Chem. Eng..

[33]  Carl-Fredrik Mandenius,et al.  Bioprocess optimization using design‐of‐experiments methodology , 2008, Biotechnology progress.

[34]  Petru-Daniel Morosan,et al.  Closed loop optimal experiment design for on-line parameter estimation , 2014, 2014 European Control Conference (ECC).

[35]  Finbarr O'Sullivan,et al.  [A Statistical Perspective on Ill-Posed Inverse Problems]: Rejoinder , 1986 .

[36]  Vivek Dua,et al.  A joint model-based experimental design approach for the identification of kinetic models in continuous flow laboratory reactors , 2016, Comput. Chem. Eng..

[37]  P. Mykland,et al.  Nonlinear Experiments: Optimal Design and Inference Based on Likelihood , 1993 .

[38]  A Kremling,et al.  Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems. , 2006, Metabolic engineering.

[39]  Kaspar Valgepea,et al.  Advanced continuous cultivation methods for systems microbiology. , 2015, Microbiology.

[40]  Eric Walter,et al.  Identification of Parametric Models: from Experimental Data , 1997 .

[41]  J Büchs,et al.  Micro-bioreactors for fed-batch fermentations with integrated online monitoring and microfluidic devices. , 2009, Biosensors & bioelectronics.

[42]  Mark Dörr,et al.  Fully automatized high‐throughput enzyme library screening using a robotic platform , 2016, Biotechnology and bioengineering.

[43]  Christoph Herwig,et al.  Model-Based Methods in the Biopharmaceutical Process Lifecycle , 2017, Pharmaceutical Research.

[44]  Julio R. Banga,et al.  Reverse engineering and identification in systems biology: strategies, perspectives and challenges , 2014, Journal of The Royal Society Interface.

[45]  Henry Y. Wang,et al.  Bioprocess monitoring and computer control: Key roots of the current PAT initiative , 2006, Biotechnology and bioengineering.

[46]  A. Zeng,et al.  Model‐based identification of cell‐cycle‐dependent metabolism and putative autocrine effects in antibody producing CHO cell culture , 2018, Biotechnology and bioengineering.

[47]  Frank Kensy,et al.  High-throughput screening of Hansenula polymorpha clones in the batch compared with the controlled-release fed-batch mode on a small scale. , 2010, FEMS yeast research.

[48]  Zorica Djuric,et al.  Modeling in the quality by design environment: Regulatory requirements and recommendations for design space and control strategy appointment. , 2017, International journal of pharmaceutics.

[49]  J. Banga,et al.  Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods , 2011, PloS one.

[50]  René Schenkendorf,et al.  Model-based optimization of biopharmaceutical manufacturing in Pichia pastoris based on dynamic flux balance analysis , 2018, Comput. Chem. Eng..

[51]  Juergen Hahn,et al.  Necessary condition for applying experimental design criteria to global sensitivity analysis results , 2013, Comput. Chem. Eng..

[52]  Rachel Legmann,et al.  A predictive high‐throughput scale‐down model of monoclonal antibody production in CHO cells , 2009, Biotechnology and bioengineering.

[53]  Jürgen Hubbuch,et al.  High Throughput Screening for the Design and Optimization of Chromatographic Processes: Automated Optimization of Chromatographic Phase Systems , 2009 .

[54]  Peter Neubauer,et al.  Detection of growth rate‐dependent product formation in miniaturized parallel fed‐batch cultivations , 2017, Engineering in life sciences.

[55]  Günter Wozny,et al.  Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design , 2015, Comput. Chem. Eng..

[56]  David Pollard,et al.  A review of advanced small‐scale parallel bioreactor technology for accelerated process development: Current state and future need , 2011, Biotechnology progress.

[57]  P Neubauer,et al.  Online optimal experimental re‐design in robotic parallel fed‐batch cultivation facilities , 2016, Biotechnology and bioengineering.

[58]  Sandro Macchietto,et al.  Model-based design of experiments for parameter precision: State of the art , 2008 .

[59]  B. Hitzmann,et al.  A bootstrap‐based method for optimal design of experiments , 2016 .

[60]  Fabrizio Bezzo,et al.  A methodology for direct exploitation of available information in the online model-based redesign of experiments , 2016, Comput. Chem. Eng..

[61]  Biao Huang,et al.  Receding horizon experiment design with application in SOFC parameter estimation , 2010 .

[62]  Gerald Striedner,et al.  Quality by control: Towards model predictive control of mammalian cell culture bioprocesses. , 2017, Biotechnology journal.

[63]  Eva Balsa-Canto,et al.  Parameter estimation and optimal experimental design. , 2008, Essays in biochemistry.

[64]  Moritz Stosch,et al.  Intensified design of experiments for upstream bioreactors , 2017, Engineering in life sciences.

[65]  Peter Neubauer,et al.  Editorial: Bioprocess Development in the era of digitalization , 2017, Engineering in life sciences.

[66]  S. Kabanikhin.  Definitions and examples of inverse and ill-posed problems , 2008 .

[67]  Tilman Barz,et al.  Real-time adaptive input design for the determination of competitive adsorption isotherms in liquid chromatography , 2016, Comput. Chem. Eng..

[68]  Dirk Weuster-Botz,et al.  Statistical vs. Stochastic experimental design: An experimental comparison on the example of protein refolding , 2012, Biotechnology progress.

[69]  Steven M Cramer,et al.  Use of MiniColumns for linear isotherm parameter estimation and prediction of benchtop column performance. , 2015, Journal of chromatography. A.

[70]  Tilman Barz,et al.  Adaptive optimal operation of a parallel robotic liquid handling station , 2018 .

[71]  Peter Knabner,et al.  Experimental design for outflow experiments based on a multi-level identification method for material laws , 2003 .

[72]  Daniel G Bracewell,et al.  Advances in product release strategies and impact on bioprocess design. , 2009, Trends in biotechnology.

[73]  Johannes Möller and Ralf Pörtner.  Model-Based Design of Process Strategies for Cell Culture Bioprocesses: State of the Art and New Perspectives , 2017 .

[74]  E. Haber,et al.  Numerical methods for the design of large-scale nonlinear discrete ill-posed inverse problems , 2010 .

[75]  E. Haber,et al.  Optimal Experimental Design for the Large‐Scale Nonlinear Ill‐Posed Problem of Impedance Imaging , 2010 .