Toward in silico CMC: An industrial collaborative approach to model‐based process development

The Third Modeling Workshop focusing on bioprocess modeling was held in Kenilworth, NJ in May 2019. A summary of these Workshop proceedings is captured in this manuscript. Modeling is an active area of research within the biotechnology community, and there is a critical need to assess the current state and opportunities for continued investment to realize the full potential of models, including resource and time savings. Beyond individual presentations and topics of novel interest, a substantial portion of the Workshop was devoted toward group discussions of current states and future directions in modeling fields. All scales of modeling, from biophysical models at the molecular level and up through large scale facility and plant modeling, were considered in these discussions and are summarized in the manuscript. Model life cycle management from model development to implementation and sustainment are also considered for different stages of clinical development and commercial production. The manuscript provides a comprehensive overview of bioprocess modeling while suggesting an ideal future state with standardized approaches aligned across the industry.

[1]  S. Hunt,et al.  Modeling Preparative Cation Exchange Chromatography of Monoclonal Antibodies , 2017 .

[2]  Keith McDonald,et al.  ICH Q11: development and manufacture of drug substances–chemical and biotechnological/biological entities , 2012 .

[3]  Anurag S. Rathore,et al.  Model‐Based Preparative Chromatography Process Development in the QbD Paradigm , 2017 .

[4]  Pedro M. Castro,et al.  Scope for industrial applications of production scheduling models and solution methods , 2014, Comput. Chem. Eng..

[5]  Jay Sanyal,et al.  A generalized approach to model oxygen transfer in bioreactors using population balances and computational fluid dynamics , 2005 .

[6]  Michael Baldea,et al.  Modular manufacturing processes: Status, challenges, and opportunities , 2017 .

[7]  René Holm,et al.  Q8(R2): Pharmaceutical Development , 2017 .

[8]  Federico Rischawy,et al.  Good modeling practice for industrial chromatography: Mechanistic modeling of ion exchange chromatography of a bispecific antibody , 2019, Comput. Chem. Eng..

[9]  Guan Wang,et al.  Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model : Towards rational scale-down and design optimization , 2018 .

[10]  Ernst Broberg Hansen Chromatographic Scale‐Up on a Volume Basis , 2017 .

[11]  Lilli Møller Andersen,et al.  Quality Risk Management , 2021, Handbook of Pharmaceutical Manufacturing Formulations, Second Edition.

[12]  Saeed Izadi,et al.  Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences , 2020, Biotechnology and bioengineering.

[13]  Eric von Lieres,et al.  Chromatography Analysis and Design Toolkit (CADET) , 2014, Comput. Chem. Eng..

[14]  Jennifer M Pollard,et al.  Prediction of lab and manufacturing scale chromatography performance using mini-columns and mechanistic modeling. , 2019, Journal of chromatography. A.

[15]  Jürgen Hubbuch,et al.  Modeling and simulation of anion-exchange membrane chromatography for purification of Sf9 insect cell-derived virus-like particles. , 2016, Journal of chromatography. A.

[16]  Jürgen Hubbuch,et al.  Calibration‐free inverse modeling of ion‐exchange chromatography in industrial antibody purification , 2016 .

[17]  Matteo Salvalaglio,et al.  Molecular modelling of the affinity chromatography of proteins: Status and perspectives , 2012 .

[18]  Sarfaraz Niazi,et al.  Pharmaceutical Quality System , 2014, Handbook of Pharmaceutical Manufacturing Formulations, Second Edition.

[19]  P. A. Rolandi,et al.  The Unreasonable Effectiveness of Equations: Advanced Modeling For Biopharmaceutical Process Development , 2019, Computer Aided Chemical Engineering.

[20]  Alois Jungbauer,et al.  Protein Chromatography: Process Development and Scale-Up , 2010 .

[21]  Modular Plants Flexible chemical production by modularization and standardization – status quo and future trends , 2017 .

[22]  Anurag S. Rathore,et al.  Preparative Chromatography for Separation of Proteins , 2017 .

[23]  Jürgen Hubbuch,et al.  Prediction uncertainty assessment of chromatography models using Bayesian inference. , 2019, Journal of chromatography. A.

[24]  Andrew L. Zydney,et al.  Bioprocess membrane technology , 2007 .

[25]  Ralf Takors,et al.  Predictability of kLa in stirred tank reactors under multiple operating conditions using an Euler–Lagrange approach , 2016 .

[26]  Rebecca Frauzem,et al.  A generic methodology for processing route synthesis and design based on superstructure optimization , 2017, Comput. Chem. Eng..

[27]  Lars Hagel,et al.  Gel filtration: size exclusion chromatography. , 2011, Methods of biochemical analysis.