Opportunities and challenges for model utilization in the biopharmaceutical industry: current versus future state

被引:22
作者
Babi, Deenesh K. [1 ]
Griesbach, Jan [2 ]
Hunt, Stephen [3 ]
Insaidoo, Francis [4 ]
Roush, David [4 ]
Todd, Robert [5 ]
Staby, Arne [1 ]
Welsh, John [4 ]
Wittkopp, Felix [6 ]
机构
[1] Novo Nordisk AS, CMC Dev & API Mfg Dev, Novo Alle 1, DK-2880 Bagsvaerd, Denmark
[2] F Hoffmann La Roche Ltd, Grenzacherstr 124, CH-4070 Basel, Switzerland
[3] Allogene Therapeut Inc, 210 E Grand Ave, San Francisco, CA 94080 USA
[4] Merck & Co Inc, 2000 Galloping Hill Rd, Kenilworth, NJ 07033 USA
[5] KBI Biopharma, 2500 Cent Ave, Boulder, CO 80301 USA
[6] Roche Diagnost GmbH, Pharma Res & Early Dev pRED, Roche Innovat Ctr Munich, Nonnenwald 2, D-82377 Penzberg, Germany
关键词
COMPUTATIONAL FLUID-DYNAMICS; ION-EXCHANGE; CHROMATOGRAPHIC BEHAVIOR; PERFORMANCE; ADSORPTION; DESIGN; VALIDATION; PREDICTION; SYSTEMS;
D O I
10.1016/j.coche.2022.100813
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Processes and manufacturing in the biopharmaceutical industry are mainly based on experimental data and statistical approaches, however, regulatory expectations of increasing understanding and insights into methods behind medicinal products have paved the way for employment of more mechanistic and first principle modeling tools and concepts. Current, advanced modeling tools can basically be divided into four groups: biophysical modeling, mechanistic modeling, computational fluid dynamics, and plant modeling. Although very useful in themselves, the first three modeling concepts may also be used to establish better plant models, where digital twins of manufacturing plants are pursued broadly in the industry. This review presents the current development stage, opportunities, and challenges of the four modeling tools, and activities needed to reach future state of modeling or 'in silico CMC' (Chemistry, Manufacturing and Controls), a state where modeling, based either on first principles or hybrid approaches combining both empirical and mechanistic approaches, can be routinely employed in lieu of solely empirical or experimental approaches. It builds largely on presentations and discussions at the recent 4th Mini Modeling Workshop from May 25, 2021.
引用
收藏
页数:13
相关论文
共 67 条
[1]   Predicting Antibody Developability Profiles Through Early Stage Discovery Screening [J].
Bailly, Marc ;
Mieczkowski, Carl ;
Juan, Veronica ;
Metwally, Essam ;
Tomazela, Daniela ;
Baker, Jeanne ;
Uchida, Makiko ;
Kofman, Ester ;
Raoufi, Fahimeh ;
Motlagh, Soha ;
Yu, Yao ;
Park, Jihea ;
Raghava, Smita ;
Welsh, John ;
Rauscher, Michael ;
Raghunathan, Gopalan ;
Hsieh, Mark ;
Chen, Yi-Ling ;
Nguyen, Hang Thu ;
Nguyen, Nhung ;
Cipriano, Dan ;
Fayadat-Dilman, Laurence .
MABS, 2020, 12 (01)
[2]   A critical review on the design and retrofit of batch plants [J].
Barbosa-Povoa, Ana Paula .
COMPUTERS & CHEMICAL ENGINEERING, 2007, 31 (07) :833-855
[3]   Use of computational fluid dynamics for improving freeze-dryers design and process understanding. Part 1: Modelling the lyophilisation chamber [J].
Barresi, Antonello A. ;
Rasetto, Valeria ;
Marchisio, Daniele L. .
EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS, 2018, 129 :30-44
[4]   Prediction of lab and manufacturing scale chromatography performance using mini-columns and mechanistic modeling [J].
Benner, Steven W. ;
Welsh, John P. ;
Rauscher, Michael A. ;
Pollard, Jennifer M. .
JOURNAL OF CHROMATOGRAPHY A, 2019, 1593 :54-62
[5]   A generic methodology for processing route synthesis and design based on superstructure optimization [J].
Bertran, Maria-Ona ;
Frauzem, Rebecca ;
Sanchez-Arcilla, Ana-Sofia ;
Zhang, Lei ;
Woodley, John M. ;
Gani, Rafiqul .
COMPUTERS & CHEMICAL ENGINEERING, 2017, 106 :892-910
[6]   A framework for supply chain optimization for modular manufacturing with production feasibility analysis [J].
Bhosekar, Atharv ;
Ierapetritou, Marianthi .
COMPUTERS & CHEMICAL ENGINEERING, 2021, 145
[7]   Analysis of complex protein elution behavior in preparative ion exchange processes using a colloidal particle adsorption model [J].
Briskot, Till ;
Hahn, Tobias ;
Huuk, Thiemo ;
Wang, Gang ;
Kluters, Simon ;
Studts, Joey ;
Wittkopp, Felix ;
Winderl, Johannes ;
Schwan, Peter ;
Hagemann, Isabell ;
Kaiser, Klaus ;
Trapp, Anja ;
Stamm, Serge M. ;
Koehn, Jadranka ;
Malmquist, Gunnar ;
Hubbuch, Juergen .
JOURNAL OF CHROMATOGRAPHY A, 2021, 1654
[8]   Prediction uncertainty assessment of chromatography models using Bayesian inference [J].
Briskot, Till ;
Stueckler, Ferdinand ;
Wittkopp, Felix ;
Williams, Christopher ;
Yang, Jessica ;
Konrad, Susanne ;
Doninger, Katharina ;
Griesbach, Jan ;
Bennecke, Moritz ;
Hepbildikler, Stefan ;
Hubbuch, Juergen .
JOURNAL OF CHROMATOGRAPHY A, 2019, 1587 :101-110
[9]   STERIC MASS-ACTION ION-EXCHANGE - DISPLACEMENT PROFILES AND INDUCED SALT GRADIENTS [J].
BROOKS, CA ;
CRAMER, SM .
AICHE JOURNAL, 1992, 38 (12) :1969-1978
[10]  
Carta G., 2010, Protein chromatography: Process development and scaleup