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Digital Twins in Biomanufacturing

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Digital Twins

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 176))

Abstract

In recent years process modelling has become an established method which generates digital twins of manufacturing plant operation with the aid of numerically solved process models. This article discusses the benefits of establishing process modelling, in-house or by cooperation, in order to support the workflow from process development, piloting and engineering up to manufacturing. The examples are chosen from the variety of botanicals and biologics manufacturing thus proving the broad applicability from variable feedstock of natural plant extracts of secondary metabolites to fermentation of complex molecules like mAbs, fragments, proteins and peptides.

Consistent models and methods to simulate whole processes are available. To determine the physical properties used as model parameters, efficient laboratory-scale experiments are implemented. These parameters are case specific since there is no database for complex molecules of biologics and botanicals in pharmaceutical industry, yet.

Moreover, Quality-by-Design approaches, demanded by regulatory authorities, are integrated within those predictive modelling procedures. The models could be proven to be valid and predictive under regulatory aspects. Process modelling does earn its money from the first day of application. Process modelling is a key-enabling tool towards cost-efficient digitalization in chemical-pharmaceutical industries.

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Abbreviations

10-DAB:

10-Deacetylbaccatin III

AI:

Artificial intelligence

APC:

Advanced process control

ATF:

Alternating tangential flow filtration

ATPE:

Aqueous two-phase extraction

ATR:

Attenuated total reflectance

CAGR:

Compound annual growth rate

CAPEX:

Capital expenditure

CCF:

Cell culture fluid

cGSP:

Continuous Good Science Practice

ChPI:

Chemical-pharmaceutical industry

COG:

Cost of goods

CPP:

Critical process parameters

CQA:

Critical quality attributes

CV:

Column volume

DF:

Diafiltration

DoE:

Design of experiments

DSC:

Differential scanning calorimetry

DSP:

Downstream processing

EMA:

European Medicines Agency

FDA:

Food and Drug Administration

FFaM:

Frankfurt am Main

FMEA:

Failure mode and effects analysis

FTIR:

Fourier-transformed infrared spectroscopy

GC:

Gas Chromatography

GGW:

Equilibrium (german: Gleichgewicht)

GLP:

Good laboratory practice

GMP:

Good manufacturing practice

GWP:

Global Warming potential

HIC:

Hydrophobic interaction chromatography

HP:

Heavy phase

HPLC:

High pressure liquid chromatography

HPWE:

Hot pressurized water extraction

iCCC:

Integrated counter-current chromatography

IR:

Infrared

ICH:

International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use

IEX:

Ion exchange chromatography

IOT:

Internet of things

IP:

Interfacial partitioning

IPC:

Inline process control analytics

LLE:

Liquid–liquid extraction

LP:

Light phase

mAb:

Monoclonal antibody

MCSGP:

Multicolumn counter-current solvent gradient purification

MPC:

Model-based process control

MRSA:

Methicillin-resistant Staphylococcus aureus

MS:

Mass spectrometry

NMR:

Nuclear magnetic resonance

NN:

Neuronal networks

OPEX:

Operational expenditure

OQ:

Operation qualification

PAT:

Process analytical technology

PCA:

Principle component analysis

PCS:

Process control system

Prot A:

Protein A chromatography

QA:

Quality assurance

QbD:

Quality-by-design

QTPP:

Quality target product profile

RPN:

Risk priority number

RTRT:

Real time release testing

SME:

Small and medium-sized enterprises

SOP:

Standard operation procedure

SPTFF:

Single-pass tangential flow filtration

TMP:

Trans membrane pressure

TÜV:

Technischer Überwachungsverein

UF:

Ultrafiltration

USP:

Upstream processing

UV:

Ultra violet light

VLP:

Virus-like particles

WHO:

World Health Organization

c [g/L]:

Concentration

Dax [cm2/s]:

Axial dispersion coefficient

Deff [cm2/s]:

Effective diffusion coefficient

dp [cm]:

Particle diameter

H:

Henry coefficient

Jv [L/m2 h]:

Flux

K [L/g]:

Langmuir coefficient

Keff [cm/s]:

Effective mass transfer coefficient

kf [cm/s]:

Film mass transfer coefficient

q [g/L]:

Loading

R [cm]:

Radius

Re:

Reynolds number

Sc:

Sherwood number

Sh:

Schmidt number

u [cm/s]:

Velocity

w:

Mass fractions

ε:

Porosity/voidage

τ:

Tortuosity coefficient

ψ:

Hindrance coefficient

col:

Column

con:

Continuous phase

dis:

Disperse phase

drop:

Drop

L:

Liquid

m:

Molecular

max:

Maximum

Mem:

Membrane

Osm:

Osmotic

p:

Pores/particles

t:

Total

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Acknowledgments

The authors would like to thank the ITVP team for Aspen Custom Modeler™, OpenFoam® and MATLAB® simulations, JMP® statistic evaluations and COSMO-RS® thermodynamic calculations. Special thanks to Mourad Mouellef and Thomas Knebel as well as Professor Siemers and the Clausthal University of Technology (TUC) process automation group for their work on advanced process control and process analytical technology. Furthermore, the authors thank the TUC working groups of Dr. J. Namyslo for NMR analytics, Professor A. Schmidt for mass spectrometry analytics, Professor A. Weber for particle measurements and Professor D. Goldmann for metal ion analytics.

Funding: The authors want to thank the Bundesministerium für Wirtschaft und Energie (BMWi), especially Dr. M. Gahr (Projektträger FZ Jülich), for funding the scientific work.

Conflicts of Interest: The authors declare no conflict of interest.

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  1. Institute for Separation and Process Technology, Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Steffen Zobel-Roos, Axel Schmidt, Lukas Uhlenbrock, Reinhard Ditz, Dirk Köster & Jochen Strube

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    Christoph Herwig

  2. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Ralf Pörtner

  3. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Johannes Möller

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Zobel-Roos, S., Schmidt, A., Uhlenbrock, L., Ditz, R., Köster, D., Strube, J. (2020). Digital Twins in Biomanufacturing. In: Herwig, C., Pörtner, R., Möller, J. (eds) Digital Twins. Advances in Biochemical Engineering/Biotechnology, vol 176. Springer, Cham. https://doi.org/10.1007/10_2020_146

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