CONTRACT MANUFACTURING

Bioprocessing 4.0 –Where Are We with Smart
Manufacturing in 2020?
Mark Demesmaeker
Head of Data Analytics

Dan Kopec
Process Analytical Technologies Expert

Artur Miguel Arsénio
Head of Product Management PAT,Automation & Software
Sartorius

Introduction
The term Bioprocessing 4.0 has been tossed around since 2018
and is derived from Industry 4.0, a national strategic initiativefrom the German government launched in 2010 with the aim ofdriving manufacturing forward by increasing digitization and theinterconnection of products, supply chains and business models.1
Bioprocessing (or Bioprocess) 4.0 today is defined as a totally endto-end connected bioprocess, where all systems and equipment in
the process are connected digitally, forming the Industrial Internetof Things (IIoT) to run, control and even improve the process via
feedback loops and artificial intelligence (AI) or machine learning.
IIoT is redefining automation architecture and simplifying theautomation pyramid by compressing many of the lower layers
and also upper layers. Sensors, instruments, and other devices areinterconnected directly to the cloud for data collection and analysis,
as well as optimized process controls. Due to the need for real-time
control capabilities of bioprocess workflows, Bioprocessing 4.0 relies
heavily on integrated data management and analytics, modelling andautomation, as well as cloud and edge-based computing for the vast
amounts of data it produces.
The biopharmaceutical industry has lagged behind other industries,
such as oil and gas, where they have been using integrated
processing since 1995, as well as finance and the semi-conductor
sectors, which have been using end-to-end digitization since 2000.
One reason for the biopharmaceutical industry being behind is thatBIOPROCESSING

4.0

unlike many other industrial processes, bioprocessing is not binaryand generally involves complex living cells where variability is highmaking measurement and predictions of bioprocess performancechallenging. The industry is also heavily regulated, with special
constraints around contamination and safety, where changes to a
Good Manufacturing Process Compliant (cGMP) locked down process
are viewed by bioprocess scientists as tricky to implement. Anotherreason is that process automation capable of culturing cells andpurifying biologics in bioprocessing was in its infancy in 2000, as werescale-down models for predicting process performance and Process
Analytical Technology (PAT) tools for real-time bioprocess monitoring.
Some might say that Bioprocessing 4.0 really began to take off
after 2004, with the publication of the FDA’s guidance on PATand (Quality by Design) QbD, which aimed to reduce process
variability and thereby improve quality, safety and/or efficiency
in drug manufacturing.2 The idea has been driven forward by
a number of industry bodies including the Biophorum (BPOG)

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with its Biomanufacturing Technology Roadmap in 2017.3 Thiswas followed by its plug and play initiative in 20184 and The
International Council for Harmonization of Technical Requirements
for Pharmaceuticals for Human Use (ICH) with its ICH Q12
guidelines.5 Each of these initiatives and guidelines has helped
galvanize forward-thinking biopharmaceutical companies and life
science equipment suppliers into action on standardization andintegration of bioprocess automation.

Who’s Embracing It?
Biopharmaceutical manufacturers are being driven to adopt
Bioprocessing 4.0 by market pressures to produce biologics in ashorter timeframe without compromising product quality and safety.
The SARS-COV-2 pandemic has magnified the need to do this becauseof the time-critical need to produce vaccines to prevent and therapies
to treat this novel Coronavirus.
The ideal Bioprocessing 4.0 manufacturing facility for rapid, flexible
production would include fully automated upstream single-use(SU) bioreactors designed for intensified processing using high celldensity fed-batch culture or perfusion culture. The bioreactors would
have associated SU in-line sensors, providing real-time information
to determine or estimate Critical Quality Attributes (CQAs), such thatscientists could gather data for release decisions while the process isrunning. These facilities would also have ‘digital twins’ of bioprocessequipment, such as bioreactors and chromatography columns, which
are in silico simulations of the process, that can be used for improved
process control or run simulations in place of physical experiments
when needed. Using this type of Bioprocessing 4.0 set-up would
mean weeks could be shaved off process runs because there would
be less waiting for off-line data testing and feedback, we could run
virtual process testing, and time for cleaning and cleaning validationof equipment would be virtually eliminated.
Companies such as Biogen are actively working towards Bioprocessing
4.0 with studies by Ahmed et al in 2019 where they have constructeda hybrid model or ‘digital twin’ of their cell culture process, which
includes cell growth, glucose consumption, lactate, glutamine,
glutamate and ammonia production, as well as titer data to simulatea high titer monoclonal antibody (mAb) production bioreactor.6

data is exchanged with raw material suppliers. The program aims tounderstand operational performance by developing data exchange
standards, using predictive models to anticipate supply issues or
identify any improvements8 and thereby ensure biologics’ quality iscontinuously achieved without any issues.

Game Changing Technology
In the past decade, a technology platform that has been helping
to move the biopharmaceutical industry closer to Bioprocessing
4.0 in the upstream is the high-throughput automated scale-downbioreactor mimic. These mini bioreactors have been shown to provide
robust estimates of process performance and product quality frombench to pilot scale in studies by Lewis et al at AstraZeneca9 and Hsu
et al at Genentech.10 They have also recently been used in 2019 as aqualified scale down model for process characterization by Manahan
et al at Merck in large‐scale commercial bioreactors (>10,000 L).11Using mini bioreactor technology with PAT tools that can betransferred between SU bioreactor scales offers a simpler method ofintegrating and digitizing an end-to-end upstream process.
Aligned with mini bioreactor technology, spectroscopy is an analyticaltechnique that is helping move the upstream Bioprocessing 4.0 dial.
Spectroscopy techniques such as Raman, Fourier-transform infrared(FTIR) and Near-infrared (NIR) are beginning to replace off-line HPLC
and autosamplers because these off-line measurement methods aretime consuming and cannot provide in-line feedback loops for realtime monitoring and control as Raman spectroscopy, for example,
can. The use of spectroscopy techniques looks set to increase in thenext decade as they are tackling some of the pain points of measuring
cell culture and monitoring CQAs of biologics.
Currently in-line Raman spectroscopy is being used in pilot andmanufacturing scale cell culture. But there are studies that indicate
this technique has the potential to be used as an automated online method to measure multiple analytes simultaneously in minibioreactors12 (Figure 1). Biopharmaceutical companies such as
Digital twins of bioprocessing equipment
are often used in Bioprocessing 4.0 facilities.

Sanofi has also embraced Bioprocessing 4.0 in its new biomanufacturing facility in Framingham, Massachusetts. The plant, which openedin 2019, is highly digitized with closed loop controls for intensified,continuous biologic production using automated data capture froma range of sensors. The cloud-based data can be accessed from anywhere in the world in real-time to assess bioprocess runs and makeprocess changes if necessary. Sanofi has also generated ‘digital twins’of its production bioreactors, so that bioprocess scientists can simulate manufacturing process changes.7
Bioprocessing 4.0 is not just about controlling process runs, andAmgen has recognized that actively managing the supply chain is an
important piece in the puzzle and has set up a Supplier Relationship
Excellence (SRE) program to create a feedback loop where electronic
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Figure 1. Mini bioreactor technology with an integrated Raman spectroscopy platform

GlaxoSmithKline are working with integrated Raman spectroscopy
from mini bioreactors through to manufacturing vessels to rapidly andmore easily build models that can control their bioreactors.13 Having
this integrated real-time PAT technology from process development
though to manufacturing scale will help to build Bioprocessing
4.0 upstream cell culture processes in many biopharmaceuticalcompanies in future.

Where are the Gaps?
In general, upstream is further down the Bioprocessing 4.0 road interms of connectedness and digitization than downstream. Thisis because downstream processing relies on more traditional, lessautomated equipment and techniques with fewer in-line sensors andit is often difficult to connect all the parts of the process, which means
there is less opportunity to collect meaningful data and controlprocesses, leading to high variability in downstream bioprocessing.

What is required now is the capability to connect downstream
equipment together more easily, (the increasing use of ballroom
style skids is helping here), as well as the use of more PAT sensors tocollect data on process variables. Also design of Experiments (DoE)studies using scale down high throughput columns and filters andmultivariate data analysis (MVDA) of the results are needed to predict
the effects chromatography resins and filter pressures, for example
have on processes and CQAs.
With the level of integrated SU technology and in-line sensors available
in the upstream, it should be easier for many biopharmaceuticalcompanies and CDMOs to be implementing Bioprocessing 4.0
here. Yet this is still not the case. One of the main reasons many
companies are not implementing Bioprocessing 4.0 in the upstream
or downstream is bandwidth and budget constraints. Many smaller
biopharmaceutical companies and CDMOs simply do not have themoney or the staff available with the right skill set that can spend

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companies are currently embracing this initiative. However, by leveraging technology advances including mini bioreactors for process development, SU scalable bioreactors, PAT tools for automatedin/on-line spectroscopy and MVDA, Bioprocessing 4.0 in the upstream at least is becoming more widely achievable. In the downstream however there is still a way to go with automation, PAT tools
and data analysis. If clear regulatory guidelines, improved access tothe right type of training for scientists and delivery by suppliers ofequipment and software that harmonizes with a plant’s digital connectedness can be achieved, then a tipping point will occur, so thattime making sure their processes and analytics are fully integrated.
This is an area where equipment and software suppliers can assist,and they should try to ensure that their products are as ready to use
for seamless integration and digitization in manufacturing facilities
as possible.
Another reason why many biopharmaceutical companies are wary ofBioprocessing 4.0 is a lack of regulatory guidance. Although, the FDA
has issued information on implementing ICH Q8, Q9, Q10,14 which is
beginning to put boundaries around processes and product quality,
guidelines around some automation and sensor technologies aremissing. For example, Raman spectroscopy sensors that measure
multiple analytes in cell culture are not fully covered. Additionally,
there is limited guidance on how to validate chemometric models
generated from Raman spectroscopy with MVDA for use in GMP
facilities. With the increase in use of continuous instead of fed-batch
culture in the upstream, there is a much greater need to validate
PAT methods such as Raman spectroscopy as these can be used forin-line monitoring and feedback control of processes that could
potentially have much longer run times and where the definition of a“batch” is unclear. The FDA has given some good strategic guidanceon spectroscopy; however, the biopharma industry needs moreprescriptive guidance, which is likely to come when the ICH Q2/
Q14 guidance (currently in draft) is published and should improve
communications between regulators and the industry.15
Finally, there is a lack of skilled technical staff to run Bioprocessing
4.0 type facilities as many educational institutes are not providing
the right kind of training with very few courses on advanced
process control currently being offered. This gap could be pluggedby equipment suppliers, if they can hire enough IT experts witha diverse skill set to develop software and automation that can beused intuitively with minimal training by operators on the shop floor.
However, this means suppliers need to invest time in understanding
the bioprocess workflow and how the different personas of people
working along it interact with the equipment from a user experience
point of view.

Conclusion
Despite Bioprocessing 4.0 with its integration and digitization
promising better process consistency to improve quality and safety
in biologics manufacturing, only a handful of biopharmaceuticalby 2030 Bioprocessing 4.0 manufacturing facilities, which can beoperated from anywhere in the world, will become commonplaceacross the biopharmaceutical industry.

References
1.

Digital Transformation Report from European Commission Germany: Industrie 4.0.
Accessed June 2020 https://ec.europa.eu/growth/tools-databases/dem/monitor/sites/
default/files/DTM_Industrie%204.0.pdf (2017).

2.

Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development,Manufacturing, and Quality Assurance. Guidance for Industry. Accessed June 2020 https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pat-frameworkinnovative-pharmaceutical-development-manufacturing-and-quality-assurance (2004)

3.

Biophorum Operations Group, Technology Roadmap Report. Accessed June 2020. https://www.biophorum.com/phorum/technology-roadmapping/downloads/ (2017)4.

Biophorum Plug and Play Workshop. Accessed June 2020 https://www.biophorum.com/
plug-and-play-workshop-how-to-make-standardized-interfaces-work-2/ (2018).

5.

TECHNICAL AND REGULATORY CONSIDERATIONS FOR PHARMACEUTICAL PRODUCTLIFECYCLE MANAGEMENT Q12. Accessed June 2020. https://database.ich.org/sites/default/
files/Q12_Guideline_Step4_2019_1119.pdf (2019).

6.

S. K. Ahmed et al. American Pharmaceutical Review https://www.
americanpharmaceuticalreview.com/Featured-Articles/517739-Hybrid-ModelIdentification-for-Monoclonal-Antibody-Production-Bioreactor-A-Digital-Twin/ (2019).

7.

B. Hargreaves. Biopharma Reporter. HTTPS://WWW.BIOPHARMA-REPORTER.
COM/ARTICLE/2019/10/16/SANOFI-OPENS-DIGITALLY-ENABLED-CONTINUOUSMANUFACTURING-SITE (2019).

8.

C. Undey, Bioprocessing Online. https://www.bioprocessonline.com/doc/amgen-s-digitaltransformation-linking-raw-material-data-from-suppliers-to-patients-0001 (2018).

9.

G. Lewis et al. Bioprocess J. 9 (1), pp 22-25 (2010). http://dx.doi.org/10.12665/J91.Wales.

10. W. T. Hsu et al. Cytotechnology 64 (6) pp 667-678 (2012). https://doi.org/10.1007/s10616012-9446-1
11. M. Manahan et al. Biotechnol. Prog. 35 (6) e2870. (2019). https://doi.org/10.1002/btpr.2870
12. R.C. Rowland-Jones, C. Jaques. Biotechnol Prog. 35(2): e2740. (2019). https://doi.
org/10.1002/btpr.2740
13. R.C. Roland-Jones et al. Biotechnol. Prog. in press (2020).
14. Applying ICH Q8(R2), Q9, and Q10 Principles to Chemistry, Manufacturing, and Controls
Review Accessed July 2020 https://www.fda.gov/media/79615/download
15. K.A. Esmonde-White, et al. Anal. Bioanal. Chem. 409, pp 637–649 (2017). https://doi.
org/10.1007/s00216-016-9824-1

Mark Demesmaeker is Head of Data Analytics at Sartorius, email: mark.
demesmaeker@sartorius.com
Dan Kopec is Product Specialist Process Analytical Technologies at Sartorius
email: dan.kopec@sartorius.com
Artur Miguel Arsénio is Head of Product Management PAT, Automation &Software at Sartorius, email: artur.arsenio@sartorius.com

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