A Look into the Future of Bioprocessing
by Dr. Svea Grieb, Kai Touw and Dan Kopec

The internet of things and robotics are already the working standard in fastadapting industries. These technologies are now also being implemented in thebiopharma industry, which is more conservative due to strong regulations andintrinsically complex processes. How is this change going to proceed and how
does it change the industry? Does it provide a bigger benefit than its risk? We
asked three experts for process analytical technologies, automation and data
analytics from Sartorius about their view on the future of bioprocessing.

Advantages of Next Generation
Manufacturing
We would first like to discuss the main advantages of implementing Process Analytical
Technology (PAT) and advanced data analytics, which enable automation of bioprocesses.
This allows for the production of higher quality and more consistent biologic drugs andregenerative therapies at reduced costs of goods (CoGS), with higher flexibility andfaster time to market1,2. We consider the five main advantages of the implementationof PAT, automation and data analytics to be the following:Consistent, High ProductQuality

Fast and Predictive
Up- and Down-Scaling

Account for Cell Variationfrom Different Sources

Consistency in product quality
and quantity is achieved, asvariations of critical processparameters (CPPs) are reduced
and process robustness isincreased. This is summarizedin figure 1.

A well characterized andmonitored process together
with scalable hardware cansignificantly reduce the costand efforts of process scale
up | down, as scale variations
can be accounted for in anautomated and predictive
fashion.

The field of personalized
medicine benefits greatly fromautomation. In regenerative
medicine applications, theautologous nature of thetreatment demands a processthat is flexible, and can
dynamically adjust to wide
variations in starting material.
PAT can account for thevariations and peculiarities of
the cells from different patientsin an automated fashion.
This result in a high process
consistency irrespective of
the starting material.

Reduced Risk of Lost
Batches and Increased
Process Safety
Reduced risks of operator errors
and of contamination throughmanual sampling. The timely
identification and correction ofprocess irregularities reduces
the risk of lost batches.

Freeing up OperatorsAdvanced Automation reduces
the requirements of operator
interference, which on theone hand reduces the risk ofoperator errors, and on theother hand allows the operators
to focus on other tasks.

Traditional Process: Post-Process Quality Testing
Fixed
process

Fixedprocess

Fixedprocess

Post-process testing of quality to ensure product is within
specification before the release of the stage

Variability
in productquality andquantity

PAT Process: Quality by Design
Variable
process

Variable
process

Variable
process

Continuous online monitoring and control of critical processparameters (CPPs) that affect the critical quality attributes(CQAs) of the productFigure 1: P AT and automation result in consistent, high product quality

2Consistency
in productquality andquantity

Key Technologies of Next Generation
Manufacturing
We would like to discuss three key players of next generation manufacturing that arecurrently driving the change.
Flexible, Automated Skids

Spectroscopy

A technological development,
which is key for use in downstream processes are flexible
automated skids, capable ofhandling different type of unit
operations, all based on S88
compliant recipes. These typeof process skids make itpossible to run standardized
and automated processes
in facilities making use ofa ‘ballroom’ concept.

We believe that spectroscopictechniques will become moreabundant in both upstream anddownstream bioprocessing, due
to its capability of label-free,
online measurements of several
analytes, cell properties andproduct quality attributes.
Hence, spectroscopy has thepotential to replace offline
measurements during thebioprocess. We envision the useof a combination of different
spectroscopic techniques, such
as NIR, Raman and UV-Vis tobe required for this. That said,
there will be a continuing need
to use and further develop
other technologies, such as
bio-capacitance, and dedicatednutrient | metabolite sensors,
for application for which
spectroscopy does not provide
a solution. Furthermore, topropagate the use in GMP,
we envision a combinationof sensors which crosscheckeach other.

Multivariate
Data Analytics
The application of sophisticated
PAT tools in combination withmultivariate data analytics has
a high impact on commercial
processing. Measurements aremoved forward in the process
to the point of controllability.
Using process fingerprints,
the state of the process canbe assessed at any time.
Furthermore through real-time
univariate and multivariate
process monitoring, data canbe used for simulation andmodelling of process designand control and ultimately leadto prescriptive analytics ofproduct quality.

3On the Challenges of Next Generation
Manufacturing
When speaking about automation of bioprocesses, we need to evaluate technicalfeasibility and cost-benefit analysis. Furthermore, there are regulatory, logistics andsafety issues that have to be solved before automation can really be adopted widelyin the biopharma industry.
Processes Challenging
to Automate
While we do not think that
there is an application in theprocess of biopharmaceutical
drug production that would
not benefit from automation
at all, we do not envision that
we will see a high degree ofautomation added to alreadyexisting pipelines, such as
well-established fed-batchprocesses. Unless, of course, theautomation adds a significant
improvement to the process, as
we have seen it for automatedtemperature shifts at a certain
viable cell density, for example.
Further, there are processes
that are possible to automate
but do not benefit massively
from the automation, as
the manual interference isvery limited, such as dead-end
filtration. Finally, there areprocesses that will be quite
challenging to find an automation for. Product quantification
with a background of manyother proteins could be one
such example.

Compatibility andInfrastructure Challenges
The seamless integrationof process equipment andprocess skids into the automation system, especially when
considering flexible manufacturing facilities, is an issue.
Communication between
competitor solutions is notalways given and there is a lackof standards that still need to4

be established. Further, there isthe challenge of aligning theprocess automation conceptof a supplier to the facility
automation concept in termsof environmental monitoring,building monitoring anda certain level of integration
into resource planning systems.

Regulatory Challenges
Some concepts of modern
automation technologies andsensor technologies are not yet
covered by regulatory guidelines. This is especially true formultivariate data analysis thattakes all available data and integrates them into a fingerprint.
The adoption of such batchfingerprinting concepts must
be considered by the regulatory
bodies. The same questions
arise for multi-analyte sensors
that are based on computational models, as it is the case forspectroscopy, for example.
How do we validate a model forthe use of GMP? What are thecharacteristics of a ‘good androbust’ model? These questions
have to be addressed. A lastcase we want to mention is thedefinition of ‘a batch’ for continuous processing. Regulations
that were once established
for a 2-weeks process have tobe adjusted to processes thatcan potentially run for months
without interruption. The Regulatory Agencies are well awareof the challenges which comewith modernizing the industry,
but are open and cooperative
to new concepts coming fromtechnical advances in the fieldof automation, PAT and advanced analytics, as is shown by
the creation of the ‘Emerging
Technology Program’ by theFDA3,4.

IT Concerns | Data Integrity
A comprehensive automation
strategy for an entire bioprocess, and potentially an entire
production site, requires
connectivity of all components
and a centralized control unit.
However, that would require
data sharing and access that implies safety risks. We experiencereluctance among our customers to adopt new technologies
such as cloud computing andwireless communication of
PAT components.
We are convinced that the taskof meeting the requirement ofnext generation manufacturing
in terms of hardware, software,data analytics and infrastructure is too demanding andcomplex to be addressed by just
one supplier. It requires thecollaboration of several industries in strong exchange withthe customers to guide newdevelopments. Sartorius hasrealized this need, as reflectedby the integration of Sartorius
Stedim Data Analytics(former Umetrics®) and thecollaboration with Siemensfor our newest automation
platform NewAP.

On the Potential of Next
Generation Manufacturing
We expect the upstream processes to benefit the most from automation, due to thehighly variable nature of the biological process. A higher degree of automation andstandardization of the process steps will lead to improved batch-to-batch consistency,
and in turn, product quality. There are three application areas that will benefit themost from automation and therefore drive the development of PAT integration andadvanced data analytics.
Intensified Processing |Continuous Processing
Intensified | continuousbioprocessing is a very hot
topic in the biopharma industry
at the moment, as it increases
the productivity of single-use(SU) facilities, while decreasing
the footprint5. This renders SU
facilities competitive toconventional stainless steel
plants for commercial supplyof biopharmaceutical drugs.
However, intensified processes
are much more complex thanconventional fed-batchprocesses and therefore requirea tighter monitoring andcontrol. PAT and automation
do not only provide this, butalso reduce the complexity forthe operator. Another reason
for why we think intensified |continuous processing will
boost novel solutions, is that
establishing new manufacturing pipelines with uniquerequirements justifies the costs
and efforts of going throughthe approval for commercial
manufacturing.

Viral Processes

Cell and Gene Therapy

When producing viral vectors
for novel vaccines or genetherapy, the product is nolonger a well characterizedmolecule, such as a monoclonal
antibody, but a complex ofvarious proteins, DNA | RNA andin some cases lipid membranes.
This complexity makes it hard
to identify and understand thefactors influencing the productCritical Quality Attributes(CQAs). Hence, these processes
benefit from a stricter control
strategy, where high levels ofautomation and implementation of PAT and advanced data
analytics play a key role.
Another crucial aspect to consider when setting up a viral
vector production process is theoperator safety. Using PAT andautomation minimizes theneed of manual sampling andoff-line monitoring, hence
reducing the risks of spills orleakages.

In personalized medicine
applications in the field ofcell therapy, every process isunique. As the starting material
are the patient’s cells, therenaturally is a high variation.
Furthermore, these processes
run at very small scales, withterrifically high costs per batchand high risk6. In these cases,
lost batches must be prevented
in any way possible. Online
sensors for monitoring andcontrol reduce the contamination risk of manual sampling
and account for process
variabilities. Because of thesmall batch size, these typeof processes will also greatly
benefit from parallelization,
where a refined automation
concept is of vital importance tolower the costs of goods (CoGs)and enhance patient safety.
Advanced data analytics in
CAR-T processes can improve
process robustness by, on theone hand, controlling thequality of viral vectors and, on
the other hand, account for
the intrinsic variation in raw
material attributes and theireffect on the patient’s response.

5A look Into the Future
2 Years from Now

5 Years from Now

10 Years from Now

In the near future, we expect
a wider spread adoption of analytics in GMP that are alreadyavailable nowadays, such as
spectroscopy for metabolite
control and bio-capacitancefor viable biomass. We also
foresee that multivariate dataanalysis (MVDA) and design ofexperiments (DOE) are adopted
by more users. Furthermore
standardization has beenrealized to allow a real plug &produce scenario in a (multiproduct-) facility setup. Also
the field of hybrid modelling,where statistical and deterministic modelling principles arecombined, will advance within
the biopharmaceutical industry,
further improving process
understanding and simulation.
Within systems biology, forexample, these approaches
are starting to be applied toenhance the production celllines commonly used in biopharmaceutical processing ina pragmatic way7,8.

In the mid-future, we expectthat modern facilities will apply
intensified and continuous
processing with advanced automation. They will be using stateof the art automated process
batch management and S88
compliant batch recipe control
functionalities, as well as plantwide visualization and electronicbatch records. Furthermore,sophisticated analysis tools, suchas HPLC and mass spectrometry
will be automated and integrated
in the bioprocess. Together withan increased use of data science,
quality by design approaches canbe applied allowing real-time
release testing of product quality
based on batch finger printing.
Robotics will take over tasks,
which cannot be automatedotherwise. E.g. materials aretransported to and from theproduction location in-time.

The far-future vision is highly
influenced by the industry 4.0
approach and related concepts
such as machine learning andthe internet of things. We willsee fully automated, continuous bioprocessing pipelines that
require no operator interventions. Processes can be monitored and controlled remotely.
Every process will have a digitaltwin that can be used for process simulation and prediction.
More and different data will begathered and will reside in thecloud, where data analytics canbe applied easily to improve
processes, regardless ofmanufacturing location.

Industry 4.0 |Internet of Things
Advanced automation
for intensified andcontinuous processing

Wider spread adoptionof analytics in GMP

6About the Authors

Dr. Svea Grieb

Kai Touw

Dan Kopec

Dr. Svea Grieb is productmanager for Process Analytical
Technology (PAT) for upstream
processes at Sartorius Stedim
Biotech. In this role, she has
global responsibility for sensors
and analytical systems. Before
joining Sartorius in 2017, Sveadid her PhD at the TU Dresdenin single-molecule spectroscopy. Throughout her education
Svea has worked at the MIT
in Boston, US, the Max Planck
Institute in Stuttgart, Germany,and the Pasteur Institute inParis, France.

Kai Touw works as (Bio)PharmaMarket Manager at Sartorius
Stedim Data Analytics.
He focusses on the applicationof advanced data analytics inthe biomanufacturing industry
and how process data can beused for statistical modellingand simulation for process
design and control. Before
taking up this role Kai workedon process intensification
as a Process Development
Consultant within Sartorius
Stedim Biotech. He has
several years of PD experience,working at Janssen Vaccines& Prevention in Leiden (NL).
Kai holds an engineering
degree from the technicaluniversity of Delft (NL).

Dan Kopec is a PAT Technology
Expert for Sartorius Stedim
Biotech, covering the North
American region. Dan is basedout of Denver, CO and has over
10 years with Sartorius Stedim
and 20+ years’ experience inprocess sensors for monitoring,
control, and automation inbiopharma, food, and chemicalindustries.

7

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