Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
Contents lists available at ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis
journal homepage: www.elsevier.com/locate/jpba
Process Analytical Technologies – Advances in bioprocess integration and
future perspectives
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⁎
Gabriella Gerzon a,b, Yi Sheng a, Marina Kirkitadze b,
a
b
Department of Biology, Faculty of Science, York University, Toronto, Canada
Analytical Sciences, Sanofi Pasteur, Toronto, Canada
a r t i cl e i nfo
a bstr ac t
Article history:
Received 25 June 2021
Received in revised form 12 September 2021
Accepted 15 September 2021
Available online 25 September 2021
Process Analytical Technology (PAT) instruments include analyzers capable of measuring physical and
chemical process parameters and key attributes with the goal of optimizing process controls. PAT in the
form of a probe or sensor is designed to integrate within the pharmaceutical manufacturing line and is
coupled with computing equipment to perform chemometric modeling for result interpretation and mul­
tilayer statistical control of processes. PAT solutions are intended for understanding bioprocesses with a
goal to control quality at all stages of product manufacturing and achieve quality by design (QbD). The goal
of PAT implementation is to promote real-time release of products to decrease the cycle time and cost of
production. This review focuses on the applications of PAT solutions at different stages of the manufacturing
process for vaccine production, the advantages, challenges at present state, and the vision of the future
development of biopharmaceutical industries.
© 2021 The Author(s). Published by Elsevier B.V.
CC_BY_4.0
Keywords:
Process Analytical Technology (PAT)
Pharmaceuticals
Biologics
Vaccines
1. Introduction
Process Analytical Technology (PAT) was defined by the Food and
Drug Administration guidance “PAT- A Framework for Innovative
Pharmaceutical
Development,
Manufacturing
and
Quality
Assurance” [1] as a mechanism to design, analyze, and control
pharmaceutical manufacturing process through the measurement of
Critical Process Parameters (CPP) with Critical Quality Attributes
(CQA) of the product. This encourages building quality into the de­
sign of the product and promoting the development of PAT solutions
to digitize and provide real-time control of various critical para­
meters that influence product output and quality. In other types of
industries including gas, petroleum [2–4] and food industries [5,6],
List of Abbreviations: PAT, Process Analytical Technology; CPP, Critical Process
Parameters; CQA, Critical Quality Attributes; QbD, Quality by Design; NIR, Near-in­
frared Spectroscopy; MIR, Mid-infrared Spectroscopy; IR, Infrared Spectroscopy; NMR,
Nuclear Magnetic Resonance; DLS, Dynamic Light Scattering; MALS, Multi-Angle Light
Scattering; ATR, Attenuated Total Reflectance; FT, Fourier Transformation; 27Al,
27
Aluminum; 31P, 31Phosphorus; LD, Laser Diffraction; FBRM, Focused Beam
Reflectance Measurement; PVM, Particle Vision Monitoring; DAD, Diode Array
Detector; mAb, Monoclonal Antibody; ANN, Artificial Neuronal Networks; MVDA,
Multivariate Data Analysis; PCA, Principal Component Analysis; PLS, Partial Least
Square Regression; FTIR, Fourier-Transform Infrared; ATR, Attenuated Total
Reflectance’
⁎
Corresponding author.
E-mail address: Marina.Kirkitadze@sanofi.com (M. Kirkitadze).
https://doi.org/10.1016/j.jpba.2021.114379
0731-7085/© 2021 The Author(s). Published by Elsevier B.V.
CC_BY_4.0
PAT solutions have been widely implemented in process design to
streamline product output. PAT technologies such as near-infrared
spectroscopy (NIR), a widely known method for its capacity for ac­
curate process monitoring of complex matrices, has been established
as a highly versatile technique with a well-established process
monitoring track record in several processing industries, e.g., to
determine fuel octane numbers and benzene levels in fuel [7]. In the
biopharmaceutical industry the nature of the products is usually
quite dynamic and complex, therefore making it more challenging to
implement the PAT solutions currently used in other industries.
However, there is ample opportunity and demand to develop PAT
solutions to streamline bioproducts in the pharmaceutical industry,
and there has been a lot of development of PAT into bioproduct
design over the recent years. Therefore, this review will discuss the
applications of PAT solutions at different stages of the pharmaceu­
tical manufacturing using vaccine production as an example, the
advantages and challenges at present state, and the vision of the
future development to meet the needs of adaptable and predictive
generations of biopharmaceutical manufacturing plants.
In an ideally regulated manufacturing industry, when a process is
launched, its progress should be easily monitored and controlled
until that process is completed. This means that at any given time,
the condition and the quality of the product with respect to yield are
transparent and known. This knowledge is lacking in current phar­
maceutical manufacturing due to a lack of information feedback
during the manufacturing process. This knowledge gap can be
G. Gerzon, Y. Sheng and M. Kirkitadze
Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
real-time product release. Therefore, the definition of different types
of PAT implementations can be misguided because the im­
plementation within a process is not directly related to whether the
technology is involved in the process, but rather how simultaneously
a product can be monitored throughout production to accurately
represent change over time.
Once applied to bioprocesses, PAT solutions can increase process
understanding and control, and mitigate the risk from substandard
drug products for both manufacturers and patients. To optimize the
benefits of PAT, the entire PAT framework must be considered and
each of the elements of PAT must be carefully selected, including
sensors, analytical technology, data analysis techniques, control
strategies, and process optimization routines. In recent years, the
diversity of PAT solutions has increasingly grown. There are many
PAT technologies at different stages of development: some are ap­
plicable for the early research setting, whereas others have ventured
into the manufacturing realm. Although more examples of PAT so­
lutions now exist in the pharmaceutical industry, the breadth of
coverage is still not comparable to some other types of industries
mentioned because of difficultly in process implementation.
Current choices for implementation of PAT instruments within
the production process are determined by information that is al­
ready known about the process, the type of current test is being
employed, and whether implementation of analytical techniques in
real-time would improve process control or higher quality/yield of
desired antigens. Certain off-line techniques e.g., immunochemical
methods are not currently technically feasible to be implemented inline or on-line. On the other hand, certain product attributes such as
concentration of various compounds, pH, osmolarity, temperature,
and conformation changes of protein antigens during absorption can
be monitored using on-line and in-line tools. Thus, various phases of
the production cycle have been shown to have improved process
measurement, characterization, and control by using analytical PAT
methods.
Portable and in-line PAT tools have been implemented in various
bioprocesses including raw material identification, fermentation
processes and downstream filtration and absorption studies to
provide more robust data, that may not be well characterized or
monitored. Raw material identification and quality testing has been
one of the most robustly utilized processes for analytical tools [8].
Since, chemical matrices are usually simple, and chemical properties
can be detected by analytical techniques such as NIR, MIR, FTIR,
NMR, and Raman spectroscopy, the implementation of PAT tools for
raw material identification have been robust [8]. Determining the
quality of raw ingredients is very important in final product quality
and safety, and implementation of robust real-time methods for raw
material identification can quality control, and process testing and
validation. A few examples of techniques explored include non-de­
structive analytical PAT tools such as FTIR, Raman, and LD, which
have been used in both off-line and in-line raw material identifica­
tion of AlPO₄ adjuvant and its characterization during intermediate
and final stages of production [9]. Off-line sampling using an NIR
probe during fermentation provided information about critical pro­
cess parameters that could be further implemented in-line [10,11].
Moreover, development using small scale studies have shown the
efficacy of monitoring adjuvants, and absorption of antigens, and
downstream filtration using both real-time and off-line PAT tools
[12–14]. Therefore, the focus is to develop and integrate real-time
release of products by streamlining at-line, on-line or in-line PAT
solutions, which will ultimately provide better control of the pro­
duction of biologics throughout the manufacturing process.
Therefore, the major goals of PAT integration into the manu­
facturing process can be summarized as the following three: the
reduction of product cycle times via real-time measurement and
control systems; reduction of waste and reworks of final product;
and the possibility for real-time product release. In this review our
Fig. 1. Various methods of implementing PAT tools into bioprocess monitoring. A)
On-line analysis includes sample cycling from the process and measured using an
analytical tool to provide real-time data B) In-line analysis implements the analytical
tool in the bioprocess to provide data in real-time as the process is occurring. C) Offline analysis requires sampling from the bioprocess and transporting it for manual
measurement in a different laboratory (i.e. Quality Control). D) At-line analysis re­
quires manual sampling from the bioprocess and measuring the sample next to the
bioprocess site, to improve acquisition time for the results. Created with
BioRender.com.
addressed by PAT solutions that report the condition of a product in
real-time and facilitate the introduction of a feedback loop to control
a process based on the reported parameters. Such an approach al­
lows to test quality of the product at different manufacturing stages
to ensure quality by design (QbD) from raw materials to inter­
mediate, and to the final drug product.
PAT has developed a broad suite of tools, including analytical,
chemical, physical, microbiological, and risk analysis. Currently,
traditional PAT approaches including off-line and at-line analysis, are
used to provide quantitative information about a process using
analytical tools. However, for these measurements the process is
sampled, and the sample is then transported to a lab for analysis,
which in turn results in a delay between process sampling and the
availability of the results [Fig. 1C, D]. At-line analysis has the benefit
that the testing is not done as far from the process site, therefore the
turnover rate to relay results is faster than off-line methods. How­
ever, the goal of PAT is to be able to streamline in-line and on-line
PAT measurements, to provide real-time data to be able to monitor
and better control a process. On-line monitoring can be done by
continuously re-directing samples outside a process to be directly
measured at the process site without interruption or manual sam­
pling, whereas in-line monitoring is directly interfaced within the
process to provide real-time continuous measurements. This can be
done by looping the sampling from the reactor to a monitoring
vessel [Fig. 1A], or by interfacing the process with the analytical
instrument in the reactor vessel [Fig. 1B], to create a feedback loop to
modify the process conditions based upon real-time analysis.
Therefore, a lot of current work focuses on method development to
create PAT solutions capable to measure data in real-time and aid
both process control and product quality.
At-line PAT solutions have traditionally been simpler to imple­
ment because the technologies do not interfere in the process they
are intended to monitor because samples are measured outside the
process. Whereas the in-line and on-line PAT solutions have been
harder to integrate due to contact with the product and thus product
integrity has to be demonstrated in order to comply with quality
requirements in a regulated industry. Further development of PAT
technology allows the use of non-invasive in-line and on-line probes
and sensors to monitor processes in-line, with a goal to implement
2
G. Gerzon, Y. Sheng and M. Kirkitadze
Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
Fig. 2. Process Analytical Technology (PAT) tools for Bioprocess monitoring throughout the Manufacturing Lifecycle. Schematic for the use of various analytical tools (i.e. Nearinfrared spectroscopy (NIR), Mid-infrared spectroscopy (MIR), Nuclear Magnetic Resonance (NMR), Raman Spectroscopy, and Differential Light Scattering (DLS)) for monitoring
different types of biological products throughout upstream and downstream production processes. Created with BioRender.com.
focus is on PAT applications for vaccines throughout various stages of
manufacturing including upstream and downstream processes, for­
mulation and filling of drug substance and drug product, and dis­
cussion of the challenges and successes, as well as future directions
essential to succeed with digital evolution in pharmaceutical and
vaccine industries.
[Fig. 2]. Upstream operations are defined by the processes involved
in growth and cultivation of a desired antigen. The upstream life­
cycle includes opportunities for process monitoring starting from
raw material preparation throughout fermentation and cultivation of
a desired antigen. Current development focuses on the im­
plementation of on-line or in-line PAT solutions to provide in­
formation about critical process parameters that impact upstream
fermentation in real-time. Downstream processes can also be
monitored to assess antigen purity and formulation for the drug
substance (i.e., adjuvant absorption) and final drug product (i.e., final
vaccine product). [Fig. 2]. Furthermore, downstream PAT applica­
tions for real-time monitoring and process control are garnering
greater interest in PAT implementation to control processes that
impact final products. Technological advancements in PAT that uti­
lizes biochemical and biophysical analyses make it possible to
monitor processes in-line and in real time [4,5] often by probe in­
sertion directly into the manufacturing line in reactors, connecters,
and vessels. Thus, by developing PAT solutions to monitor various
phases of the manufacturing lifecycle, it will help monitor product
consistency to streamline the faster development of high quality and
more affordable biological products. Therefore, the subsequent sec­
tion will explore current development of various analytical PAT tools
including NIR, IR, NMR, Raman, DLS, and particle count and imaging
probes during various phases of the manufacturing lifecycle.
2. PAT in pharmaceuticals and vaccines
Recently, development of PAT solutions for the monitoring of
bioprocesses has been investigated to understand and control more
complex and dynamic biological products such as vaccines. Vaccine
technologies are complex and created with different bioprocesses.
There are many different types of technological vaccine platforms
including live attenuated pathogens, inactivated whole pathogens,
toxoid antigens, or parts of pathogens including natural or re­
combinant proteins, conjugated or unconjugated polysaccharides,
virus-like particles, or DNA/RNA vaccines [15]. The development of
each type of vaccine requires different biological systems and re­
quirements. Therefore, PAT tools are being used to better understand
and control different phases of the manufacturing lifecycle [Fig. 2].
Although the manufacturing lifecycle is adapted for specific types
of antigen production, the overall processes are similar, and include
opportunities for various PAT tool applications during every phase
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Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
2.1. Near-infrared spectroscopy (NIR)
development of fiber optic probes using attenuated total reflectance
(ATR) and Fourier Transformation have helped make the method
better suited for monitoring bioprocess solutions [29,30]. Compared
to NIR, the pathlength in ATR fiber optic probes are commonly much
shorter and therefore are not commonly used to measure biomass,
however many other metabolic components of biological solutions
such as glucose, lactate, ethanol, methanol, and ammonia were
measured using this technology [29–31].
The implementation of IR using in-line and on-line studies have
been developed in smaller scales to measure upstream bioprocess
parameters including glucose, lipids, and proteins to provide realtime process information [32,33]. Other studies have explored the
use of in-line ATR-FTIR probes in small scale fermenters to measure
media composition from biomass, which was possible, however
current work has not been able to accurately correlate CPP para­
meters in culture fermentation media at that scale [34]. However,
the implementation of these probes in large scale bioreactors needs
to be further explored as current probes are not robust enough to be
implemented in large scale bioprocesses [31]. Moreover, although IR
probes have been used to measure critical process parameters in
upstream bioprocesses such as fermentation, there is less informa­
tion about its measurement and use in downstream processes. Re­
cently work has been shown using an in-line ATR probe in a smallscale study to monitor the composition of aluminum phosphate
adjuvant throughout the manufacturing process, including raw
material, intermediate products, and final adjuvant product, as well
as its absorption to an antigen [9,12]. In addition, IR can be used ty to
measure process residuals in-line, e.g., surfactant concentration in
viral vaccine product [13]. This work further demonstrates the ap­
plication and use of IR technology to monitor bioprocesses but also
further shows its capability to be implemented in-line to provide
real-time data throughout the production process.
NIR spectroscopy is a non-invasive analytical method that oper­
ates based on the principle that the atoms of molecules are in con­
stant motion and vibrate at specific frequencies. The two main
vibration types are stretching and bending, the type and frequency
of each vibration are molecule specific and directly dependent upon
the mass of each atom in the molecule and strength of the chemical
bond between atoms [16–19]. Light frequencies that correspond to
molecular vibrations are absorbed by the sample and the resulting
infrared spectrum (IR) comprises peaks of well-defined frequencies,
band shapes and heights that can be correlated to molecule con­
centrations present in the sample. Theoretically, the concentration of
any molecule containing C–H, N–H, S–H or O–H bonds can be
measured. Compared to IR that detects fundamental vibrations, NIR
measures higher energy wavelengths that create combination and
overtone vibrations. Fundamental vibrations are defined as a tran­
sition of energy from the ground state to a first excited state [16–20].
However, NIR vibrational frequencies comprise of combinations or
overtones which are defined as excitations from the ground state to
a level above the first excited state. Thus, an advantage of NIR is that
it displays weak absorbance bands that return more signal to the
detector. Since the weaker absorbance bands cause less specular
reflectance, therefore samples do not require dilution to be mea­
sured. [16–19,21,22].
Although NIR is very sensitive, the analysis of the spectra could
be complex. As most mixtures in fermentation contain a variety of
different molecules, this results in overlapping overtones [16–19].
Therefore, differences in peak intensities resulted from different
variables are not easily determined from the spectra and require
multivariate statistical models to extrapolate information about
different parameters. Thus, calibration models are built for each
process parameter and are then validated against a reference set
[16–19]. In recent years there has been a lot of progress for devel­
oping models and methods to measure fermentation both on-line
and in-line to provide real time information to further understand
and control upstream bioprocesses. Measurement of parameters
such as glucose, ammonium, lactate, glutamate, optical density/
biomass, glycerol, and pH, have been developed using NIR in a
variety of different organisms and explored during manufacturing
fermentation [10,11,23–28]. The ability to measure these parameters
in real-time help to better understand growth processes and fer­
mentation dynamics to better control bioprocesses to produce op­
timized yields. Studies have shown statistical models developed for
individual parameters based on reference methods, however, the
emergence of process control using batch consistency or optimal run
calibrations have also further allowed the development of NIR
models without the need for reference calibration methods [11,23].
Thus, the use of either modeling methods can allow real-time de­
cisions to be monitored and controlled in real-time and have allowed
the rapid development and use of NIR spectroscopy in bioprocess
monitoring.
2.3. Nuclear magnetic resonance (NMR)
NMR is a spectroscopic technique that utilizes magnetic fields to
study atomic nuclei of a sample. Since this method provides in­
formation for each nucleus in the entirety of a sample and is directly
proportional to the intensity of the signal measured, NMR can be
used to provide information about the characterization, and quan­
tification of a variety of different materials [35,36]. Moreover, when
utilized as a PAT technology, similar statistical methodology can be
applied for the pattern recognition of complex processes derived
from NMR data.
There has been previous work investigating upstream biopro­
cesses and metabolite products measured by NMR off-line. However,
recent studies have shown that sampling differences might exist
between off-line and on-line NMR measurements [37], depending
on the type of information about a process that is necessary. During
reactions, conditions including temperature, pressure, and sample
mixing, can contribute to various physical and chemical modifica­
tions of reactions including changes in solubility (precipitation),
sample evaporation, or production of chemical intermediates
[37,38]. Therefore, on-line measurements in real-time provide the
most robust information about the true reaction process compared
to off-line sampling. Furthermore, continuous monitoring for kinetic
models is better measured on-line to have more relevant re­
presentation of the reaction [37]. Currently, various flow tube de­
signs have been implemented to allow on-line reaction monitoring
for magnets of various sizes, with the capability of continuous or
stop-flow designs from reaction vessels to collect sample informa­
tion [39,40]. Furthermore, flow NMR designed for larger super­
conducting magnets also allows sample acquisition into the
spectrometer and accounts for various flow effects that are re­
presentative in the acquired data, without the need for instrument
2.2. Infrared spectroscopy
Infrared spectroscopy is a type of vibrational spectroscopy that
measures the infrared region of the electromagnetic spectrum.
Different areas of the spectrum are associated with different vibra­
tional energies and measurement of the Mid-IR (MIR) region
(400–4000 cm-1) can collect a molecular fingerprint of a compound
through the measurement of its fundamental vibrations [29,30]. In
contrast to NIR, the resulting MIR spectrum can directly assign and
resolve molecular peaks associated to organic molecules and can be
used as a direct measurement to assign components in a solution
[29,30]. Although one drawback of the technique was the inter­
ference of water to detect components of aqueous solutions, the
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Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
modification, and provides comprehensive data for measuring var­
ious chemical reactions [41].
Although excellent methods have been developed for off-line and
flow monitoring of bioprocesses using high-field NMR magnets, the
implementation of these instruments in production has been chal­
lenging [39]. However, in recent years low-field NMR spectrometers
containing permanent magnets, that do not require the use of
cryogens, have been developed to monitor reaction processes be­
cause of the simple instrument maintenance and capability to pro­
vide real-time process information [42,43]. These smaller “benchtop” NMR spectrometers helped to mitigate many of the issues with
implementing high-field NMR spectrometers into production en­
vironments and can monitor processes in real-time using similar
stop or continuous flow on-line reaction monitoring design systems
[39]. In-process sample testing using these bench-top NMR spec­
trometers are being developed, and studies done inside fume hoods
in chemical industries demonstrated the feasibility of using these
devices to monitor chemical reactions in real time [39].
Apart from chemical reactions, there have been recent ad­
vancements of methods to monitor biologically relevant upstream
fermentation processes using low-field NMR spectrometers. For
example, recent studies have shown the capability of low-field NMR
spectroscopy to monitor sucrose hydrolysis on-line [44]. Further­
more, recent advances in bioprocess monitoring have shown im­
proved methods of monitoring nutrient and metabolite profiles of
both yeast and fungi [42], and microalgae lipid profiles [43] as well
by on-line NMR using low-field NMR spectrometers. These devel­
opments can be applied to biopharmaceutical relevant processes, to
monitor reaction progress in a robust way without the need to
characterize various components individually. In downstream pro­
cesses, the study of sedimentation of aluminum adjuvanted sus­
pensions, and freezing variability have been investigated in order to
find out more about production process that can affect final drug
products in a non-invasive manner. For both studies low field NMR
spectrometers were able to non-invasively determine differences in
sedimentation characteristics of suspensions with aluminum ad­
juvants, as well as freezing variability between vials using the same
type of NMR methodology [45,46]. In addition, 27Al and 31P NMR
methods have also been developed to quantitatively measure total
aluminum and free unbound phosphate in adjuvanted vaccines that
can be measured throughout different phases of production [47].
These studies provide further information for downstream para­
meters involved in formulation and stability for vaccines and could
be applied for reaction monitoring of adjuvanted drug substances
on-line.
Since, NMR can provide robust and direct quantitative sample
information, and can measure many nuclei (i.e. hydrogen, carbon,
phosphorous, aluminum) it can be used for a variety of bio­
pharmaceutical relevant processes and is unique among other ana­
lytical methods. Due to these unique advantages, NMR has been
shown as a good on-line reference technique for chemical reaction
progress monitoring for other analytical techniques including NIR
[48]. As a reference method, NMR can quantitatively show reaction
intermediates that are not represented by other current off-line re­
ference techniques [48]. Thus, further development of NMR for online monitoring in biological processes could also be used as a
quantitative and robust on-line reference method to support other
analytical techniques.
result in the inelastic scattering of photons that produce vibrational
energy from excitation by a laser [49]. These vibrational modes are
mapped and result in spectra that can provide both chemical and
physical information about a sample of interest.
Over recent years advances in Raman spectrometers and analy­
tical software have allowed for better spectral information and have
made Raman a common analytical technique used in industry. One
of the many reasons this technology has become more popular for
industrial bioprocess monitoring is because Raman spectroscopy is
rapid to implement, cost effective, non-destructive to samples, easily
reproducible and can measure a variety of both organic and in­
organic substrates [23]. Raman can be implemented in-situ and has
been implemented to provide consistent sample measurements for a
variety of process that can include both liquids, solids, gases, and
aerosols, making the technique very versatile [23]. Due to the
technique versatility, there has been a lot of studies showing the
implementation and use of Raman spectroscopy in pharmaceutical
industries to measure raw materials and various chemical progres­
sion steps including tablet mixing and drying [49]. Further devel­
opment of Raman spectroscopy for monitoring applications in
biopharmaceutical industries has been adapted to provide real-time
process information.
Similarly, to NIR, the implementation of Raman probes on-line
and in-line have provided the opportunity to obtain process in­
formation in real-time and have been coupled with chemometrics to
provide robust information about a bioprocess. Studies have shown
the development of models using in-line Raman probes to detect a
variety of substrates important for fermentation and cell cultivation
monitoring including glucose, glutamine, lactate, ammonia and
glutamate in various types of cell systems [50–52]. Moreover, Raman
spectroscopy has been implemented as on-line PAT solutions in
bioprocesses to monitor and control looped systems to measure
metabolites and viable cell density which is important for fermen­
tation [53,54]. Furthermore, real-time just-in-time learning (RT-JITL)
frameworks are under development to help maintain industrial
Raman models that can be used for real-time fermentation mon­
itoring [55]. These developments further demonstrate the evolving
use and benefit of implementing Raman PAT solutions for bioprocess
control. Further development of amino acid monitoring in bior­
eactors in-real time using Raman spectroscopy has also been studied
to provide further information about parameters that influence cell
culture growth processes [56]. These models can help provide realtime bioreactor monitoring for fermentation parameters that can
affect the growth, expression, and selection of various cell systems to
better control a process to produce a desired yield [52].
Furthermore, the ability to measure various components in­
volved in the manufacturing process for vaccine development helps
exemplify the versatility of using Raman in both upstream and
downstream processes to provide a robust and singular method to
identify and measure a variety of components important for vaccine
development that can be further implemented in real-time. Raman
spectroscopy has also been developed to measure variability be­
tween various vaccine products with similar antigens to assess
quality and identity between batch releases and various products to
ensure safety and quality of released biopharmaceuticals [57,58].
Thus, further implementation of Raman spectroscopy in-line in
these industries can help further develop analytical tools to provide
real-time information about biological products during manu­
facturing processes.
2.4. Raman spectroscopy
2.5. Dynamic light scattering (DLS)
Raman spectroscopy is a form of vibrational spectroscopy that
can provide information about chemical properties and molecular
interactions of various compounds, which in turn can provide a
molecular fingerprint of any given compound [49]. Raman active
compounds are molecules that have changes in polarizability and
Dynamic light scattering (DLS) is a technique that correlates the
Brownian motion and resulting light scattering of particles in solu­
tion and is used to provide information about particle size and dis­
tribution [59]. The technique has been widely developed and used to
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Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
measure a variety of different biological particles. DLS has been used
to provide information about biological molecules with relevance to
the production and understanding of biopharmaceuticals. A recent
study used DLS to measure pre-absorbed antigens to examine con­
sistency of antigen size distribution prior to association with ad­
juvants [14,60], and to further compare with the adsorbed state, i.e.,
the consistency of size of antigens associated adjuvants for the for­
mulation of vaccine products measured by laser diffraction (LD) and
in-line by Focused Beam Reflectance Measurement (FBRM) [14,60].
The application of DLS has also been developed to provide in­
formation about particle size and stability of a variety of pharma­
ceutically relevant proteins. Most recent studies have shown the use
of DLS for monitoring the effect of temperature on SARS-CoV-2 spike
and RBD proteins to determine the stability of the proteins after
storage at 4 °C, by correlating their molecular weight [61].
Recently, the development of an on-line application of DLS cap­
able to measure size distribution of protein antigens and oil-inwater adjuvants can provide real-time characterization [62] and
enable decisions to develop and optimize manufacturing processes.
Statistical modeling using artificial neuronal networks (ANN) have
been developed using DLS to measure average yeast cell size
throughout stages of cell growth by monitoring the diameter of the
cells [63]. The development of this model in cell culture suspension
can be used to further study the particle size of cells during up­
stream processes to provide information about cell growth. This
could also be further applied to other organisms to provide another
technique to measure applications of biomass which are currently
not well characterized or understood. Another technique adaptable
for the on-line application is real-time MALS, which in addition to
size also reports molecular weight distribution [16]. However, the
PAT methods using DLS and MALS in bioprocess monitoring during
manufacturing would need to be further developed for applicable
real-time process control.
In downstream formulation process, equipped with an imaging
probe, FBRM can be used to monitor the loss of fine particles prior to
filtration, which can markedly decrease the time needed for filtra­
tion. Other techniques employed include particle vision monitoring
(PVM), which can be used to monitor the transition between oiling
and crystallization. In 2005, Pfizer researchers considered the power
of PAT to understand processes as self-evident and described usage
of PAT for crystallizations on scale and redox reactions [67]. There
are success stories of PAT integration into manufacturing process of
the pharmaceuticals especially for the manufacturing of solid for­
mulations.
The use of real-time measurements for the exploration and
manufacturing of new vaccines enables new efficient ways to gain
initial understanding on the association of adjuvant with an antigen
of interest. Currently, the utility of an adjuvant to a given vaccine
antigen is determined on a case-by-case basis. By characterizing and
trending the adsorption process in real time, PAT has the potential to
dramatically reduce precious investigation time required for next
generation vaccine manufacturing by providing a mechanism of vi­
sualizing antigen-adjuvant complex chemical structure changes.
This technology may also assist in selecting the most suitable ad­
juvant for the antigens being absorbed. Therefore, future regulatory
support of PAT implementation for measuring drug substance and
drug products, aims to integrate the use of these technologies in the
next generation of vaccine manufacturing [1].
3. PAT in digital manufacturing
3.1. PAT is a portal to digital
The significant impact of implementing analytical tools using
PAT, is to create digitized processes with feedback loop control, to
better understand, control and optimize bioprocesses. By under­
standing critical process parameters (CPPs) that influence biopro­
cesses, that knowledge can be adapted and optimized to use in
various platforms. Thus, when generating large amounts of data, a
virtual digital copy (i.e. Digital Twin) could be created to provide
information about a process and expected data readout with asso­
ciated software, to simulate a manufacturing process, along to
change and adapt equipment and procedures for a new platform.
Thus, the digital copy containing all the critical information about a
process can be transferred from various plants or physical locations
to a new process [68,69]. Ideally the creation of a digital twin
blueprint to transfer technology, knowledge and analysis could be
implemented to adapt current processes to development of various
antigens without the need to begin validation and optimization of a
bioprocess from scratch [70]. Thus, digital twins can provide in­
formation about the process, i.e. digital blueprint, that would allow
its quick shipment and adaptation in a novel environment [Fig. 3].
However, in the biopharmaceutical industry the development of
digital twins is complex because of the dynamic nature of the pro­
ducts. Therefore, transfer of technology is not as easily implemented
as in other industries (i.e. petrol and automotive industries) because
knowledge about the various parameters that impact a process is not
fully understood, or sometimes similar between platforms, therefore
the development of a digital twin copy is inherently more com­
plex [68,69].
Although digital twins are more complex to develop in bio­
pharmaceuticals, there is a large opportunity to maximize opera­
tional gain in the industry. This includes the application of PAT
solutions, to create more efficient processes with greater control and
regulation, that can ultimately improve the quality of biological
products. This can help provide solutions to decrease the cost of
goods, development of new processes, improve product quality, and
better satisfy requirements outlined by various regulators to help
2.6. Particle count and imaging probes
Technological advancements in PAT that utilizes biochemical and
biophysical analyses make it possible to monitor processes in-line
and in real time [4,5] often by probe insertion directly into the
manufacturing line in reactors, connecters, and vessels. The im­
plementation of PAT to monitor antigen adsorption may facilitate
new product development and process optimization for commer­
cialized products [64], further process understanding as a whole
[14], and reduce lost batches by allowing for continuous monitoring
and decision-making in real time [6]. It will also accelerate and
streamline testing processes by reducing downtime of waiting for
test results to release a batch, and providing robust, continuous data
collection. During development of formulations, it can be used in
scaled-down models and then used to verify characteristics at full
scale, simplifying and accelerating process definition.
Particle size is a quality attribute of adjuvanted vaccines that
requires monitoring [65] during processing as large adjuvanted
particles can potentially decrease potency of a vaccine [12,66]. One
of the methods that monitors the number, size, and shape of parti­
cles through chord length distribution is focused beam reflectance
measurement (FBRM™) [14,64]. It was shown that ParticleTrack and
EasyViewer probes with iC FBRM™ and iC Vision™ software, re­
spectively, detect differences between a normal adjuvanted for­
mulation and one that contains aggregates [64]. They are also able to
detect subtle particle size distribution changes during the antigen
adjuvant adsorption processes [64]. Measurements were made using
both the real-time probes and with off-line benchtop laser diffrac­
tion instrument as an orthogonal testing assay to understand simi­
larities. In addition, in-line particle sizing by FBRM and mid IR
probes were shown as useful tolls for in-process characterization
and identification of adsorbed vaccine drug product [14].
6
G. Gerzon, Y. Sheng and M. Kirkitadze
Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
Fig. 3. Schematic design for the implementation of a Digital Twin in the Biopharmaceutical industry. Physical processes and digital processes are combined to create digitized
models and are developed using machine learning to better predict outcomes of important biological process parameters. Copies of these models are then implemented to
different products or sites to create templates with required parameters to measure and predict outputs of a new design. Created with BioRender.com.
maximize operational gain and faster development and release of
new biopharmaceuticals to the market.
To date there are not any digital twins being directly applied in
biopharmaceutical applications, there has been a lot of work in de­
veloping models to create digital twin frameworks that can be used
for bioprocesses in biopharmaceutical manufacturing. There has
been work mapping CHO cell culture kinetics using genetic modeling
to predict metabolic changes in the cell platform [71]. Since these
cells are often used as platforms for growth of different recombinant
proteins, these models can help to develop better metabolic mon­
itoring strategies for digital twins using the CHO cell platform [71].
Further work has also demonstrated the feasibility of using Raman
spectroscopy and Diode array detector (DAD) as PAT methods to
predict concentrations of mAbs using a simulation study to de­
termine process control prediction that could be applied to a future
digital twin framework concept [72]. Interestingly, a study outlining
the prior use and of knowledge of various PAT bioprocess methods,
have been integrated to propose a complete manufacturing digital
twin framework for a production of a SARS-COVID-19 mRNA vaccine
[73]. This framework could be further tested in a larger industrial
scale to try and improve production understanding and scale-up
timing for the manufacturing production of a COVID-19 vaccine to
meet requirements [73]. Thus, the importance of the development of
digital twin frameworks in biopharmaceutical manufacturing is
important in order to be able to quickly respond to consumer needs,
while providing robust methods to monitor various production
processes for different biological products, in order to achieve op­
timal product production and safety.
3.2. PAT data analysis
Since different types of analytical tools require different types of
data processing, choosing an appropriate analytical method is im­
portant, so that it is able to provide continuous process information
about critical process parameters of interest, in a stable, linear, ro­
bust and repetitive manner [31]. Moreover, the type of modeling
selected to incorporate empirical, data driven, and statistical ap­
proaches have been explored using PAT for Design of Experiments
(DoE) and Hybrid modeling, that have been effective for process
modeling using multivariate data analysis [74,75].
Technologies such as Mid-IR, require more simplistic univariate
analysis, and can be used to provide information about analytes of
interest using band height or area of peaks that directly correlate to
chemical information of the modeling system. However, other
technologies that provide secondary information about processes
with many collinearities such as NIR, require multivariate modeling.
Thus, the analytical method chosen, and appropriate data processing
methods, will reflect the results of the entire process and determine
if the method is able to provide sensitive and selective monitoring of
analytes that fit within the model’s scope of measurement under
varying conditions that can be predictively replicated [31]. Thus, the
robustness and reliability of measurement of critical process para­
meters relies on data analytics that are appropriately determined for
each process and type of analytical sensor.
Most commonly multivariate data analysis (MVDA) is necessary
to reveal hidden patterns in data sets and interactions between
parameters for methods that cannot provide direct chemical or
7
G. Gerzon, Y. Sheng and M. Kirkitadze
Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
physical information about processes [23]. MVDA analysis is em­
ployed when there is a large amount of data points that cannot be
modeled individually and require the reduction of data in order to
find useful information about all the variables [23]. The two most
common types of MVDA data analysis employed during bioprocess
monitoring is Principal component analysis (PCA) and Partial least
square regression (PLS) [23,31,76,77]. Both types of analysis are
sensitive to data pre-treatment or preprocessing methods which can
help to reduce noise and find better information about process
parameters based on their chemical and physical attributes that
impact the spectral signal [23,31]. There are various pre-treatments
to normalize differences and scaling between parameters, and other
pre-treatment methods to help correct baseline shifts which can
help to reduce the impact of scattering and instrument drifts [23,78].
Once appropriate pre-processing treatments are employed to
spectral data, models can be built to determine information about
bioprocess data from the spectra. PCA analysis can provide qualita­
tive information about data without requiring reference methods,
and can monitor the structure, variance and distribution of data
generated directly from measurements. Quantitative information
about bioprocess parameters can also be determined using reference
methods for model calibration determined by partial least square
regressions (PLS). Both types of information can be very useful to
monitor bioprocesses using PAT tools. Qualitative models can pro­
vide information about bioprocess progression, by comparing batch
data to previous or ideal batch consistencies to provide information
about the quality or performance of the process. Quantitative
models can help provide information between process variables and
data, to better monitor various parameters throughout the biopro­
cess and provide further understanding of the process [31]. Both
methods require robust calibration models to be created, and vali­
dated using new data sets, in order to ensure predictability of the
model and robustness, in order to account for drifts and changes
throughout the data and process parameters changes due to the
dynamic nature of bioprocesses and materials used [31]. Therefore,
model maintenance is important to account for differences in
starting material and changes in the system over time. Guidance for
MVDA modeling and how they can be applied for bioprocesses is
outlined in USP chapter 1039 and provides excellent framework to
design chemometric models based on regulatory guidance [79,80].
In order to implement MVDA modeling into manufacturing infra­
structures, there are IT programs available that meet 21 CFR part 11
compatibility that streamline data from analytical instruments and
are able to process and visualize MVDA data [79]. IT Programs such
as SynTQ®, SIPAT®, BioPAT SIMCA® and Unscrambler Process Pulse II®
are commercially available programs to streamline various analytical
instruments and associated data [79]. Thus, implementation of these
programs can help standardize data collection and analysis for var­
ious PAT instruments for use in industrial environments.
The use of data driven models can be further applied using ma­
chine learning in biopharmaceutical manufacturing. However, the
challenge with implementing PAT models using machine learning
into the manufacturing suites is to demonstrate the robustness of
the models for GMP use, and ensure the compliance regulatory re­
quirements. Li et al., describe an framework called a stage-gate
model, to provide guidance for PAT progression to design and im­
plement PAT technologies into biomanufacturing processes while
maintaining GMP practices and strict regulatory requirements [79].
Part of the framework that Li et al. describe is the necessity to define
the robustness of the technology that is planned to be implemented
in a real time process and determine its comparability to previously
established methods so that it is equally compared to historical data
to provide valid results [79]. Moreover, process design and under­
standing of the stringent parameters of a specific part of the process
is also important in determining which technologies to implement
and what kind of variability in the model is acceptable to produce
accurate results and quality data [79].
This provides a challenge when implementing machine learning
models into bioprocesses because the processes are often quite
complex and variable in nature so proper training and use of various
types of modeling may be required to provide understanding of
parameter limits that are acceptable. Then, machine learning in the
context of bioprocess monitoring can be applied to systems in the
digital realm to predict the response on changing conditions and
control the process without the continuous addition of empirical
data, relying on MVDA models created for critical process para­
meters. To date there have been improvements in implementing
machine learning to predict bioprocess monitoring. Off-line meta­
bolic analysis using CHO cells were adapted using machine learning
to provide better prediction of amino acid concentration throughout
a bioprocess, which can be adapting into production to control nu­
trient feeding and potentially be further built using in-line PAT
technologies [81]. Further work has shown implementation of realtime machine learning algorithms to predict various parameters in
cell culturing monitoring that adapt a specific model in-real time to
provide better prediction and control of the process from the model
with the lowest prediction error [82]. The implementation of ma­
chine learning for model maintenance shown for Raman models
measuring metabolites in bioprocesses, is an example of how these
systems could be implemented in manufacturing suites to maintain
accuracy of models to satisfy strict quality regulations [55]. Thus,
models such as these can be further implemented in complex pro­
cesses with various process parameters throughout the production
process to implement machine learning algorithms to provide better
forecasting and control, that can maintain valid prediction limits to
fit regulatory requirements compared to current models. This helps
advance the ultimate goal to predict product outcome over a variety
of processes in the manufacturing plant generation 4.0, to drastically
reduce downtime and allow for a remote workforce, and promote
optimal quality for real-time release products.
3.3. Decisions in real time – compression of time for
faster product development
The necessity of developing and implementing PAT into biopro­
cesses is instrumental to control and understand biological product
development in real-time. This will provide insight into processes
throughout the manufacturing lifecycle, and how various process
parameters can impact final vaccine antigen or biological product
yield and quality. Moreover, real-time monitoring of bioprocesses
can help apply critical parameters commonly used amongst various
product platforms to adapt to new bioprocesses and develop in-line
monitoring of new antigens. The importance of being able to quickly
transfer real-time process monitoring and knowledge to new anti­
gens or platforms is important for faster development and scale-up
for new projects that are important for industrial and consumer
needs. This can be exemplified in recent applications seen, due to the
need of rapid vaccine development in response to the COVID-19
pandemic. The consequences of the pandemic helped biopharma­
ceutical industries to understand the necessity of innovation in
process understanding and control, and its importance to competi­
tively create and understand new biological products and scale-up
for quick development and validation of both upstream and down­
stream processes to develop biological products in response to
medical need.
PAT methods developed to date could be further improved and
tested for robustness to begin implementation in real bioprocess
production suites to ensure stable and accurate models that will
satisfy regulatory requirements. To integrate these PAT tools
throughout various manufacturing processes, the focus of future
8
G. Gerzon, Y. Sheng and M. Kirkitadze
Journal of Pharmaceutical and Biomedical Analysis 207 (2021) 114379
development should also adapt current PAT technologies using
single-use bioreactor systems. Thus, by adapting current technolo­
gies and implementing them in adaptable single-use formats, real
production data can be further collected to provide more informa­
tion about full scale manufacturing and help build robust process
models. Moreover, the development of single use sensors and bior­
eactor systems will help the industry to comply with strict reg­
ulatory requirements involved in production and will help promote
agile development of new models for various bioproduct platforms
[83,84]. This ultimately could help implementation of these tech­
nologies into current and future manufacturing spaces, while redu­
cing lead time to develop PAT methods for automated real-time
release products. Therefore, the implementation of PAT tools in the
biopharmaceutical industry should be further developed to provide
better understanding of complex biological processes, to create
better streamlined and digitized manufacturing processes for future
biological products.
[11]
[12]
[13]
[14]
[15]
[16]
Declaration of Competing Interest
[17]
The authors declare that they have no known competing fi­
nancial interests or personal relationships that could have appeared
to influence the work reported in this paper.
[18]
Acknowledgements
[19]
Graphical abstract and all figures were created using
BioRender.com. Gabriella Gerzon has a paid subscription to
BioRender.com at time of publication. This research is kindly sup­
ported through grants funded from the Mitacs Accelerate IT17772,
and NSERC-CRD CRDPJ 538347-18 discovery fund.
[20]
[21]
[22]
Conflicts of Interest
Gabriella Gerzon and Yi Sheng are employees of York University,
and Marina Kirkitadze is an employee of Sanofi Pasteur. The authors
have no relevant affiliations or financial involvement with any or­
ganization or entity with a financial interest in or financial conflict
with the subject matter or materials discussed in the manuscript.
[23]
[24]
[25]
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