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\begin{document}

\title{Monitoring Product Quantity, Purity and Potency of Biopharmaceuticals in
Real-time by Predictive Chemometrics and Soft sensors}



\author[1]{Astrid Dürauer}%
\author[1]{Alois Jungbauer}%
\author[2]{Theresa Scharl}%
\affil[1]{Universitat fur Bodenkultur Department fur Biotechnologie}%
\affil[2]{Universitat fur Bodenkultur Wien Institut fur Statistik}%


\vspace{-1em}



  
  \date{May 28, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The biopharmaceutical industry is still running in batch mode, mostly
because it is a highly regulated industry sector. In the past, sensors
were not readily available and in-process control was mainly executed
off-line. The most important product parameters are quantity, purity and
potency besides adventitious agents and bioburden. There is increasing
economic pressure on time-to-market and also on the environmental
sustainability of biopharmaceutical manufacturing. New concepts for
manufacturing using disposable single-use technologies and integrated
bioprocessing will dominate the future of bioprocessing. In order to
ensure the quality of pharmaceuticals initiatives such as Process
Analytical Technologies, Quality by Design and Continuous Integrated
Manufacturing have been established. The vision must be that these
initiatives together with technology development pave the way for
process automation and autonomous bioprocessing without any human
intervention. Then a real-time release would be realized leading to a
highly predictive and robust biomanufacturing system. The steps toward
such automated and autonomous bioprocessing are reviewed in context of
monitoring and control. Starting from statistical treatment of single
and multiple sensors, establishing soft sensors with predictive
chemometrics and hybrid models. A scenario is described how to integrate
soft sensors and predictive chemometrics into modern process control.
This will be exemplified by selective downstream processing steps such
as chromatography and membrane filtration, the most common unit
operations for separation of biopharmaceuticals.%
\end{abstract}%



\sloppy


For submission to Biotechnology and Bioengineering 2023 Special Edition
Recovery of Bioproducts

\textbf{Monitoring Product Quantity, Purity and Potency of
Biopharmaceuticals in Real-time by Predictive Chemometrics and Soft
sensors}

Astrid D\selectlanguage{ngerman}ürauer\textsuperscript{1*}, Alois
Jungbauer\textsuperscript{1,2}, and Theresa Scharl\textsuperscript{3}

\textsuperscript{1}Institute of Bioprocessing Science and Engineering,
University Natural Resources and Life Sciences, Vienna Austria

\textsuperscript{2}Austrian Centre of Industrial Biotechnology, Vienna,
Austria

\textsuperscript{3}Institute of Statistics, University Natural Resources
and Life Sciences, Vienna Austria

*Corresponding author:

Institute of Bioprocessing Science and Engineering

Muthgasse 18

1190 Vienna

Austria

e.mail: astrid.duerauer@boku.ac.at



\section*{Abstract}

{\label{abstract}}

The biopharmaceutical industry is still running in batch mode, mostly
because it is a highly regulated industry sector. In the past, sensors
were not readily available and in-process control was mainly executed
off-line. The most important product parameters are quantity, purity and
potency besides adventitious agents and bioburden. There is increasing
economic pressure on time-to-market and also on the environmental
sustainability of biopharmaceutical manufacturing. New concepts for
manufacturing using disposable single-use technologies and integrated
bioprocessing will dominate the future of bioprocessing. In order to
ensure the quality of pharmaceuticals initiatives such as Process
Analytical Technologies, Quality by Design and Continuous Integrated
Manufacturing have been established. The vision must be that these
initiatives together with technology development pave the way for
process automation and autonomous bioprocessing without any human
intervention. Then a real-time release would be realized leading to a
highly predictive and robust biomanufacturing system. The steps toward
such automated and autonomous bioprocessing are reviewed in context of
monitoring and control. Starting from statistical treatment of single
and multiple sensors, establishing soft sensors with predictive
chemometrics and hybrid models. A scenario is described how to integrate
soft sensors and predictive chemometrics into modern process control.
This will be exemplified by selective downstream processing steps such
as chromatography and membrane filtration, the most common unit
operations for separation of biopharmaceuticals.

Keywords: Real-time release, machine learning; process control,
continuous integrated biomanufacturing;

Introduction

The biopharmaceutical industry is still running in batch mode, mostly
because it is a highly regulated industry sector (Kumar et al., 2020),
but there is increasing economic pressure on time-to-market and also on
the environmental sustainability of biopharmaceutical manufacturing. New
concepts for manufacturing using disposable single-use technologies are
being increasingly implemented and manufacturing is turning away from
dedicated manufacturing suites to ballroom facilities suited for
manufacturing multiple products (Müller et al., 2022). Integrated
continuous biomanufacturing will expedite and leverage these
developments because it is a way to reduce the footprint of a
manufacturing plant and reduce the time required to produce clinical
batches(Cataldo et al., 2020; Ding et al., 2022; Farid, 2019;
Konstantinov et al., 2015; Patil et al., 2018). The status-quo is still
batch processing with quality by testing. After each step samples are
taken, tested and then the subsequent step is executed. The PAT
initiative (''Guidance for Industry: PAT-A Framework for Innovative
Pharmaceutical Development, Manufacturing, and Quality Assurance,''
2004) has encouraged to implement advanced analytical technologies in
form of at-line, on-line and off-line analytics to ``design, develop and
operate processes consistently to ensure a predefined quality at the end
of the manufacturing process'' (Figure 1). It was obvious from the start
that chemometric approaches will allow us to get better process
understanding instead of interpreting the analytical results one by one
(Lopes et al., 2004; Wasalathanthri et al., 2020).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Approaches for Quality Assurance in Biopharmaceutical
Production (A) Quality by testing approach -- after process steps
samples are drawn to determine product quality and quantity in the
laboratory. As this is a retrospective approach, it does not allow any
process control and leads to failure prone production even though it is
costly and time consuming. (B) Quality by Design approach -- the process
is monitored in real-time and therefore product quality and quantity is
known at each stage of the process. If the process is performed within
the set specifications, the product will finally have the desired
quality. Real-time monitoring of the process allows process control and
therefore enhances product quality and reduces time and material
consumption for offline analysis.

Then the subsequent quality by design initiative's main aims (Q8-Q11
quality guidelines of the International Council for Harmonization of
Technical Requirements for Pharmaceuticals for Human Use (ICH) (The
International Council for Harmonization of Technical Requirements for
Pharmaceuticals for Human Use (ICH),) should ``ensure that all sources
of variability affecting a process are identified, explained, and
managed by appropriate measures. This enables the finished medicine to
consistently meet its predefined characteristics from the start''. This
considers the raw material variations and all variations of the process
parameters. Furthermore, recently a guidance has been released to
recommend continuous integrated biomanufacturing. This guidance also
recommends an in depth process understanding but does not recommend
process control. The guideline of real-time release has been developed
because ``under specific circumstances an appropriate combination of
process controls (critical process parameters) together with pre-defined
material attributes may provide greater assurance of product quality
than end product testing and the context as such be an integral part of
the control strategy principle''. Furthermore, ``RTRT may be applied
during the stages of manufacture of chemical and biological products
resulting in the elimination of all, or certain, specific tests in the
specifications of the finished active substance or finished medicinal
product''. The ultimate goals must be to monitor and control the process
in real-time so that we are able to efficiently control the variability
of raw materials, variability of the process parameters and in the
inherent variability of the biological system(Wasalathanthri et al.,
2020). If we want to control the process, we need real-time information
on quantity, purity and potency, - the critical quality attributes (CQA)
- because only then can we control the critical process parameters. This
does not differ from current approaches, but everything will be
real-time instead of retrospective. It is obvious that we do not have
dedicated sensors for quantity, purity and potency. We must use indirect
methods, but there is a reluctance in industry and academia to apply
soft sensors. A soft sensor (Luttmann et al., 2012; Mandenius et al.,
2015; Roch et al., 2016), also known as a virtual sensor, is not a real
existing sensor, but a simulation of interdependencies between
representative measured variables and a target variable such as product
concentration, aggregate content, dsDNA content, or a biological
activity of a biopharmaceutical for instance. Then the soft sensors can
be used to control a system(Ender et al., 2003). Especially in
downstream processing when executed in batch we have limited possibility
to really control the process. Therefore, a further incentive for
continuous integrated manufacturing beyond the economic gain and
improvement of the environmental footprint is the ability to fully
control and automate a process and to arrive at system runs
autonomously. Such a system would also contribute to a digital twin of a
manufacturing plant, but such a twin is not required for an automated
process. Process control is already well established in upstream
processing but not in downstream processing. Control of oxygen and pH
has been already done in the very early days of production of
biopharmaceuticals in bioreactors. In an advanced bioreactor control
systems air, O\textsubscript{2}, CO\textsubscript{2}, in the off gas and
dissolved oxygen and temperature are measured in-line together with NIR,
Raman, turbidity and capacitance to infer nutrient concentration and
waste products or on-line sampling is conducted measured it directly by
at-line enzymatic assays or HPLC (Zhao et al., 2015).

In advanced downstream processing, chromatographic workstations are
equipped with UV 280 flow cell, pH, conductivity probes (Carta et al.,
2010). Advanced sensors such as NIR, AT -FTIR, Raman multiangle light
scattering, and dynamic light scattering are not routinely used as
sensors for downstream process monitoring, although they would provide
very useful information that goes far beyond the standard information.
The signal from such sensors yields a spectrum that must be further
interpreted using a so-called deconvolution process, for example, to
assign a particular wavelength to a molecular property. These
chemometric approaches were developed simultaneously with the
development of the sensor hardware. Recently, it was found that the peak
profile itself could be used to correlate it with certain properties of
the process fluid or biomolecule, such as the glycosylation pattern of
antibodies, aggregate content, or truncated variants. In such a case,
the entire spectrum is used instead of a specific wavelength, and if the
spectral information is trained with off-line data, it can be considered
a soft sensor(Feidl et al., 2019; Kornecki et al., 2018).

Decades ago, bioprocessing originally started with fixed time/volume
processes and even processes are performed without a single sensor. The
next stage is threshold-based processing based on either a single sensor
or multiple sensors. More sophisticated methods for using sensor data
include chemometric methods, advanced statistical methods, and hybrid
models (Figure 2). Hybrid models and model predictive control are the
culmination of a monitoring and control system that ultimately enables
real-time release.

Fixed Time/Volume\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Threshold based\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Model based online monitoring\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Model predictive control\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 2: Typical approaches how to conduct a biopharmaceutical process
shown for a chromatographic step exemplarily. It can be either (A) Based
on Fixed time / volume for loading and elution; (B) Threshold based e.g.
stop loading or start collecting the eluate at a certain UV absorbance;
(C) Model based online monitoring with sensors implemented before and /
or after the column to predict product quality and quantity in feed and
/ or eluate; (D) Model predictive control. While (A) and (B) rely on
time and material offline analysis and do not allow in process control,
(C) and (D) do need offline analysis only for the model training and
enable real-time decision making and real-time release.

In this review, we will elaborate how we can arrive at a system for
monitoring and control to run a bioprocess, autonomously; we will mainly
on downstream processing. An ideal bioprocess from the point of view of
real-time monitoring, control and real-time release is outlined.

\textbf{Statistical modelling concepts}

In real-time monitoring we rely on establishing correlations between
sensor signals, process parameters and off-line measured quantity and
quality parameters. It is pivotal how a correlation is established
because it will impact highly correlated predictors (Figure 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 3: Concept how a soft sensor is generated by correlation of
offline measurement with on-line signals from sensors and process
parameters for Critical Quality Attributes QCAs.

Correlation analysis is used to measure the extent of the relationship
between variables (Varmuza et al., 2009). Typically, only the extent of
the linear relationship is considered using Pearson correlation.
However, there are also methods available to measure nonlinear
relationships, e.g., Spearman's rank correlation, which is a
nonparametric measure of rank correlation, reporting the statistical
relationship between the rankings of two variables (Lee et al., 2000).
The correlation structure in the data has a major impact on the
subsequent analysis of the data. It is important to note that
correlation does not imply a causal relationship between the variables,
i.e., one variable is affected by another.

Often, we want to model a critical quality attribute (CQA) of a
downstream process based on one or many input variables. Typically,
measuring the CQA is laborious, cost intensive and takes hours or even
days. On the other hand, the input variables are easy to measure online
using sensors like the standard UV detector, the standard pH and
conductivity probe or spectroscopic data. We distinguish between
situations where the relationship can be described by a fundamental
scientific law (first-principle model), by a relatively simple
mathematical equation (based on physical/chemical knowledge) and purely
data-driven models where we only assume that relationships exist
(Varmuza et al., 2009). Coming up with a fundamental scientific law in
downstream processing is a highly complex task which requires an immense
number of experiments, therefore data-driven approaches appear to be
more suitable (Rathore et al., 2022a). A compromise is sometimes found
in so-called grey-box models or hybrid models (Hong et al., 2018; Simon
et al., 2015a). Hybrid modelling approaches have already been
successfully used in downstream processing to model the flux evolution
and duration of ultrafiltration processes (Krippl et al., 2020; Krippl
et al., 2021) and capture chromatography (Narayanan et al., 2021)
(Figure 4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\par\null

Figure 4: Categories of models based on the underlying data and
assumptions exemplified for the mass balance of a chromatographic
separation. \textbf{Mechanistic models} are based on biophysical
relationship and first principle knowledge packed into a mathematical
model. These models are sometimes synonymously called ``white box
models'' as the have to be based on full process understanding. All
parameters are determined by independent experiments.
For\textbf{mechanistic models with fetch factor} individual parameters
are determined experimentally, they are data driven. \textbf{Data driven
models} are mathematical models solely based on statistical
relationships of data -mostly derived by online sensors and offline
analysis. They are not based on biophysical relations. Therefore, they
are also called ``black box models''. However, these models depict the
entity of the process space. \textbf{Hybrid models} combine mechanistic
and data driven models and can compensate the first ones for lack of
understanding of processes by data collected throughout the process.
They are of special interest for bioprocess modelling as in most cases
those processes are not fully understood and describable.

Multiple linear regression (MLR) is one of the most frequently used
methods in multivariate data analysis. It can easily be applied and
interpretation of the effect of the individual predictors on the
response is straightforward. However, this is only true if there are
many more observations than predictors in the model and there is no
correlation structure among the predictors. In such situations linear
regression can be used to model a clearly understood mechanistic
relationship between variables. On the other hand, if the predictors are
highly correlated, interpretation of the individual effects of those
predictors is no longer possible. Individual effects can be masked by
several correlated variables. Therefore, it is mandatory to check the
correlation structure between the input variables prior to setting up an
MLR model. In the case of multi-collinearity, no solution can be
calculated at all as the covariance matrix becomes singular (Varmuza et
al., 2009). In many practical situations MLR is still used even if the
predictors included in the model are moderately correlated. In such
situations the model is selected due to fitting the response well rather
than for interpretability of the individual effects of the input
variables. This is a very pragmatic and purely data-driven approach
where model selection is based solely on the performance measure.
Spurious correlations are used, there is no need for a causative
correlation, because we exploit the data structure, e.g., we control and
measure the pumps, pH, conductivity and pressure in a chromatography run
and search for a relationship with multiple sensors. Finally, we can
predict CQAs.

Model Selection, Training and Overfitting

For model selection the performance of different models on so-called
training data must be evaluated accordingly. Typical performance
measures are the (MSE), the root-mean-squared-error (RMSE) or the mean
relative deviation (MRD).

The MSE measures the squared differences between the observed (measured)
values and the ones predicted by the model and takes the mean over all
these differences. The MSE is often used during model building and
parameter optimization. However, it is not suitable for evaluation of
the prediction error as it is a squared quantity. To get an idea of the
size of the error the root of the MSE is used, the RMSE, which is in the
same unit as the CQA that was modelled. The MRD is the mean absolute
difference between the observed and the predicted value standardized by
the observed value. Often it is given in percentages.

It is not valid to calculate a performance measure on the data the model
was built on. Instead, an independent test set needs to be used. A split
of the available data into a training set (ca. 50\% of the objects), a
validation set (ca. 25\% of the objects) and a test set (ca 25\% of the
objects) is often recommended (James et al., 2021a; Varmuza et al.,
2009).

Here the training data is used for model building and the validation
data is used for parameter optimization. This split of the data is very
useful to avoid overfitting, i.e., the model performs very good on the
training data but does not generalize well to new data the model has not
seen during model building. The performance of different models can then
be evaluated on the independent test set in order to check how well it
generalizes. If only a limited number of observations are available for
model building the split must be adapted. Depending on the statistical
method used, an individual validation set might not be necessary as
parameter optimization is done on the training data via
cross-validation, e.g., when using PLS. In this case 75\% or even 80\%
of the data can be used for model training (Westad et al., 2015).

Experimental Design vs Design of Experiments -- how much experimental
data do we need?

A model will only perform well on the test set if the study was designed
properly. Still, as we are working with biological material the choice
of splitting the data into training -- validation -- and test set has a
major impact on the performance measure. Therefore, multiple splits
and/or cross validation as an efficient method to reuse the data are
recommended (Gareth et al., 2021; James et al., 2013, 2021a). When
designing a study, it is important to keep the goal of the study in
mind. The prediction model could be used in a biopharmaceutical
production process where the process conditions are fixed, and only
little deviations are expected. Alternatively, real-time monitoring
could also be used during process development where many different
process conditions need to be explored. In (Felf\selectlanguage{ngerman}ödi et al., 2020) it was
shown how the precision of the analytical method also impacts the data
needed to set up a prediction model. For model training, the number of
fractions for the off-line analysis together with the number of
chromatographic runs performed is crucial. The prediction model can only
be of high quality if the off-line analysis is very accurate (expressed
as coefficient of variation). Therefore, a compromise must be found
between the number of fractions analyzed, the number of chromatographic
runs and the performance of the prediction model expressed as RMSE
(Figure 5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure 5: Example to demonstrate how many fractions and how many runs
are required to train a statical model in chromatography. Modeling error
as a function of number of fractions per run (\emph{n}
\textsubscript{f}) for values of coefficient of variation (\emph{cv} )
representing accuracy of analytical method. (B) Pseudo-3D contour plot
with prediction error as a function of both\emph{n} \textsubscript{r}
and \emph{n} \textsubscript{f}. From (Felf\selectlanguage{ngerman}ödi et al., 2020)

Interpolation versus extrapolation

When setting up a prediction model the design space of critical process
parameters has to be considered.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Figure 6: The basic principles of the quality by design approach, where
more flexibility is obtained by a characterized space; adapted from
(Carta et al., 2010).

Ideally, the full design space is covered by the training, validation
and test data. In this ideal situation interpolation within the design
space will be very good (Figure 6). Rolinger et al. (Rolinger et al.,
2021) five runs in their study. Four runs covered the design space, and
the center point was used as an independent test set. It is not
surprising that the model performed well. Sauer et al and Walch et al.
(Sauer et al., 2019; Walch et al., 2019) on the other hand simulated the
situation of a manufacturing run under clearly defined process
conditions and used eight runs performed under identical process
conditions for model building. The performance was then evaluated on
purification runs performed under identical conditions but with
biological material from a new fermentation and under different process
conditions. It was shown that the model performed well on new biological
material (Sauer et al., 2019) and that model extrapolations are possible
but are limited to modifications in the column load and small changes in
buffer composition (Walch et al., 2019).

\textbf{Machine Leaning Methods}

Multiple statistical methods can be used to set up a predictive model.
In simple situations with linear relationships and only a few possible
predictors, even linear models can be sufficient. Depending on the aim
of the model, fast and simple methods like PLS can be used to get a
rough idea of the prediction performance in process development. If the
model should be used in real-time in a production process, more advanced
non-linear methods such as Random Forest or Neural Networks should be
considered. Here we give an overview of the advantages and disadvantages
of some selected machine learning methods (Table 1).

Table 1: Most prominent statistical methods used for prediction of
quality and quantity in bioprocess monitoring and control\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Method} & \textbf{Number of Variable} & \textbf{Variable
Selection necessary} & \textbf{Applications} &
\textbf{Implementation}\tabularnewline
\midrule
\endhead
MLR & Small, uncorrelated & yes & Simple, linear relationships & Fast
and simple\tabularnewline
Partial Least Squares Regression PLS & Large, typically correlated & no
& First idea of a prediction model, process development & Fast and
simple\tabularnewline
Structured Additive Regression STAR & moderate & yes & Production
process & Computationally very intensive\tabularnewline
Random Forests RF & large & no & Quick idea of a prediction model,
variable importance, process development & Rather fast, some parameter
tuning required\tabularnewline
Support Vector Machines Regression SVM & large & no & Production process
& A lot of parameter tuning required\tabularnewline
Neural Networks NN & moderate & yes & Production process & Rather
slow\tabularnewline
Deep Learning DL & huge & no & Production process & Computationally very
intensive\tabularnewline
Gaussian Process Regression GPR & moderate & no & Process development &
Rather fast\tabularnewline
\bottomrule
\end{longtable}

Partial Least Squares Regression - PLS

When the number of predictors is large or even larger than the number of
observations and the predictors are highly correlated, e.g., when using
spectroscopic data, Partial Least Squares (PLS) regression (Wold et al.,
2001) is frequently used (Brestich et al., 2018; Christler et al., 2021;
Felf\selectlanguage{ngerman}ödi et al., 2020; Rüdt et al., 2017; Walch et al., 2019). PLS can
easily be applied and has the big advantage that model training is very
fast. PLS transforms the original predictor variables into a set of
latent variables, which are also linear functions of the original
predictors. Linear combinations are determined such that a maximum
covariance between the scores (the values of the latent variables) and
the response is achieved. The number of components (latent variables) is
an optimization parameter. It is usually determined within the framework
of cross-validation (CV). These latent variables are then used as
predictors in a multiple linear regression model for the CQA. If you
want to optimize a PLS model different subsets of predictors should be
considered as inputs as selection of the number of latent variables
alone is usually not sufficient (Walch et al., 2019). Ultimately, PLS
models are a good starting point for modelling CQA when setting up
real-time monitoring or in process development based on spectroscopic
data. However, if you want to implement a real-time prediction model
more advanced methods should be considered.

Structured Additive Regression - STAR

The relationships between the response and the different input variables
are usually non-linear. PLS however, is a linear regression method.
Obviously, non-linearities are present in complex natural product
mixtures. These can be incorporated using Structured Additive Regression
(STAR) models (Fahrmeir et al., 2004), an extension of linear models.
Within this framework the machine learning technique boosting is often
used for variable selection (Hofner et al., 2015; Hofner et al., 2011).
This method works very well when the number of predictors is moderate as
optimization is computationally very intensive (Sauer et al., 2019).

Support Vector Machines Regression - SVM

A further method is Support Vector Machines (SVM) regression (e.g., (Li
et al., 2007)). SVMs can achieve an optimum network structure by a
compromise, balancing the quality of the approximation of the given data
and the complexity of the approximation function. SVMs are boundary
methods, they do not try to model a group of objects. Because the
boundary can be very complex attention should be paid to the problem of
overfitting. SVM was originally used for classification purposes, but
its principles can be extended easily to the task of nonlinear
regression by the introduction of an alternative loss. The basic idea of
SVM regression is to map the original data set into the mapped data set
in a high dimensional feature space via a nonlinear mapping function
(so-called kernel functions), and then perform a linear regression in
this feature space. Defining the linear regression function in this
feature space, nonlinear function regression in the original space
becomes a linear function regression in the feature space. SVMs have
been included in recent studies on continuous biomanufacturing (Nikita
et al., 2022). The selection of an appropriate kernel function is data
dependent and needs expert knowledge. Additionally, hyperparameter
tuning must be performed in order to avoid overfitting.

Gaussian Process Regression

Gaussian Process Regression (GPR) is a non-parametric, Bayesian machine
learning method that infers probability distributions rather than the
exact measured values (Rasmussen et al., 2021). These models are
particularly attractive as they can be used for small data sets, do not
only provide flexible models but also give a model-based estimate of the
prediction error. Recently, Gaussian Process Regression (GPR) has
received increasing attention in the field of biotechnology. di Sciascio
(di Sciascio et al., 2008)used GPR for the development of a biomass
concentration estimator, whereas Hutter-2021 (Hutter et al., 2020) used
GPR to efficiently learn from multiple product spanning process data.
However, in contrast to MLR, different software implementations will
give different error estimates due to different parameter definitions
and different numerical optimization (Erickson et al., 2018). A further
limitation is that GPR they do not scale well with increasing data size.

Tree-based methods

Alternative methods are tree-based models like regression trees or
Random Forests (Breiman, 2001). A regression tree is a hierarchical
model where observations are recursively split into binary partitions
based on their predictor values. Random Forests are ensemble learning
methods where the predictions are obtained by averaging over hundreds or
even thousands of trees built on bootstrap samples, i.e., samples taken
from the training data with replacement. Recently it was shown that
these methods perform very well in downstream processing (Nikita et al.,
2022). Random Forests are very popular due to the build-in
permutation-based variable importance measure. This approach was used to
find suitable inputs for Artificial Neural Networks (Melcher et al.,
2015). Model tuning is very important but setting up a prediction model
is straightforward and fast, especially in comparison to ANNs.

Artificial Neural Networks - ANN

Artificial Neural Networks (e.g., (James et al., 2021b; Lecun et al.,
1989)) are nonlinear statistical models that have been used in
bioprocess modelling since the early days (Glassey et al., 1997). They
can simulate highly nonlinear dynamic relationships of the process
without prior knowledge of the model structure. In a
single-layer-neural-network (often called shallow network, (Lee et al.,
2018)), different linear combinations of the input variables are built
and then a nonlinear function, e.g., the sigmoid function is applied.
Since these new variables are not directly observed, they are called
hidden units, and often they are arranged in a graphical representation
as a hidden layer. The new variables can be used in a linear or
nonlinear regression model resulting in an output variable. In classical
ANNs, more hidden layers can be used but with a great tendency to
overfitting. Further disadvantages of classical ANNs are related to
convergence speed, network topology and bad local minima. After 2010,
neural networks gained further attention mainly in the field of image
classification with the name Deep Learning where ``deep'' refers to the
number of hidden layers. The architecture of a Convolutional Neural
Network (CNN) was particularly successful (Lecun et al., 1989). They are
now very successful in comparison to other machine learning techniques
due to major computer hardware improvements, the use of graphical
processing units and much larger data sources. Nowadays, modern neural
networks use the ReLU activation function instead of the sigmoid
function and consist of multiple hidden layers. Deep learning was
recently used for real-time quality prediction and process control
(Nikita et al., 2022). They applied deep neural networks and compared it
to SVM, decision trees regression and random forests on time series data
of UV, conductivity and pH probes of 84 batches. In this study, random
forests and decision trees outperformed the deep neural networks maybe
due to the relatively small number of predictors. Rolinger-2021
(Rolinger et al., 2021) compared PLS to CNNs for their ability to
quantify the mAb concentration in the column effluent based on UV and
Raman spectroscopy. In this study there was also no need to use deep
learning as a UV-based PLS model was already sufficiently precise. Deep
learning was developed for large or big data in terms of both
experiments and input variables.

Variable selection and feature importance

For classical ANNs, variable selection had to be performed prior to
setting up the model due to slow convergence. Random forests were
successfully used to come up with an optimal set of inputs for the ANN
(Melcher et al., 2015) and extensive variable selection was performed
prior to establishing the STAR and PLS models (Sauer et al., 2019; Walch
et al., 2019). For this purpose, prior knowledge such as amide bands and
fingerprint regions of the spectral data was used to select informative
predictors. Additionally, highly correlated wavelengths were removed by
reducing the resolution of the spectra. This required a considerable
amount of domain knowledge and engineering skills. In deep learning,
these preparatory steps are no longer required as the method is now
capable of dealing with a very large number of inputs and weights
thereof are computed automatically (Lecun et al., 1989). Of course,
using a very large number of inputs for a prediction model comes at the
expense of interpretability of the model.

Explainable Machine Learning

In the machine learning community, a lot of research is currently going
on in trying to explain what is going on inside prediction models
(Roscher et al., 2020; Zhong et al., 2022). Statistical, purely
data-based models are often called black box models in the field of
hybrid modelling (Simon et al., 2015a; Simon et al., 2015b). Of course,
they are no black boxes. The impact of individual input parameters on
latent variables in PLS or the weights in ANNs can be extracted and
interpreted. If the software including the underlying code to generate a
certain model is freely available such models can be considered as
white-box models as all information is available. Black-box models only
occur if the code is not shared as usually the case in commercial
software (Winter et al., 2021). However, especially if the machine
learning models are based on high-dimensional data, the interpretation
of the model parameters can be complex. This process is more complex
than understanding first-principal models with a clear mechanistic
relationship between the input and output variables. This makes the
efforts in the field of explainable machine learning even more important
and promising for the future of predictive chemometrics.

\section*{Real-time monitoring}

{\label{real-time-monitoring}}

Real-time monitoring of critical quality attributes (CQA) or critical
process parameters (CPP) using real-time monitoring by means of soft
sensors (Mandenius et al., 2015), (Roch et al., 2016) has enormous
advantages over the determination of those off-line after the unit
operation. It is much faster and can assess attributes, where no
dedicated sensor or analytical method is available. It also provides
information in real-time if the process is within the operating range.
These soft sensors are also called prediction models (e.g., (Sauer et
al., 2019)). Often, we also want to monitor other attributes, which are
beyond the CQA, because they are relevant for process economics, but not
relevant in respect to patient safety. In a conventional process,
particularly in DSP the samples are taken from a pool or intermediates
and then subjected to further off-line analysis. Interventions in the
upstream process do not require a fast reaction because cells grow
slowly and a response after up to an hour is sufficient. Even an at-line
measurement using automatic sampling devices with a 30-min lag time is
considered as a ``real-time'' measurement because the return of the
measurement is fast enough to control the process (Zhao et al., 2015).
Whereas in a lot of situations in downstream processes the decision must
be made within seconds, e.g., when a stream is diverted from waste to
product collection. Then the decision must be made within seconds and
time-consuming analytics become useless.

\textbf{Multiple-sensor predictive chemometrics approach}

If only a single quality attribute is monitored such soft sensors are
often based on single sensors, e.g., the monitoring of the product
quantity by UV spectroscopy (Rolinger et al., 2021) or attenuated total
reflection Fourier-transform infrared (ATR\selectlanguage{english}-FTIR) spectroscopy (Rudt et
al., 2019). However, multiple sensors must be combined even if only the
process signals are used that a chromatographic workstation is typically
equipped with, e.g., UV, conductivity and pH probe (Nikita et al.,
2022). If several CQAs need to be modelled in real-time, it is
indispensable to use a multiple sensor approach in order to capture the
different properties of these CQAs (Sauer et al., 2019). Such an
approach can be regarded as data fusion (Borras et al., 2015; Liggins et
al., 2017; Rolinger et al., 2021). A multiple sensor approach has many
advantages, e.g., in the case of sensor saturation where the combination
of different input variables leads to redundancies that can compensate
for the information loss.



\textbf{Example for predictive chemometrics approach}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

Figure 7: Workflow for model-based real-time monitoring of a
chromatographic step adapted from Sauer et a. 2018. In addition to the
sensors for pH, conductivity, UV absorbance and pressure which a
chromatography workstation is typically equipped with, four online
sensors have been implemented in the flow after the column. Online data
were obtained for 8 identical runs of a chromatographic purification
step. The eluates were aliquoted and collected as 15 fractions and
offline analysis was carried out to determine the desired quality
attributes, product quantity and impurity content. Part of this data set
was then used to establish mathematical models for each quality
attribute by relating the offline with the online data. The models were
selected via the lowest root mean squared error (RMSE) and then
evaluated via their predictability for independent test data sets which
have not been used for the model training before. Implemented in the
stirring software of the chromatographic workstation, the established
models give information on all quality attribute they have been trained
on in real-time (\textless{}1sec) and enable real-time decision making
for e.g. pooling of the eluate for the next step.

Sauer et.al. equipped a chromatographic workstation with multiple
sensors (Sauer et al., 2019). Besides standard detectors (UV, pH and
conductivity), multi-angle light scattering, refractive index,
attenuated total reflection Fourier-transform infrared and fluorescence
spectroscopy were included. The real-time monitoring system was used in
a cation exchange capture step of fibroblast growth factor 2 expressed
in \emph{E. coli} . Eight training runs were performed where 15
fractions of the eluate were analyzed to get information about the
product quantity, host cell protein and double-stranded DNA impurities
as well as endotoxins and Monomer/aggregates (Figure 7). Prediction
models were generated for each individual response variable using
cross-validation. The same system was used for an antibody capture
process (Walch et al., 2019). The input data of the various devices was
time-aligned considering the different void volumes and time resolution.
Individual preprocessing methods were applied to the individual sensors
together with a variable selection procedure specific for the sensor.
Finally, the online signals were averaged over the time intervals of the
collected fractions that were analyzed off-line. A multiple sensor
approach is only feasible if the chromatographic workstation is equipped
with a central database (Oliveira, 2019; Steinwandter et al., 2019). For
these multiple sensors the software solution XAMIris (Evon, Austria) was
used for the recording of various signals, starting of the
chromatographic runs, data export, time-alignment as well as the
implementation of soft sensors for real-time monitoring of several CQAs
(Christler et al., 2021; Sauer et al., 2019; Walch et al., 2019).

Impact of different sensors

In same multi sensor setup UV/Vis spectroscopy was used as it mainly
measures the primary structure, such as the content of aromatic amino
acids (UV280nm), polypeptide backbone (UV214nm) or DNA content (UV260nm)
(Christler et al., 2021; Sauer et al., 2019; Walch et al., 2019). The
refractive index (RI) was included as it was previously used to quantify
protein (Zhao et al., 2011). ATR-FTIR can distinguish between HCP and
target protein (Capito et al., 2013). Intrinsic fluorescence of the
aromatic amino acids can used to measure the tertiary structure of
proteins and to detect structural changes induced by polarity
(Ghisaidoobe et al., 2014; Rathore et al., 2009). Light scattering
methods (Minton, 2016) are used to determine their quaternary structure,
for example, protein aggregation. Fluorescence spectroscopy, as well as
light scattering techniques, have been used for at-line determination of
quality attributes (Patel et al., 2018; Rathore et al., 2009; Yu et al.,
2013).

Multiple CQA monitoring- single sensors vs multiple sensors

If multiple CQAs are monitored using multiple sensors decent model
selection is required. Extensive investigation of the impact of the
individual sensors on the prediction performance was done (Sauer et al.,
2019; Walch et al., 2019). Typically, a prediction model is as simple as
possible but as complex as necessary. Therefore, models based solely on
one single sensor were compared to models with two, three up to all
available sensors. The best model was selected based solely on the
prediction error, e.g., the root mean squared error (RMSE) of prediction
on an independent test set, a purely data-driven approach. However, if
the performance of an extensive model including fluorescence and/or
ATR-FTIR data only slightly outperformed a basic model, it was still
recommended to use the basic model (Sauer et al., 2019). For all
investigated CQAs, the finally selected models contained more than one
single sensor.

Robustness of the monitoring system - sensor fouling / sensor shift

For the set-up of a real-time monitoring system, it is recommended to
implement multiple prediction models as sensor fault, shift or fouling
can easily distort the input data and make the prediction models
useless. The model used impacts the pooling decision (Walch et al.,
2019). Even though the performance of the more complex models was
superior on the independent test set, the simpler models were smoother
and more robust. An optimal monitoring system should always be based on
several prediction models based on different sensor combinations in
order to react on sensor fouling or sensor shifts. If one sensor fails,
the real-time monitoring can easily be based on an alternative model
where the specific sensor is not used. The technology transfer of the
monitoring system with multiple sensor (Sauer et al., 2019; Walch et
al., 2019) revealed that only a subset of possible prediction models
could be used for real-time prediction at the different sites as the
fluorescence device was not robust enough (Christler et al., 2021).
Simpler models without fluorescence could still be used at the different
sites. However, due to the very specific properties of the individual
sensors, the performance of the prediction models could be considerably
improved by new model training.

\section*{Towards real-time monitoring, control and real-time
release}

{\label{towards-real-time-monitoring-control-and-real-time-release}}

Real-time monitoring provides information if the process runs within the
specified operating range. This is useful information and can be linked
to release criteria, but it could be further exploited for process
control. Then raw material variations, fluctuations in process
parameters could be controlled and a much more robust process can be
obtained. This is then also the step towards automation, because human
intervention could be minimized. In bioreactors real-time monitoring is
used to control the process to deliver optimal nutrients to reach a high
titer or product quality. A typical example is the addition to manganese
ions to trigger the glycosylation of antibodies (Tharmalingam et al.,
2015) addition of amino acids or control the level of glucose at a
minimum level. Bioreactor control is much more advanced compared to
downstream processing. Besides pooling there are not a lot of
opportunities to control batch chromatography. An interesting approach
is to control the gradient shape in batch chromatography to optimize
resolution and binding capacity (Sellberg et al., 2017). The authors
named it salt trajectory. An optimizer adapts the shape of the gradient
according to the retention behavior of product and impurities and the
salt gradient is modulated. The wider application of such a controls
system would require a fast-real-time monitoring of the effluent stream
of the chromatography column, because they still used off-line
measurements of the column effluent. Such a system maximizes
productivity while resolution is not affected. Usually increasing
productivity will be obtained at the expense of reduced resolution.
Implementation of soft sensors such as the (Walch et al., 2019) into a
column chromatography with a gradient optimizer would lead to an optimal
system where product quality and quantity is monitored, and the
information is further utilized to control the system and operate under
high resolution.

Another dilemma is the optimization of dynamic binding capacity. When
chromatography is operated with constant velocity an optimum of
productivity at a certain residence time is achieved. At this residence
time the column utilization is rather low, but by increasing the column
utilization the productivity is reduced. At high velocity/low residence
time the breakthrough curve is shallow and at low velocity steep,
therefore the dynamic binding capacity and column utilization is higher
at low velocity (Carta et al., 2010; Eslami et al., 2022a; Eslami et
al., 2022b). One solution is countercurrent chromatography, which allows
maximizing column utilization and productivity, but on expense
equipment/instrument complexity(Heeter et al., 1996). Countercurrent
loading or periodic counter current chromatography is a way to render
batch chromatography into continuous chromatography and in addition
column utilization is improved. In its simplest form a column is
overloaded, and the breakthrough loaded on the second column.
Concurrently the third column is washed, eluted, and regenerated. This
concept has been extensively used for purification of recombinant
antibodies with protein A affinity chromatography(Badr et al., 2021;
Davis et al., 2021; Farid et al., 2015; Gerstweiler et al., 2021;
Gomis-Fons et al., 2020; Rathore et al., 2022b; Scheffel et al., 2022;
Sun et al., 2022; Vetter et al., 2021; Vogg et al., 2018). In order to
control the overloading of the first column a control algorithm using
absorbance at 280 nm has been implemented. First a breakthrough of the
impurities is observed and when the column is close saturation a second
breakthrough is caused by the product. This signal can be used as a
control algorithm for switching columns (Chmielowski et al., 2017;
Gerstweiler et al., 2022; Godawat et al., 2012). This will only work
when rather pure feedstock such as an antibody produced in chemical
defined media is purified. Crude feedstocks would fully saturate the UV
sensor and the difference between the signals of the impurities and
product might be too low in order to get useful information to control
the loading. In this case a soft sensor such as the Walch et. al.
approach (Walch et al., 2019) will be a general solution to control
loading in PCC. Near infrared spectroscopy (NIRS) has been also placed
at the column entrance and exit and calibrated with a chemometrics
approach (Thakur et al., 2020). It is not yet clear if this sensor alone
would work for crude solutions or if only the concentration range is
wider compared to a UV-cell. If only a wider concentration range must be
covered flow VP is a good solution.

Another way to solve the problem of productivity and column utilization
is to change the flow rate during loading. A dual flowrate during
loading has been proposed by Lacki and coworkers (Ghose et al., 2004a;
Ghose et al., 2004b) and then extend to either multiple steps
(Ramakrishna et al., 2022) or a linear flow-rate gradient (Chen et al.,
2021)or a model predicted gradient (Eslami et al., 2022a; Eslami et al.,
2022b; Gomis-Fons et al., 2021; Sellberg et al., 2018)). The loading is
started with a high velocity and then subsequently reduced. In order to
establish an optimized system an optimizer and controller are necessary.
Such a model predictive control results in high productivity and high
column utilization but with less complex equipment. In order to run such
a controlled process a soft sensor is required at the column in-let and
outlet when crude feedstocks are purified and UV280 is not sufficient to
monitor product concentration (Eslami et al., 2022a; Eslami et al.,
2022b; Sellberg et al., 2018). Such a controller has been implemented in
the XAMIris (Evon, Austria) (Figure 8).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

Figure 8: Principle of a flow rate gradient for optimization of
productivity and resin utilization. (A) set-up of sensors in the
chromatographic workstation (B) change of flow rate vs time or volume
loaded onto the column and breakthrough curves at low and high residence
time blue line high residence time, redline low residence time, (C)
resin utilization productivity and yield of an unoptimized process and a
process with model predictive control, data from (Eslami et al., 2022a;
Eslami et al., 2022b).

The system can be also used for continuous chromatography. Then two
columns are operated in tandem, while one column is loaded and the
second column is washed, eluted and regenerated. If the wash, elution,
and regeneration cycle is longer than loading then four columns must be
installed. The selection of column number follows the same rules as for
counter current chromatography. Furthermore, the velocity gradient
approach can be extended to wash, elution and regeneration and thereby
even higher productivity could be obtained. During loading in counter
current chromatography two columns are interconnected and therefore the
maximal system pressure and flowrate is constrained by this situation.
This is not the case when columns are operated in parallel with velocity
gradients during loading.

How fully integrated continuous biomanufacturing may look like was been
previously proposed by the Morbidelli group (Feidl et al., 2020). They
propose a process wide control of the integrated process. The unit
operations are individually controlled and process wide by a supervisory
system. The challenge is to integrate all unit operations and to
maintain a constant mass flow and to avoid propagation of process errors
or deviations. Therefore, always surge tanks between unit operations are
placed to dampen fluctuations in flow and pressure and also might be
used as intermedia storage when a unit operation stops working.
Fluctuations flow rate and concentrations are inherent when a
conventional batch manufacturing process is rendered into a continuous.
Then preferably perfusion culture is connected to a counter current
chromatography. Constants harvest conditions in perfusion culture could
be only expected if be fully growth arrested. This is not the case and
therefore the excess of cells must be removed from the bioreactor by a
so-called bleeding leading to a change in harvest flow rate. The
concentration is simultaneously changing. Therefore without proper
control these fluctuations would propagate through the entire process
and eventually amplify and lead to a derailed process. Model predictive
control ensures either constant harvest flow or concentration
(Pappenreiter et al., 2022).

Conventional feedback control is not sufficient, because it is designed
for rapid changes in process conditions, but in perfusion cell culture
conditions change slowly (Zhao et al., 2015) . Then harvest is sent to a
surge thank and then captured by a counter current chromatography. The
loading flow rate is constant but elution after the columns is saturated
leads to a periodically change of mass flow and in order to warrant a
constant contact time in the virus inactivation the elution fractions
are pooled and then very often subjected to a batch virus inactivation,
although many continuous virus inactivation methods have been
established. By following such a process design the benefits of
continuous biomanufacturing are reduced in part automation and on-line
control and real-time release become very difficult to implement. Surge
tanks have a deteriorating effect RTD and wide RTD makes a process slow
and batch definition difficult(Lali et al., 2022). In an ideal process
the mass flow is never interrupted, and surge tanks are avoided. The
perfusion bioreactor produces a constant harvest flow and can be further
processed by flocculation and precipitation and polished with flow
through chromatography methods. After concentration adjustment by e.g.,
single pass tangential flow filtration a continuous virus filtration can
be added as recently suggested (Figure 9).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

Figure 9: Concept of the ideal process based on precipitation, tow stage
filtration and flow through chromatography, data from (Burgstaller et
al., 2019; Li et al., 2019) drawing courtesy G. Recanati.

By combing existing elements of continuous biomanufacturing with
real-time monitoring concepts it is possible develop a fully automated
system (Helgers et al., 2022; Vetter et al., 2021) which runs under
optimal conditions without large safety margins it will run
autonomously, and a real-time release concept could be realized.

\section*{Conclusion}

{\label{conclusion}}

Although the biopharma industry is highly regulated, there are many
opportunities to develop better and more robust processes. This is also
encouraged by health authorities with the various guidelines that have
been adopted in the past, such as PAT, QbD, Continuous Manufacturing and
Real-time Release. All the elements for technical implementation are in
place and there is acceptance by the authorities. There is a perception
that real-time monitoring is difficult to implement and requires a lot
of change. It is possible to integrate real-time monitoring gradually
and it should be considered from a soft sensor perspective. This concept
has been successfully implemented in other industries. It required
relatively simple model training and the use of established statistical
tools such as multivariate statistics or neural networks. Such
approaches have been practiced for decades and are known as chemometrics
when multiple analytical methods were required to measure a particular
analytical attribute. A soft sensor could also be described with
predictive chemometrics. A fully controlled and automated process would
also help reduce drug shortages, improve process economics, and reduce
the environmental footprint.

\section*{Acknowledgments}

{\label{acknowledgments}}

The COMET center: acib: Next Generation Bioproduction is funded by BMK,
BMDW, SFG, Standortagentur Tirol, Government of Lower Austria und Vienna
Business Agency in the framework of COMET - Competence Centers for
Excellent Technologies. The COMET-Funding Program is managed by the
Austrian Research Promotion Agency FFG.

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