# Applications of Machine Learning in Biopharmaceutical Process Development
and Manufacturing: Current Trends, Challenges, and Opportunities

Thanh Tung Khuat<EMAIL_ADDRESS>Robert Bassett
<EMAIL_ADDRESS>Ellen Otte<EMAIL_ADDRESS>Alistair Grevis-
James<EMAIL_ADDRESS>Bogdan Gabrys
<EMAIL_ADDRESS>Complex Adaptive Systems Laboratory, The Data Science
Institute, University of Technology Sydney, NSW 2007, Australia  CSL
Innovation, Melbourne, VIC 3000, Australia

###### Abstract

While machine learning (ML) has made significant contributions to the
biopharmaceutical field, its applications are still in the early stages in
terms of providing direct support for quality-by-design based development and
manufacturing of biopharmaceuticals, hindering the enormous potential for
bioprocesses automation from their development to manufacturing. However, the
adoption of ML-based models instead of conventional multivariate data analysis
methods is significantly increasing due to the accumulation of large-scale
production data. This trend is primarily driven by the real-time monitoring of
process variables and quality attributes of biopharmaceutical products through
the implementation of advanced process analytical technologies. Given the
complexity and multidimensionality of a bioproduct design, bioprocess
development, and product manufacturing data, ML-based approaches are
increasingly being employed to achieve accurate, flexible, and high-performing
predictive models to address the problems of analytics, monitoring, and
control within the biopharma field. This paper aims to provide a comprehensive
review of the current applications of ML solutions in a bioproduct design,
monitoring, control, and optimisation of upstream, downstream, and product
formulation processes. Finally, this paper thoroughly discusses the main
challenges related to the bioprocesses themselves, process data, and the use
of machine learning models in biopharmaceutical process development and
manufacturing. Moreover, it offers further insights into the adoption of
innovative machine learning methods and novel trends in the development of new
digital biopharma solutions.

###### keywords:

biopharmaceuticals , machine learning , upstream , downstream , bioprocesses ,
digital twin , soft sensors

††journal: Journal of …

##  1 Introduction

Over the past few years, biopharmaceutical products, also known as biologics,
such as monoclonal antibodies (mAbs) and therapeutic proteins have become the
best-selling drugs in the pharmaceutical market [Lu et al., 2020], and in
2021, seven of the top ten best-selling drugs worldwide were biologics
[Urquhart, 2022] (see Fig. 1). According to the definition given by US Food &
Drug Administration (2018) [FDA], biologics include vaccines, monoclonal
antibodies, blood and blood components, allergenics, somatic cells, tissues,
gene therapy, and therapeutic proteins. Unlike small molecule and chemically
synthesized drugs, biologics are complicated, large mixtures of sugars,
proteins or nucleic acids, and other substances which are not easy to identify
and exactly characterise [Peters & Hennessey, 2020]. Most of the biologics are
produced by biotechnology in a living system such as microorganisms, plant,
animal, or human cells, in which mammalian cells like the Chinese hamster
ovary (CHO) cells, mouse myeloma (NS0), baby hamster kidney (BHK), human
embryo kidney (HEK-293) and human retinal cells are typically used [Wurm,
2004]. In recent years, the market for biologics has explosively grown with a
percentage of new biological products approved by FDA every year since 2014
for treating various human diseases including cancers, autoimmune, metabolic
and infectious diseases, always exceeding 20% of the total number of new
approved drugs [De la Torre & Albericio, 2022] (see Fig. 2 for more details).

Figure 1: Top ten drugs by sales globally in 2021 (produced from the data in
Urquhart [2022]). The biological products are highlighted by red color. Figure
2: New drugs have been approved by FDA over the last 25 years and the
percentage of biologics over total drug approved each year (adapted from the
data in De la Torre & Albericio [2022]).

Among biological products, mAbs emerge as the leading product in the rapidly
growing market of high-valued biologics [Papathanasiou et al., 2019]. As
reported by Grand View Research [2022], the global therapeutic monoclonal
antibody market was valued at about US$185.5 billion in 2021 and is expected
to achieve a compound annual growth rate of 11.30% from 2022 to 2030 to reach
a revenue of around $494 billion by the end of 2030. To achieve this target,
it is necessary to employ more cost effective solutions and smart
manufacturing while simultaneously not sacrificing the process robustness and
product quality when aiming for higher process productivity, more affordable
end-products and shorter production times [Xenopoulos, 2015].

The manufacturing process of mAbs and therapeutic proteins is complex with
many interconnected elements. It begins with the culture of genetically
modified cells in small- and medium-sized bioreactors and expanding them to
the large production bioreactors within upstream processes. After that,
various downstream purification steps are performed to separate the product of
interest from cell-derived and process-related impurities [Papathanasiou &
Kontoravdi, 2020]. Before the Quality by Design (QbD) has been introduced by
FDA as a standard technique [Rathore & Winkle, 2009], the assurance of final
product quality in the conventional manufacturing of mAbs and therapeutic
proteins has usually relied on costly and time-consuming quality by test
methods [Zhang & Mao, 2017]. In the quality by test approaches, the root
causes of failure and/or significant levels of uncertainty within bioprocesses
are often not well understood. Hence, it is frequently time-consuming, costly,
and potentially adversely affecting product safety when the manufacturing
procedures have to be repeated until the root causes of failure are detected
and resolved. In contrast to the quality by test, QbD methods are based on the
holistic understanding of products and how materials and critical process
parameters (CPPs) influence the critical quality attributes (CQAs) and yield
of the final products via different unit operations [Zhang & Mao, 2017].

In the QbD framework, the product specifications, also known as the quality
target product profiles (QTPPs), are placed at the heart of the design
strategies of experiments to guarantee the quality and consistency of the
final products [Papathanasiou & Kontoravdi, 2020]. This leads to the crucial
roles of in-process analytical measurements and process analytical technology
(PAT). Moreover, it is desired to understand the relationship between process
conditions and product quality. These characteristics provide opportunities
for the use of computational models as promising alternatives to experimental
approaches to optimise and control the process behaviors more systematically
[Smiatek et al., 2020], to extract useful knowledge from acquired experimental
data and to build predictive models for in-silico experiments. As presented in
Papathanasiou & Kontoravdi [2020], there are three main challenging areas in
the implementation of QbD principles in biomanufacturing: (i) product
heterogeneity, (ii) various limitations of measurements for process
monitoring, and (iii) the lack of online process control strategies. These
challenges can be mitigated by taking advantage of knowledge-driven and data-
driven approaches for (real-time) bioprocess monitoring and control based on
the collected bioprocess data.

By monitoring the relationships among variables and extracting valuable
knowledge from bioprocess data, machine learning (ML) models can acquire novel
insights into the interdependence between CPPs and CQAs in biopharmaceutical
process development and manufacturing. These insights have the potential to
assist in the development of more effective process control strategies. Apart
from real-time monitoring and control of upstream and downstream bioprocesses,
supervised ML algorithms can be applied to discovery and design of
biopharmaceuticals such as mAbs selection, media screening, design of feeding
strategies, and selection of substrate for purification. ML algorithms can
also be used in product formulation and stability steps. As a result, the
deployment of ML methods for bioprocess development and manufacturing can
contribute to reducing the time and production cost of bioproducts and, in
turn, lowering the price of cell and protein therapy [Puranik et al., 2022,
Rathore & Singh, 2015]. Over the past few years, there has been observed a
significant increase in the use of multivariate data analysis (MVDA), which is
often considered to be a sub-field of ML focusing on a group of statistical
approaches, as a PAT tool for cell cultivation [Banner et al., 2021, Rathore &
Singh, 2015] to explore the available data from pilot or manufacturing plants
aiming to find the correlation between process variables and product quality
attributes, enhance the bioprocess comprehension, and online monitoring and
control of unit operations. As summarised in Mercier et al. [2014], popular
applications of MVDA consist of the analysis of spectroscopic measurements and
data profiles of unit operations like cell culture and chromatography,
quantitative evaluation of process comparability, root cause analysis of
failures, and raw material selection. MVDA methods have been recommended by
regulators as a critical tool for QbD and PAT frameworks to improve
understanding of bioprocesses and to increase the levels of process monitoring
and control and, in turn,increasing the probability of achieving the desired
final product quality [Mercier et al., 2014].

As shown in Banner et al. [2021], it should not be surprising if the use of ML
algorithms within biopharmaceuticals would grow explosively in coming years to
take benefits from explored insights and the power of predictive models to
optimise, monitor, and control bioprocess development and manufacturing
operations. The main trends can focus on the use of advanced data analytics,
the integration of soft sensors and PAT approaches to formulate advanced
control strategies [Mandenius & Gustavsson, 2014], and the development of
hybrid data-driven and knowledge-driven models associated with integrated
designs of production elements such as process, product, and cells [Badr &
Sugiyama, 2020, Walsh et al., 2022]. Moreover, the biopharma field could
witness a rapid shift to digital transformation with the increased adoption of
queryable and structured centralised repositories such as a data lake or data
warehouse for recorded data [Steinwandter et al., 2019]. This digitisation
would allow the application of ML models to all data gathered from multiple
sites across various scales and unit operations of biomanufacturing plants
thanks to data consolidation and accessibility [Banner et al., 2021]. This
creates an important premise for the fully automated data-driven
biopharmaceutical manufacturing facilities equipped with knowledge of
physicochemical properties of substances, bioprocesses, and products in near
future.

This paper aims to provide a comprehensive review of the existing applications
of ML to various stages of process development and manufacturing for mAbs and
therapeutic proteins, identify challenges regarding bioprocesses themselves
and process data, then proposing potential research directions in this area.
We do not aim to provide statistics of the numbers of algorithms used for
specific problem domains year over year. The interested readers can found more
information for this theme in recent reviews [Banner et al., 2021, Guerra &
Glassey, 2018, Mowbray et al., 2021, Pham et al., 2022]. Instead, existing
trends regarding how ML algorithms are used in the process development and
manufacturing of biological products across different unit operations are
reviewed in detail. This paper is an expansion of the recent review paper
[Puranik et al., 2022], which covers only typical applications. On the
contrary, this paper presents a much larger number of applications of ML to
biopharmaceuticals and identifies numerous potential directions for the
applications of advanced ML methods to address existing challenges within
bioprocess data aiming to the development of digital twin systems for
biopharma 4.0.

The rest of this paper is organized as follows: Section 2 summarises some
background of a typical biopharmaceutical process for manufacturing mAbs and
therapeutic proteins. Next, Section 3 presents challenges regarding
bioprocesses and process data characteristics, which can cause difficulties in
building ML models. After that, Section 4 reviews popular applications of ML
in early stages of bioproduct development, while Section 5 covers different
applications of ML models in upstream processing processes. Section 6 is
dedicated to presenting typical applications of ML in the downstream
purification process. The use of ML algorithms to deal with problems in
product formulation and stability is presented in Section 7. Section 8
collects valuable open data sources to support ML research in
biopharmaceuticals, while Section 9 discusses potential research directions of
ML in the construction and development of Biopharma 4.0. Concluding remarks of
this paper are described in Section 10.

##  2 A typical biopharmaceutical process for production of monoclonal
antibodies and therapeutic proteins

The standard manufacturing and purification process of mAbs and therapeutic
proteins includes two main stages: upstream (USP) and downstream processing
(DSP) in combination with the final product formulation and stability phase.
The upstream process comprises the cultivation of cells in a series of
bioreactors and the production of the interested product, while the downstream
process includes a sequence of separation/purification steps to capture the
product of interest and eliminate various process, host cell, and product
related impurities with minimal yield loss [Shukla & Thommes, 2010, Hong et
al., 2018, Papathanasiou et al., 2019, Badr & Sugiyama, 2020, Papathanasiou &
Kontoravdi, 2020]. Currently, these operations are performed in fed-batch
cultivation systems and batch separation processes [Xenopoulos, 2015], and
sometimes continuous cultivation systems also use these procedures. Fig. 3
represents a flow diagram for a typical manufacturing process platform of mAbs
and recombinant proteins.

Figure 3: Production process of mAbs and therapeutic proteins (adapted from
[Papathanasiou et al., 2019, Smiatek et al., 2020, Shukla & Thommes, 2010,
Hong et al., 2018, Badr & Sugiyama, 2020])

The upstream process begins with a vial thaw followed by the inoculum growth
of cells from a cell bank and suitable media, which is performed in shake
flasks or spinner flasks. After that, the upstream process formally starts
with the cell culture in seed bioreactors with the progressive increase in
size and/or volume. The cells are then transferred to the production
bioreactor for protein expression in the medium [Shukla & Thommes, 2010,
Papathanasiou et al., 2019]. Finally, a typical harvest procedure is conducted
to eliminate cells and cell debris by centrifugation followed by depth and
membrane filtration prior to be transferred to purification steps in the
downstream [Shukla & Kandula, 2008]. Fed-batch approach is usually used for
the upstream process, where small volumes of feed nutrients are added to the
bioreactors during the cell growth stages. Apart from the monitoring of
nutrient and metabolite concentration, other factors of bioreactors in the
upstream processing are also frequently controlled during the cell cultivation
including dissolved oxygen (pO2), pH, temperature and mass transfer of oxygen
and CO2 [Shukla & Thommes, 2010].

The downstream process for the manufacturing of mAbs and therapeutic proteins
starts with Protein A chromatographic capture. This type of affinity
chromatography relies on the specific binding interactions between the Fc area
of mAbs and the Protein A ligand. As indicated in Shukla & Thommes [2010],
Papathanasiou et al. [2019], Protein A affinity chromatography step can lead
to over 98% purity because of high binding affinity and specificity of the
Protein A to Fc-fusion proteins. This specificity enables impurities such as
host cell proteins, DNA and other contaminants to pass through while the
products of interest binds to the stationary phase. The products are then
eluted from Protein A affinity adsorbents under low pH conditions for virus
inactivation and reduction of the affinity of Protein A to the Fc regions.
Next, one or multiple polishing chromatographic steps are conducted to further
remove process and product related impurities, which cannot be removed by the
Protein A affinity chromatography such as aggregates and product variants
because of their chemical similarity with the derived protein as well as a
byproduct related to the process like leached protein A. Cation exchange (CEX)
chromatography and anion exchange (AEX) chromatography are the most commonly
used polishing approaches [Shukla & Thommes, 2010], which may be used
individually or in combination. After polishing steps, a viral filtration step
is deployed for virus removal. The final step in the downstream process is
typically an ultrafiltration/diafiltration (UF/DF) to decrease storage volumes
and pass the product through the buffer for the formulation of the drug
substance.

The last stage of the manufacturing process of mAbs and related proteins
focuses on optimising and analysing the therapeutic products. Main steps of
this stage include the studies regarding the design of product formulations
assuring long shelf-lives and high stability of the therapeutic protein in the
buffer solution in line with the desired delivery method [Smiatek et al.,
2020].

Within the QbD scheme, each biopharmaceutical manufacturing process has to
comply with predefined standards to ensure the QTPPs of the final product. In
particular, the QbD method introduces a set of CQAs which present the suitable
limit, range, or distribution of physical, chemical, biological, or
microbiological properties or characteristics of in-process and finished
products [Mitchell, 2013]. As claimed in Eon-Duval et al. [2012], these CQAs
are frequently chosen based on risk-based analyses, where potential or known
product quality attributes are assessed for their potential effects on patient
safety and product efficacy. Quality attributes of recombinant therapeutic
proteins can be divided into three groups, i.e., product-related impurities
and substances, process-related impurities, and contaminants [Eon-Duval et
al., 2012, Papathanasiou et al., 2019]. These commonly used attributes are
presented in Table 1.

Table 1: Critical quality attributes are commonly used to assess the final recombinant therapeutic proteins (adapted from Eon-Duval et al. [2012], Papathanasiou et al. [2019], Chan [2019]) Product-related impurities and substances |  Process-related impurities |  Contaminants
---|---|---
Aggregation |  Residual host cell proteins (HCPs) |  Adventitious agents (e.g. potential virues, bacteria, mycoplasma, bioburden, and fungi)
Fragmentation |  Residual DNA |  Endotoxins
N/C-terminal modifications |  Raw material-created impurities (e.g. leached protein A, cell culture media, and buffers) |
Oxidation |  Leachables (from product contact materials) |
Deamidation/Isomerization |  |
N-linked Glycosylation |  |
O-linked Glycosylation |  |
Glycation |  |
Conformation |  |
Disulfide bond modifications/free thiols |  |
GlcNAc, N-acetylglucosamine |  |

This section only summarises the main steps and their functionality within a
typical manufacturing platform of mAbs and recombinant therapeutic proteins.
The review of impacting factors and the way of enhancing productivity and
modifying product quality along with development strategies can be found in
Radhakrishnan et al. [2018] for the upstream and in Baumann & Hubbuch [2017],
Rathore & Kapoor [2014] for the downstream processes. According to Badr &
Sugiyama [2020], the optimisation activities of the upstream cultivation
processes are usually conducted aiming to raise product quantity represented
by titer and productivity, while the optimisation of the downstream
purification processes is performed to obtain the desired product profile.

The manufacturing process of the mAbs and theurapeutic proteins is associated
with tight regulations defining the purity and composition of the final
products and the limitation of product and process-related impurities
[Papathanasiou et al., 2019]. Therefore, rigorous process monitoring becomes
critical for the manufacturing process. In that regard, control strategies for
tracking the real-time values of impurities and secondary products may be of
great potential and high demand. These are the opportunities for the
deployment of data-driven modelling and optimisation for the whole
biopharmaceutical manufacturing process from the discovery and design stages
to upstream, downstream, and product formulation and stability. This paper
will review key areas where ML algorithms can be used for these purposes.
However, first we will discuss challenges associated with processes and
bioprocess data.

##  3 Challenges of biopharmaceutical processes with respect to ML solutions

###  3.1 Challenges associated with characteristics of biopharmaceutical
processes

The unique aspects and specificities of the biopharmaceutical industry give
rise to various challenges related to data management and analysis, as well as
the comprehension of complete bioprocesses. According to Steinwandter et al.
[2019], these obstacles can be tackled by the deployment of digital
transformation solutions and the use of smart algorithms for monitoring,
controlling, and optimisation of manufacturing processes. These current
obstacles and potential solutions are presented in Table 2. In this section,
we explore the distinctive features of the bioprocess life cycle that may
present difficulties and challenges when it comes to creating and integrating
digital solutions for the development and production of mAbs and therapeutic
proteins.

Table 2: Current challenges of biopharmaceutical manufacturing processes and potential digital solutions for addressing them (taken from Steinwandter et al. [2019]) Current challenge |  Potential digital solution
---|---
Development and approval of new biologics is costly and the time from production to clinic is too long |  Increased use of digital tools for experimental design, data-driven decision-making, and bioprocess optimisation
Bioprocesses are not transparent |  Using predictive soft sensors to indirectly estimate difficult or impossible-to-measure variables
Routine manufacturing processes are not cost-effective, operate sub-optimally, or lead to failed batches |  Applying optimisation approaches to optimise performance of already established processes; implement enhancements for product life-cycle management
Impact of multidimensional changes in process parameters on CQAs is usually unknown |  Use process modelling approaches to achieve data-driven estimates for the changes of process parameters in consideration of whole process value chain
Shortage of highly skilled workers in statistical analysis and machine learning |  Deploy automated algorithms to replace the manual data analyses
It is difficult for humans to analyse and understand high-dimensional bioprocess datasets |  Deploy data-driven methods for data interpretation

####  3.1.1 Challenges in achieving an effective manufacturing process

In the manufacturing process of therapeutic proteins, cell culture plays a
vital role and a high-performing media formulation is one of the key factors
to obtain high cell densities, high productivity levels and quality protein
products [Biedermann et al., 2022]. Therefore, enhancement and optimisation of
cell culture media formulations becomes one of the key challenges to achieve
the high performance and quality of the final products across multiple cell
lines [Ritacco et al., 2018]. In addition to cell culture media optimisation,
the effective nutrient usage is also an important factor to achieve high
performing cell cultures. As a result, monitoring and optimisation of process
inputs represent a major theme to reach more efficient bioprocess development
and manufacturing [Vaessen, 2021]. Therefore, PAT tools are frequently
deployed to measure and quantify in-process parameters. As an example for the
use of PAT tools, new sensors have started to be frequently used for real-time
process monitoring and control in addition to routine off-line analytics
[Dutton, 2021]. PAT data can also be employed by ML algorithms for the
analysis and optimisation of production and improving the product quality.
Together with the development of automation and intelligent devices, smart
learning algorithms have enormous potential to be integrated into bioprocess
manufacturing systems. This integration enables enhanced monitoring and
adaptation of unit operations to accommodate dynamic changes in cell cultures
and the purification of biological products. The ultimate goal is to increase
yields and improve the quality of the final products. Although automated and
adaptive ML solutions have been widely used for many industry processes
[Salvador et al., 2016, Bakirov et al., 2016, 2015, Kadlec et al., 2011,
Kadlec & Gabrys, 2011], there is still no study in the literature deploying
automated and adaptive ML methods for real-time monitoring and control of
biopharmaceutical processes. This is due to the high complexity and rigorous
regulations regarding assessment of process and product related measurements
besides the challenges in bioprocess data and a process comprehension. With
the recent innovation in automated and autonomous machine learning
(Auto/AutonoML) [Khuat et al., 2022, Dong et al., 2021, Bakirov et al., 2021,
Kedziora et al., 2020], however, the integration of Auto/AutonoML into the
whole manufacturing processes from upstream, to downstream and formulation
stages is more likely to become a feasible solution to address challenges in
real-time monitoring, control, and improvement of product quality of
bioprocessing.

####  3.1.2 Domination of batch based bioprocesses

Although continuous processes are very common in many process industries
[Kadlec et al., 2009], biopharmaceutical manufacturing relies mainly on the
batch processes, in which each process can be split into serially performed
unit operations [Shukla & Thommes, 2010, Papathanasiou et al., 2019,
Steinwandter et al., 2019, Smiatek et al., 2020]. Given a process, the numbers
of unit operations, splitting and pooling steps depend on the product. The
variation in one of the CPPs of one unit operation can result in the change in
process conditions and CQAs of the products in subsequent steps [Steinwandter
et al., 2019]. This poses challenges and opportunities for the use of ML
solutions for real-time monitoring of CPPs based on the feedback data of
offline analytics and online PAT data.

In contrast to continuous processes, batch processing has to be fully
completed prior to running another batch. This characteristic offers several
benefits, including the ability to control mutations in a cell line, which can
impact process efficiency and product quality, as well as providing
flexibility in the production schedule. Notwithstanding, batch bioprocessing
shows downsides in the production time and scaling up abilities [Schaeffer,
2019]. The quality and uniformity of each batch needs to be verified to
improve the performance of future batch runs which leads to an increase in the
production time. In addition, the products are harvested only once from each
batch, so batch manufacturing is a labor-intensive process, ideally, to be
performed at a large scale. To meet sufficient number of cells for
bioprocessing, the batch will be extended in successively larger bioreactors.
The cost of the equipment for the growth of successively larger cell cultures
is significant. Hence, fitting a new batch process into existing equipment is
considered as one of the biggest challenges that bioprocess manufacturers have
to face [May, 2020]. As a result, the shift to continuous operation modes can
lead to the significant reduction in operating time and machinery cost for
scaling up, which in turn also reduces the problems related to mathematical
modelling concerned with the effects that scaling up can produce [May, 2020].
The transition to continuous manufacturing is a potential opportunity for the
deployment of automated and adaptive ML solutions for the real-time monitoring
and dynamic optimisation of control strategies for the whole manufacturing
process. It is because the process control is crucial for achieving the
steady-state of continuous processing and managing the potential dynamic
effects and drift associated with biological processes and the product quality
concerns over time. All of the above issues can be, in principle, addressed by
the use of automated and adaptive ML algorithms.

####  3.1.3 Diversity in interoperability of unit operations

As mentioned above, the manufacturing process of biologics includes several
serial process unit operations (PUOs), also known as the bioprocess trains,
each having a specific impact on the final CQAs and overall productivity. The
interaction among the parameters of various unit operations can lead to the
generation of many impacts on the CQAs of the final products similarly to a
contribution of parameters of an individual unit operation [Jiang et al.,
2017]. Hence, the CQA profiles are desired to be known prior to the execution
of the next PUO for the effective operation of interconnected bioprocess
trains. To achieve this goal, online CQAs measurements should be specified at
every PUO if possible [Jiang et al., 2017]. As presented in Eon-Duval et al.
[2012], the product CQAs are defined based mainly on the risk assessment of
the process development within a QbD framework, so the implementation of
sensory systems across multiple PUOs can play a crucial role for this purpose.
As a result, the integration of the monitoring, data analysis and finally
modeling of CQAs across the various PUOs of the bioproduct manufacturing
process shows a crucial importance, especially in a continuous operation mode
[Guerra et al., 2019].

One of the characteristics of an industrial production of bioproducts is the
large-scale operation. However, in practice, large-scale heterogeneity usually
causes higher variability of CQAs [Guerra et al., 2019, Brunner et al., 2017].
Though several studies in the literature have presented the effects of CPPs on
CQAs, few studies describe the correlation between process heterogeneity and
the variability of CQAs and the level of metabolic reactions [Guerra et al.,
2019]. The main reason for this fact is the scarcity of real-time monitoring
tools for product CQAs during larger scale operations, which limits the
comprehensive understanding of the dynamic operational environment. Therefore,
it would be very desirable to take full advantage of automation and machine
learning solutions for monitoring and analysis of all related PUOs and
generation of detailed understanding of bioprocesses as well as dynamic
changes during the operational time to improve the yield and quality of final
products, especially in industrial large scale operations. Nevertheless, the
large amount of collected data could pose new challenges for information
technology infrastructure, data storage and governance as well as effective
raw data mining algorithms.

####  3.1.4 Challenges in time to market

As stated in Denny-Gouldson [2019], one of the main challenges in the
development and manufacturing of biologics is the constant pressure in
delivering novel and high quality therapeutics to the market faster and more
cost effectively, while still meeting high standards of regulatory compliance.
The sooner the process runs and regulatory approvals for the products are
completed, the sooner those essential therapeutics become available for
patients. However, due to the complexities of biologics and long production
cycles, it is tough for biopharma companies to balance the specificity,
quality, cost, and speed. Any small accident or production failure can result
in destructive ripple effects.

To accelerate the manufacturing life-cycles, biopharma organisations have
invested heavily in innovative solutions such as the high throughput process
development, the expanding use of single-use technology [Dutton, 2021],
process analytics, cross-scale predictive modelling, and continuous processing
[Denny-Gouldson, 2019]. These changes pose new challenges but also open
opportunities for the implementation of automation and ML solutions for real-
time data analytics, continuous monitoring and control of the whole system
from the upstream to the downstream processes.

####  3.1.5 Constant pressure to lower costs

No business can avoid the cost pressures including the pharmaceutical
industries. Process intensification may be considered as an efficient approach
to lower the cost of goods through enhanced process efficiency without
sacrificing productivity [Boodhoo et al., 2022]. Bioprocess intensification is
defined as a rise in a bioproduct output related to cell concentration, time,
bioreactor volume or cost [Whitford, 2020]. The implementation of process
intensification may reduce up-front capital investment, optimise outputs,
streamline labor expenses, and reduce other overhead costs like water, energy,
waste disposal, and cleanroom maintenance [Boodhoo et al., 2022].

There are some similarities between the intensification and optimisation, in
which both methods aim to enhance the performance to increase productivity
[Keil, 2018]. However, the intensification is associated with more step
changes in equipment and/or process design leading to not only a significant
increase in productivity but also a considerable enhancements in other
environmental and economic metrics such as energy consumption and carbon
footprint [Boodhoo et al., 2022, Boodhoo & Harvey, 2013]. One of the
fundamental strategies in terms of the philosophy behind the process
intensification is the shift from batch to continuous processes [López-
Guajardo et al., 2021]. Continuous bioprocessing is attracting more attention
because of its advantages in terms of constant nutrient conditions,
elimination of by-products, a capability of formulating high density cultures
and no lag phase once the system is operating [Boodhoo et al., 2022].

As shown in López-Guajardo et al. [2021], ML is an important tool for the
innovation of the process intensification with the ability to extract
insights, pattern recognition and predictions in the process industry. These
abilities can assist in the sustainability design, system integration,
advanced process and quality control, intensification, and data-driven process
modeling. However, the applications of ML for the process intensification face
several following challenges:

  * •

Real-time operation. This challenge requires the ML models to capture,
interpret, and analyse the large amount of data instantly. This feature
becomes prominently important in the context of continuous processes to handle
constantly streaming data. The real-time operation ability could intensify
closed-loop systems to result in automated and self-driven assemblies
[Sagmeister et al., 2021].

  * •

Interoperability. Bioprocess manufacturing operations require the exchange and
communication of information among multiple unit operations. This poses
challenges in data synchronisation between various interconnected equipment
and devices through technology infrastructure to allow the coordinated actions
among devices, sensors, software, process models, and humans [Sharma et al.,
2021].

  * •

Information transparency. This requirement poses the challenges in readiness
of information from the entire process and external knowledge. It requires the
continuous process and visualisation of the large amount of data in an
interpretable approach which could be utilised immediately for the adaptation
of the production processes to meet the market demands and supporting the
process enhancements and intensification strategies [Sivo et al., 2021].

  * •

Distributed decision making. This feature requires the subsystems or unit
operations to be capable of making localised data-driven decisions yet also
having the ability to coordinate and cooperate among constituent subsystems
[Daoutidis et al., 2019]. This results in the challenges in computational
efficiency, flexibility, coordinated and parallelised communication of data-
driven decision making algorithms within each unit operation.

###  3.2 Challenges stemming from various characteristics of bioprocess data

As stated in May [2020], some of the greatest challenges in bioprocessing
today are the democratisation and the implementation of potential digital
solutions, especially focusing on ML models, for knowledge extraction and
supporting informed decisions based on the data sources derived from cell-line
development to manufacturing and product formulation. However, due to the
complexities of biologics, process manufacturing and mechanisms underlying the
biological systems, bioprocess data present many challenges for the ML
modelling and analysis. This section presents several typical characteristics
of bioprocess data including the diversity of data sources, heterogeneity,
high dimensionality, data integrity, big data, small training data with
infrequent feedback, and the lack of open datasets. These characteristics can
result in many challenges in the deployment of ML solutions for
biopharmaceutical process development and manufacturing.

####  3.2.1 Diversity of data sources

According to Duong-Trung et al. [2022], the toughest challenge for automated
model building which needs to be overcome is the diversity, distribution, and
multi-structures of data sources. Steinwandter et al. [2019] summarised the
most critical data sources during the bioprocess life cycle (see Table
LABEL:data_source). As we can be see there are often many different data
sources used in the biopharmaceutical development and manufacturing life
cycle. Several data sources such as file-based exports are easy to set up but
they represent several disadvantages for analysis and model building due to
the inconsistency in formats and much effort needed to restructure and clean
such data. Some of the data sources from data warehouses and historians
contain structured and standardised data which can be useful and easy to
access for the development of ML models. However, these structured databases
require significant effort to set up. Although the diversity of data sources,
as presented in Table LABEL:data_source, can present many challenges when
building effective machine learning models, optimal solutions for the
practical problem typically require the usage of multiple data sources (e.g.,
an Open Platform Communications (OPC) connector exporting to a data
warehouse).

Table 3: The diversity of data sources within a bioprocess life cycle (data are taken from Steinwandter et al. [2019]) |  |  |  |  |
---|---|---|---|---|---
Source |  Examples |  Pros |  Cons |  Rapid setup and low IT/OT costs |  Usability for ML
Data warehouses |  Microsoft SQL Server, Oracle Database, SAS Data Management, Amazon Redshift |  Easy and fast access for analytical purposes; store data from various departments and unit operations |  Considerable effort to set up the systems |  * |  *****
Data historians |  Wonderware, OSI Pi |  Able to store large amounts of mostly time-series process data; often offer various APIs to acess to data |  Considerable effort to set up and maintain the systems |  * |  *****
Tabular files |  .csv, .txt files, Excel, Ambr exports automatically or manually generated |  Easy to set up and use; portable file format without requiring complex IT environment |  Too many various variants and inconsistencies due to manual file changes result in vast effort for data collection and cleaning when analysing a large number of files |  ***** |  ***
LIMS systems |  Starlims, McKesson Lab, LabWare, LabVantage |  Standardized data source for analysis results from laboratory |  Need additional meta data to interconnect these data with process data from other systems |  * |  ***
SCADA/MES systems |  Sartorius MFCS, Infors Eve, Applikon BioXpert |  Structured data formats and easy to access using standardised interfaces; many available process data |  Require set up and availability of interface; metadata to the process itself are usually missing |  *** |  ***
Electronic batch record |  Emerson Syncade, ampleLogic eMBR |  Extremely valuable data but requiring less effort for data collection and cleaning than paper-based batch record |  Huge amount of data; still need much manual work and comprehension |  * |  ***
Direct interface to analytical devices or sensors |  OPC, serial ports, field bus |  Simple set-up; real-time direct access to data; most direct manner to implement smart sensors |  Usually missing metadata; only simple algorithms can be deployed without any further additional data sources |  *** |  **
Distributed control systems |  Emerson Delta-V, Siemens Simatic PCS7 |  Real-time connection to connected systems |  Often difficult to access (historical) data due to its main purpose is automation, not data delivery |  ** |  **
Data lakes |  Distributed file systems (e.g., Apache Hadoop, Azure Data Lake, Amazon S3) |  Able to store structured and unstructured data easily; no data cleansing and preparation necessary to store data |  Needs data preparation for unstructured data prior to analysis |  *** |  **
Hand-written laboratory results |  Paper-based laboratory notebook |  No IT require |  Not able to perform automated data analysis; need to transfer data into another system first |  ***** |  *
Paper-based batch records |  - |  Extremely valuable data that cannot be found anywhere else (e.g., holding times, exceptions, or out-off-specification events) |  Extremely significant effort for data collection and cleaning |  *** |  *
Others |  .pdf,.docx files, images |  Large amounts of available data in these formats |  Data are usually unstructured and need much manual work to make them usable for ML tools |  **** |  **
*IT: Informational Technology, OT: Operational Technology

####  3.2.2 Big raw data issues

Pharmaceutical organisations usually derive and store a large amounts of data
from many different data sources for the business and research purposes. As
indicated in Tobyn et al. [2018], the estimation of the data under storage of
one large pharmaceutical company for all purposes are around 6 petabyte of
which 2 petabyte are generated from the R&D departments. The biopharmaceutical
production sectors also create a lot of data. Hundreds of process parameters
including material inputs, process outputs, control actions and physiochemical
parameters from both production scales and the cell expansion in the inoculum
trains are regularly collected and stored electronically [Charaniya et al.,
2008, 2010]. With advances in real-time PAT tools and the increased use of
high throughput and automated bioreactor systems, the development and
manufacturing processes of mAbs and therapeutic proteins are generating more
and more bioprocess data [Pham et al., 2022]. Consequently, the
biopharmaceutical industry could face the challenges in data synchronisation
and moving from a data-heavy environment to the one where data are converted
to information resulting in useful knowledge. Huge amounts of raw data also
poses many obstacles for ML algorithms in data preparation and engineering,
model building, and knowledge extraction because analytics tools need large
amounts of good quality data rather than huge amounts of raw data with noise
and redundancies. To overcome this challenge, a significant amount of
resources and effort should be allocated to implementing solutions for data
preprocessing and synchronisation prior to the development of ML models.

####  3.2.3 Data heterogeneity

Data heterogeneity is identified as the most prominent problem in the process
development and manufacturing of mAbs and therapeutic proteins [Pham et al.,
2022, Charaniya et al., 2008]. Two sources resulting in bioprocess data
heterogeneity are time scale and type [Charaniya et al., 2008, 2010].

Bioprocess parameters can be classified into online and offline types
depending on the frequency of measurements. While a vast majority of process
parameters are measured online such as pH, temperature, dissolved oxygen and
CO2 concentration, PAT data, and gas flow, a few critical parameters, such as
viable cell density and product concentrations and several metabolite and
nutrients concentration measurements, are acquired off-line [Charaniya et al.,
2008]. Many online measurements are collected in real-time, while measuring
the off-line parameters is performed periodically within a production cycle.
In addition, several key parameters such as product concentration and quality
indices are only available at specific time points during the operational
process due to the time and expense associated with their acquisition. These
unique characteristics of bioprocess data result in high level of
heterogeneity with regard to the measurement frequency. Many ML algorithms are
not designed to work well on heterogeneous data collected over different
sampling time intervals [Hong et al., 2018]. It is required to modify the
inner mechanisms of learning algorithms [Charaniya et al., 2010, Le et al.,
2012, Severson et al., 2015] or use techniques to preprocess time
heterogeneity such as dynamic time warping [Undey et al., 2002, Vaughan &
Gabrys, 2016].

In addition to different measurement frequencies, the data type is also a
source contributing to the heterogeneity of the bioprocess data. While many
process parameters such as nutrient and metabolite concentration, cell and
product concentration, pH, and temperature are continuous, other parameters
are discrete or even binary like valve settings for nutrient feeding and gas
sparging as ON/OFF state [Charaniya et al., 2008]. The data type heterogeneity
can be tackled in data preprocessing stages prior to model building by
encoding techniques, or using learning models which have the ability to learn
from mixed-attribute data such as CART decision trees [Breiman et al., 2017]
or fuzzy-neural networks [Khuat & Gabrys, 2023].

####  3.2.4 Small training data issues

While for established or legacy biologics, many pharmaceutical companies have
collected large amounts of historical data of experiments spanning years or
decades, process data for novel bioproducts are limited, sometimes amounting
to as little as only one or two runs for a new product at the manufacturing
sites [Tulsyan et al., 2018b, Banner et al., 2021]. This characteristic
results in a Low-N problem, in which the number of training samples is much
smaller than the number of data dimensions. Furthermore, in practice, products
are regularly moved to various production sites to accommodate other products
and their life cycles. This operation type also contributes to the Low-N
problem as there is a limited number of historical experiments available at
new manufacturing facilities. With the increased use of PAT tools within
bioprocess steps, large amounts of diverse measurements and information can be
collected in real-time while there are only a small number of experiments that
can be conducted [Banner et al., 2021]. This typically makes it quite
challenging for ML algorithms.

The limited number of observations together with high input dimensionality
results in sparse sample spaces. Learning models which are built using such
training sets have a much greater risk of being overfitted [Verleysen &
François, 2005] leading to their potential poor predictive performance (i.e.
something that in the ML literature is referred to as an ML model’s poor
generalisation ability). A possible solution to overcome the challenges within
small training sets is the generation of larger training data sets necessary
for ML models using probabilistic algorithms such as Gaussian Processes to
derive artificial data from small data sets with several assumptions [Tulsyan
et al., 2019a, 2018a]. Another solution includes the deployment of Digital
Twins to yield unlimited simulated data sets, which can be used to build and
assess the ML models while waiting for the availability of experimental data
[Goldrick et al., 2019]. Despite several existing solutions, the Low-N problem
is still a longstanding challenge which has not been completely tackled when
applying ML solutions to bioprocess data.

####  3.2.5 High dimensionality

Bioprocess data are high dimensional with routinely tens or even hundreds of
different parameters related to the process and products being monitored over
time to assure a high-quality process [Puranik et al., 2022]. Moreover, with
the integration of PAT tools such as spectrometers into the bioprocess steps
for online monitoring of process parameters, the number of dimensions of the
obtained data can be thousands of variables as in the Raman and infrared
spectroscopy data [Gautam et al., 2015]. This, in turn, results in the issue
known in the ML literature as the curse of dimensionality. Learning from high
dimensional feature spaces with a limited number of training samples is a huge
challenge for ML algorithms.

To reduce the impact of high dimensionality on the performance of learning
models, feature selection and dimensionality reduction methods need and are
commonly employed to select a small subset of features that are significantly
correlated to the process outcomes. For example, in Charaniya et al. [2010],
clustering of parameters was used to identify the representative parameter
from each cluster and remove redundant parameters representing the same
information. However, the process data usually show the temporal nature and
time-dependency, so the feature selection approaches need to consider the
sequence of events. The dynamic nature and time-series setting of process data
is a considerable challenge for common feature selection methods because of
the interdependence among parameter values across time [Fidaner et al., 2016].
Statistical methods can be used in this context to assess the relative
importance of temporal process variables in discriminating runs from various
clusters [Charaniya et al., 2008].

####  3.2.6 Data integrity and quality

Data integrity and quality are also some of the key factors which can affect
the performance of ML models. For instance, missing values and incompleteness
of datasets are the inevitable features of bioprocess data. There are several
reasons for missing/incomplete information in bioprocess databases ranging
from biomanufacturing settings such as a sensor breakdown during the
operation, inconsistent sampling rates, the utilisation of incorrect format in
data logging, malfunctions in data acquisition devices, errors in recording of
real-time parameters to the change or update of instrumentation systems and a
variety of human factors [Severson et al., 2017, Mante et al., 2019,
Gangadharan et al., 2021].

The way of handling missing values accounts for one of the main challenges in
mining manufacturing data of biologics because the missing value handling
method has a strong influence on the performance of successive analyses of the
bioprocess datasets [Mante et al., 2019]. The high complexity of underlying
mechanisms of the biological systems poses additional challenge to the
efficient handling of missing values as the employed methods need to take into
account the correlation of individual parameters and complex interactions
among them when dealing with missing information [Del Rio-Chanona et al.,
2019]. As presented in Mante et al. [2019], the missing values of parameters
in bioprocess data usually occur at random. Majority of these missing values
are associated with parameters which are measured and fetched automatically.
Therefore, there are three possible approaches to deal with such missing
values [Little et al., 2012], i.e., eliminating incomplete samples, imputing
the missing data and using the learning algorithms and analysis methods not
requiring a complete dataset such as the methods in Gabrys [2002], Folch-
Fortuny et al. [2017], Khuat et al. [2021a]. Discarding a large number of
observations may introduce biases to analytic results, whereas the
implementation of effective imputation methods, as claimed in Mante et al.
[2019], has not introduced any bias to the subsequent analyses of production
datasets of mAbs.

In addition to missing information, the presence of outliers in bioprocess
data, which can result from errors in recording and measurements, unknown data
structure, or new phenomenon, can also negatively affect the performance of
subsequent data analyses and ML models. Therefore, identifying and excluding
outlier points in the dataset is also an important step prior to ML model
building. The methods based on the standard deviation, statistical metrics and
tests are usually used for outlier detection in bioprocess data [Kirdar et
al., 2007].

####  3.2.7 Lack of open datasets for reproducibility of ML solutions

To assess the effectiveness of the ML solutions in the literature as well as
performing comparative analyses between different algorithms, it is required
to run algorithms on the same datasets. However, the reproducibility of ML
solutions for bioprocess data is a significant challenge due to the lack of
publicly available benchmark datasets. Bioprocess data are expensive to obtain
as conducting biological experiments is time-consuming, labor-intensive and
requires expensive materials [Duong-Trung et al., 2022]. In addition, the
bioprocess data are associated with strategic products and business secrets of
pharmaceutical companies, thus it is unlikely that they are publicly shared
with the research community. These facts prevent the explosive growth of ML
solutions for biopharmaceuticals in comparison to other fields such as natural
language processing and computer vision.

##  4 The applications of ML in early stages of biologics development

Multiple applications of ML algorithms to the discovery and development of
small chemical molecules over the past few decades have resulted in some
outstanding outcomes. Several recent surveys such as Schneider et al. [2020],
Vamathevan et al. [2019], Chan et al. [2019] have summarised the algorithms,
remarkable achievements, and challenges in applying ML to the discovery of
small molecule drugs. In contrast to the small chemical molecules, the
utilisation of ML for the early discovery and development stages of biologics
is still limited. As mentioned in Narayanan et al. [2021a], there are a number
of reasons for this trend including the complexity of biological products and
the sparse, heterogeneous, and smaller datasets of biomolecules in the
available data warehouses of biopharma organisations in comparison to small
molecules. This section reviews the up-to-date applications of ML to the early
discovery and development stages of biologics.

###  4.1 Discovering prospective biologic candidates and assessing their
computational developability

Evaluation of potential biological candidates in terms of safety,
immunogenicity risks, and chemistry, manufacturing and control challenges
including conformational stability, aggregation, high viscosity, and chemical
degradation sites at the early stages of discovery and development is critical
for risk mitigation of costly delays and failures during later product
development stages, especially for antibody-based therapeutics [Kumar et al.,
2018] and the development of biosimilar products [Zurdo, 2013]. Therefore,
computational tools have been increasingly employed to assist the discovery of
biologic drug candidates. To effectively apply computational algorithms,
especially ML models for this purpose, it is necessary to build a library
containing sequence information and structures of biologics together with
their chemistry, manufacturing and control (CMC) attributes including
expression levels, cell lines, physicochemical stability, and analytical
characterization results as well as physicochemical properties. Such library
can be explored by ML based methods to discover the insights regarding how
molecular sequences and structural properties form attributes such as
concentration-dependent viscosity behaviours, chemical degradation,
solubility, and aggregation [Kumar et al., 2018]. This knowledge enables to
enhance the candidate selection process. Khetan et al. [2022] provided a list
of useful databases and datasets which can be used to train, validate, and
evaluate biopharmaceutical informatics tools for the discovery and development
of antibody-based biologic drug candidates.

In the discovery phase of biological products, the protein sequence
information is usually available quite early in the libraries of prospective
binders to the given product targets [Kumar et al., 2018]. This information in
the libraries frequently results from hybridoma, phage display technique, next
generation sequencing technology or other similar activities [Glanville et
al., 2015], and it can be applied to specifying aggregation prone regions,
immune epitopes, or potential physicochemical degradation liabilities in
biologic candidates using computational and ML tools [Kumar et al., 2018,
Puranik et al., 2022]. In addition, combining protein structural information
based molecular modelling with the analysis of sequence information can
provide valuable insights regarding molecular origin to develop optimisation
strategies to reduce safety issues for biologic candidates. For instance,
Kumar et al. [2012, 2016] presented the identification of common molecular
regions underpinning both aggregation and immunogenicity of biologics. Kumar
et al. [2018] stated that it is feasible to predict critical quality
attributes of a biologic candidate such as chemical degradation sites based on
its amino acid sequence by computational modelling. The integration of
molecular modelling and data analytics can lead to novel, accelerated, and
cost-effective bioprocesses and products [Zurdo, 2013].

According to Zurdo [2013], product design, lead selection, and manufacturing
process development contain significant risks for biopharma industry due to
their impacts on final product quality, costs, biological activities, and
safety. As a result, the introduction of a developability assessment to the
lead selection and optimisation phase of biologic candidates can facilitate
tackling crucial quality aspects regarding the manufacturability, safety and
pharmacology of novel biologic candidates. The concept of developability is
defined as the probability for the successful translation of a lead candidate
to a biologic drug which ensures stability, manufacturability, safety, and
specificity during production and formulation [Jarasch et al., 2015].
Therefore, developability is a multi objective optimisation issue, where ML
can be integrated with high-throughput experimental approaches to form a
potential screening method aiming to enhance efficiency and determine optimal
properties at early stages of the biologics discovery process [Narayanan et
al., 2021a]. Unlike the QbD method, developability focuses mainly on the
product design and discovery stages rather than on the control of the
manufacturing processes [Jarasch et al., 2015]. The developability assessments
at the early development stages of biologics bring many benefits including
significant reduction of risks at later development pipelines, saving time and
resources, providing a chance for re-engineering of the molecule to identify
and alleviate sequence or structural liabilities. It also contributes to the
selection of better prospective molecule candidates representing similar
efficacy and more favorable development ability [Khetan et al., 2022].

In practice, the deployment of experimental methods for the production of a
large number of candidates and the biophysical characterisation assays to
assess biophysical properties is costly and time consuming [Hebditch &
Warwicker, 2019]. Therefore, the in silico candidate screening approaches are
usually preferred to identifying high value lead candidates and novel
engineering targets aiming at accelerating the biotherapeutic development
process [Shan et al., 2018]. However, these approaches require the
availability of experimental datasets for biophysical properties of approved
biological products. Several such databases have been presented in the
literature such as Jain et al. [2017]’s dataset and Thera-SAbDab [Raybould et
al., 2020]. Using these datasets of biophysical properties of approved mAbs,
ML models can be developed to make prediction of biophysical properties and
antibody developability based on the information of amino acid sequences.
Hebditch & Warwicker [2019] developed ML models trained on the experimental
dataset [Jain et al., 2017] for all the 12 biophysical properties to predict
the performance in terms of biophysical characterisation based on the amino
acid sequence features. This is a crucial step for evaluating the
developability of antibody-based biotherapeutics. Chen et al. [2020a]
demonstrated the success of ML models in predicting antibody developability
from amino acid sequence information based on physicochemical and learned
embedding features, which can avoid the determination of antibody structures
experimentally or computationally. The authors used a dataset of over 2400
antibodies [Dunbar et al., 2014] to extract features based on antibody
sequence data. Then, six ML models including Gaussian Bayes, Logistic
Regression, Support Vector Machines (SVM), Random Forest, Gradient Boosting,
and Multilayer Perceptron were trained on the physicochemical features and
embedding features extracted from sequence and amino acid data to predict the
developability of antibodies. Out of the six ML models, the SVM showed the
best performance in capturing antibody developability from the sequence data.
Gentiluomo et al. [2019] built the artificial neural networks to predict
critical biophysical properties of therapeutic mAbs including aggregation
onset temperature, melting temperature of unfolding, and protein-protein
interactions in diluted solutions using only the amino acid compositions of
mAbs. The predictive model could be used to select the mAb candidates with
good physicochemical properties among thousands of mAbs sequences before
expression in cells and purification for the next step of the developability
assessment.

The main requirements for developability assessments are to identify critical
quality attributes at early stages and to evaluate the influence of detected
molecule liabilities for the selection of the best candidates [Lobo et al.,
2021]. Typical liabilities in terms of chemical and physical stability
attributes consist of methionine (Met) and tryptophan (Trp) oxidation,
asparagine (Asn) deamidation, and aspartic (Asp) isomerisation [Xu et al.,
2019]. Chemical and physical stability is a key issue in the development of
protein therapeutics because of its effects on both efficacy and safety of the
product [Jia & Sun, 2017]. Therefore, stability studies have an important role
to play in initial developability assessment of therapeutic proteins and their
formulations [Puranik et al., 2022]. Several ML algorithms have been used as
in silico predictors of specific liabilities, which show the capability of
virtual screening of developable biologic drug candidates. For example, Fang
[2020], Cao et al. [2019], Jokinen et al. [2018] demonstrated the use of ML
algorithms for prediction of the stability changes in proteins based on single
or multiple mutations. However, Fang [2020] claimed that these ML models have
not yet achieved a sufficient performance level for practical use which could
have resulted from the limited amount of experimental data and informative
features. In terms of prediction of the chemical stability from amino acid
residues to eliminate or reduce the liabilities as early as possible in the
selection stage of biologic candidates, Jia & Sun [2017] evaluated the
effectiveness of six ML models including SVM, random forests (RF), naive
Bayes, K-nearest neighbor, artificial neural networks (ANN) and partial least
squares to predict Asn deamidation based on the available experimental and
structural data of deamidated proteins. Meanwhile, Delmar et al. [2019]
introduced the use of random forest models to predict if an Asn is liable for
deamidation and determine the deamidation rate. The random forest model was
trained on the sequence data of mAb peptides. Likewise, SVM, RF, and ANN were
also used to predict Met oxidation sites [Aledo et al., 2017] and risk [Sankar
et al., 2018] based on the features extracted from the sequence and structure
data of proteins and antibodies. Yang et al. [2017] demonstrated the use of RF
regression to estimate solvent-accessible surface area of Met residues from
the antibody sequence data to be used as an alternative metric for prediction
of oxidation propensity. In addition to chemical stability, biophysical
stability is also commonly used to identify the stability of proteins and
mAbs. King et al. [2011] used the SVM model for the prediction of the thermal
and pH stability of antibodies based only on their sequence data.

In recent years, there has been much emphasis on the development of high
concentration antibody formulations for low volume, subcutaneous delivery, and
moving to appropriate and patient-centric dosing schemes and patient self-
administration [Lai et al., 2022, Puranik et al., 2022, Whitaker et al.,
2017]. The developability properties of mAbs including low viscosity, low
aggregation propensity, and high solubility are important for the development
of novel high-concentration mAb-based therapeutics [Lai et al., 2022, Puranik
et al., 2022]. Nevertheless, the assessment of such properties of mAbs at high
concentrations in early stages of discovery and screening of candidates is
difficult because of the limitation in the sequence and biophysical property
data of molecules [Lai et al., 2022]. Therefore, it is highly desired for the
construction of predictive models which can estimate the developability of
high concentration antibody formulations at early development stages. Lai et
al. [2021a] demonstrated the use of Decision Trees trained on features
extracted from 27 FDA-approved mAbs including net charges and high viscosity
index (HVI) to predict antibody viscosity at a high concentration (150 mg/ml).
Lai et al. [2022] extended the research by combining datasets of 27 approved
commercial antibodies with 20 preclinical and clinical stage mAbs to train
logistic regression, SVM, KNN, and Decision Trees for prediction of low and
high viscosity of antibodies. In another study, Lai et al. [2021c] showed that
the combination of coarse-grained models with hydrodynamic calculations and
HVI parameters derived by ML models can predict viscosity of mAb candidates at
various concentrations. For aggregation, there are several in silico models
using ML algorithms for predicting the protein aggregation rates, which is a
key aspect in the development of biologic drug candidates because protein
aggregation can affect safety, efficacy, and immunogenicity of protein-based
therapeutics [Hermeling et al., 2004]. Lai et al. [2021b] deployed the
k-nearest neighbors and support vector regression models trained on
structural-based features extracted from molecular dynamics simulations of 21
mAbs to predict the antibody aggregation rates at 150 mg/ml concentration. In
later study, Lai et al. [2022] evaluated the performance of ML regression
models such as linear regression, support vector regression, and k-nearest
neighbors regression in predicting antibody aggregation rates for 20
preclinical and clinical stage mAbs using features extracted from molecular
dynamics simulations of the full-length antibody. [Obrezanova et al., 2015]
developed tree-based ensemble models to predict the intrinsic aggregation
propensity of mAbs based on physicochemical properties derived from amino acid
sequence data. [Gentiluomo et al., 2020] applied ANN models to prediction of
real-time aggregation of proteins under 24 different real storage conditions
based on accelerated stability studies and commonly used biophysical
properties such as the first apparent temperature of unfolding and onset
temperature of aggregation. The proposed method can provide better insights
regarding the long-term thermal stability of proteins without having to
provide the primary sequences and protein structural inputs. For in silico
sequence-based protein solubility predictors, gradient-boosted machines [Rawi
et al., 2018], deep neural networks in combination with a novel data
augmentation algorithm based on generative adversarial nets [Han et al.,
2019], and convolutional neural networks (CNNs) [Khurana et al., 2018] are the
current state-of-the-art models. Moreover, ML models such as random forests
[Yang et al., 2016], can be deployed to predict the effects of mutations such
as individual amino acid substitutions on the solubility of the target
proteins, which can be used as a cost-effective method for screening of
soluble mutational variants of therapeutic proteins. In spite of high
potentials of ML on the development of in silico tools, it is noted that one
common drawback of these frameworks in the early stages of biologic drug
discovery is the input of these models using only protein sequences or
structure-based information without consideration of the effects of
formulation conditions [Narayanan et al., 2021a]. More detailed discussions
regarding the application of ML algorithms in screening of molecule candidates
based on biophysical and chemical properties at early stages of development
and discovery of biologic drug candidates can be found in a recent review
[Narayanan et al., 2021a].

###  4.2 Design of mAbs

Section 4.1 presented the applications of ML models to addressing the issues
regarding biophysical properties in the discovery of potential biotherapeutic
drug candidates and their developability assessments including thermal
stability and colloidal stability (viscosity, solubility, and aggregation).
This section will review the potential of using ML algorithms to tackle the
problems concerning the design of therapeutic mAbs. Together with the
increasing availability of sequence databases (e.g., International
Immunogenetics Information System, Abysis, DIGIT, iReceptor), structure
databases (e.g., Protein Data Bank (PDB), Structural Antibody Database
(SabDAb)) and experimental databases (e.g., PDBBind, Ab-Bind, or SKEMPI
containing antibody–epitope interactions) [Norman et al., 2020], ML models
have a great potential to be used as computational tools in supporting the
design of mAbs [Narayanan et al., 2021a]. As a result, we are moving from the
random design era to the one where mAbs, their structure and underlying
mechanisms related to how the mAbs bind to the target have been comprehended
to the level that enables these mAbs to be designed de novo [Denny-Gouldson,
2019]. As shown in Akbar et al. [2022a], there are three main pillars for ML-
based design of mAbs, i.e., the learning and understanding of rules underlying
antibody–antigen interactions and binding, the capability of modular and non-
linear optimisation of interdependent antibody design parameters, and the
ability to synthesize novel antibodies beyond the training datasets
(unconstrained generation).

####  4.2.1 Prediction of antibody-antigen binding

For the investigation of antibody-antigen binding, there are three key
prediction problems where ML models can be applied: a) prediction of the
antibody-antigen binding interface including epitope, paratope, and
epitope–paratope interactions; b) binding affinity prediction, and c) binding
partner prediction including single and/or many-to-many epitope-paratope
binding partners [Robert et al., 2021]. The comprehension of rules of the
epitope–paratope interactions at the atomic level is essential for
antibody–antigen recognition in the effective design of therapeutics. However,
experimental approaches used as a gold standard are time-consuming and
expensive. Therefore, computational methods based on ML models which can
predict antibody–antigen contact surfaces could be deployed as an alternative
to assist rapidly in the design of therapeutic mAbs [Norman et al., 2020].

Epitope prediction can be grouped into two distinct application classes, i.e.,
antibody-agnostic epitope prediction and antibody-aware epitope prediction
[Akbar et al., 2022a]. While the former is used to identify the most desirable
epitopes without prior insights of the corresponding antibodies, the latter is
usually employed to determine the epitope to which a given antibody will bind.

There are several applications of ML models for the prediction of antibody-
agnostic epitopes. Saha & Raghava [2006] evaluated the effectiveness of
recurrent neural networks (RNN) for predicting linear B-cell epitopes on a
antigenic sequence dataset including 700 B-cell epitopes taken from the BciPEP
database and 700 non-epitope peptides taken randomly from the Swiss-Prot
database. The input peptide length (sliding window for the input layer of the
network) ranging from 10 to 20 amino acids was tested, and the best
performance using 5-fold cross validation was about 66% accuracy achieved at a
window size of 16 amino acids. Manavalan et al. [2018] built a non-redundant
dataset including 5,550 experimentally validated B-cell epitopes (BCEs) and
6,893 non-BCEs from the Immune Epitope Database. Then, the authors proposed a
new ensemble method which is a fusion of extremely randomized tree (ERT) and
gradient boosting (GB) classifiers to predict linear BCEs based on the
features regarding the combination of physicochemical properties with amino
acid composition, amino acid index, dipeptide composition, and chain-
transition-distribution extracted from the constructed dataset. There are also
other prediction tools for classification of linear BCEs and non-BCEs using
SVM models such as BCPred [EL-Manzalawy et al., 2008], BEST [Gao et al.,
2012], COBEpro [Sweredoski & Baldi, 2009], and AAP antigenicity scale approach
[Chen et al., 2007]. These approaches combined physicochemical properties
associated with antigenicity and information generated from a sequence
conservation, predicted 2D structural features, and related solvent
accessibility. However, the vast majority of epitopes (about 90%) are
conformational [El-Manzalawy & Honavar, 2010] and discontinuous in nature
[Sweredoski & Baldi, 2008], and thus, linear epitopes are unlikely to capture
discontinuous epitopes within antigen sequences [Akbar et al., 2022a].

The prediction approaches of discontinuous epitopes usually require the
information regarding three-dimensional structure of the antigenic protein
[Liang et al., 2010]. Compared to the prediction of linear epitopes, there
have been limited numbers of methods for the effective discontinuous epitope
prediction due to the limitation of available antigen-antibody complex
structures. Existing methods for antibody-agnostic predictions of
discontinuous epitopes train learning models on the antibody-antigen
structures and make epitope prediction on the antigen structures alone [Akbar
et al., 2022a]. These models use amino-acid propensity scales together with
geometric predictors or clusters with different shapes representing the
spatial compactness of surface residues and secondary structure composition
such as PEPITO [Sweredoski & Baldi, 2008], SEPPA [Sun et al., 2009], DiscoTope
[Kringelum et al., 2012], and EPSVR [Liang et al., 2010]. Furthermore, in a
recent study, [Lu et al., 2022] proposed to use both local and global features
for the prediction of BCEs. In their approach, Graph Convolutional Networks
are used to capture local spatial neighborhood information in parallel with
Attention-Based Bidirectional Long Short-Term Memory networks which are used
to extract global information from the whole antigen sequence. The
experimental results confirmed that global features play a critical role in
the prediction of BCEs. However, these methods need the information of antigen
structures because they used structure-based residue features as input. In
contrast, the two-stage method proposed by Ren et al. [2017] can predict
conformational B-cell epitopes using only antigen sequence information. In the
first stage, sub-classifiers are deployed to learn general epitope patterns
from each propensity type separately extracted from a large data set of
antigen sequences containing computationally defined epitopes. In the second
stage, a decision tree classifier trained on antigen sequences with
diversified experimentally determined epitopes is used to identify the
heterogenous complementarity of the propensities which constitute a basis for
antibody-antigen interactions. Because the method only needs antigen sequence
information, it can be deployed for large-scale predictions of discontinuous
epitopes and broader applications, such as the discovery of novel epitopes and
their corresponding antibodies,or development of new antigens for a given
pathogen.

As argued in Akbar et al. [2022a], antibody-agnostic prediction of epitopes is
an ill-defined problem because an epitope only become useful in a specific
context associated with an antibody. Therefore, antibody-aware epitope
prediction approaches have been introduced to address this issue. Sela-Culang
et al. [2014] used a Random Forest model to predict the likelihood of a given
residue in an antibody complementary determining region (CDR) to pair up a
given residue on the antigen surface based on the features of these residues.
For each antigen residue, the features comprise secondary structure, estimated
disorder, surface accessibility, predicted interaction hotspots, the
considered amino acid and its amino acids neighbors in antigen sequence. For
each antibody residue, the extracted features include the residue position in
the antibody, the considered amino acid and its amino acids neighboring in the
sequence of antibody. Another study [Jespersen et al., 2019] enhanced the
prediction of antibody-specific epitopes by exploring features generated from
structures of antigens and their associated antibodies and using them for
statistical and ML algorithms. The study identified the correlation of
geometric and physicochemical features to the interacting paratope and epitope
patches. The inclusion of these features in the training set for feed-forward
neural networks can increase the prediction accuracy of the related antigen
target for a given antibody and the antibody target for a given antigen.

Although the prediction of epitopes provides useful information related to the
antigen regions which can be bounded by the antibody, this prediction does not
directly give the insights into the specific antibody residues which can be
mutated to change their functionality [Krawczyk et al., 2014, Norman et al.,
2020]. This issue may be addressed by the antibody–antigen docking technique,
which provides a list of potential orientations and poses binding two
molecules to each other given the structure of the antibody and the antigen.
Therefore, there were several studies aiming to use the structure-based
antibody-specific epitope predictions to enhance the quality of antibody-
antigen docking algorithms (using mathematical and mechanistic models) by
including geometrical information of both antibody and antigen to re-rank the
list of possible poses provided by docking algorithms. For example, the
EpiPred method [Krawczyk et al., 2014] combined the conformational matching of
the antibody-antigen interfaces and a knowledge-based specific antibody-
antigen score to return a ranked list of likely epitope regions for a given
antibody. These predicted epitope regions can facilitate docking procedures
when they can increase the quality of raw antibody–antigen poses provided by
docking frameworks. Schneider et al. [2022] built 3D antibody structures from
their sequence and generated rigid-body docking poses from this modelling in
combination with given antigen epitopes. This information was then used to
train a convolutional neural network. The trained model can be used to rerank
docks generated by the docking algorithms.

Together with antigenic epitope prediction, accurate prediction of antibody
paratope can provide useful knowledge for the investigation of antibody-
antigen interaction mechanisms and antibody design [Lu et al., 2021].
Similarly to the prediction of epitopes, paratope prediction approaches can be
divided into antigen-agnostic and antigen-specific groups [Akbar et al.,
2022a]. In each group, the structure or sequence data of antibodies and
antigens can be used to train paratope predictors.

There have been several ML models proposed for the antigen-agnostic paratope
prediction task which are based on a sequence or structure information of the
antibody. Liberis et al. [2018] proposed the Parapred method, which uses a
combination of CNN and long-short term memory networks to predict the
paratopes based on only the amino acid sequence of CDRs. Amino acid sequences
were encoded as tensors using the one-hot encoding method prior to being
passed through the deep learning models. Experimental results showed that
Parapred outperformed the proABC method [Olimpieri et al., 2013], which is
also a paratope prediction model using a random forest classifier trained on
features extracted from amino acid residue sequences of an antibody. In
contrast to Parapred, the proABC method requires additional features on the
entire antibody sequence such as the predicted canonical structure associated
with each CDR and hypervariable loop length, and germline family together with
antigen volume apart from the heavy and light chain sequences of the antibody.
In an enhanced version, proABC-2 [Ambrosetti et al., 2020] was introduced, in
which the random forest classifier is replaced with a CNN model. Otherwise,
the data processing method and set of features were kept the same as the
proABC. However, the experimental outcomes indicated that proABC-2
outperformed Parapred [Liberis et al., 2018]. In addition to the prediction of
paratope residues, proABC-2 is able to predict types of interactions including
general, hydrogen bond and hydrophobic interactions of the paratope. Beside
the antibody sequence information, a structure of the antibody molecule can
also be used for the paratope prediction. Daberdaku & Ferrari [2019] proposed
a novel approach for the paratope prediction from antibody structures using 3D
Zernike descriptors and the SVM model. The proposed method first extracted
geometrical representation together with physicochemical and biochemical
properties of the residues on the antibody surface from the given
experimentally obtained 3D structures of the antibodies. For each spherical
patch uniformly sampled from the antibody surface, the rotationally invariant
local features based on 3D Zernike moments are computed. The randomized
logistic regression is used within the 3D Zernike descriptors for feature
selection. From the obtained features, SVM models are trained to distinguish
interface surface patches belonging to the paratope from non-interface ones.
To identify paratope residues, the predicted interface surface patches are
then mapped on the underlying residues. Each residue in the antibody is given
a score representing its probability of belonging to the paratope based on the
minimum distance from its atoms to the centre of predicted paratope surface
patches. Next, each residue in the antibody may be predicted as antigen
binding or non-antigen binding by determining the threshold value for its
final score. The experimental results showed that this proposed method
outperformed other antigen-binding interface prediction tools such as Parapred
[Liberis et al., 2018]. Moreover, the predicted local surface patches can help
docking algorithms to narrow down the conformational search space to only
surface patches belonging to the paratope.

Similarly to the case of antibody-agnostic epitope prediction, the paratope
prediction without considering the context of a specific antigen may not
provide much insightful knowledge for the antibody design [Akbar et al.,
2022a]. Therefore, antigen-aware paratope prediction methods have been
introduced to take benefits from the knowledge of the specific epitope
residues to refine the paratope prediction. Deac et al. [2019] proposed a
method to expand the Parapred [Liberis et al., 2018] using the information
about residue sequences on the target antigen apart from the features
extracted from the residue in the CDR including amino acid type, six heavy and
light chain sequences and seven additional features describing physical,
chemical, and structural properties of the given amino acid type. These
features are then used to train a deep learning architecture combining dilated
convolutional layers and a self-attention layer. The proposed method slightly
improved the paratope prediction accuracy compared to the original Parapred
version. In addition to sequence information, several antigen-specific
paratope prediction approaches have used the features extracted from the
structures of antibody and antigen as input. Pittala & Bailey-Kellogg [2020]
introduced a model based on the Graph Convolution Attention Network, called
PECAN, which can predict binding interfaces for both paratope and epitope.
Graph convolutions are used to capture the local spatial connections of the
interfaces, while an attention layer is deployed to learn distant information,
and a base network trained on general protein–protein interaction data is used
as a transfer learning technique for fine-tuning of hyper-parameters for
epitope and paratope prediction networks. In another study, Del Vecchio et al.
[2021] proposed a novel architecture for the deep learning model using
epitope-paratope message passing (EPMP) for the prediction of both paratopes
and epitopes. The proposed architecture for the paratope model sequentially
handles the input antibody residue sequences in lower layers by CNNs followed
by graph structure features of residues in higher message passing neural
network layers. In contrast, the epitope model uses purely structural features
extracted from antigen residues to train graph neural network layers in
combination with contextual cues from the given antibody. Recently, a
geometric deep learning has appeared as one of the most promising approaches
for the generation of representations of protein structure and the molecular
surface [Atz et al., 2021] which can be employed to predict antibody–antigen
interacting interfaces [Gainza et al., 2020]. This method expands the neural
networks to integrate the geometric knowledge regarding structure and symmetry
of the antigens and antibodies to refine the quality of the predictive models.

Besides the prediction of the paratope or epitope for the known binding pairs,
the extraction of paratope-epitope matching rules and generation of all
possible binding partners for a given antibody or antigen are still a
challenging issue. This is due to the lack of large antibody-antigen structure
and affinity datasets to build the many-to-many binding partner prediction
models [Akbar et al., 2022a]. Therefore, Akbar et al. [2021] performed some
initial work on taking advantage of antibody-antigen complexes in the public
antibody database, graph theory and deep learning to formulate a set of
antibody-antigen structural interaction motifs. These motifs have illustrated
the potential predictability of antibody–antigen interactions in general and
paratope-epitope pairs in particular. The generated structural interaction
motifs enable to predict paratope-epitope interfaces of unrelated antibody-
antigen complexes. The initial results in this study indicated that the
availability of general interaction vocabulary of antibody–antigen interfaces
may facilitate learning and extraction of antibody–antigen interaction rules.

Another key problem in the prediction of antibody-antigen binding is the
prediction of binding affinity changes of the antibody toward a given antigen
based on the mutations in the amino acid sequence of the antibody. The
improvement of binding affinity is essential for increasing the efficacy and
reducing the amount of antibody per dose to lower price [Kurumida et al.,
2020]. ML models have been used to predict the antibody-antigen interaction
binding affinity changes based on mutations. Pires & Ascher [2016] developed a
web server, namely mCSM-AB, to predict the effect of amino-acid substitutions
on the antibody–antigen affinity changes of an antibody for a given antigen
based on graph-based signatures. For each single-point mutation on the
antibody-antigen complex, the proposed method extracts the wild-type residue
environment. The wild-type residue environment is then used to generate a
structural signature and the pharmacophore count difference between wild-type
and mutant residues. These features along with experimental antibody-antigen
affinity difference from the literature are used to train K-Nearest
Neighbours, Regression Trees, and Gaussian Processes models for prediction.
However, mCSM-AB has used only structural information for the prediction of
binding affinity changes upon mutations, while evolutionary information and
energetic terms which have shown significant impacts of the mutation on the
antibody binding affinity has not been used for the prediction. Therefore, in
an updated and refined version of mCS-AB, namely mCS-AB2 [Myung et al.,
2020b], the learning models have been trained on a larger and more
comprehensive dataset, which utilises not only graph-based signatures
(pharmacophore and distance pattern) but also evolutionary and energy-based
features as well as interatomic interactions to better capture structural and
sequence-based information for an improved predictive performance of antibody-
antigen binding affinity changes due to mutations. The learning models in the
mCSM-AB2 are popular classifiers available in the scikit-learn library
including Extra Trees, Random Forest, Gradient Boosting and XGBoost
regression. Kurumida et al. [2020] also proposed to use ensemble models to
predict the antibody affinity changes upon single point mutations. The authors
used the predicted outcomes of antibody affinity changes from 11 molecular
mechanics predictors and the mCSM-AB model as input features to train Gaussian
process regression and random forest regressor to provide the final prediction
for the antibody affinity changes upon mutations. The obtained results showed
better classification performance than molecular mechanics-based affinity
predictors. While these studies predict the antibody-antigen binding affinity
changes using a single point mutation at a time, Myung et al. [2020a] proposed
an approach, called mmCSM-AB, with the ability to predict the binding affinity
changes upon multiple point mutations using graph-based signatures, sequence
and structure based information of antibody-antigen complexes. A wide range of
ML models including Random Forest, Extra Trees, Gradient Boosting, XGBoost,
SVM and Gaussian Process was considered in the mmCSM-AB tool.

Although the applications of ML models for the prediction of antibody and
antigen binding have made considerable progress in recent years, as presented
above, there are five key challenges still pertaining to the learning models
for this topic including predictability, generalisation, interpretability,
uncertainty, and data completeness [Akbar et al., 2022a]. These challenges are
summarised in Fig. 4 within a typical ML workflow. Predictability is the
capacity of ML models to accurately predict properties of antibody-antigen
binding, which is essential to assist the design of antibodies. However, the
accuracy of predictive models is usually impacted by the biological complexity
and the restricted information of the considered datasets. The generalisation
of learning models refers to the performance of the trained models on
different datasets without the need for additional training. The
generalisation is important for the ML-based mAbs design. To achieve good
generalisation performance, a learning model needs to consider the similarity
of antibodies in terms of both sequence similarity and binding behaviors
because sequence-similar antibodies may bind to different antigens. Another
challenge for the ML models to be used for mAbs design in practice is the
ability to explain the reasons behind their predicted outcomes, i.e., showing
the rules for prediction and generalisation. The interpretability of ML models
would reduce the risks of results generated from data-dependent properties and
provide insights for development of novel antibody sequences. This
characteristic is still a challenge of ML models for antibody-antigen binding
prediction as most of the predictive approaches are deep learning models which
are inherently difficult to extract interpretable knowledge from. Model
uncertainty is also another challenge for the application of ML models to this
theme. It is because training data are often noisy and sparse, so it is
difficult for learning models to exhaustively learn the underlying rules for
the prediction. As a result, the learning models could converge to an
approximate set of biased rules or short-cut solutions. The completeness of a
training dataset is the amount of information contained in this dataset to
assist ML models in inferring predictive results. It is still challenging to
assess the completeness of training sets for the prediction of antibody-
antigen binding.

Figure 4: Five key challenges for the applications of ML to the
antibody–antigen binding prediction (adapted from Akbar et al. [2022a])

####  4.2.2 Learning and optimisation of interdependent antibody design
parameters

In order for the potential candidates of therapeutic mAbs to be manufactured
at commercial scales and successfully go through clinical procedures, they
need to overcome several developability problems [Raybould et al., 2019]. The
developability of an antibody is evaluated based on a number of biophysical
and biochemical properties including the propensity to stability, aggregation,
immunogenicity, plasma half-life and clearance [Bailly et al., 2020]. Early
screening of developability with properties satisfying manufacturing
requirements is essential to reduce the time and cost for the choice of lead
mAb candidates. The development of in silico tools equipped with ML algorithms
for the prediction of developability parameters could help to enhance the
quality of mAb designs. According to Akbar et al. [2022a], the design process
needs to take into account the interdependence and non-linearity of antibody
design parameters.

The applications of ML models for the prediction of antibody design parameters
in terms of developability properties based on aggregation and stability
including thermal and pH stability, solubility, and concentration-dependent
viscosity were presented in Section 4.1. This section will discuss the
applications of ML algorithms to the estimation and identification of antibody
design parameters in terms of the plasma half-life, clearance, and
immunogenicity.

The serum half-life and clearance are important factors influencing the
efficacy and optimal dosing regimen of mAbs [Goulet et al., 2018]. The half-
life of Immunoglobulin G (IgG) of mAbs can be adjusted by optimising several
physicochemical parameters such as the Neonatal Fc-receptor (FcRn) affinity,
the isoelectric point (pI), binding poly-specificity reagent (PSR) and thermal
stability [Akbar et al., 2022a]. These key parameters have been identified
through several studies with the support of ML algorithms. Goulet et al.
[2018] deployed the least absolute shrinkage and selection operator regression
(LASSO) to determine the key combination of biophysical parameters for highly
accurate prediction of half-life and clearance with the mAbs clinical half-
life and clearance data. The analytical results showed that FcRn binding at pH
7.0 and thermodynamic stability (particularly the change in heat capacity upon
unfolding) result in the most accurate prediction of half-life and clearance
for mAbs. In a recent study, Grinshpun et al. [2021] analysed the impact of 40
biophysical and sequence-based parameters (12 measurements in vitro and 28
computed in in silico) on mAb clearance over 48 IgG1 antibodies. The authors
used the Random Forest model for the classification of the mAb linear
clearance (normal clearance and fast clearance). The ranking analysis of
biophysical variables over 10,000 loops of the RF model showed that in silico
computed sequence-based pI and PSR are the most important parameters
influencing the human clearance. These results aligned with the findings in
previous studies on the impacts of pI and PSR on the pre-clinical clearance
[Igawa et al., 2010, Kelly et al., 2015, Datta-Mannan et al., 2015].

Antibodies developed from non-human sources such as B-cell repertoires of
animals is more unlikely to be tolerated by humans and so it can lead to
immunogenic responses. This phenomenon is known as immunogenicity. These
immunogenic responses can result in negative effects on safety and
pharmacokinetic properties of therapeutic mAbs and the generation of
neutralising antibodies causing the loss of efficacy [Lu et al., 2020].
Therefore, the evaluation of immunogenicity of mAbs is a critical step in the
design of mAbs. Humanization is one of the methods to reduce immunogenicity
while still preserving the mAb efficacy [Marks et al., 2021]. Humanization
approach of a mAb involves the partial substitution of non-human CDR sequences
using a referenced human scaffold. An efficient humanization method needs the
ability to accurately identify the humanness of a sequence. Higher humanness
scores are usually associated with lower immunogenicity levels. Several
studies have applied ML models to the assessment of antibody humanness and
acceleration of mAb humanization via in silico solutions. Clavero-Álvarez et
al. [2018] proposed to use the multivariate Gaussian model to estimate the
degree of humanness in consideration of the correlations between pairs of
residues within and among amino acids at different positions. Although the
proposed method may accurately classify human and murine sequences, the
humanness score showed only a weak negative correlation to experimental
immunogenicity levels. In another study, Wollacott et al. [2019] trained a bi-
directional long short-term memory network on human antibody sequences and the
model showed the better performance in discriminating between the antibody
sequences of human and other species than the method proposed in Clavero-
Álvarez et al. [2018]. The trained model can be used for selecting suitable
scaffolds and mutations to assist in humanization of antibodies. Marks et al.
[2021] built RF classifiers to discriminate each human V gene type from non-
human sequences. The model is trained on large-scale repertoire sequence data
including human and murine precursor sequences taken from the Observed
Antibody Space (OAS) database [Kovaltsuk et al., 2018]. The trained RF model
is then used to develop a humanization tool, called Hu-mAb, that can humanize
both the VH and VL sequences of potential mAbs by proposing mutations
increasing humanness. The Hu-mAb tool could suggest optimal solutions which
can minimise the number of mutations made in the sequence to reduce the effect
on efficacy of mAbs. The experimental results showed that the humanness scores
provided by the RF model represent a negative correlation with experimentally
observed immunogenicity. It means that the sequences associated with a higher
humanness score could have lower immunogenicity levels. As a result, the
mutations proposed by the Hu-mAb are similar to those provided by experimental
humanization studies. However, the non-human sequences in the OAS database are
almost all murine sources, and thus the Hu-mAb can only perform effective
humanization on murine precursor sequences. In addition, the tool only
supports V-typed antibody formats. The OAS database was also used in the study
of Prihoda et al. [2022]. The authors developed an open-source platform for
humanization (Sapiens) and humanness evaluation (OASis). The OASis can provide
granular and interpretable humanness scores using 9-mer peptide search in the
OAS which is able to precisely separate human and non-human sequences with
results correlated with clinical immunogenicity. The Sapiens is a deep
learning humanization technique, which train a deep neural network with the
Transformer encoder architecture using language modeling. The input sentences
of the model are amino acid sequences of antibody variable regions, while the
word tokens are individual amino acid residues. The experimental outcomes
showed that the Sapiens can achieve a higher humanness enhancement than Hu-mAb
and comparable to experimental methods. The tool can provide viable point
mutations that can be experimentally validated, while preserving specificity
and binding affinity of mAbs.

Another approach to estimate the immunogenicity of mAbs is based on the
adaptive immune system activation mechanism by analysing the peptide
presentation on the Major Histocompatibility Complex class II (MHC II)
molecules recognised by T cells. This characteristic is called
immunopeptidome, which can provide valuable knowledge for immunogenicity
estimation [Sofron et al., 2016, Dudek et al., 2016]. With the popularity of
immunopeptidome public databases such as Immune Epitope Database
111www.iedb.org (IEDB) and ones presented in Doneva et al. [2021], various in
silico tools have been developed to predict protein immunogenicity using
immunogenic peptides. Different algorithms including the use of ML models such
as ANNs and SVM, discussed in a recent review of Doneva et al. [2021], could
utilise the sequence or structural data of proteins to estimate their efficacy
to stimulate a T-cell response.

In summary, although there have been many advanced algorithms proposed and
improvements made in efficiency and accuracy of in silico predictive
approaches of developability parameters for potential mAb candidates, several
challenges remain unsolved [Akbar et al., 2022a]. For example, computational
immunogenicity predictions have not yet completely substituted the in vitro
testing in animals because of the safety requirements accompanying the
specific parameters [Doneva et al., 2021]. Kuroda & Tsumoto [2020] argued that
it is likely to be tough to base only on in silico approaches for the
assessment of immunogenicity due to the limitation in the understanding of
biological rules for immunogenicity. Moreover, finding a multi-objective
global optimisation for all developability parameters for a given antibody is
still a challenging requirement because the enhancement of a developability
measure could lead to the sacrifice of another. For instance, Kuroda & Tsumoto
[2020] indicated that the improvement of antibody stability might lead to the
increase of aggregation. A promising solution to learn and optimise multiple
design parameters for a given mAb is the combination of many ML models trained
on data from various experimental campaigns [Liu et al., 2020].

####  4.2.3 Generation of in silico antibody sequences

As presented in a recent survey [Akbar et al., 2022a], the in silico
generation of antibody sequences has been dominated by the deep learning
models with three typical architectures namely Variational Autoencoders (VAE),
Generative Adversarial Networks (GAN), and Autoregressive (AR) models
including long short-term memory recurrent neural network (LSTM-RNN) and a
transformer. These generative models can be trained on general or customised
datasets to learn sequence space representations and synthesise novel
sequences. The autoregressive models are able to generate highly diverse mAb
sequences and meaningful sequence embeddings reducing the demand for manual
features. In the similar manner, the VAEs and GANs have been used to generate
biologically meaningful latent representations and condition them on extra
features (e.g., viscosity, solubility). As a result, these models could be
deployed for de novo sequence generation, conditional or out-of-distribution
generation, and optimisation of multiple mAb design parameters. Transfer
learning approaches can be used to infer higher-order and function-specific
interactions from a small number of available sequences based on the insights
learned from general large datasets. To assess the effectiveness of generative
models, it is necessary to have external computational or experimental
validators (oracles) for resulting antibody sequences after generation.
Experimental evaluation methods may be time-consuming and costly, so it is
desirable to develop in silico oracles. This generation process of in silico
antibody sequences is illustrated in Fig. 5.

Figure 5: Main steps of an in silico generation of potential candidates in the
mAb design process (adapted from Akbar et al. [2022a])

GAN models are usually used to explore the prospective distribution of an
actual dataset by deploying a generator and a discriminator in a zero-sum
game. The best performance will be obtained as the discriminator no longer has
the ability to distinguish the outputs of the generator from the actual
distribution. Therefore, GANs posses a great potential for generation of mAb
sequences with a particular property or novel sequences (out-of-distribution
sampling) from existing distributions of available sequence spaces. Amimeur et
al. [2020] illustrated the use of the GAN model trained on 400,000 light and
heavy chain human-repertoire sequences randomly taken from the OAS database
for generation of novel antibody therapeutics. The resulting sequences
generated by the GAN model can mimic typical human repertoire features like
diversity and immunogenicity. Specifically, the authors showed that the
proposed method can bias the model to achieve the improved developability and
chemical and biophysical properties of interest by continued training of the
learned model with a data subset of specific desirable properties. For
example, as shown in the outcomes, this transfer learning approach can reduce
negative surface area patches of antibodies (recognised as a potential
aggregation source, thermal instability, and possible half-life decreasing),
bias of a higher pI to lower aggregation, lower binding affinity toward MHCII,
or going towards longer CDR3 lengths (identified as a method to increase
diversity and formulate more effective therapeutics for a given target). The
authors experimentally validated their method in vivo by expressing GAN-
generated antibody sequences via phage display and in stable Chinese hamster
ovary (CHO) cells. The experimental results showed that the generated
antibodies exhibit multiple desirable biophysical properties.

In the mAb design, VAEs could be employed to lower the dimensionality of a
dataset, formulate biologically meaningful representations and clusters,
generate de novo sequences, and create the mAb sequences with the expected
function by interpolating in the latent space. Eguchi et al. [2022] proposed a
method for novel and high-quality 3D backbone structures of antibodies using a
VAE trained on the structural data collected from the antibody structure
database AbDb/abYbank [Ferdous & Martin, 2018]. The experimental results
showed that the backbones generated by the VAE model are new and chemically
accurate and able to be used in other design protocols to achieve
biochemically realistic sequences. For example, the use of a constrained
optimisation and simulation framework such as Rosetta [Leaver-Fay et al.,
2011] does not result in a large change in the overall structure of antibodies
generated by the VAE model but there is a refinement of chemical validity. In
addition, by constraining and optimising a structure in the latent space of
the VAE model, it can generate structures satisfying user-defined features.
The authors demonstrated these characteristics by integrating the proposed
method with Rosetta to generate a single-domain-binder to the ACE2 epitope of
the SARS-CoV2 receptor binding domain via discrete sampling and constrained
optimisation of the mAb structures in the latent space. The outcomes showed
that the latent space of VAEs can be used to generate new binding proteins and
provide rational mAb candidates for further experimental validation. In
another study, Friedensohn et al. [2020] also introduced a VAE model trained
on antibody repertoires from the bone marrow of antigen-immunized mice to
model the underlying B-cell receptor recombination process, in which a
Gaussian mixture model is applied to latent space to cluster potentially
antigen-specific antibody sequences. This method can provide both the latent
embedding and cluster labels for similar sequences. Hence, the proposed VAE
model could synthesise new sequences and group input sequences to distinct
clusters based on the learned latent space embedding. The authors
experimentally verified the proposed method by recombinantly expressing twelve
antibody sequence variants from one cluster mapped to one of the confirmed
binders. The results confirmed that all of these 12 variants were antigen-
specific.

Due to inherently sequential nature of autoregressive models, they are usually
suitable for modeling biological sequences, where the order of amino acids is
fixed and sequential (one amino acid after the other). In a benchmarking
research regarding the antibody sequence design, Melnyk et al. [2021]
indicated that AR models are appropriate choices for the specific antibody
design tasks including grafting specific regions like a short CDR3 within an
input sequence and generation of diverse sequences with broader coverage while
still preserving the consistency with a given input sequence. AR models can be
used to generate antibody sequences with the optimised binding affinity. Saka
et al. [2021] demonstrated the use of a LSTM neural network for the generation
of high antibody against kynurenine, which is a metabolite concerning the
niacin synthesis pathway. The LSTM model is trained on enriched antibody
sequences by next-generation sequencing techniques. Then, virtual sequences
are generated from the trained LSTM model by mimicking enriched sequences, and
the priority is put into the promising sequences with high affinity based on
the likelihood scores of binding affinity computed by the trained model. The
outcomes confirmed that the likelihood values of antibody sequences generated
from the proposed model are highly correlated with the quantitative binding
affinity. Compared to frequency based screening techniques, the generated
sequences by LSTM models also have a greater affinity. The LSTM model can also
be used to learn distributions of antibody sequences together with a wide
range of design parameters including paratope, epitope, affinity, and
developability. This characteristic was illustrated in the study of Akbar et
al. [2022b]. The authors generated in silico training dataset of 1D native
antibody sequences (CDR-H3, the third CDR of the antibody heavy chain) (taken
from Greiff et al. [2017]) annotated with their corresponding paratope,
epitope, and affinity to each 3D antigen (from the Antibody Database [Ferdous
& Martin, 2018]) using the antibody-antigen binding simulation framework,
called Absolut! [Robert et al., 2021]. Then, these 1D antigen-aware CDR-H3
sequences were used to train an LSTM generative model. Note that the LSTM
model was not trained on any explicit 3D information on the paratope, epitope,
affinity, nor the developability parameters of a given sequence. The trained
model was then deployed to generate novel CDR-H3 sequences, which were
assessed for their antigen-specificity including affinity, paratope, epitope
associated with developability parameters using Absolut!. The obtained results
showed that the generated CDR-H3 sequences had the developability parameters
and affinity values similar or better than those of training sequences. This
research confirmed that the AR models are capable of learning 3D-affinity,
paratope, and epitope information from 1D antibody sequence data. In another
study, Shin et al. [2021] showed the efficiency of the AR model in designing
new single-domain antibody sequences (also known as nanobodies) from existing
natural nanobody repertoires. The authors employed dilated convolutional
neural networks trained on around 1.2 million llama nanobody sequences from
the immune repertoires to learn the constraints characterising functional
nanobodies. The model learned conditional distributions over CDR-H3 sequences
conditioned on the CDR-H1 and CDR-H2 sequences to eliminate CDR-H3 sequences
which are not chemically compatible with the other CDR-Hs. The experimental
results confirmed that the new CDR-H3 sequences generated by the causal CNNs
have similar distributions of hydrophobicity and isoelectric point in
comparison to the reference natural CDR-H3 sequences.

In conclusion, the current studies on in silico generative modeling of
antibodies have mainly focused on the generation of a new 3D backbone
structure of antibodies with expected properties [Eguchi et al., 2022],
sampling antibody sequences with high affinity [Saka et al., 2021], clustering
and interpolation of the latent space for discovering new antibodies [Eguchi
et al., 2022, Friedensohn et al., 2020], and the generation of new and diverse
antibody sequences satisfying the desirable developability parameters [Amimeur
et al., 2020, Akbar et al., 2022b]. However, the synthesis of new antibodies
using generative models also faces many challenges. The first one is the lack
of effective external (computational or experimental) oracles to assess the
outputs of generative models. The second issue is the difficulty in the
simultaneous optimisation of multiple developability parameters and binding
affinity during the generation of new antibodies as the label (e.g., binding
affinity, developability, and plasma half-life ) of new antibodies is not
known a priori. Therefore, it is highly desirable to develop high-throughput
experimental oracles.

###  4.3 Design of other therapeutic proteins

Protein engineering is a progressive process of designing or developing
proteins which meet a set of valuable properties to be used in scientific or
medical applications [Yang et al., 2019]. The discovery of proteins with
expected properties and functions is usually resolved through three approaches
of protein engineering or design including rational design, directed
evolution, or de novo design [Bojar & Fussenegger, 2020, Narayanan et al.,
2021a]. This section will review the typical applications of ML to protein
engineering and design of general proteins through these three main routes.
While the rational design of proteins focuses on building predictive models
from structure or sequence data to predict the properties (e.g., affinity,
stability) of a given input protein, the directed evolution workflow aims to
combine the predictive modelling with optimisation algorithms to generate new
proteins with pre-defined desired properties. Meanwhile, de novo protein
design is directed towards building generative models to explore the input
space together with perturbing the input data for generation of new resulting
proteins unrelated to those in nature on the basis of physical principles of
intra- and inter-molecular interactions [Huang et al., 2016].

Rational protein engineering methods aim to develop quantitative models for
protein properties, and deploy them to more efficiently discover the protein
fitness landscape (the relationship between a given set of mutations and the
protein function of interest) to bypass the directed evolution challenges
[Alley et al., 2019, Rocklin et al., 2017, Huang et al., 2016, Romero et al.,
2013]. Such rational design needs a comprehensive and predictive understanding
of structural stability and quantitative molecular function of the proteins
[Alley et al., 2019]. As a result, the quantitative structure-activity
relationship (QSAR) modeling using ML algorithms have been introduced for
virtual screening of prospective biological active molecules from millions of
candidate compounds cheaply and rapidly [Wang et al., 2015]. The predictive
models usually use the sequence or structure data of proteins represented
under the form of input feature vectors or matrices and predict the target
protein properties. The details of the use of traditional ML models and deep
learning for QSAR approaches can be found in several surveys such as Zhang et
al. [2017], Xu et al. [2020], Mao et al. [2021].

Directed evolution techniques are used to repeatedly enhance an expected
function of a given protein through mutation and selection steps of an initial
protein sequence. Machine-learning-assisted directed evolution is a branch of
protein engineering aiming at optimisation of protein functions [Yang et al.,
2019]. Data driven ML models can be used to predict the way of mapping protein
sequences to corresponding functions without the demand for a detailed
approach of the underlying physics or biological pathways. Such approaches
enable to accelerate the directed evolution by learning from the properties of
given variants and utilising this learned knowledge to choose sequences likely
to express the enhanced properties. As shown in Yang et al. [2019], machine
learning has been highlighted as a potential tool in the directed evolution
workflow. By reducing the number of variants which need to be verified and
learning from each directed evolution round [Bojar & Fussenegger, 2020], ML-
guided directed evolution has resulted in new approaches for optogenetics
[Bedbrook et al., 2019], the engineering of reference green fluorescent
proteins [Saito et al., 2018], protein thermostability [Romero et al., 2013],
and demonstrated some potential to address the problem of epistatic effects
derived from combinatorial mutations [Wu et al., 2019].

De novo methods aim to take advantage of the protein folding principals to
design new polypeptide sequences which can fold into a stable 3D structure and
are associated with an existing or new desirable functionality. Similarly to
the design of mAbs, generative DL models including GAN, VAE, and
Autoregressive models have been used for de novo design of protein sequences
with expected functionalities like solubility, stability, and affinity [Karimi
et al., 2020]. Repecka et al. [2021] proposed a new GAN architecture using
temporal convolutional networks and a self-attention layer to learn the
meaningful sequence motifs and evolutionary relationships directly from the
complex multidimensional amino-acid sequences. The trained model then can
generate novel and highly diverse sequence variants exhibiting natural-like
physical properties within the given biological constraints. Costello & Martin
[2019] proposed a VAE-based protein sequence generation method using residual
networks, dilated convolutions, and an autoregressive module on top of the
decoder trained on the protein sequence and phenotype data. The latent space
of the proposed architecture is able to accurately reconstruct sequences with
a set of desirable functions and phenotypes. The research is a step forward to
build a model able to learn general structures of protein sequences and design
general-purpose structure free proteins. Rives et al. [2021] proposed to use a
Transformer network and Alley et al. [2019] used LSTM-RNNs trained on large
unlabelled sequence databases to generate sequence embeddings which are able
to capture the secondary and tertiary structures and funtionalities of a
protein as well as amino acid biochemical properties. Biswas et al. [2021]
demonstrated how such sequence embeddings can be used for de novo protein
evolution. First, the general embeddings were generated by an LSTM-RNN network
trained on 20 million general protein sequences. Then, these embeddings were
fine-tuned on sequences of proteins of engineering interest, followed by the
sampling procedure of a small number of mutants, quantifying their particular
functional properties and developing a linear regression model with inputs
being embedding representations and outputs being quantified protein
properties. The trained model is then used to propose the optimal designs
evolved from a starting protein sequence by mutating, embedding, and
prediction of the desirable properties. This process is repeated until the
expected functionality of the protein is achieved. There have been several
studies demonstrating the capability of conditional transformer models, which
have been successfully used for text generation, for protein engineering
problems, i.e., de novo protein design. Ingraham et al. [2019] proposed a
conditional transformer model for designing protein sequences using a graph
representation of their 3D structures. The proposed architecture can
effectively employ the spatial locality of dependencies in molecular
structures for the generation of new protein sequences. Another study
performed by Madani et al. [2020] can create new functional protein sequences
with existing folding motifs by training a Transformer network on diversified
sequences with conditioning tags which encode annotations like organism,
function, location, and process information. The proposed model is able to
learn these conditional probabilities over amino acids and conditioning tags
of interest. The experimental results indicated that the protein sequences
derived from conditional labels are different from the similar sequences in
the training data, and to some extent the model can generate out-of-
distribution sequences.

###  4.4 Cell-line selection

Cell-line selection is a gene transfection process of protein of interest into
host cells to build a heterogeneous cell pool. Cells are then sorted into
single-cell cultures, and the cell-line leading with regard to the highest
quantity, quality and stability across sequentially scaled-up cultures is
chosen for the master cell bank, which are usually utilised for manufacturing
phases in clinical trials and commercial use later on [Narayanan et al.,
2021a]. Traditional approaches of cell-line selection are time and labour
consuming and dramatically restricted by the number of clones which can be
screened to obtain high-production clones [Browne & Al-Rubeai, 2007].
Therefore, the high-throughput automation tools and image analysis techniques
have been developed to quickly screen high quality clones from a large
population of heterogeneous cells.

Golabgir et al. [2016] compiled a bibliography of more than 1000 articles on
CHO cells and analysed the data using multivariate statistical methods. They
found significant differences among cell lines regarding specific
productivity, growth rate, and viable cell density. Despite this variation,
they were able to draw some general conclusions that were independent of the
particular cell line. For example, they observed a correlation between titer
and cell viability, as well as a reverse correlation between growth rate and
both specific productivity and titer. Therefore, the identification of cell-
line and clone specific characteristics is crucial for the design of
integrated bioprocesses [Badr & Sugiyama, 2020]. Changes in these
characteristics can have a significant impact on upstream processes and,
consequently, downstream process design.

As reported in the literature, ML models have been deployed for image analysis
in fluorescence-based cell sorting aiming to separate the heterogeneous pool
of cells into single-cell cultures. Gu et al. [2019] developed a real-time
image guided cell sorting and classification system using image processing
methods and an SVM model. The constructed system can generate bright field and
fluorescence cell images under the form of time domain waveforms. Cell images
are then rebuilt from such time domain waveforms. After that, the system
extracts image features and derives image-based gating criteria. Finally, cell
images are handled, and the cell sorting process is performed in real time
using the SVM model based on the image-guided gating criteria. Nitta et al.
[2018] introduced an intelligent image-activated cell sorter method based on
image analysis and classification using CNN models. The system combined high-
throughput cell microscopy, focusing, and sorting within a hybrid software and
hardware data management framework, which allows real-time automated execution
from data acquisition, processing, decision making to actuation. The images of
sorted cells can be employed to further train the deep CNN models for
improving sort decisions and optimisation of the recognition of extremely rare
cells, e.g., circulating tumor cells, antigen-specific T cells and cancer stem
cells.

##  5 The applications of ML in understanding, prediction, optimisation,
monitoring and control of upstream processes

In biopharmaceutical process development manufacturing, the upstream process
refers to the initial stages of production including the cultivation and
harvesting of cells that produce the active pharmaceutical ingredient
[Papathanasiou et al., 2017]. The main role of the upstream process is to
ensure the growth and reproduction of the cells producing the active
pharmaceutical ingredients in large enough quantities to meet the production
demand [Satheka, 2022]. Data analytics and modelling techniques are frequently
deployed in the upstream process development mainly because it plays an
important role in achieving the maximum quality and quantity of products and a
large amount of valuable data is usually derived during this stage [Sokolov et
al., 2017, Richelle et al., 2020]. Therefore, this section will present a
comprehensive review of how ML models applied to the selection and
optimisation of cell culture media, the determination of the variations in the
upstream process performance and product quality with regard to the changes in
operational conditions and CPPs of bioreactors as well as understanding and
extracting insights from upstream processes based on the collected data. This
section also reviews the applications of ML models to real-time monitoring and
controlling of the upstream process as well as transferring knowledge across
different scales of cell cultivation experiments.

###  5.1 Efficient screening of cell culture media supplements

Cell culture media optimisation is an effective way to enhance the quality of
recombinant proteins produced in cell culture in addition to the development
of cell lines and culture conditions [Reinhart et al., 2015, Ivarsson et al.,