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11 • Systems Biology Approaches 291
Apart from omics tools and techniques, several types of mathematical models are
also leveraged for drug target identication and validation. These include disease mod‑
els, biotherapeutic models, dynamic pathway models, and data‑driven statistical learning
models. In the disease model, a systems‑level mathematical or statistical framework is
developed to describe a given disease in silico. Conventionally, kinetic parameters and
rate constants from experiments, literature, and/or informed guess are leveraged to model
the disease pathophysiology with ordinary differential equations (ODEs), also known as
mechanistic models. Data from multi‑omics such as transcriptomics, proteomics, and
metabolomics are also being leveraged to improve the predictability of disease models.
Another dimension of complexity in disease models is the interaction of components at
multiple scales. This is generally captured by multi‑scale hybrid models such as ABMs
(Ji etal. 2017; Menezes etal. 2020). In some cases where data availability is limited to
develop quantitative models, a systems‑level qualitative model may be developed that
is based on molecular cause‑and‑effect relationships. Table 11.4 captures a few of the
recent examples of disease models/metabolic models. For example, Ji etal. developed a
hybrid agent‑based model to capture cell‑cell interactions in bone marrow under multiple
myeloma conditions (Ji etal. 2017). The model was utilized to predict the treatment effects
of three key therapeutic drugs. The study found that the combination of these three drugs
TABLE11.4 Recent examples of studies performed in drug target discovery and target
validation of biopharmaceuticals utilizing computational systems biology approach
MODELING
APPROACH
Mechanistic Metabolic
Multi-scale
mechanistic
Hybrid ODE
and ABM
Multi-scale and
biophysical
Hybrid ODE
and ABM
DISEASE
MODEL STUDY AND MAJOR OUTCOME REFERENCE
Study performed simulation of adiponectin
disease
Kidney
disease
Cancer
(multiple
myeloma)
Cancer
(Glioma)
Tuberculosis Captures various aspects of TB disease and
exocytosis in in response to the reduction of
β3ARs observed in adipocytes from animals
with obesity-induced diabetes.
Developed a complete nephron model and
tested inhibition of Na
2 (SGLT2) along the proximal convoluted
tubule. Results predicted that the segment’s
Na+ reabsorption decreased signicantly,
resulting in natriuresis and osmotic diuresis.
Developed and applied myeloma growth
model to predict the treatment effects of
three key therapeutic drugs. Study found that
the combination of these three drugs can
potentially suppress the growth of myeloma
cells and reactivate the immune response.
The model predicts that cell migration depends
on the relative balance between random
motility and strength of chemo-attractants.
predict that biomarkers in the blood may only
faithfully represent events in the lung at early
time points after infection.
+
-glucose cotransporter
Lövfors
etal.
(2021)
Layton and
Layton
(2019)
Ji etal.
(2017)
Kim etal.
(2015)
Joslyn etal.
(2022)
(Continued)
292 Biopharmaceutical Informatics
TABLE11.4 (Continued) Recent examples of studies performed in drug target discovery
and target validation of biopharmaceuticals utilizing computational systems biology approach
MODELING
APPROACH
ABM
host-pathogen
model
Mechanistic Diabetic
Mechanistic COVID-19 Model tests the hypothesis that SARS-CoV-2
Machine
learning
DISEASE
MODEL STUDY AND MAJOR OUTCOME REFERENCE
Pneumonia Identied key contributors to alveolar infection
in a mechanistic fashion. Results suggest that
S. pneumonia interactions with alveolar
epithelial cells contribute to overall infection
dynamics as compared to interaction with
macrophages.
The study addresses the challenge of
kidney
disease
Lung cancer Developed machine learning model to explore
heterogeneity for clinical trials of novel
anti-inammatory therapies, by modeling
how chronic inammation affects kidney
function in ve compartments. Such models
could be utilized for drug target validation.
infects immune cells and, for this reason,
induces high-level productions of
inammatory cytokines.
novel antibody sequence space and to
accelerate the development of highly potent,
drug-like antibodies.
Santos
etal.
(2018)
Hofherr
etal.
(2022)
Reis etal.
(2021)
Makowski
etal.
(2022)
can potentially suppress the growth of myeloma cells and reactivate the immune response.
Similarly, Layton and Layton (2019) developed a multi‑scale mechanistic model for neph‑
rons. The model was utilized to test the inhibition of Na+‑glucose cotransporter 2 (SGLT2)
along the proximal convoluted tubule. The model predicted that the segment’s Na+ reab‑
sorption decreased signicantly, resulting in natriuresis and osmotic diuresis, thus pro‑
viding insights into potential drug targets for kidney disease. In summary, multi‑omics
analyses and mathematical models have been utilized to gain insights into disease models
and identify potential drug targets using a systems biology approach.
11.5 APPLICATION OF SYSTEMS
BIOLOGY IN BIOPHARMACEUTICAL
DEVELOPMENT–OPTIMIZING
GROWTH AND PRODUCTIVITY
Once the therapeutic target has been identied and validated, the next step in the bio‑
pharmaceutical pipeline is to produce the therapeutic biomolecule or protein of inter‑
est. This means to develop a scalable process to generate material for various stages
11 • Systems Biology Approaches 293
of studies, including regulatory toxicology studies and phase 1 and phase 2 clinical
trials prior to approaching the Food and Drug Administration (FDA) for evaluation (Hu
2020). This set of steps, in the broader sense, can be termed biopharmaceutical develop‑
ment. It encompasses both the discovery and development of the drug target. A typical
biopharmaceutical process development paradigm includes developing a cell line that
can stably produce the biotherapeutic molecule (cell line development), generating the
biomolecule in cell culture (upstream process development), purication of cell culture
material to remove impurities (downstream process development), and the nal formu‑
lation of the therapeutic into drug product (drug product development).
The upstream process development of biotherapeutics such as monoclonal antibod‑
ies is typically accomplished using recombinant DNA (rDNA) technology to engineer
producer cell lines such as Chinese Hamster Ovary (CHO) cells, mouse myeloma (NS0)
cells, among others (Dhara etal. 2018). As such, there are two major goals for biophar‑
maceutical product development: (i) enhance the productivity of the biologics (directly
linked to the reduction in the cost of manufacturing) and (ii) enhance and maintain the
target range for product quality attributes (directly linked to safety and efcacy of the
drug product) (Figure11.4). On both these fronts, systems biology techniques such as
multi‑omics analyses and process modeling are implemented to gain process/cellular
enhancements.
The productivity of biotherapeutics in cell culture processes directly depends on the
amount of cell biomass available and its capability to produce the protein of interest. Cell
concentration is generally depicted by viable cell density (VCD). Specic productivity,
the rate of protein production per cell per unit time, is depicted by q
. Together, these two
P
key factors determine overall volumetric productivity (VP) as VP is an integral of qP*VCD
over the batch duration (time) and vessel volume. Conventionally, bioprocess parameters
such as pH, temperature, dissolved oxygen, rate of agitation, etc., are utilized as levers to
FIGURE11.4 Systems approach toward biopharmaceutical development.
294 Biopharmaceutical Informatics
increase growth and productivity. In addition, perturbations in cell culture medium com‑
position, feeding strategy, and perfusion are also leveraged to enhance cell growth and
productivity (Ritacco etal. 2018; Hiller etal. 2017). However, these conventional levers
have limitations as they cannot be utilized to perturb the cellular physiology from within
to push the biological limit for growth and productivity (Hoang etal. 2022). Moreover,
these methods require a signicant number of experiments to determine the statistically
optimal condition, as they are mostly trial‑and‑error based. The tools and techniques
in systems biology can be leveraged to push the limits of growth and productivity by
identifying physiological bottlenecks and developing mitigation strategies (Kildegaard
etal. 2013). Moreover, as the design of experiments is based on mechanistic insights, it
also helps in reducing the number of experiments needed for achieving optimal growth
and productivity. Table11.5 summarizes a few recent examples where a systems biology
approach has been implemented to achieve improved cell growth and protein productivity.
For instance, it is well established that high concentrations of lactate and ammonia result
TABLE11.5 Recent examples of studies performed to enhance cell growth and
productivity of biopharmaceuticals using systems approach
TOOLS/
SYSTEMS
APPROACH
Multi-omics
and
functional
analyses
Multi-omics
and
functional
analyses
Omics and
functional
analyses
Multi-omics
and
functional
analyses
Multi-omics
and
functional
analyses
TECHNIQUES
EMPLOYED STUDY AND MAJOR OUTCOME REFERENCE
Transcriptomics,
proteomics,
metabolomics,
and glycomics
Proteomics and
metabolomics
Proteomics The study highlighted key pathways for
Transcriptomics,
proteomics, and
metabolomics
Transcriptomics,
and
metabolomics
Performed multi-omics proling to
investigate effects of pH on growth and
productivity. Study concluded that pH set
points differentially regulated various
intracellular pathways including vesicular
trafcking, cell cycle, and apoptosis,
thereby impacting growth and
productivity.
Study demonstrated that modications to
cellular environment by modifying feed in
cell culture can be guided by omics studies
to achieve enhanced cell growth.
targeted engineering to generate desirable
CHO cell phenotypes with enhanced
growth for biotherapeutic production.
The study identies key metabolic enzymes
and metabolites as indicators of growth
and productivity. Also demonstrates that
redox balance is key to cellular health
during cell culture processes.
The study identies and demonstrates
intermediates and byproducts of amino
acid metabolism as secondary growth
inhibitors in cell culture for biotherapeutics
production.
Lee etal.
(2021)
Blondeel
etal.
(2016)
Bryan etal.
(2021)
Ali etal.
(2019)
Chandra
Mulukutla
etal.
(2017)
(Continued)
11 • Systems Biology Approaches 295
TABLE11.5 (Continued) Recent examples of studies performed to enhance cell growth
and productivity of biopharmaceuticals using systems approach
TOOLS/
SYSTEMS
APPROACH
Mathematical
modeling
Mathematical
modeling
Mathematical
modeling
Mathematical
modeling
Mathematical
modeling
TECHNIQUES
EMPLOYED STUDY AND MAJOR OUTCOME REFERENCE
Genome-scale
modeling
Genome-scale
modeling
Constraint-based
modeling
Metabolic ux
analysis
Genome-scale
metabolic ux
modeling
The study evaluates and compares
parameters for the two phases of cell
culture, i.e., growth phase and production
phase.
Developed a metabolic network-based
modeling approach utilizing genome-scale
model, and implemented it to develop
feeding strategies to potential increase
protein productivity.
The study identies unconventional
objective functions to minimize
non-essential nutrient uptake rate toward
cell growth and productivity improvements
in various CHO derived cell lines.
The study suggests that productivity is
related to the oxidative state of
metabolism whereas cell growth can be
characterized by glycolytic metabolic state.
Implemented genome-scale model to
identify feed supplements to enhance
biotherapeutic productivity.
Schinn
etal.
(2021)
Fouladiha
etal.
(2020)
Chen etal.
(2019)
Templeton
etal.
(2013)
Huang
etal.
(2020)
in growth inhibition in cell cultures, thereby reducing the overall productivity (Pereira
etal. 2018). Several process‑related and cell engineering strategies have been devised to
reduce the amount of these two growth inhibitors in cell culture. However, even with low
concentrations of lactate and ammonia, cells in culture tend to plateau out in terms of
growth (Chandra Mulukutla etal. 2017). Mulukutla etal employed omics techniques to
identify and quantify several byproducts or intermediates of amino acid metabolism that
accumulate in fed‑batch cell culture and impact cell growth. Further, transcriptomics and
metabolomics analyses resulted in the identication of cell engineering targets that could
reduce the biosynthesis of these growth inhibitors. Metabolic engineering of CHO cells
by knocking out such a gene target resulted in signicant improvement in cell growth
(Mulukutla etal. 2019). Similarly, mathematical modeling tools within systems biology
have also been utilized to enhance protein productivity in producer cells. For instance,
Huang etal implemented a genome‑scale model along with transcriptomic and metabo‑
lomic analyses to systematically evaluate CHO cell culture and gain metabolic insights
for bioprocess development (Huang etal. 2020). The genome‑scale model was leveraged
to identify an experimentally optimal condition for improved productivity. These are a
couple of examples that demonstrate how systems biology tools and techniques, includ‑
ing multi‑omics analyses and mathematical modeling, can be leveraged to enhance cell
growth and productivity of biotherapeutics in cell culture.
296 Biopharmaceutical Informatics
11.6 APPLICATION OF SYSTEMS
BIOLOGY IN BIOPHARMACEUTICAL
DEVELOPMENT–CONTROLLING
PRODUCT QUALITY ATTRIBUTES
Product quality attributes of biotherapeutics refer to the way the produced molecule
exists in the drug substance apart from the amino acid backbone structure. Attributes
such as post‑translational modications (glycosylation, deamidation, glycation, etc.),
charge variants, fragmentation, and aggregation may be directly linked to the safety and
efcacy of the biopharmaceutical drug product. While optimizing cell growth and pro‑
ductivity are the major drivers for developing a cost‑effective biotherapeutics production
process, controlling the aspects of the product quality attributes is equally important
for developing safe and efcacious biotherapeutics. In cases where the product quality
attributes could have a potential impact on drug product safety and/or efcacy, they are
termed ‘critical quality attributes’ (CQAs). A well‑dened control strategy is required to
maintain a prescribed or clinically tested range of these CQAs during the manufactur‑
ing process (Rathore and Winkle 2009).
Conventionally, the biopharmaceutical development process involves the design of
experiment (DoE) studies to identify process parameter ranges that are expected to
result in products with CQAs within the prescribed range. However, given the complex‑
ity and heterogeneity within the biological systems, control of CQAs has been a chal‑
lenging task. This is where systems biology tools can be implemented to gain detailed
mechanistic insights into the inuence of cell physiology and extracellular environ‑
ment on quality attributes, and how it is impacted by cell culture process parameters
(Kildegaard et al. 2013; Sha etal. 2016). As an example, this section will focus on
N‑linked glycosylation (N‑glycosylation) to understand how systems biology can be
implemented to gain better control and modulation of various glycosylated species dur‑
ing cell culture process development.
N‑glycosylation is a post‑translational modication that impacts various aspects
of therapeutic proteins, including effector functions, pharmacokinetic clearance,
safety, immunogenicity, stability, and shelf life. Various studies have been performed
to understand the effect of cell culture process parameters and media components on
N‑glycosylation (Sha etal. 2016). However, to precisely control and modulate the glyco‑
sylated species, a deeper mechanistic insight is required. To this end, both multi‑omics
studies as well as mathematical modeling have been utilized to further the understand‑
ing of N‑glycosylation control and modulation. Table 11.6 summarizes a few recent
examples where systems biology has been implemented to achieve similar goals of con‑
trolling and modulating post‑translational modications such as N‑glycosylation. For
example, Krambeck etal developed a detailed reaction kinetics model for protein gly‑
cosylation to predict the various glycosylated species that can arise in cell culture when
enzymes linked to N‑glycosylation are perturbed (Krambeck et al. 2009). By lever‑
aging mathematical modeling and integrating glycomics data from proteins produced
11 • Systems Biology Approaches 297
TABLE11.6 Studies performed to enhance and control product quality attributes of
biopharmaceuticals using systems approach
TOOLS/
SYSTEMS
APPROACH
Omics and
functional
analyses
Omics and
functional
analyses
Omics and
functional
analyses
Omics and
functional
analyses
Omics and
functional
analyses
Mathematical
modeling
Mathematical
modeling
Mathematical
modeling
Mathematical
modeling
TECHNIQUES
EMPLOYED STUDY AND MAJOR OUTCOME REFERENCE
Transcriptomics
and
metabolomics
Transcriptomics
and
metabolomics
Proteomics Differential activation of oxidative
Transcriptomics,
proteomics,
and
metabolomics
Transcriptomics The study implements omics analysis to
Genome-scale
modeling
Mechanistic,
reaction
kinetics model
Mechanistic,
reaction
kinetics model
Mechanistic,
reaction
kinetics model
The study reveals the effect of oxidative
stress on protein sialylation using
multi-omics approach.
The study identies the root cause for
temporal heterogeneity of glycan
species in therapeutic proteins
produced in cell culture and suggest
mitigation strategies based on
transcriptomic and metabolomics
analyses.
phosphorylation may result in
differences in post-translational
modication as well as charge variants
of therapeutic proteins produced in
CHO cell culture.
The study identies key genes and
pathways that are perturbed by pH
variation in cell culture, leading to an
impact on N-glycosylation, protein
aggregation, and charge variant
proles apart from mAb productivity.
understand biological causes of
aggregation and also identies
potential strategies to control this
product quality attribute.
Modeling framework to investigate the
effect of ammonium of sialylation of
monoclonal antibodies.
Modeled structure-specic turnover
rates of N-glycans, and prediction of
N-glycan heterogeneity for
glycosylation enzyme inhibitors.
Extension of GLYMMER model to ten
different CHO cell lines with varied
activities of enzymes involved in
N-glycosylation and NSD transport.
Utilized kinetic model to design and
build 23 different transgenic pools to
enhance galactosylation in protein
therapeutics.
Lewis etal.
(2016)
Sumit etal.
(2019a)
Strasser etal.
(2021)
Lee etal.
(2021)
Barzadd etal.
(2022)
Savizi etal.
(2021)
Arigoni-Affolter
etal. (2019)
Krambeck etal.
(2017)
Stach etal.
(2019)
(Continued)
298 Biopharmaceutical Informatics
TABLE11.6 (Continued) Studies performed to enhance and control product quality
attributes of biopharmaceuticals using systems approach
TOOLS/
SYSTEMS
APPROACH
Mathematical
modeling
TECHNIQUES
EMPLOYED STUDY AND MAJOR OUTCOME REFERENCE
Machine
learning and
AI tools,
glycomics
This study reviews the implementation
of machine learning and AI tools on
large set of glycomics data and
proposed that such models can be
analyzed to gain mechanistic insights
into glycosylation machinery and how
the machinery shapes glycans under
different scenario.
Li etal. (2022)
by a variety of cell lines, they were able to further improve the understanding of gly‑
can heterogeneity in therapeutic proteins (Krambeck etal. 2017). Such models have
signicantly helped improve the predictability and controllability of N‑glycosylation.
Similarly, Sumit etal. (2019) utilized multi‑omics functional analyses to identify tem‑
poral bottlenecks in the N‑glycosylation pathway during fed‑batch production of thera‑
peutic proteins. The study helped develop mitigation strategies to minimize temporal
heterogeneity of glycan species in fed‑batch processes toward better controllability of
the system. Mathematical modeling and multi‑omics analyses can also be utilized to
identify gene targets that can modulated to precisely control N‑glycosylation in cell
culture (Chang etal. 2019). These examples demonstrate that systems biology tools and
techniques, including multi‑omics analyses and mathematical modeling, can be lever‑
aged toward precision control of product quality attributes such as N‑glycosylation for
developing safe and efcacious biotherapeutics.
11.7 BIG DATA APPROACH IN
BIOLOGICS DRUG DEVELOPMENT
During biopharmaceutical drug development, in particular cell culture process develop‑
ment, a vast amount of time series and discrete datasets are generated. This includes
but is not limited to, process data from bioreactors, bioanalytical data such as nutrient,
metabolite and byproduct concentrations, cell growth and viability, product titer and pro‑
ductivity, as well as data for product quality attributes. With limited success in phase 1
and phase 2 stages, it is imperative to utilize all the data generated from various programs
toward a better process understanding and control (Hay etal. 2014). To this end, digital
twins for upstream process development have been conceived and developed (Hoang
etal. 2022). Such digital twins utilize hybrid models that incorporate mechanistic under‑
standing from the systems biology approach and statistical learning approach for data
with limited mechanistic insights (Macdonald 2022; Park etal. 2021). An overview of
11 • Systems Biology Approaches 299
FIGURE11.5 Digital twins and big data systems approach in biopharmaceutical drug discovery and development.
upstream digital twins is shown in Figure 11.5. Briey, we can think of three major
levers that can control process performance, improve productivity, and modulate product
quality. The rst lever is biological, and it includes the transcriptome, metabolome, pro‑
teome, and redox state of the cells, among many others. The second lever is chemical,
and it includes medium composition, nutrient feed composition and feeding strategy, pH,
transmembrane equilibrium of macro and micronutrients, etc. The third lever is a physi‑
cal lever, also known as process control, and it includes agitation, power distribution per
unit volume, heat transfer, scaling considerations, etc. As all three levers can inuence
productivity and product quality, efforts have been made to develop an integrated hybrid
model that accounts for all these three levers. Thus, an integral part of the upstream
digital twin is the omics state of the cells and its dynamics integrated into a mechanistic
or genome‑scale model that is part of a bigger hybrid model incorporating other sets of
data. Such digital twins can help in process optimization, predicting course corrections,
enhancing process robustness, and expediting development timelines (Park etal. 2021).
11.8 CONCLUSIONS AND FUTURE DIRECTIONS
Biopharmaceutical drug discovery and development is a challenging process owing
to complexity the biological systems entail. The systems biology approach offers
experimental and computational tools such as multi‑omics analyses and quantitative
300 Biopharmaceutical Informatics
mathematical modeling to dissect such complexities. These tools allow for systems‑level
analyses, integrated model development, multiple hypotheses testing, and simulation
under various constraints, resulting in increased probability of success during drug
discovery and development. The systems biology approach has been applied to drug
target identication and validation by analyzing and comparing omics states of control
and disease conditions both at the molecular level as well as functional level. In addi‑
tion, systems‑level quantitative disease models have been utilized to dissect underlying
mechanisms of regulation. These models also provide a platform for in silico testing
of hypotheses for drug target validation. Applications of the systems biology approach
in drug development include enhancement of cell growth and productivity of producer
cell lines engineered to produce recombinant biopharmaceuticals. This is achieved by
utilizing functional analyses and mathematical modeling to identify metabolic bottle‑
necks and developing mitigation strategies either via the cell engineering approach or
through media and process changes. Similar approaches have also been successfully
implemented to dissect regulations underlying variations in product quality attributes of
the biopharmaceuticals and have signicantly aided in the control of these attributes for
enhanced safety and efcacy.
With recent advancements in computational capabilities and automation, biophar‑
maceutical drug discovery and development are steering toward the next phase of accel‑
eration to reduce development timelines and bring drugs faster to the patients. To this
end, the systems approach (omics and computational modeling) is getting integrated
with process analytical technologies (PATs) and advanced sensors and actuators to
develop cyber‑physical systems (CPS) that enable big data analysis and real‑time control
of process (Rathore etal. 2022). The use of hybrid models and statistical learning tools
along with CPS will enable the industry to potentially control and modulate the system
at a more precise level with predictive course correction. In summary, recent advances
in the big data approach along with the systems biology approach appear to have a very
positive outlook and tremendous scope in contributing signicantly to biopharmaceuti‑
cal drug discovery and development. Together with advances in data analytics, systems
biology approach can signicantly reduce the cost of drug discovery and development
and bring novel biotherapeutics faster to the patients.
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