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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5629_Библиотеки_им_академика_М_И_Перельмана.pdf
Use of Systems
Biology
11
Approaches toward
Target Discovery,
Validation, and
Drug Development
Madhuresh Sumit and Venkata Gayatri Dhara
11.1 INTRODUCTION (INTRODUCTION TO
SYSTEMS BIOLOGY AND ITS SCOPE IN
DRUG DISCOVERY AND DEVELOPMENT)
Systems biology is a holistic approach comprised of computational and experimental
tools employed to understand a complex biological system at multiple scales and dimen‑
sions (Kitano 2002). These tools are leveraged to capture the spatial and temporal com‑
plexity of a biological system. Dissecting such complexity through a systems approach
can help understand non‑linear and non‑intuitive relationships among various compo‑
nents of a biological system as well as their emergent properties. It also provides insights
into potential ways of modulating the system from within and from outside.
The systems biology approach allows for integrated model development, testing
of multiple hypotheses, simulation of biological systems under various constraints,
281
282 Biopharmaceutical Informatics
FIGURE11.1 Overview of systems biology in biopharmaceutical R&D.
and mechanistic interpretation of the data at hand. Consequently, hypothesis testing
is signicantly informed and more predictive as compared to the hit‑and‑trial method
and thus optimizes drug discovery and development processes (Butcher etal. 2004).
Figure11.1 shows a schematic of the components of the systems biology approach. The
approach allows generating and in silico testing of biologically relevant hypotheses.
This ability of systems biology has a two‑pronged scope within biopharmaceutical
informatics. On one hand, it enables the discovery of new biomarkers for diseases and
identies potential drug targets, by identifying cause‑and‑effect pathways potentially
involved in a disease. On the other hand, it can provide insights into ways of improv‑
ing productivity and product quality in biopharmaceutical development. Thus, it helps
reduce development timelines and resource requirements with an enhanced probability
of clinical success. Overall, the systems approach is utilized to potentially help reduce
the cost of drug discovery and development, bringing novel biotherapeutics to market
faster, and increasing their affordability and accessibility at the same time.
11.2 EXPERIMENTAL METHODS FOR SYSTEMS
APPROACH IN BIOPHARMACEUTICAL
DRUG DISCOVERY AND DEVELOPMENT
The systems biology approach can be classied broadly into experimental methods and
computational methods (Figure11.2). The experimental approach is generally based on
11 • Systems Biology Approaches 283
FIGURE11. 2 Systems biology tools utilized toward biopharmaceutical drug discovery and
development.
creating a map of the cell with the genes, metabolites, proteins, and their glycoforms.
The key components of cellular metabolism include metabolic pathways composed of
enzymes and other proteins, and metabolites such as substrates, intermediates as well
as end products. Deciphering the cellular metabolism involves identication and char‑
acterization of these components within the cell. This can be accomplished through a
holistic analysis of its metabolites (metabolome) and the proteins/enzymes involved in
those interactions (proteome), respectively. A comprehensive analysis of all the genes
encoded (genome) and expressed (transcriptome) provides additional information about
whether the cell contains the genetic code to manufacture the enzyme and the maximum
level at which the quantity of the enzyme can be produced by the cells. Thus, experi‑
mental methods of systems biology rely on investigating the biological system at hand
through a holistic analysis of its genome, transcriptome, proteome, glycome, lipidome,
and/or metabolome (Piña etal. 2018). Table11.1 summarizes a few of the major ‘omics’
approaches, their scope at the molecular level, and corresponding predictive capacity.
The ‘omics’ methods are often utilized in biopharmaceutical drug discovery and
development in tandem to gain a multi‑dimensional insight into complex biological
systems (Hasin etal. 2017). For example, the integration of multi‑dimensional omics
information from diseased primary tissues can provide insights into mechanisms that
underlie the disease development (Kreitmaier etal. 2023). Kreitmaier etal. summarized
the insights from multi‑omics analysis for several diseases including type 2 diabetes,
osteoarthritis, and Alzheimer’s disease. Similarly, drug discovery requires screening of
several potential targets to narrow down the top candidates that can be most causally
related to a disease condition. As an example, Li etal. (2020) developed experimen‑
tal and bioinformatics pipelines incorporating multi‑omics analyses to identify poten‑
tial targets for ovarian cancer. Such pipelines may include microarray studies, disease
model studies, and multi‑omics studies among others. The overall goal is to understand
how cells work and respond to their environment when external triggers, such as a
TABLE11.1 Omics tools and techniques employed in biopharmaceutical drug discovery and development
SYSTEM/
OMICS
METHODS MOLECULE PREDICTIVE CAPACITY METHODOLOGY
Transcriptome mRNA Actual processes (what a tissue/cell is set to do in
its current state)
Proteome Proteins Physiological activities (what a tissue/cell is actually
doing)
Metabolome Metabolites Metabolic status (what the tissue/cell is actually
consuming and producing)
Microarray,
RNA-seq
a
Protein digestion/
MALDI-TOF
GC-MS,
LC-MS,
ANNOTATION TYPICAL OUTPUT
NA,
BLAST
Peptide
HMDB, YMDB 30–500metabolites
NMR
Lipidome
Lipids Complements transcriptome, proteome, and
metabolome through interconnectedness
GC-MS,
LC-MS
Lipid
b
Lipid Maps
Glycome Glycans Microheterogeneity of glycosylation linked to
HILIC, LC-MS SNFG ~300–3,000glycan
effector functions/biophysical properties of
biotherapeutics
Source: Adapted from Piña etal. (2018).
a
Relative uorescence values in case of microarray, counts in case of RNA-seq.
b
Lipidome is sometimes considered a subset of metabolome.
MOLECULAR
SPECIES
libraries
Database,
284 Biopharmaceutical Informatics
20,000–34,000
transcripts
100–1,000 peptides
(counts)
(peak areas)
100–1,000lipids
(areas)
species
11 • Systems Biology Approaches 285
diseased state, are introduced to modify their regular function. A cellular‑level scrutiny
to decipher the functions and response of different pathways can be achieved using these
omics technologies developed over the past few decades.
Once the omics data is generated, the data can be utilized to perform statistical
and functional analyses in the context of the cell and disease model under investigation.
These analyses could be performed at a ‘molecular level’ or at a broader ‘functional
level.’ Molecular‑level analyses involve comparing transcripts, proteins, or metabolites
side by side for the control and disease conditions. For example, differential gene expres‑
sion analysis methods such as DESeq, edgeR, limma, etc., are utilized to compare the
transcriptome across the control and disease conditions (Rapaport etal. 2013). The mol‑
ecules, however, are generally part of a broader biological function that they represent.
Hence, analysis of transcriptome across various functions is desirable as well. These
functions, or a priori dened set of genes/proteins or metabolites, could provide statisti‑
cally stronger inferences while comparing different biological states. One such example
of functional analyses is the gene set enrichment analysis (GSEA) (Subramanian etal.
2005). The a priori denition of gene set could be based on functional groups such as
gene ontology, canonical pathways of metabolism, or specic signatures for a cell type
or disease type. Additional complexity for the analyses at molecular and functional
levels could be added by temporal variations in the omics state (Sumit etal. 2019a).
Cellular pathway functional analyses identify the affected pathways due to a disease
condition. Gene ontology functional analyses focus on the higher‑level cellular func‑
tionalities that are impacted between the control and disease conditions. Overall, the
omics and functional analyses tools available for biopharmaceutical drug discovery and
development can potentially help in many ways. Prominent areas of impact of such
analyses include a reduction in the percentage of false positives that emerge from the
potential targets in drug discovery, identication of multiple informed choices for drug
target validation, and enhancement of productivity and product quality attributes during
the development of biologics or biotherapeutics.
11.3 COMPUTATIONAL METHODS FOR
SYSTEMS APPROACH IN BIOPHARMACEUTICAL
DRUG DISCOVERY AND DEVELOPMENT
Systems biology integrates experimental techniques such as omics and functional anal‑
yses (as described in Section 11.2) with mathematical modeling and simulation. The
construction of quantitative/mathematical models for a systems‑level biological net‑
work requires the application of computational methods to understand and dissect the
behavior of the system. Several types of mathematical models have been developed to
study biopharmaceutical drug discovery and development. These models and modeling
approaches could be broadly classied into mechanistic or reaction kinetics modeling
approach and structural constraints‑based approach (Bruggeman and Westerhoff 2007).
Additionally, with advancements in experimental data generation and computational
286 Biopharmaceutical Informatics
TABLE11.2 Mathematical modeling approaches utilized to develop quantitative models
for disease and development
MODELING
APPROACH
Constraint-based
approach
Mechanistic
approach
Statistical
learning-based
approach
INFORMATION
UTILIZED
Reaction network
and stoichiometry,
metabolic uxes
Stoichiometry and
kinetic parameters,
initial concentrations
Dependent and
independent
variables; extensive
amount of
experimental data
TEMPORAL
INFORMATION
Not
incorporated
Possible
Possible
DATA
REQUIREMENT
+
++
+++
TYPICAL
OUTCOME
Phenotypic
range and
systems
limitations
Systems
dynamics and
non-linearity
Prediction for
systems
wherein
mechanism
not fully
understood
capabilities, a third approach viz. statistical and machine learning methods is being uti‑
lized in various cases of drug discovery and development. These approaches vary in the
amount of information and data requirements as well as expected outcomes. Table11.2
summarizes these three approaches with additional details.
Mechanistic models are based on fundamental reaction engineering principles
and consist of all the reaction kinetics within a system under study (Kremling 2013).
Generally, the system is described by a series of differential equations depicting enzy‑
matic/non‑enzymatic reactions along with stoichiometric balance. Information about
interacting network motifs could also be added in the form of negative and positive
feedback and feedforward loops (Milo etal. 2002). Additionally, mathematical models
may incorporate biochemical variability and/or stochasticity to better explain the noise
in the biological systems (Sumit etal. 2019b). In cases where kinetic parameter and rate
constant details may not be available, or there is an experimentally known rate limiting
step, a few linked equations could also be clubbed together to modulate the granular‑
ity of the model. Initial concentrations of the components and kinetic rate constants
are estimated or inferred from experimental data or from literature. The set of dif‑
ferential equations is then solved for temporal or non‑temporal inputs that could either
depict a disease condition or a potential modulator of the disease condition. An exam‑
ple of utilizing mathematical models to depict the disease condition is modeling the
tumor microenvironment to understand cancer metabolism from the perspective of the
Warburg effect (Shamsi etal. 2018). In contrast, an example of utilizing mathematical
models to understand the potential modulation or treatment of a disease condition is the
mechanistic modeling of tumor progression and how tumors would respond to cytotoxic
drug administration as a treatment strategy (Vavourakis etal. 2018). On the other hand,
the constraint‑based approach has become more popular in systems biology recently,
especially with the increasing genome sequencing capabilities. For example, one can
develop a metabolic model consisting of information about all the genes a species con‑
tains and can be expressed in the form of transcripts and proteins. These transcripts and
11 • Systems Biology Approaches 287
proteins maybe involved in a vast complex network of metabolic reactions, and such
models are generally termed genome‑scale models. While it is challenging to develop
a reaction kinetics/mechanistic model for a species owing to the lack of information
on kinetics parameters, it is relatively easier to constrain stoichiometric uxes for the
entire network based on the transcriptomic state of the system and experimentally mea‑
surable uxes. This is a great example where experimental systems biology and com‑
putational systems biology approaches are applied in tandem to develop systems‑level
understanding of uxes in a metabolic network (Orth etal. 2010). Genome‑scale meta‑
bolic ux balance models have been utilized in drug target discovery as well as dur‑
ing drug development for biopharmaceuticals. For example, Jerby and Ruppin (2012)
utilized a genome‑scale modeling approach to predict drug targets of cancer. Another
example from drug development for biopharmaceuticals is to develop feeding strategies
for improving productivity using the genome‑scale modeling approach (Huang etal.
2020). Finally, the third approach, viz. the statistical learning approach is becoming
more popular with the availability of more computational capabilities as well as a vast
amount of experimental systems data such as transcriptomics, proteomics, or glycomics
(Guerra and Glassey 2018; Rathore etal. 2022). For systems that may be constrained by
limited mechanistic understanding, but have a large set of experimental data available,
statistical learning approaches can be employed to perform supervised and unsuper‑
vised learning and make predictions about conditions not fully understood.
While mathematical modeling using one of these approaches could be a standalone
exercise to develop a quantitative understanding of a biological system, when imple‑
mented together with experimental systems biology studies such as genomics, transcrip‑
tomics, proteomics, metabolomics, and/or glycomics, it can provide invaluable insights
into the mechanism, controllability, and predictability of the biological system. Further,
to incorporate the complexity of a biological system across multiple scales, multi‑scale
models such as agent‑based models (ABMs) are also developed to gain insights into the
system (Bonabeau 2002). In addition, in some cases, statistical learning approaches are
incorporated into mechanistic models to generate hybrid models that leverage mecha‑
nisms and big data to gain deeper insights. Thus, such an integrated approach has the
potential for the identication and validation of novel drug targets as well as precision
control of the drug development processes (Figure11.3). In the next sections, we explore
such examples further.
11.4 APPLICATIONS OF SYSTEMS
APPROACHES TO TARGET IDENTIFICATION
AND VALIDATION IN DRUG DISCOVERY
Drug target identication and validation is the process of identifying therapeutic targets
and subsequently developing therapeutic agents that could potentially produce effec‑
tive modulation of the target to bring about a positive change in a disease condition in
a safe and efcacious manner. It involves two major steps: (i) selection of a therapeutic
288 Biopharmaceutical Informatics
FIGURE11.3 Systems approach toward drug target identication and validation.
target, and (ii) preclinical assessment of target modulation through potential interven‑
tions for safety and efcacy. The assessment is based on the effect of target modulation
on the clinical endpoint, which is generally a direct or indirect marker of the diseases.
The overall goal of drug discovery is to assess whether, and to what extent, this clini‑
cal endpoint is affected by the modulation of potential therapeutic targets (Young and
Michelson 2011).
Identication of drug targets that have a high probability of success toward meet‑
ing the end goal is of prime importance to minimize failures during clinical trials and
reduce the overall cost of drug discovery. Conventional methods of drug target identi‑
cation are primarily based on literature surveys and/or phenotypic screening. However,
such endpoint focused approach poses a big limitation as there is a limited possibility
of reassessment for the choice of the target in case of a failure (Swinney and Anthony
2011). Swinney and Anthony performed the data analysis of success of a target‑centric
approach for rst‑in‑class drugs including biologics approved by the US Food and Drug
Administration between 1999 and 2008. In their analysis, they concluded that a lack of
proper understanding of molecular mechanism of action (MMOA) results in an increas‑
ing number of failures and lower productivity in pharmaceutical research and develop‑
ment. As the approach is overall trial‑and‑error based, failure implies that one must
start again from the beginning to identify new targets. While this target‑based approach
has been very successful in the past few decades (Swinney and Anthony 2011; Vincent
etal. 2022), it is increasingly becoming inefcient and expensive due to lower odds of
success in phase I and phase II trials (Seyhan 2019; Hay etal. 2014). Therefore, we need
a more holistic approach that can signicantly improve the odds of success in clinical
trials. This is where systems biology tools such as multi‑omics, computational models,
statistical learning, and big data analytics are being utilized to make a more informed
decision about the drug target identication and potential ways for its modulation (Ekins
etal. 2019; Vincent etal. 2022; Paananen and Fortino 2020).
11 • Systems Biology Approaches 289
There are several advantages to the systems biology approach in drug target identi‑
cation and validation. First, the approach is mechanistically driven and therefore, reduces
the odds of failure during clinical trials. Second, it utilizes the top‑down disease‑centric
scope, which is holistic and results in multiple drug target identication. Therefore, one
always has backup options with the next‑best target. Third, as an informed approach, it
helps accelerate the drug development timeline and decreases the resource burden from
discovery to development to commercialization. In these contexts, the systems biology
approach can be implemented toward multi‑omics functional analyses to dissect disease
pathophysiology and develop mathematical models that describe such pathophysiology,
and/or their interaction with potential biotherapeutics.
Several experimental tools within systems biology such as genomics, transcrip‑
tomics, proteomics, and metabolomics have been leveraged to study disease models
for drug target identication. Table11.3 shows a few recent examples of studies that
incorporate omics analyses toward various disease models for drug target identication.
In most cases, multiple omics dimensions are integrated to gain a deeper insight into
TABLE11.3 Recent examples of studies performed in drug target discovery and target
validation of biopharmaceuticals utilizing systems approach (omics tools and techniques)
OMICS TOOLS/
TECHNIQUES
Genomics Breast
Genomics Parkinsons Identied candidate drugs for Parkinsons
Transcriptomics Breast
Transcriptomics Kidney
Transcriptomics Ovarian
Proteomics Lymphoma Described a pharmaco-proteomics approach to
DISEASE
MODEL STUDY AND MAJOR OUTCOME REFERENCE
cancer
cancer
disease
cancer
Putative target genes for treating breast cancer
were identied using genomics and
bioinformatic tools
disease by a method using GWAS data and
in silico databases to screen the target loci
known for the disease
Clonal transcriptomics helped discover
pathway of action for drug resistance
providing direction to a combinatorial therapy
for triple negative breast cancer in mouse
model
Leveraged single cell transcriptomics data and
functional analysis to reveal that collecting
duct cell plasticity, driven by Notch signaling,
results in abnormal cell populations in chronic
kidney disease
Implemented multiple functional analysis tools
to nd signicantly higher expression of
CREB1 in normal ovarian tissues; also
identied 25 CREB-related proteins as
potential targets
map the interactome of a tumor-enriched
isoform of HSP90 (teHSP90)
Baxter
etal.
(2018)
Uenaka
etal.
(2018)
Wild etal.
(2022)
Park etal.
(2018)
Li etal.
(2020)
Goldstein
etal.
(2015)
(Continued)
290 Biopharmaceutical Informatics
TABLE11.3 (Continued) Recent examples of studies performed in drug target
discovery and target validation of biopharmaceuticals utilizing systems approach (omics
tools and techniques)
OMICS TOOLS/
TECHNIQUES
Metabolomics Melanoma The study identied 21 plasma metabolites
Metabolomics Tumor Study identied that plasma concentrations of
Multi-omics Type 2
Multi-omics Pneumonia Genomics, transcriptomics, and metabolic
Omics + ML Tuberculosis A whole-blood RNA therapy-end model was
Multi-omics COVID-19 Curated a highly informed integrated drug
Multi-omics Silicosis Study revealed that arachidonic acid (AA)
Multi-omics Breast
DISEASE
MODEL STUDY AND MAJOR OUTCOME REFERENCE
Ang etal.
diabetes
cancer
including amino acids, propionyl carnitine,
phosphatidylcholines, and sphingomyelins as
signicantly altered in two B-RAF-mutant
melanoma xenografts; identied selective
MEK inhibitor as a potent drug
26metabolites, including amino acids,
acylcarnitines, and phosphatidylcholines
decreased in mice bearing PTEN-decient
tumors compared with non-tumor-bearing
controls; identied class I PI3K inhibitor
pictilisib as a potent drug
A review that characterizes multi-omics
disease model for type 2 diabetes mellitus to
identify overall change in the intestinal ora
and metabolic disturbances
information were integrated in order to
prioritize candidate targets in the purview of
multi-drug resistance
developed to identify hypothetical individual
end-of-treatment time points with 22 RNAs
shortlist by combining structural diversity
ltering along with experts’ curation and
drug target mapping on the depicted
molecular pathways
pathway metabolites, prostaglandin D2
(PGD2), and thromboxane A2 (TXA2) were
signicantly upregulated in silicosis lungs
Identied three highly connected modules,
EED, DHX9, and AURKA as potential
candidate molecular targets for triple
negative breast cancer
(2017)
Ang etal.
(2016)
Wang
etal.
(2021)
Ramos
etal.
(2018)
Singhania
etal.
(2018)
Tomazou
etal.
(2021)
Pang etal.
(2021)
Turanli
etal.
(2019)
the disease and its modulation. For example, Turanli etal. utilized transcriptomic and
proteomic analyses to identify three highly connected modules as potential drug targets
for breast cancer (Turanli et al. 2019). Similarly, Ramos et al. integrated genomics,
transcriptomics, and metabolomics information that helped prioritize candidate targets
for an infectious disease in the purview of multi‑drug resistance (Ramos etal. 2018).