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eferences 253
106 Andreotta, A.J., Kirkham, N., and Rizzi, M. (2022). AI, big data, and the future of consent. AI &
Society 37 (4): 1715–1728.
107 Javaid, M., Haleem, A., Singh, R.P. et al. (2022). Significance of machine learning in healthcare:
features, pillars and applications. International Journal of Intelligent Networks 3: 58–73.
108 Kelly, C.J., Karthikesalingam, A., Suleyman, M. et al. (2019). Key challenges for delivering clinical
impact with artificial intelligence. BMC Medicine 17 (1): 195.
109 Felzmann, H., Fosch-Villaronga, E., Lutz, C., and Tamò-Larrieux, A. (2020). Towards
transparency by design for artificial intelligence. Science and Engineering Ethics 26 (6): 3333–
3361.
110 Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business
Ethics 160 (4): 835–850.
111 Tsamados, A., Aggarwal, N., Cowls, J. et al. (2022). The ethics of algorithms: key problems and
solutions. AI & Society 37 (1): 215–230.
112 Cavazzoni, P. (2023). FDA Releases Two Discussion Papers to Spur Conversation about Artificial
Intelligence and Machine Learning in Drug Development and Manufacturing. [cited 2023];
Available from: https://www.fda.gov/news-events/fda-voices/fda-releases-two-discussion-papers
spur-conversation-about-artificial-intelligence-and-machine#:~:text=The%20regulatory%20
uses%20are%20real%3A,of%20therapeutic%20areas%2C%20and.
255
12.1 Introduction
The current process of drug development, which focuses on a single target, one disease, and one
drug, has serious flaws in terms of safety, efficacy, and sustainability. It has only now been realized
how valuable network biology and polypharmacology (multi-targeted therapy) methods are for
integrating omics data and creating medications that target numerous targets. Network pharma-
cology is a revolutionary paradigm that emerged from the merging of these two approaches; it
studies the effects of drugs on the interactome and the disease at some levels [1–3]. The complex
formulations used in Ayurveda, the traditional Indian medicine system, include a wide variety of
ingredients and bioactive compounds. However, the mechanisms and scientific thinking underly-
ing these formulations have hardly received any research attention. To better understand the
possible effects, indications, and mechanisms of medications, evidence-based Ayurveda can make
use of natural product approaches. In today’s pharmaceutical landscape, natural products play a
significant role, particularly in disease treatment [4, 5]. A wealth of valuable resources have been
bestowed upon humanity by natural products throughout the millennia. To assess the pharmaco-
logical efficacy of herbal treatments, drug discovery has developed high-throughput approaches
based on network pharmacology. This chapter provides a comprehensive overview of the technique,
significance, and application of network pharmacology to cure a wide spectrum of complicated
disorders using Indian traditional medicines [6, 7].
12.1.1 Background of Drug Discovery Future Challenges
Drug discovery is a complex process. The field encompasses the disciplines of chemistry and
pharmacology and has yielded life-saving therapeutic interventions for diseases. The primary
objective is to identify chemicals that fulfil unaddressed medicinal needs, particularly in critical
circumstances. Notwithstanding advancements, the process of discovering new drugs remains
challenging [7, 8]. The process of introducing a new pharmaceutical compound can be time-
consuming and expensive. The procedure involves utilizing extensive chemical databases,
12
Network-based Methods in Drug Discovery
Ghanshyam Parmar
1
, Ashish Shah
1
, Jay Mukesh Chudasama
1
, Priya Kashav
1
,
and Vanesa James
2
1
Department of Pharmacy, Sumandeep Vidyapeeth (Deemed to be University), Vadodara, Gujarat, India
2
Department of Regulatory Affairs and Quality Assurance, LJ Institute of Pharmacy, Ahmedabad, Gujarat, India
12 Network-based Methods in Drug Discovery256
optimizing the basic constituents, and conducting comprehensive testing [9]. The creation of
medication has always relied on the use of chemical synthesis and experimental biology.
Nevertheless, the utilization of structural-based design and other digital tools is accelerating
the process of drug discovery which is represented in Figure 12.1. There are many diseases that
do not have effective therapies, and the pharmaceutical sector needs to increase output. The
intricate nature of human biology poses a significant challenge in the development of drugs
that have specific effects and minimal side effects [9, 10]. Notwithstanding these challenges,
the discipline continues to develop. Anticipate that the integration of advanced technology and
collaborative efforts will enhance the efficiency of drug research and yield a greater number of
life-saving medications [10, 11].
In light of the rising costs and pricing restrictions associated with medication research, pharma-
ceutical corporations and other interested parties are seeking ways to increase efficiency. Three
specific areas of focus include the application of big data, implementation of adaptive trial designs,
and establishment of public–private partnerships. Public–private partnerships can assume various
forms, each presenting distinct benefits and drawbacks, encompassing financial and legal conse-
quences. Adequate preparation is essential for adaptive trial designs, which may involve adaptive
licensing [12]. Big data can be utilized for medication development by leveraging existing informa-
tion, while privacy concerns must be resolved. Overall, these novel alternatives possess the capac-
ity to enhance the efficiency of future pharmaceutical development [13].
Drug research success in this era of rapidly evolving science and technology needs a well-
thought-out plan. Scientists are discouraged, and progress is slowed by bureaucratic techniques,
Food and
secondary
metabolites
IMPPAT Indian
medicines
database
Protein encoded
from recombinant
DNA
Drug
discovery
Drug
repurposing
Plant based
secondary
metabolites
Marine based
secondary
metabolites
Nucleic acids
with different
structures
Human based
secondary
metabolites
Prokaryotes
based secondary
metabolites
Microbes based
secondary
metabolites
Figure 12.1 The representation of different drug applications discovery in various fields of
fundamental science.
12.1 Introduction 257
budget cuts, and isolated research attempts. Funding models should incorporate public and
academic feedback to guarantee appropriate research methodologies. Collaboration and targeted
research are impeded by the academic community’s dispersion, especially among faculty mem-
bers [14]. Due to the multidisciplinary nature of drug development, larger networks are necessary
for collaboration beyond the confines of translational institutions, where industry, scientists, and
physicians can work together. Every system in a business needs to incorporate modern technology.
Recognizing and appreciating the unique skills of each individual involved in pharmaceutical cre-
ation is essential. In spite of the difficulties, open innovation and public–private partnerships are
essential for making the most of everyone’s abilities. The public and patients can play an active role
in advancing progress through effective education and interdisciplinary training [15]. To success-
fully develop novel drugs, all parties involved must maintain an optimistic outlook and foster an
environment of trust. There may be scholarly and business shifts brought about by the COVID-19
pandemic. By the year 2040, advances in disease biology coupled with modern technology have the
potential to enhance disease prevention, management, and potential cures. To better people’s
health, scientists and doctors work tirelessly. A comprehensive strategy that prioritizes collabora-
tion, foresight, and a nurturing environment can propel drug research forward [16, 17].
The efficacy of most treatments for complex disorders is negligible, and there is a decline in the
process of discovering new drugs. Obstacles in fundamental and preclinical research include
inadequate quality, challenges in replicating discoveries, and limited relevance to real-world appli-
cations. The focus on particular organs in the field of medicine and the belief that each disease has
a singular target and therapy also impede innovation [18, 19]. The fields of systems and network
medicine, in conjunction with network pharmacology, are revolutionizing our understanding,
identification, management, and eradication of diseases. Distinct endotypes, identified by causa-
tive, multi-target signaling modules, explain the presence of comorbidities and supplant the need
for extensive descriptions of specific symptoms. The combination of synergistic multicompound
network pharmacology and medication repurposing offers a highly accurate and efficient approach
to therapeutic intervention. This approach eradicates the drug discovery protocols and accelerates
the translation of clinical applications [18, 20].
12.1.2 Single Target Approach Limitations
Population-based trials of commercially available medications do not demonstrate patient benefits.
Indeed, the top 10 most profitable US medications do not enhance the health of the majority of
patients, resulting in a high number needed to treat (NNT). The NNT is lower in high-risk indi-
viduals, indicating treatment effectiveness. However, the issue continues. As a result, it is critical
to transition from continuous symptom management to a more accurate and, ideally, curative
treatment that works for the majority of patients. According to Eroom’s law, our ability to transfer
biological knowledge into medical development has deteriorated since the 1950s [21]. To overcome
this impediment, it is necessary to employ entirely novel medical approaches and identify at least
two critical factors that are impeding innovation. Reproducing preclinical and foundational
research is difficult due to low study quality, including insufficient statistical power and a bias in
scientific journals toward favorable findings. The second issue is that we lack conceptual under-
standing of the majority of disease definitions. Except for viral and uncommon disorders, chronic
diseases are defined by their phenotypes or organ symptoms. Nowadays, medicine focuses on
organs. Our preclinical animal illness models frequently reproduce similar symptoms, but there is
no conclusive evidence that the mechanism is the same in people. We focus on managing symp-
toms rather than treating the ailment since we do not understand the underlying causes of illness.
12 Network-based Methods in Drug Discovery258
Primary hypertension, which has no known cause, accounts for 95% of all high blood pressure
cases [22]. Until the problem goes away, blood vessel vasodilators such as calcium channel block-
ers or thiazide-type diuretics lower blood pressure by interacting with different proteins [23].
Because the molecular mechanism of hypertension is unknown, we prioritize preventing myocar-
dial infarctions and strokes above treating them. Despite proper antihypertensive medication,
most high-risk individuals will experience severe adverse effects. Complex disorders necessitate
long-term care because present medications are neither curative nor exact. Rare diseases with pre-
cise and monogenetic ethologies are an exception to these constraints [24].
Network pharmacology finds protein–protein interactions that affect illness outcomes.
Multiomics methods and computer technologies are being used to precisely document
the collective metabolic response in people, enabling the study of more complicated diseases.
Here, we examine biomedical applications of network pharmacology depicted in
Figure 12.1 [25].
Since the discovery of monotherapy for challenging illnesses, resistance, and side effects have
prompted the strategy to evolve beyond mono-triple therapy. When monotherapy is ineffective,
multitarget pharmacological treatment provides hope (Figure 12.2). Polypharmacology is
becoming increasingly popular among researchers and pharmaceutical corporations as a means
of developing multi-target medications. Scientific research suggests that multi-target pharmaco-
logical treatment may be effective in eliminating medication resistance. First, computationally
search protein networks to identify proteins that interact [26]. Proteins that assist parasites,
bacteria, and fungi in surviving communicable diseases are highlighted below. Parasites such as
P. falciparum create a large number of these proteins because they interact in hostile environ-
ments or infiltrate the host. Targeting these proteins necessitates a complete understanding of
their structures and functions as they interact directly or indirectly [27, 28]. Using this
Applications of
network pharmacology
Traditional medicine
Pharmaceutical formulations
Pharmacology
• Scientific evidence
• Understanding rational of
herbal formulations
• MOA of traditional
medicines
• Network based designing of
herbs
• Analysis and identification
of bioactive compounds
• Identification of biomarkers
• Finding out novel targets
• By insilco reduce time and cost
• Identifying novel pathways of disease and medication
• Targeting multiple genes, discovery of disease causing gene
• Design of diagnostic biomarkers and study of multi drug
resistence
• Development of novel
pathways by herbs to treat
diseases
• Understanding MOA of
drugs
• Determination of side
effects and indications
• Drug drug interaction
• Drug repurposing
Figure 12.2 Various applications of network pharmacology, in contrast to traditional medicines and
pharmaceutical formulations, and for establishing the pharmacological mechanism of drugs or
phytoconstituents.
12.1 Introduction 259
information, molecules with several targets can be developed, which is represented in Figure12.3.
Since multi-targeting medications are our only option for treating complex disorders, we will
most likely continue to look for them [29]. Scientists have been interested in developing nano-
particles (NPs) that target sick cells while protecting healthy tissue to reduce the side effects of
chemotherapy since the development of cancer nanomedicine. Researchers are now designing
biomimetic NP platforms that resist immune recognition and accumulate in tumors. Initially,
they focused on bioconjugation of targeted molecules to NPs [30, 31]. Here, we examine the
targeted techniques’ pros and cons. First, we examine bioconjugation developments that encap-
sulate NPs with biomolecules for cell-specific interaction. Biomolecules can be antibodies,
aptamers, peptides, or tiny compounds. Even while bioconjugated NPs have better pharmacoki-
netics and biodistribution than unmodified ones, the mononuclear phagocytic system (MPS)
rejects them as “foreign.” Several biomimetic techniques have been developed to reduce MPS
clearance and immune recognition of NPs. Some biomimetic NPs imitate the structure of natu-
rally occurring ones, while others are completely camouflaged by natural features. Overall, bio-
conjugated and biomimetic NPs offer great potential for future healthcare and can reduce
off-target effects through site-specific delivery. We discuss each strategy, its future design, and its
potential therapeutic impact on cancer treatment [32].
12.1.3 Emergence of Network Biology and Polypharmacology
There has been a continual evolution in human diseases, particularly infectious ones. Having said
that, they are taking their time to establish therapeutic techniques. It is not possible to treat all
diseases with the same medication because some have several causes and extremely complicated
pathologies. Medication based on Traditional Indian Medicine (TIM) is an innovative approach to
healthcare in India. Some of the botanicals included in TIM formulations have multi-targeting
Cost
Toxic and adverse effect
Target 01
Target 01
Target 02
Target 02
Target 03
Target 03
Target 01
Target 02
Target 03
Toxic and adverse effect
Toxic and adverse effect
Efficacy
Toxicity
Toxic and adverse effect
Multicompartment medication
Multiple ligand
Therapeutic effect
Therapeutic effect
Therapeutic effect
Therapeutic effect
One target
One drug
One disease multi target
Drug-drug interaction
Toxicity
Adhesion to therapeutic
Regimen
Metabolites generation
Efficacy
Cost
Chemical incompatibility
Toxicity
Patent extension
Efficacy
Cost
Toxicity
Resistance and compensation
More than one drug
Figure 12.3 Compression between one target and multitarget approach to treat complex diseases.
12 Network-based Methods in Drug Discovery260
characteristics, the focus of drug research has shifted from the well known “one target, one drug”
paradigm to a new “multi-target, multi-drug” strategy, which attempts to systematically regulate
several targets, as a result of our growing understanding of complex disorders [33, 34]. As a new
strategy to combat the declining efficiency of pharmaceutical research, polypharmacology has
emerged. Still, it is not easy to find methods to evaluate multicomponent treatments and rank
synergistic combinations of drugs [35].
Novel analytical approaches to study Nature’s chemical and pharmacological properties are
needed to find sustainable and environmentally friendly drugs to battle pandemics. Our study
introduces polypharmacology-labeled molecular networking (PLMN), a new analytical tech-
nology approach. Polypharmacological high-resolution inhibition profiling and positive and
negative ionization tandem mass-spectrometry-based molecular networking are used in this
method. Identifying complicated extract bioactive components quickly and efficiently is the
goal [36, 37]. To find antihyperglycemic and antibacterial components, the raw extract of
Eremophila rugosa was examined using PLMN. Nodes in the molecular network show the
activity of each component in the seven assays in this proof-of-concept study using polyphar-
macology scores, pie charts, and microfractionation variation scores. There were 27 diterpe-
noids that were not canonical and were generated from diphosphate. Ferruletane esters show
antihyperglycemic and antibacterial properties [37]. In the fight against the epidemic and
other clinically significant methicillin-resistant Staphylococcus aureus strains, oxacillin and a
few of these esters work in tandem. A number of esters attach to the active site of protein
tyrosine phosphatase 1B in a saddle-shaped fashion. The identification of drugs utilizing natu-
ral components with several pharmacological effects may be altered if PLMN’s coverage of
testing was to be expanded or contracted [38, 39].
12.2 Network Pharmacology: Practical Guide
12.2.1 Common Network Pharmacology Databases
Advanced technology for assessing massive datasets has enabled more compelling and efficient
diagnostic and treatment solutions. Understanding how proteins disrupt the complex regulatory
system is critical. The field of network biology, which postulates that complex biological systems
are subject to general rules, has had a profound impact on our understanding of disease. Data min-
ing and other computer approaches were recommended for regulatory networks in the 21st century.
These strategies examined genotype-illness phenotypic relationships. Advances in network biology
show that targeting individual proteins does not treat complicated diseases [40]. Drug developers
realized that polypharmacology was useless and needed to be abolished to generate a viable treat-
ment with several targets. Through network pharmacology, highly specialized drugs that target a
single receptor have given way to treatments that target multiple receptors. Later, system biology
and polypharmacology merged in various health areas [41].
In the last decade, indigenous people have used therapeutic plants without scientific research.
Traditional treatments used many medicinal plant species. Since therapeutic plants are inexpen-
sive and natural, they affect people’s health. However, unsustainable collection and use of these
plants have caused the demise of many valued plant species. Traditional remedies can treat com-
plex ailments due to their holistic approach and extensive testing with numerous components.
Herbal formulas distinguish traditional medicine. Understanding the complex combination of
herbal formulae and their mechanisms can help modify conventional medicines in the age of data
12.2 etwork Pharmacology: Practical Guide 261
analysis. Modern network pharmacology allows systematic study of herbal formulations’ compli-
cated molecular makeup and interactions with complex diseases. Traditional medicine herbs have
better molecular compatibility than single drugs, which may improve network responsiveness.
Network-based techniques are widely used in innovative drug development research [42, 43]. The
key molecule responsible for synergy and efficacy in understanding novel medicines is derived
from natural sources. It has been demonstrated that these procedures are compatible with a num-
ber of herbal formulations used in traditional medicine. One cutting-edge method for identifying
medicinal herbs’ active ingredients and molecular targets is network pharmacology [44]. This
comprehensive method provides a baseline for evaluating bioactive chemicals in medicinal plants
and proposes a fresh therapeutic idea for studying their mechanisms of action in sickness therapy.
Network pharmacology provides innovative approaches to discovering active compounds, bio-
markers, and the scientific basis of conventional medicine by leveraging the intricate biological
processes of the human body [45].
12.2.1.1 Network Pharmacology-Related Databases and Data Analysis Tools
One modern approach to drug discovery is network pharmacology, which is supported by biologi-
cally relevant databases that store massive volumes of data about biomolecule correlation
(Table 12.2). Free and easy access to all of these resources is a major selling point when it comes to
collecting data for studies in network pharmacology. Table 12.1 lists the major network pharma-
cology databases and the ones that are associated with them [46].
12.2.1.2 Exploring IMPPAT Network Pharmacology Databases
Drug development based on natural products and traditional knowledge can be accelerated by the
compilation, curation, digitization, and exploration of the phytochemical space of medicinal plants
used in India. In the IMPPAT, a revamped and enlarged database that includes 4010 medicinal
plants from India, 17,967 phytochemicals, and 1095 medicinal uses, all of which have been hand-
picked by experts. Notably, IMPPAT offers a nonredundant, in silico stereo aware library containing
17,967 phytochemicals from medicinal plants in India, and it constructs associations at the level of
plant parts. This library complies with standards. To facilitate simpler chemical space exploration,
the phytochemical library has been annotated with numerous relevant features. We have also nar-
rowed the search to 1335 phytochemicals that show promise as medications, the vast majority of
which are completely unique from any already available pharmaceuticals [47]. Our team has used
cheminformatics to analyze the phytochemical space of medicinal plants from India, characteriz-
ing their molecular complexity and molecular scaffold-based structural diversity. We have also
compared this space to other chemical libraries. Accessible at https://cb.imsc.res.in/imppat/,
IMPPAT 2.0 is a comprehensive phytochemical atlas of medicinal plants used in India that has
been painstakingly compiled by hand [47].
Traditional medicine and ethnopharmacology are famous in India. Complex traditional Indian
medicine compositions have many components. Their medicinal efficacy comes from empirical
knowledge, not mechanistic comprehension of the mixture’s active ingredients. Before recently,
old Indian literature [47, 48] and old Ayurvedic literature [49] were the primary authors who
documented traditional Indian medicine, particularly the use of plants and their remedies.
Because it was not digitized, this material could not be used for the development of new medicinal
treatments. This knowledge can be utilized to create a database that covers all aspects of Indian
medicinal plants, including their phytochemistry, ethnopharmacology, and computational drug
discovery [50].
12 Network-based Methods in Drug Discovery262
Table 12.1 Data retrieval and analysis of network pharmacology data is facilitated by a variety
of databases, each of which is described along with its usage.
Sr. no. Database name Brief description Use of database
1 BioCarta Pathway database Pathway analysis and
visualization
2 BioGRID Biological general repository for
interaction datasets
Protein–protein interaction
dataset
3 C2Maps Network pharmacology tool Connectivity map for drug–target
interactions
4 CB (Chemical book) Chemical compound information Chemical information and
structure
5 ChEMBL Bioactivity database for drug
discovery
Drug discovery and
pharmacology
6 ChemProt Chemical biology resource Protein–chemical interactions
7 ChemSpider Chemical structure database Chemical structure information
8 CHMIS-C Traditional Chinese medicine
database
Traditional Chinese medicine
information
9 COGs Clusters of orthologous groups Evolutionary relationships of
genes
10 CPDB Cancer gene–drug associations
database
Cancer-related gene and drug
interactions
11 Cytoscape Bioinformatics software Network visualization and
analysis
12 DAVID Database for annotation,
visualization, and integrated
discovery
Functional annotation of genes
13 DIP Database of interacting proteins Protein–protein interactions
14 DrugBank Drug database Drug information and
interactions
15 GeneCards Human gene database Information on human genes
16 Guess Network pharmacology tool Predicting drug–target
interactions
17 HAPPI Human annotated and predicted
protein interaction database
Protein interaction information
18 HIT (Herbal ingredients’
targets identification)
Herbal ingredients and their
targets
Identification of targets for
herbal ingredients
19 HPRD Human protein reference
database
Human protein information
20 InterPro Protein families and domains
database
Protein domain and family
information
21 KEGG Kyoto Encyclopedia of genes and
genomes
Pathway and functional
information
22 LookChem Chemical marketplace Chemical information and
marketplace
23 MMsINC Database of microRNA-mediated
molecular interactions
microRNA-mediated interactions