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Preface
constructive, and unambiguous manner with adequate consistency in ow,
continuity, and technical clarity.
This book primarily focuses on the state-of-the-art drug repurposing
strategies and techniques currently being utilized for the discovery of therapeutic candidates from existing drug molecules by experimental (in vitro/in
vivo) and/or computational (in silico) strategies. Through this book, readers/
users can update their knowledge and skills with sufcient technologically
advanced tools, computational techniques, and database available for the
discovery of drugs by drug repurposing and computational approaches.
In this book, drug repurposing and computational approaches for the
discovery and development of drugs against certain emerging and lifethreatening diseases including microbial infections (bacterial, fungal, viral,
COVID-19), parasitic diseases and neglected tropical diseases (NTDs),
malignant diseases (cancer), inammatory diseases, cardiovascular disorders, diabetes, and aging and neurological (CNS) disorders are covered. In
addition, challenges and regulatory issues commonly encountered in drug
repurposing and computational drug discovery programs are also addressed.
Some of the key highlights of the book are as follows:
• Presents/details the scopes available for the discovery of drugs by drug
repurposing approaches and computational strategies with potential
advantages and clinical utilities in the treatment of infectious illness,
malignant diseases, rare and difficult to treat diseases.
• Summarizes latest developments on the application of drug repur-
posing strategies and computational approaches in drug discovery.
• Describes/depicts drug discovery approaches from existing drug
candidates or lead molecules by computational/database screening
and experimental screening/assays.
Having contributions from experienced academicians, scientists, and
researchers from across the globe, this book is intended to be a useful
resource for a wide audience, particularly working in the area of drug
discovery research including drug discovery scientists, medicinal chemists,
pharmacologists, toxicologists, phytochemists, biochemists, clinicians,
discovery scientists (R&D), biomedical researchers, health care professionals and researchers. The most likely users of the book are postgraduate
students, doctoral researchers, senior academic researchers, professors, etc.
of pharmaceutical, biomedical, and allied sciences from the higher academic
institutions, universities, and pharmaceutical and biotechnology companies.
CHAPTER 1
Drug Repurposing in Future Drug
Discovery and Development
Drug Repurposing and Computational Drug Discovery: Strategies and Advances.
Mithun Rudrapal, PhD (Ed)
© 2024 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)
KIRTI AGRAWAL, MALAVIKA SAJI, and DHRUV KUMAR
Amity Institute of Molecular Medicine and Stem Cell Research
(AIMMSCR), Amity University, Noida, Uttar Pradesh, India
ABSTRACT
Rising necessity of potential drugs for treating chronic, rare, and untreated
diseases is the main motive for the development of repurposed drugs. Drug
repurposing or repositioning is the technique of discovering novel therapeutic uses for drugs, either already approved or under investigation for a
new indication. This method has many advantages over the conventional
methods of drug discovery. Reduced risk of failure is one factor that makes
drug repurposing an approachable option. The number of drugs that fail in
Phase III of clinical trials is disturbing, and only very few are approved for
clinical use. Since repositioned drugs are already proven safe, they have less
chance of failure, saving 5–7 years of safety testing. De novo drug discovery
and development is a time-taken and high investment process while drug
repurposing process is efficient and economical. There are generally four
types of drug repurposing approaches: computational, knowledge-based,
biological-experimental, and mixed approaches, which are broadly used in
repurposed drug development. In this chapter, we have further elaborated
traditional and high-throughput drug repurposing approaches and emphasized over their features, their validation techniques, noteworthy applications for treating a wide range of untreated diseases, and challenges of drug
repositioning.
2
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
In the face of advances in science and technology with improved knowledge of human diseases, transformation of these benefits into therapeutic
developments has been slower than expected. Pharmaceutical industries are
facing a lot of challenges globally to bring new drugs into the market as it
1
is a time-consuming and high-cost process.
To combat these challenges, the
drug repurposing strategy has been considered for screening new usages of
preexisting drugs and for developing new drugs in pharmaceutical research
and development (R&D). Drug repurposing/repositioning/reprofiling is
an interesting approach to identify various new uses of approved or under
clinical trial drugs for new medical indications. This effective approach offers
numerous gains over developing a completely new drug for a specified indica-
2
The most important benefit of this strategy is the risk of failure is lesser
tion.
because the reprofiled drug has previously been checked to be suitably safe
in preclinical models and/or humans trials. Thus, it is less expected to fail
in terms of safety in following efficacy trials. The second important factor
is that the time taken for a repurposed drug development can be effectively
reduced as most of the preclinical testing, safety evaluation, and formulation
development (in some cases) has been already completed. Also, low investment is required that usually depends on the developing process and stage of
the repurposing drug.
3
The regulatory and phase III costs may remain more or
less the same for a repurposed drug as for a new drug in the same indication,
but there could still be substantial savings in preclinical and phase I and II
costs.4 Together, these advantages have the potential to result in a less risky
and more rapid return on investment in the development of repurposed drugs,
with lower average associated costs once failures have been accounted for
(indeed, the costs of bringing a repurposed drug to market have been estimated
to be USD300 million on average, compared with an estimated ~USD2–3
billion for a new chemical entity). Finally, repurposed drugs may reveal new
targets and pathways that can be further exploited. It takes around 12–15 years
for a de novo drug approval process.5 The importance of drug repurposing
over de novo drug synthesis has been illustrated in Figure 1.1. In general, to
select a candidate drug for further developmental pipeline, drug repositioning
approaches require a three-step process:
• Hypothesis formulation through identifying a candidate molecule for
a specified target.
• Systematic assessment of the effect of drug in preclinical models.
3 Drug Discovery and Development
• Efficacy assessment in the clinical trials (phase II) after compiling
adequate safety data from phase I trials.
FIGURE 1.1 Major steps and expected time in new drug discovery and development process
versus repurposed drug development process.
4
However, there are some historical instances of drug repositioning that exists,
but this concept has come into light in year 2004 when Ashburn and Thor, in
an article, primarily defined drug repurposing as the practice of discovering
2
new and better uses of existing drugs.
Sildenafil drug which was primarily
used for antianginal medication was later repositioned in the middle of the
2000s to cure erectile dysfunction, and morning sickness drug thalidomide
was repositioned for multiple myeloma.6 These achievements open up new
opportunities and create a vast interest in drug repositioning which stemmed
in the establishment of many startup companies focusing on repurposing.
Also, market research reports have confirmed that drug repositioning has
an imperative share in the R&D marketing with spending 10–50%.7 The
term “polypharmacology” means one drug-multiple hits or off-target effects,
and its principle has been understood since the beginning of drug discovery.
Conventionally, the aim of drug discovery and development was to detect
the possible therapeutic agents by means of a one drug-one target model that
4
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
signifies that high selectivity would maximize the efficiency and minimalize
their side effects. To find out such particular compounds, problems arise
because a huge majority of compounds mediate often undesired effects.8 Due
to this observation, the theory of polypharmacology has been emerged. For
better understanding the scope of drug repositioning, Nancy et al. reported a
bibliometric examination by studying a few drugs. For instance, chlorpromazine drug synthesized for regulating mental disorders and as a preoperative
medicine that was later tried for treating several diseases like whooping
cough and for treating the symptoms of radiation therapy in cancer patients.
An antimalarial compound chloroquine that was synthesized in 1934 was
later used for targeting several diseases including fever, skin rash, and other
parasitic diseases.7 Numerous drug classes show polypharmacological
features like antipsychotic,9 cholinesterase inhibitors,
10
selective serotonin
reuptake inhibitors,11 and thrombolytic agents. Amantadine drug was originally developed for treating influenza but later redirected for Parkinson’s
disease.12 Likewise, zidovudine was earlier used as a cancer-treating drug,
but after redirection, it is in use for targeting HIV/AIDS.13 Although every
drug has the ability to hit multiple molecular targets, it is important to understand its clinical efficiency toward new targets that requires reinvestigation
of prevailing drugs for therapeutic redirection.
Many strict regulations are required to develop, validate, and bring any
new drug into the market and this process also needs 12–15 years with
substantial investment due to diversity in physico-chemical properties of
the compounds and exponential increase in the drug production. Still the
whole world is trying to eradicate the COVID-19 strains; it is difcult to wait
for 12–15 years for anti-COVID-19 drug development and this pandemic
situation has already proved the power and efciency of drug repurposing.
This drawback promotes various pharmaceutical companies and/or research
centers to quickly and prociently employ the already-approved and existing
14
drugs for new interventions.
Also, those molecules that were earlier failed
to show their efcacy toward predetermined targets can typically get a
good start for their reproling for other new indications that can be further
developed as potential therapies for rare diseases that are still facing lack
of diagnosis, treatment, and resources. Drug repositioning is a short-term
and less expensive approach that brings effective treatments to patients in
comparison with time-taken drug discovery and development practices.
15
Additionally, this method also helps to overcome the increasing overhead
costs for drug development, as a result of lowering down the medication cost
for patients’ treatment.
5 Drug Discovery and Development
Nowadays, the modern and progressive drug approaches have produced a
huge amount of data like gene expression data, mutational analysis, drug
chemical structure outlines, association between drug-disease, drug targeted
proteins, etc. which could be most useful to identify the right drug for a
specific target. There are various systematic approaches that are in use for
developing intriguing drug repurposing strategies such as computational
approaches experimental approaches, knowledge-based approaches, and
mixed approaches in which computational and experimental approaches and
their validation techniques have been displayed in Figure 1.2.
FIGURE 1.2 Representation of drug repurposing and validation approaches.
1.3.1 COMPUTATIONAL APPROACHES
Computational approaches, also called in silico approaches, are mainly
based on data-driven tools that allow systematic data analysis of any type
like gene expression analysis, mutational analysis, genotypic or phenotypic
traits, proteomic data, chemical structure profiles, or electronic health
6
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
records (EHRs), which led to generating drug repurposing hypothesis.
16
These findings would be helpful to treat incurable diseases, cancer, etc., that
require essential and adequate data to carry out the proposed research. Some
commonly used computational databases and resources for drug repurposing
are listed in Table 1.1.
Drug Repositioning.
Database/ Resource type Description URL
resource
PubChem Drug database
Human Protein Disease and
Atlas drug database
TCGA (The DiseaseCancer Genome related genetic/
Atlas Program) genomic data
GEO (Gene Disease-related
Expression transcriptional
Omnibus) genomics data
repository
ChEMBL Compound
information
resource
ClinicalTrials Drug–disease
associations
Allen Brain Disease
Atlas database
An open database holds
chemical information of more
than 90 million compounds
with their bioactivities, gene,
and protein targets
Mapping of all the human
proteins in cells, tissues, and
organs using integrative omics
technology
Characterization (RNAseq,
microarray, sequence
information) of over 33 types
of cancer
Database of next-generation
sequencing (NGS),
microarray, high-throughput
functional, and transcriptional
genomics data of diseases
Database for more than 2.1
million bioactive compounds
with drug-like properties
Web-based resource of
public- and private-supported
clinical data of diseases and
conditions
Gene expression database
for human and mouse brain
diseases and conditions
https://pubchem.ncbi.
nlm.nih.gov/search/
search.cgi
https://www.
proteinatlas.org
http://cancergenome.
nih.gov/
http://www.ncbi.nlm.
nih.gov/geo/
https://www.ebi.ac.uk/
chembl/
https://clinicaltrials.
gov
http://www.
brain-map.org/
Database/ Resource type Description URL
resource
COSMIC Cancer An online database of somatic http://cancer.sanger.
(Catalogue mutation mutations in human cancer. ac.uk/cosmic
of somatic database
mutations in
human cancer)
GTEx Gene Catalog of relationship https://www.
(Genotype- expression between genotype and gene gtexportal.org/home/
Tissue database expression in multiple human
Expression) tissues
STRING Protein Web resource of identified https://string-db.org/
association and predicted protein–protein cgi/input.pl
database interactions (PPI), network
analysis
Drug Bank Drug database Covers 11,000 drugs that https://www.
contain more than 200 data drugbank.ca/
fields of chemical data and
drug targets
e-Drug3D Drug database It allows exploring http://chemoinfo.
FDA-approved drugs and ipmc.cnrs.fr/MOLDB/
active metabolites index.html
ChemSpider Drug database Database of 64 million drug http://www.
chemical structures chemspider.com/
SIDER Drug database Data of marketed medicines http://sideeffects.
and their documented embl.de/
adverse reactions (side
effect frequency, drug–target
relation)
Drug Central Drug database Online drug information http://drugcentral.org/
resource contains info on
chemical entities, active
ingredients, drug mode
of action, pharmaceutical
products, indications, etc.
8
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
Text mining techniques are useful to find out the data associated with drugs,
genes, diseases, and then to organize the appropriate information from the
retrieval data based on the co-occurrence among the applicable entities or
by employing natural language processing techniques. Along with a lot of
information that is available about drug repurposing, enormous relationship
conceptual data of novel biological entity are accessible from publications.
Text mining allows us to discover fresh data by mining from numerous
published resources with computational method. Hearst proposed a common
definition for text mining (TM) as “It is the discovery of novel, formerly
unknown information through automatic information extraction from various
17
written sources using the computer machine.
” Usually, TM in the field of
biology comprises four steps as follows:
• Data/information retrieval (IR), in which biological study-related
documents are mined from the literature. These related documents
require filtration due to the presence of some useless conceptions in
the documents.
• Biological name entity recognition (NER), in which useful biological
conceptions are recognized using precise vocabularies.
• Biological information extraction (IE); and
• Knowledge discovery (KD)-relevant information is mined for
knowledge discovery about biological theories to eventually build a
knowledge graph with the extracted information.
18
Text mining also helps to determine the relationship between drug–target
and drug–disease. The concept of text mining techniques in the medical eld
was originated from the Swanson (ABC) model. For example, if the drug A
is related to the gene B, and the gene B is associated with the disease C, then
19,20
the drug A may have a new association with the disease C.
Based on this
concept, Li et al. established a method to build disease-denite drug–protein
connection maps through linking network and text mining. Primarily, they
mined the relationship between disease–protein using network mining,
followed by searching of drug terms that are indirectly linked with particular
diseases like Alzheimer’s disease in PubMed using text mining.21 Lastly,
the drugs and proteins could be connected through disease–protein or drug–
disease relationships.
9 Drug Discovery and Development
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Semantics-based computational approaches are broadly used to retrieve
information, image, and other fields including drug repurposing. The pipeline of semantic technology mainly comprises three steps:
• Biological entity relationships are mined from previous data from
large medical databases to create the semantics network.
• Construction of semantics networks using ontology networks through
the addition of the previously acquired information.
• Lastly, for the prediction of new relationships in the semantics
networks, mining algorithms are planned to calculate new relation-
22
ships in the semantics networks.
Based on a hypothesis in which
similar drugs are correlated with similar targets and similar targets
are connected to similar drugs, Guillermo et al., in their research,
illustrated an algorithm to calculate drug–target relationships using
semantic link prediction techniques and edge partition approaches
and built a semantic network containing (drug–drug, target–target)
relationships which made precise predictions of drug–target association.23 Likewise, Chen et al. constructed a statistical-based model of
semantic linked subnetwork between drug and target for the prediction of drug–target association. The suggested model successfully
recognized some known and random drug–target pairs with high
accuracy and also identified drugs for repurposing. For instance, a
drug named barbiturate earlier used for curing migraines was predicted
24
for treating insomnia with biological literature support.
Similarly,
a study demonstrated that tamoxifen drug that was used in treating
breast cancer can also cure ovarian cancer that was also supported by
the literature.
25
Drug repurposing through computational approaches has progressed over
the past two decades starting from drug similarity attempts that usually used
single source of medical or biological data into a pioneering application
area for machine learning techniques. Similar to machine learning models,
computational drug reprofiling necessitates wide-ranging data to disclose
the fundamental relationships between the biomedical and the biological
entity. The workflow of machine learning normally includes four steps: (1)
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