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120
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
According to the report of the International Agency for Research on cancer
of 2018, there are 9.6 million deaths due to cancer. The most predominant
38
type of cancer is the pulmonary, memory gland, and colorectal.
The high
prevalence rate of mortality is associated with pancreatic and stomach cancer
might be due to limitations in treatment options. The high prevalence of death
in cancer is dependent upon certain factors such as chemoresistance or tumor
heterogeneity as well as metastases. Due to high demand drug and production
crises emerged in the research and development of pharmaceuticals which
lead to the formulation of novel drug delivery therapeutics.39 However,
the discovery and development of new drug molecules represent timeconsuming and costly processes with low success rates. This might be due to
less understanding of the association between dose, exposure, and its effect
on the target. The interesting approach in the discovery of novel anticancer
therapeutics is to the repurposing of old FDA-approved molecules for new
indication termed as a repurposing of drug.
40
The main advantages of drug
repurposing are to minimize risk and cost for the development and shorten the
time gap in the discovery process due to the availability of pharmacokinetic
and pharmacodynamics and clinical data. Additionally, drug repurposing has
an opportunity to find new molecules for the treatment of rare cancer that is
often neglected by the R&D department of pharmaceutical companies due
to less marketing values.41 In silico drug repurposing approach has typical
benefit to transform biological data into a prediction of druggable targets.
Computational methods facilitate the use of data generated through different
omics tools, that is, genomic, proteomic, transcriptomic, and metabolomics
into the understanding of the biology of old and new targets along with the
mechanism of action of drugs (Fig. 5.1).
Prediction of druggable targets is crucial for repurposing. Hence, the
acquisition of biological data on targets can be collected from in vivo and in
42
vitro studies or from computational in silico methods.
Even though, targeted
therapy has signicant potential to improve patient life span. Despite this in
silico drug repurposing is based on the hypothetical approach that uses huge
data to predict the drug molecules against the cancer targets. This approach
has the unique ability to transform biological data of cancer phenotyping and
identication of druggable targets. For a successful computational pipeline,
the algorithms are necessary for the integration of huge biological data. In
this process, one of the essential steps is the appropriate collection and crossexamination of available omics data about cancer biology and mechanism.
121 Drug Discovery for Malignant Diseases
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FIGURE 5.1 A general overview of computer-aided drug repositioning approaches in
oncology.
Source: Reprinted with permission from Ref. [64]. © 2021 Elsevier.
5.5.1 ROLE OF IN SILICO DRUG REPURPOSING IN THE PREDICTION
OF DRUG THERAPIES
In vitro and in vivo experiment-based drug repurposing is often the result
of chance discovery and not hypothesis-based. This might result from an
experimental drug screening or by the identification of target similarities
42
among different diseases.
Disulfiram was approved for the treatment of
alcoholism in repurposing effectively in the treatment of cancer.
convulsant drug valproic acid was effective as an anticonvulsant drug.
However, valproic acid is proven as an anticancer drug in multiple clinical
46
trials.
A similar example such as nelfinavir was originally employed in
the treatment of acquired immune deficiency virus (AIDS) infection that
is currently in pipeline to treat various cancers such as lung, breast, and
47
melanoma.
Brivudine was first indicated in the treatment of herpes viral
infection. Brivudine is a thymidine analogue that stops viral replication in
humans. Later, based on the structural interaction brivudine also binds to
the human heat shock protein Hsp27 and inhibits its antiapoptotic activity.
Ibrutinib was identified as a typical Bruton’s tyrosine kinase inhibitor via
43–45
Anti-
48
122
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
BTK inhibition, but later it was proved to be a VEGFR2 inhibitor in cancer
therapy. So it is an example of a drug having the same drug acting on different
targets.49 Similarly, nilotinib was validated as a potent MAPK14 inhibitor;
50
additionally, it was proved as a potential anti-inflammatory drug.
Based on
the structural system pharmacology platform levosimendan a PDE inhibitor
for heart failure was developed as a serine/threonine-protein kinase inhibitor
in cancer.
51
5.5.2 IMPORTANCE OF IN SILICO DRUG REPURPOSING IN
PERSONALIZED MEDICINE IN CANCER
Computational driven drug repurposing might help to improve the efficacy
of personalized drug therapy in cancer which offers novel therapeutic indications. The key challenge that might be faced in oncology is the development of targeted and personalized therapy to treat cancer with minimal
52
side effects with a high response rate in each patient.
The targeted drug
therapies result from either the use of a drug promisingly effective against
defined molecular targets identified in a cancer cell or in the tumor microenvironment or more ideally based on the selection of patients who could
likely benefit from a specific treatment (Table 5.2). However, the concept
of targeting typical mutated tumor protein such as oncogene appeared
as a successful pharmacological approach in preclinical models.53 Other
challenging factors such as interindividual variation in patient’s physiological parameters, the bioavailability of drug, tumor microenvironment,
the aggressiveness of the tumor, and metastatic stage of tumor are also
limiting factors in the development of personalized therapy. To overcome
these limiting factors computational approaches for drug repurposing
might be the appropriate solution to develop targeted therapy in cancer.
Computation approaches can take advantage of various biological data
considering tumor biology, the clinical outcome of patient’s biomarkers
along with drug pharmacokinetics and pharmacodynamics modeling for
the prediction of a drug in cancer therapy.
54–56
Moreover, in silico methods
have also the capability to repurpose the old and off-target compounds to
increase the likelihood in selected patients and predict better response in
tumors.37 In 2011, the FDA-approved drugs vemurafenib and crizotinib
were repurposed in metastatic melanoma and lung cancer, respectively.
57,58
Everolimus was repurposed in pancreatic tumors with the mutation in
mTOR pathway genes.
59
123 Drug Discovery for Malignant Diseases
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TABLE 5.2 Identification of Drug Candidates Targeting Hallmarks of Cancer.
Sl. Cancer hallmarks Types of Example
no. therapy
1 Sustaining proliferative signalling Mono Rapamycin, prazosin,
indomethacin
2 Evading growth suppressors Combinatorial Quinacrine, ritonavir
3 Resisting cell death Mono Artemisinin, chloroquine
4 Enabling replicative mortality Combinatorial Curcumin, genistein
5 Genome instability and mutation Combinatorial Spironolactone, mebendazole
6 Reprogramming energy Mono Metformin, disulfiram
metabolism
7 Inducing angiogenesis Combinatorial Thalidomide, itraconazole
8 Activating invasion and metastasis Combinatorial Berberine, niclosamide
9 Tumor-promoting inflammation Combinatorial Aspirin, thiocolchicoside
10 Evading immune destruction Mono Infectious disease vaccines
60
Development and progression of cancer involves multiple factors, genetic
alterations, and mutation of various cellular components. Various types of
cancer hallmark already have been discovered that activates positive regulators for cancer cell proliferation, survival, and genetic mutations. Some of
the hallmarks are useful to predict disease outcome and prognosis. Deeper
understanding of these hallmarks is essential to implement target-based anticancer therapy. Multiple layers of omics studies on specific targets provide
disease-related data that helps in in-silico repurposing.
61,62
5.6.1 THE CANCER GENOME ATLAS (TCGA) STUDY
TCGA is a public project that has a collection of over 11,000 tumors, representing 33 most common types of cancer. The goal of this project is to create
a comprehensive atlas of molecular alterations that occur in cancer cells and
to discover genetic alterations using multidimensional platforms. The multidimensional platform integrates genomic, epigenomic, and transcriptomic
data from various types of cancer to define their molecular subtypes. TCGA
studies also compare multiple cancer types to identify common molecular
124
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
features that help to do accurate molecular classification of tumors. Multidimensional studies of TCGA provide detailed information about cancer
biology that can be applied for various computational approaches for drug
repurposing. The availability of this large publicly available data will boost
translational cancer research and could be powerful resource to support drug
repurposing.
63
5.6.2 CANCER TARGETS RELATED OMICS DATA
Activation of oncogene and inactivation of tumor suppressor genes are one
of the fundamental reasons for development and progression of cancer.
Currently, targeted anticancer agents that are approved or under clinical
trial are targeting oncogenic activation pathways. However, development of
resistance is one of the major drawbacks of targeted therapy. Certain mutated
oncogenes like K-RAS, β-catenin are irresponsive to targeted cancer therapy
that turns into development of resistance. Due to this reason current targeted
therapy still shows limited clinical success, thus opening the opportunities
of drug repurposing.
Omics studies provide deeper insights into understanding of oncogenic
signaling activation or tumor suppressor genes that support cancer cell proliferation and survival. In certain studies, multiple omics approach had been
implemented that focused on specic oncogenes (like C-MYC, K-RAS, and
β-catenin) and tumor suppressor genes (like PTEN and p53) for implementa-
tion of modulators or inhibitors in a clinical setting. For example, multiple
omics studies on K-RAs help to dene biology of K-RAS in different types
of tumors. Various in vivo and in vitro models are developed for better
understanding.
64
Cancer is one of the major threats for human health. Every year around
10 million people die from various forms of cancer. Cancer is the second
leading disease that causes human death. Drug discovery and development
process take around 12 years with around 2.7 billion USD cost for the development of new molecules. Considering heterogeneity of cancer, the drug
development process for cancer is even more complicated. Undruggable
target, chemo-resistance, and tumor heterogeneity are major barriers for safe
and effective cancer chemotherapy. Traditional drug discovery approach
125 Drug Discovery for Malignant Diseases
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fails to provide an effective solution. Computer-aided drug repurposing has
a potential to improve cancer therapy due to major three reasons: (1) each
cancer hallmark is deeply investigated by Omics approaches. (2) Use of
traditional algorithm with computational approaches can expand the ability
of data integration even in the absence of clinical hypothesis. (3) Prediction
of synthetic lethality using computational tools can be useful for effective
clinical outcomes.
The era of computer-aided drug repurposing is just started. In the future,
the drug repurposing approach can solve therapeutic limitations of current
cancer therapy. The combination of useful predictions generated by various
computational tools with experimental validation can speed up the anticancer
drug development process.
KEYWORDS
• in silico repurposing
• malignant diseases
• computational tools
• SBDD approach
• omics
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