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exploration process. In contrast to blinded approaches, target-based methods
enhance the likelihood of medicine discovery by directly linking targets to the dis-
ease/syndrome mode of action. The integration of target evidence and DR amplies
the potential for identifying therapeutically advantageous compounds (Benson etal.
2013; March-Vila etal. 2017).
16.3.3 Knowledge-Based Methods
Utilizing bioinformatics/cheminformatics methodologies, these methods incorpo-
rate various data sources into drug repositioning studies, encompassing details
about medicament, drug-target networks, target and drug chemical structures, clini-
cal studies data, FDA approvals, as well as their signalling and metabolic pathways.
However, the informational content of both blinded and target-based approaches
might be insufcient to uncover novel mechanisms away from the established
targets.
In contrast, knowledge-based methods integrate existing data to predict unknown
mechanisms, like identifying new drug targets, revealing hidden drug-drug like-
nesses, and pinpointing novel disease biomarkers. By incorporating substantial
known information, knowledge-based methods enhance the DR process, thereby
improving the accuracy of predictions (Benson etal. 2013; March-Vila etal. 2017).
16.3.4 Signature-Based Methods
Methods for DR based on signatures involve the utilization of gene signatures
extracted by using disease omics data, either with the treatment or without treat-
ment, to unveil undiscovered off-target or disease mode of action. The sequence
read archive (http://www.ncbi.nlm.nih.gov/Traces/sra/), NCBI-GEO (http://www.
ncbi.nlm.nih.gov/geo/), SRA, CMAP Connectivity Map, and CCLE, Cancer Cell
Line Encyclopaedia, are among the publicly accessible databases from which the
genomic data used in these analyses is sourced. Signature-based approaches play a
vital role in unravelling previously unidentied mechanisms for compound and
drugs. Computational techniques employed in this context often focus on molecu-
lar-level mode of action, such as genes that undergo signicant changes (Benson
etal. 2013; March-Vila etal. 2017).
16.3.5 Pathway or Network-Based Methods
This method reconstructs pathways specic to a given disease by using genetic
disease information, available signalling or metabolic pathways, and protein inter-
action networks. Through this process, broad signalling networks that previously
involved many proteins are rened to more particular networks with a smaller num-
ber of proteins or targets, which helps identify important targets for drug
R. Sharma et al.
405
repurposing. Utilizing pathway exploration or network biology approaches, these
techniques explore into genetic, metabolic information, proteomic, and genomic
related to diseases to pinpoint essential pathways, uncovering new targets suitable
for drug repositioning. For example, exploring the signalling mode of action for
metastatic subtypes in breast cancer poses a challenge due to the difculty in clari-
fying these subtype-specic signalling mechanisms through already known breast
cancer pathways or gene signatures (Benson etal. 2013; March-Vila etal. 2017).
16.3.6 Targeted Mechanism-Based Methods
To clarify the unknown mechanism of drug action, this method combines treatment
omics (genetic) data, pre-existing signalling pathway data, and protein interaction
networks. The objective is to uncover the mechanisms of drug mechanism by iden-
tifying off-target or targeted pathways affected by drug treatment, utilizing drug
omics data collected earlier and afterward the administration of the drug. For
instance, addressing drug resistance in cancer therapy is critical. Patients often have
resistance to a medication after a few months of treatment, even with initial positive
responses. In order to nd more effective therapeutic targets, further understanding
of the mechanisms underlying drug action is necessary for successful drug treat-
ment. However, there are a very few or limited studies available on mechanism-
based methodology that focus on targets and employ sophisticated in silico study to
predict drug effects and their associated pathways. This scarcity is attributed to the
challenges involved in developing actual computational models (Benson etal. 2013;
March-Vila etal. 2017).
16.3.7 Pharmacovigilance-Based Drug Repurposing
Pharmacovigilance-based drug repurposing is a recent concept in which repurposed
candidates are identied from pharmacovigilance data using data mining approaches.
Recently, few studies have used pharmacovigilance-based approach in the identi-
cation of potential repurposing drugs against various diseases (Table16.1). Recently,
in our lab, we have used this approach for the identication of potential drugs
against bacterial infections.
16.4 Virtual Screening (VS)
Virtual screening (VS) is a useful in silico technique used in the search for new
medicines (Fig. 16.2). A crucial prerequisite for engaging in virtual screening
involves having access to the 3D type of structure of every target protein (Miró-
Canturri etal. 2019; de Mello etal. 2020). Consequently, several databases have
been established to house 3D molecular structures. The application of VS has nowa-
days become widespread in the identication of novel drug molecules and has
16 Drug Repurposing andVirtual Screening
406
Table 16.1 Few examples of repurposed drugs
S.
no
Drug name Drug known for Repurposed for
Type of
study
1. Auranon Rheumatoid arthritis Antibacterial and
antifungal (Miró-Canturri
etal. 2019)
In vitro
2. Aripiprazole Antipsychotic/
anti-depressant
Active against fungal
biolms (Rajasekharan
etal. 2019)
In vitro
3. Aspirin and
ibuprofen
NSAIDS Antibacterial and
antifungal (Ogundeji etal.
2016)
In vitro
4. Dacarbazine Anti-tumour Active against fungal
biolms (Wakharde etal.
2018)
In vitro
5. Niclosamide Helminthiasis/
anti-infective
Antibacterial and
antifungal and active
against fungal biolms
(Hamdoun etal. 2017)
In vitro
and in
silico
6. Artesunate Anti-infective Active against fungal
biolm (De Cremer etal.
2015)
In vitro
7. Auranon Rheumatoid arthritis Anti-bacterial and
antifungal (Siles etal.
2013)
In vitro
8. Bithionate disodium Anti-infective Active against fungal
biolms (De Cremer etal.
2015)
In vitro
9. Bleomycin Anti-tumour Active against fungal
biolms (Wakharde etal.
2018)
In vitro
10. Avermectin B1a Anti-infective Active against fungal
biolms (Siles etal. 2013)
In vitro
11. Bromperidol Anti-psychotic/
anti-depressant
Active against fungal
biolms (Holbrook etal.
2017)
In vitro
12. Benzbromarone Vasodilator Active against fungal
biolms (Siles etal. 2013)
In vitro
13. Carboplatin Anti-tumour Active against fungal
biolms (Wakharde etal.
2018)
In vitro
14. Broxyquinoline Anti-infective Active against fungal
biolms (De Cremer etal.
2015)
In vitro
(continued)
R. Sharma et al.
407
Table 16.1 (continued)
S.
no
Drug name Drug known for Repurposed for
Type of
study
15. Celecoxib Anti-inammatory/
immunomodulatory
Active against fungal
biolms (Alem and
Douglas 2004)
In vitro
16. Clarithromycin Anti-infective Active against fungal
biolms (Fernández-
Rivero etal. 2017)
In vitro
17. Chloroquine Anti-malarial Active against fungal
biolms (Shinde etal.
2013)
In vitro
18. Cyclophosphamide As immune-modulator in
autoimmune diseases
Breast cancer (Aggarwal
etal. 2021)
19. Doxepin Anti-psychotic/
anti-depressant
Active against fungal
biolms (Caldara and
Marmiroli 2018)
In vitro
and
invivo
20. Disulram
(Antabuse)
Reduces ethanol
tolerance in alcoholism
Metastatic breast cancer
(The Institute of
Molecular and
Translational Medicine,
Czech Republic 2023)
Phase II
clinical
trial
21. Diazepam Anti-psychotic/
anti-depressant
Active against fungal
biolms (Kathwate etal.
2015)
In vitro
22. Dexpramipexole ALS and other
neurological diseases:
phase 3 trials did not
meet the endpoint
Hypereosinophilic
syndrome (National
Institute of Allergy and
Infectious Diseases
(NIAID) 2017)
Clinical
trial
23. Ebastine Anti-inammatory/
immunomodulatory
Active against fungal
biolms (Dennis and
Garneau-Tsodikova 2019)
In vitro
24. Etodolac Anti-inammatory/
immunomodulatory
Active against fungal
biolms (Alem and
Douglas 2004)
In vitro
25. Edaravone Neuroprotective agent in
acute ischemic stroke and
ALS
Multiple sclerosis
(Eleuteri etal. 2017)
Clinical
studies
26. Exemestane Ovulation induction Breast cancer (Zucchini
etal. 2015)
Clinical
studies
27. Eltrombopag Anti-inammatory/
immunomodulatory
Active against fungal
biolms (Ko etal. 2020)
In vitro
28. Everolimus
(Votubia, Evertor)
Immunosuppressant
during organ transplants,
wound healing
Metastatic breast cancer
(Royce and Osman 2015)
Clinical
studies
29. Favipiravir Inhibitors of RNA-
dependent RNA
polymerase of virus
(anti-viral drug)
Effective against
SARS-CoV-2 (COVID-
19) (Wang etal. 2020)
Clinical
studies
(continued)
16 Drug Repurposing andVirtual Screening
408
Table 16.1 (continued)
S.
no
Drug name Drug known for Repurposed for
Type of
study
30. Finasteride Benign prostatic
hyperplasia
Anti-bacterial and
antifungal (Chavez-Dozal
etal. 2014)
In vitro
31. Fulvestrant Anti-oestrogen Breast cancer (Turner
etal. 2018)
Clinical
studies
32. Fluvastatin Lipid lowering Active against fungal
biolms (Rana etal. 2019)
In vitro
33. Gemcitabine Anti-viral drug Breast cancer (Stemmler
etal. 2011)
34. Goserelin Prostate cancer, uterine
broids, assisted
reproduction
Breast cancer (Noguchi
etal. 2016)
Clinical
studies
35. Imipramine Anti-psychotic/
anti-depressant
Active against fungal
biolms (Caldara and
Marmiroli 2018)
In vitro
36.
γ-Secretase
inhibitors (GSI)
Alzheimer disease:
prevent amyloid
precursor cleavage
Several inhibitors are
being tested against a
variety of cancers (Schein
2020)
Clinical
trails
37. Ivermectin Anti-parasitic Effective against
SARS-CoV-2 (COVID-
19) (Caly etal. 2020)
Clinical
trails
38. Iodoquinol Anti-infective Active against fungal
biolms (Wall etal. 2019)
In vitro
39. Letrozole Ovulation induction Breast cancer (Finn etal.
2016)
Clinical
trails
40. Ketoprofen Anti-inammatory/
immunomodulatory
Active against fungal
biolms (Abdelmegeed
and Shaaban 2013)
In vitro
41. Itraconazole Antifungal Anti-cancer (Kim etal.
2010)
In vitro
42. Ketorolac Anti-inammatory/
immunomodulatory
Active against fungal
biolms (Kim etal. 2010)
In vitro
43. Lopinavir/ritonavir Anti-viral drug Effective against
SARS-CoV-2 (COVID-
19) (Cao etal. 2020)
Clinical
studies
44. Lorazepam Anti-psychotic/
anti-depressant
Active against fungal
biolms (Domhan etal.
2008)
In vitro
45. Losartan Blood pressure reduction Alzheimer disease (Schein
2020)
Pre-
clinical
trials
46. Mycophenolic acid Immunosuppressant Anti-cancer (Domhan
etal. 2008)
In vitro
and
invivo
(continued)
R. Sharma et al.
409
already played a signicant role in bringing compounds to market. Some notable
examples of medications that are sold through virtual screening are zanamivir, cap-
topril, ritonavir, and indinavir, as well as saquinavir, tiroban, dorzolamide, and
saquinavir (Maia etal. 2020).
16.4.1 Molecular Docking
It is a exible method for estimating the structure and evaluating the intensity of the
interaction between a protein and a minor chemical in a complex. Extensive sets of
ligands and targets can be quickly and effectively screened via molecular docking,
Table 16.1 (continued)
S.
no
Drug name Drug known for Repurposed for
Type of
study
47. Verapamil Anti-arrhythmic Active against fungal
biolms (Yu etal. 2013)
In vitro
48. Tocilizumab Immunosuppressive drug
for cytokine release
syndrome
Severe COVID-19
infection (Toniati etal.
2020)
Clinical
studies
49. Ribavirin Anti-viral drug Effective against
SARS-CoV-2 (COVID-
19) (Elky 2020)
In silico
50. Rifampicin Anti-infective Active against fungal
biolms (Fernández-
Rivero etal. 2017)
In vitro
Fig. 16.2 Methods of virtual screening
16 Drug Repurposing andVirtual Screening
410
which provides a large number of sample options (Kitchen etal. 2004; Sgobba
etal. 2012).
16.4.2 Ligand-Based Virtual Screening (LBVS)
LBVS utilizes ligands through established biological action to uncover compound
sharing analogous structures within virtual compound public library. In this method,
the structure of the molecular target is disregarded, with LBVS relying on the prem-
ise that molecules possessing structural resemblance might demonstrate compara-
ble biological activity. Thus, the goal of LBVS is to identify molecules with similar
molecular scaffolds or pharmacophore components in order to increase the likeli-
hood of nding compounds that are biologically active. In LBVS, compounds
stored in databanks are equated with the descriptors or qualities of recognized mol-
ecules derived from reference molecules. This comparison is conducted using simi-
larity measures, with the Tanimoto coefcient being widely employed for this
purpose (de Oliveira etal. 2023). One prevalent LBVS classication technique is
based on the number of dimensions represented by the 1D, 2D, or 3D descriptor:
1D descriptors: 1D descriptors include molecular characteristics such as molecular
weight, the number of acceptor and donor groups in hydrogen bonds, rotatable
bonds, types and counts of atoms, and computed physicochemical parameters
such as logP and water solubility (logD), among others.
2D descriptors: Molecular linkage is the foundation upon which 2D descriptors,
which are constructed, are built. In the form of topological indices and structural
descriptors, these appear (Siles etal. 2013). Binary vectors or 2D charts are two
ways that structural descriptors can be used to illustrate a substance by describ-
ing its chemical substructure. Descriptors that are used to calculate molecular
similarity include the carbo index, connection index, and count of aromatic rings.
These are examples of 2D descriptors.
3D descriptors: When it comes to the spatial arrangement of atoms, their character-
istics, or chemical groups, 3D descriptors provide molecular insights. These
descriptors take into account the conformations of molecules, necessitating the
examination of chemical groups, particle distribution in space, and molecular
characteristics (Holbrook etal. 2017). Studies have revealed that when it comes
to virtual screening, no particular descriptor performs better than others overall
(Alem and Douglas 2004). Therefore, a variety of descriptors are usually used to
describe molecules. Van der Waals forces and electrostatic potential are two
examples of 3D descriptors (de Oliveira etal. 2023).
16.4.3 Pharmacophore Modelling
Pharmacophore modelling is a rmly established approach utilized for screening
vast databases in the initial stages of medicament development. Pharmacophore
R. Sharma et al.
411
modelling VS is an in silico method which used identication of new drug by
matching the structural and chemical features of a small compound (pharmaco-
phore) with the target site on a biological macromolecule, like a protein receptor.
This technique helps in the efcient identication of large chemical public library
to predict compound that are likely to bind and interact with the target, thus aiding
in the discovery of a new drug (Giordano etal. 2022).
16.4.4 Similarity Searching
Similarity searching has become a straightforward and cost-efcient method for
comparing information across different chemical databases to discover connections
between active structures in the database. With this method, it’s now simpler to
track down the original active aspects based on how much the structures resemble
each other. Because of its ease and effectiveness, much chemo informatics software
relies on likeness searching with single target structure methods. To conduct several
searches or investigate target structures that aren’t structurally associated, similarity
searching is carried out using chemical databases such as a MDL Drug Data Report
(MDDR) (Bastikar etal. 2022).
16.4.5 Machine Learning (ML)
Machine learning (ML) is a computational approach used in drug development and
materials science to expedite the identication of promising compounds with
desired properties. It inuences machine learning algorithms to analyse large data-
sets of chemical structures and their associated properties, predicting which com-
pounds are most expected to exhibit the desired biological actions or material
characteristics (Carpenter and Huang 2018).
16.4.6 Structure Based
Target-based virtual screening (TBVS), another name for structure-based virtual
screening (SBVS), is a technique that predicts the ideal combination of two chemi-
cals to create a stable molecule. This technique involves various approaches to
investigate the 3D structure of the molecular target. SBVS is the favoured approach
when there is a 3D structure of the molecular target which has been empirically
determined. It endeavours to anticipate the probability of interaction among appli-
cant ligands and the target protein, taking into consideration the binding strength of
the resulting complex. Among SBVS techniques, molecular anchoring is commonly
utilized due to its cost-effectiveness and reliable outcomes (Maia etal. 2020).
In the 1980s, molecular docking emerged with Kuntz etal.’s development of an
algorithm for investigating the geometrically plausible docking of a compound and
target (Pinzi and Rastelli 2019). Their work demonstrated the ability to achieve
16 Drug Repurposing andVirtual Screening
412
structures closely resembling the correct ones post-docking. This method aims to
forecast the optimal positioning and alignment of a ligand within a molecular tar-
get’s binding site to facilitate the formation of a stability of complex (Shah etal.
2024). SBVS is commonly employed with proteins and enzymes like receptors, but
it can also extend its application to other structures like carbohydrates and
DNA.Despite its promising potential, it wasn’t until the 1990s that SBVS saw
widespread adoption, facilitated by advancements in techniques, increased compu-
tational capabilities, and enhanced accessibility to structural data of target mole-
cules (Smith 2016).
16.4.7 Molecular Dynamics Studies
Molecular dynamics studies are usually done after the molecular docking studies to
check the stability of drugs in the active site of the target. These simulations depend
upon the Newton second law, i.e. F = m∗a where F is force, m is mass, and a is
acceleration. Further, change in velocity with respect to time is acceleration. Overall,
simulation is provided using high-performance computational techniques to analyse
the physical movements of atoms and molecules. The stability of ligand in the active
site of the target is analysed by calculating various parameters like root mean square
deviation (RMSD), root mean square uctuations (RMSF), radius of gyration,
potential energy, principal component analysis (PCA), free energy landscape (FEL),
and so on.
16.4.8 Quantitative Structure-Activity Relationship (QSAR)
Quantitative structure-activity relationship (QSAR) is a mathematical as well as
computational approach aimed at establishing a statistically meaningful connection
between the structure and function of molecules, employing chemometric tech-
niques. The method of ligand-based drug design known as QSAR analysis was
originated by Hansch and Fujita back in 1964, marking over 50 years since its
inception (Bastikar etal. 2022).
QSAR assists in identifying compounds possessing specic characteristics by
analysing their chemical details and their impact on biological functions. It consid-
ers factors such as how they interact with their environment and the presence or
absence of certain chemical traits. QSAR aims to establish connections between a
compound’s structure, chemistry, and physical attributes with its biological effects,
employing diverse mathematical techniques. The resulting QSAR models are uti-
lized to anticipate and categorize the biological behaviours of novel chemical mol-
ecules (Bastikar etal. 2022). QSAR models are benecial for regulatory purposes
when there is limited or no empirical data available, and there is a need to assess
toxicological or pharmacological potential at regulatory endpoints. The division of
Applied Regulatory Science (DARS) focuses its research efforts in computational
pharmacology and toxicology on developing QSAR models and carefully selected
R. Sharma et al.
413
datasets for endpoints that are pertinent to regulatory matters. Two statistical QSAR
models were recently developed to predict drug permeability across the blood-brain
barrier (BBB). These models are designed to help regulators assess if drug metabo-
lites or related chemicals have the potential to be abused. They can also forecast
whether an unknown substance of abuse will be able to cross the blood-brain barrier
and cause the effects that the public health assessment via structural evaluation
(PHASE) approach predicted. A sophisticated computer technique called PHASE is
employed to determine the possible threat that recently developed street drugs rep-
resent to public safety. It has been used in the past to evaluate kratom alkaloids and
derivatives of fentanyl. Pharmaceutical companies can use BBB models to identify
novel treatments for illnesses of the central nervous system (CNS) (Research C for
DE 2023).
The reason for creating in silico QSAR models delves into and encompasses
the following factors:
• Using mathematical techniques to predict the biological action of molecules and
comprehend their physical-chemical properties. QSAR models can be developed
for various drug classes to study and forecast the activity of compounds.
• Understanding and explaining the mechanisms across a set of chemicals. By
constructing a QSAR model based on this established mode of action for a series
of molecules, it becomes possible to predict the action of unidentied molecules.
Typically, molecules that is similar in structure exhibit similar types of activity
within a certain range. Therefore, when a novel compound is discovering belong-
ing to a similar type of class, its action can be also predicted using a QSAR
model. This aids in enhancing the activity of the molecule and designing
new ones.
• Cost savings in compound development (e.g. drugs, pesticides) through reduced
expenses in molecule synthesis, manufacturing, and in vitro/in vivo testing.
Mathematical validation of improved activity among newly designed molecules
allows for selective synthesis, saving time and resources compared to traditional
drug design methods.
• Predictions could reduce the need for costly animal testing, addressing ethical
concerns and sacricing animals for every new molecule, in terms of price, time,
and ethics. QSAR helps in side-step unnecessary animal tests for new compound
(Bastikar etal. 2022).
QSAR Application
• Identifying unidentied leads with medicinal, biocidal, or pesticide actions is
dependable when using the QSAR model.
• The QSAR makes it possible to nd harmful substances in the early phases of
ligand creation or when scanning various databases of already-existing
compounds.
16 Drug Repurposing andVirtual Screening