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Drug Repurposing and Computational Drug Discovery: Strategies and Advances
• Ligand interaction studies reveal what amino acid residues interact
with a ligand. This is important because a ligand must interact with
the crucial amino acids of a target protein. For example, in the case
of SARS-CoV-2, ligands (potential inhibitors) must interact with the
HIS41-CYS145 catalytic dyad and other amino acids at the catalytic
cavity of the main protease.
86
Molecular dynamics (MD) simulation is a computational technique that is
widely used to validate the results obtained from the MDSS.
89,90
a preliminary computational study to investigate the binding affinity of a
ligand toward the active site of a target protein.
84
On the other hand, MD
simulation is used to evaluate the stability of a protein–ligand complex for
a specific period (nanoseconds) in a computationally simulated environ
ment to mimic a real-life biological environment.
supported by in vitro data, MD simulations are generally not carried out.
91
92,93
However, in the absence of in vitro data, MD simulations are conducted to
validate the MDSS results.
91
Following are a few key pieces of information
that are necessary to understand the basics of MD simulations:
• MD simulation is used to validate MDSS. From MDSS, we can get
information regarding the binding affinity and protein–ligand interac
94
tions.
However, the stability of a ligand at the active binding pocket
of a protein is not provided by MDSS. Therefore, MD simulations are
used to check and evaluate the stability of a protein–ligand complex.
MD simulations are carried out in conditions that mimic real-life
biological environments where a ligand will supposedly interact with
a protein.
91
• Root mean square deviation (RMSD) of a protein and ligand is the
most common property evaluated to check the stability of protein–
ligand interaction. The RMSD trajectory of a protein and ligand is
evaluated for a specified period. The original docked pose of ligand
at the active binding pocket of a protein is taken as the reference
structure. From this, the stability of the protein and the ligand is
determined by observing their movements in the form of an RMSD
trajectory plot. For small proteins, an RMSD fluctuation between 1
and 3 Å is considered acceptable. The RMSD of a ligand should not
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differ significantly from the RMSD equilibrium of the protein that it
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is complexed with.
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• Root mean square fluctuation (RMSF) is an important parameter
generated by MD simulations for each amino acid residue of a protein.
The RMSF of a protein can be used to determine specific changes
occurring at a particular amino acid residue during the MD simula
tions. By taking the RMSF of a protein as a reference, the changes
occurring to a particular amino acid in a protein that is complexed
with a ligand can be found out. The smaller the RMSF value, the more
stable is the protein.
• The radius of gyration (rGyr) is another important parameter gener
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ated by MD simulations. rGyr determines the compactness of a
protein–ligand complex during the MD simulations. For a protein–
ligand complex, it is desirable to have a small value of rGyr.
95
4.3.2 LIGAND-BASED DRUG DESIGN
In the late 1990s, the term “virtual screening” (VS) was coined wherein
VS refers to the use of computational models and algorithms to screen out
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potential lead compounds.
Following are a few key pieces of information
that are necessary to understand the basics of VS:
• VS can be used to identify potentially toxic compounds from a small
to a large compound library. For example, the median lethal dose
), toxicity class, and organ toxicity of a compound can be deter
(LD
50
mined by an online web tool “ProTox-II
DataWarrior may also be used for toxicity studies.
19
” Offline software such as
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In this way, from
a library of 1000 compounds, all compounds with potential signs of
toxicities can be identified and discarded from a study.
• VS can be used to identify compounds with desired drug-like prop
erties. For example, the physicochemical properties, lipophilicity,
water-solubility, pharmacokinetics, bioavailability, and synthetic
accessibility of a test compound can be studied with the help of an
18
online tool “SwissADME.
” When we have hundreds or thousands
of compounds, it is not possible to synthesize or carry out MDSS for
all those compounds. Also, it is not rational to carry out MDSS or MD
simulations for a compound with toxicities and undesirable drug-like
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Drug Repurposing and Computational Drug Discovery: Strategies and Advances
properties. To avoid such types of issues, VS can be used to remove
toxic compounds with undesirable drug-like properties from a study.
In this way, the chances of failure at a preclinical or clinical level will
97
be reduced. In addition to SwissADME, DataWarrior
and Molin
spiration Chemoinformatics (https://www.molinspiration.com/) may
also be used for VS.
• Among VS, quantitative structure–activity relationship (QSAR) is a
powerful computational technique that is used to study the relation
ship between the chemical structure of a compound and its biological
activity. The presence or absence of certain functional groups or the
attachment of a functional group at a particular position can alter the
biological activity of a compound.
98
QSAR models can be created,
validated, and subsequently be used to predict the inhibitory concen
tration (IC50) value of compounds against target proteins or specific
99
pathogens.
QSAR models are mathematical equations that are built
using molecular descriptors (i.e., properties of a compound such
as lipophilicity, polar surface area, etc.). With the help of machine
learning techniques, the molecular descriptors of a compound are
correlated with its biological activity using the developed and vali
dated QSAR models. In this way, the biological activity of novel
compounds can also be tested. The ultimate aim of QSAR is not to
replace in vitro or in vivo studies, but rather, to rationalize the process
of compound selection that will be tested in vitro or in vivo.
100
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According to the International Union of Pure and Applied Chemistry, the phar
macophore model is defined as “an ensemble of steric and electronic features
that is necessary to ensure the optimal supramolecular interactions with a
specific biological target and to trigger (or block) its biological response.”
101
Pharmacophore modeling is a powerful computational technique that is used
for virtual screening, de novo drug design, lead optimization, multi-target
drug design, activity profiling, and target identification.
steric features of a compound that are most responsible for eliciting interac
tions and biological activity are identified using pharmacophore modeling.
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When a ligand structure and target protein are not known, pharmacophore
modeling is impossible. When a ligand structure is known but the target
protein is not known, ligand-based pharmacophore modeling can be carried
out. When a ligand structure is not known but the target protein is known,
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structure-based pharmacophore modeling can be carried out.
Pharma
cophore modeling can be used with other computational techniques such
as MDSS and QSAR.
104,105
identify nontoxic compounds with desirable drug-like properties, pharma
cophore modeling may be used to identify potential leads before they are
subjected to further studies.
Artificial intelligence (AI) has made significant progress in big data analysis
for screening and discovery of potent active compounds for various types
of diseases. Several deep learning and machine learning methods have been
discussed in several reviews regarding their usefulness in drug discovery
106,107
and optimization.
Simulation modeling, computation, and prediction
methods that apply AI have become more powerful and accurate, with excel
lent accessibility. These methods have been used for repurposing existing
drugs, approved natural products, and clinical trial candidates. Here are some
applications of AI in the discovery of antiparasitic drugs and NTDs drugs:
• One of the first uses of the machine learning (ML) method was to
troubleshoot the formulation of benznidazole to treat Chagas disease
106
(American trypanosomiasis).
In this research, they used chitosan
microparticles to improve its pharmacokinetic properties. Particle
size, encapsulation efficiency, and dissolution rate were modeled
using JST to optimize their oral absorption.
• The use of ML models was also reported by Guera et al. in which
72 compounds were tested in vitro against Trypanosoma cruzi, with
descriptors generated by the CODES software.
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successful in predicting the activity of compounds in the test set
with the standard error (SE) prediction and root-mean-square error
(RMSE) of 0.17 and the value of the area under the receiving operator
curve (AROC) of 0.7 (value 0.5 is random).
• Research conducted by de Souza et al. showed that the use of artificial
neural networks (ANN) and kernel-based PLS (KPLS) in modeling
363 compounds as anti-T. cruzi has the good predictive ability by
producing r square and RMSE values of 0.85 and 0.75, respectively .
109
The KPLS model was used to provide a comprehensive analysis of the
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Drug Repurposing and Computational Drug Discovery: Strategies and Advances
effect of molecular features on increasing or decreasing the activity of
antitrypanocidal compounds.
• A good predictive model of antitrypanosomal drug activity has been
developed by Kryshchyshyn et al. using stochastic gradient boosting
(SGB), random forests (RF), Gaussian process regression (GP), and
multivariate adaptive regression splines (MARS).
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has the highest predictive power followed by SGB and GP, while
MARS gives the worst prediction. Logit transform can improve the
predictive power of the model.
• Analysis of the activity of anti-T. cruzi compounds. cruzi was also
studied by Luchi et al. by adopting different types of molecular
descriptors.
111
They used the support vector machines (SVM) method
to understand the basic molecular activity by selecting 87 features.
However, the SVM method tends to overestimate the data so the use
of a large number of features is not recommended.
112
In the process of drug discovery and development, computational tech
niques serve multiple purposes. They are used to identify compounds for
web-lab testing, or are used to predict the potential molecular mechanism, or
are used to optimize the properties or biological activity of lead compounds.
Different computational techniques have been used to identify potential drug
candidates against many parasitic diseases and NTDs (Table 4.3). Molecular
docking, MD simulations, EIIP ltering, 3D-QSAR, pharmacophore
modeling, virtual screening, homology modeling, MM-PBSA binding free
energy calculations, and target shing are a few examples of computational
techniques that are used for evaluating compounds against parasitic diseases
and NTDs.
Many drugs have been repurposed for parasitic diseases and NTDs (Tables
4.1 and 4.2). Also, several compounds that showed potent activity against
parasitic diseases and NTDs are highlighted in Table 4.3. For all the
compounds that are highlighted in the paper, the majority of them have been
repurposed or tested only at a preclinical level. Even at the preclinical stage,
the majority had been tested at the in vitro level only. For those compounds
that have the potential to be repurposed for a different disease, or those newly
identified compounds that showed potential against parasitic diseases and
NTDs, exhaustive in vivo studies are recommended so that these potential
and NTDs.
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Disease Compound Possible mechanism of Computational Stage Reference
action technique involved
Malaria 1-(heteroaryl)-2-((5-
nitroheteroaryl)methylene)
hydrazine derivatives
Inhibition of Molecular docking
plasmodium falciparum
lactate dehydrogenase
Preclinical; in vivo; Peter’s
test, Compound 2, 99.09%
parasitemia inhibition at 125
[113]
mg/kg/day
18β-glycyrrhetinic acid
Inhibition of Molecular docking
plasmodium falciparum
lactate dehydrogenase
Preclinical; in vitro, IC
68–100% suppression at
[114]
50
62.5–250 mg/kg
Dengue
Quercetin Inhibition of NS5
Gallic acid Inhibition of NS5
Chikungunya
Aminopiperidine analogue Inhibition of envelope
(compound 11) protein
1,3-thiazolodin-4-one Inhibition of nsP2
derivatives (compound 8)
Leishmaniasis N’-[(5-Bromo-2-
thiophenyl)methylene]
thiophene-2-carbohydrazide
Molecular docking
Preclinical; in vitro; EC =
protein 19.2 µg/mL
Molecular docking
protein
Molecular dynamics
simulations
Molecular docking
Preclinical; in vitro; EC =
25.8 µg/mL
Preclinical; in vitro; EC =
1.6 µM
Preclinical; in vitro; EC =
1.5 µM
Inhibition of arginase
(L. donovani)
EIIP filtering,
3D-QSAR,
Preclinical; in vitro; IC =
2.18 µM
molecular docking
[115]
50
[115]
50
[116]
50
[117]
50
[118]
50
95 Parasitic Diseases and Neglected Tropical Diseases (NTDs)
Disease Compound Possible mechanism of Computational Stage Reference
action technique involved
5-acetyl-6-((4-(5-(4hydroxyphenyl)-4,5dihydro-1H-pyrazol-3-yl)
Inhibition of pteridine
reductase 1 (L.
donovani)
Molecular docking
Preclinical; in vitro; IC
µM 1.5
=
[119]
50
phenylamino)methyl)-1methyl35 4-phenyl-3,4dihydropyrimidin-2(1H)-one
Zika virus Asunaprevir
Simeprevir
Buruli ulcer Euscaphic acid
ZINC95485880
Chagas disease 28SMB032
Inhibition of NS2B/NS3
protease
Inhibition of NS2B/NS3
protease
Potential inhibitor of
isocitrate lyase
Potential inhibitor of
isocitrate lyase
Inhibition of DNA
Pharmacophore
anchor model,
molecular docking
Pharmacophore
anchor model,
molecular docking
Molecular docking,
virtual screening
Molecular docking,
virtual screening
Homology
modeling, molecular
docking
Preclinical; in vitro;
enzymatic assay; IC
µM
= 6.0
50
Preclinical; in vitro;
enzymatic assay; IC
µM
= 2.6
50
Preclinical; in silico; −8.6
kcal/mol
Preclinical; in silico; −8.6
kcal/mol
Preclinical; in vitro, IC
= 0.54 µM; in vivo, 40%
50
parasitemia reduction,
[120]
[120]
[121]
[121]
[122]
mg/kg for 5 days,0.1
intraperitoneal
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Drug Repurposing and Computational Drug Discovery: Strategies and Advances
TABLE 4.3 (Continued)
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Disease Compound Possible mechanism of Computational Stage Reference
action technique involved
4-[4-(2-Chloro-benzyloxy)phenyl]-3,6-dimethyl-4,8dihydro-1H-pyrazolo[3,4-e]
[1,4]thiazepin-7-one
Inhibition of CYP51TC
Molecular docking
Preclinical; in vitro,
intracellular strain (EC
= 3.86 µM), bloodstream
strain (EC
vivo, 43% parasitaemia peak
= 4.00 µM); in
50
[123]
50
reduction
Fascioliasis HTS12701
Inhibition of cathepsin
L3
Virtual screening,
molecular docking,
Preclinical; in silico; −10.68
kcal/mol
[124]
MD simulations,
MM-PBSA
binding free energy
calculations
BTB03219
Inhibition of cathepsin
L3
Virtual screening,
molecular docking,
Preclinical; in silico; −7.16
kcal/mol
[124]
MD simulations,
MM-PBSA
binding free energy
calculations
Human African 2,3,4,5-tetrahydrobenzo[F]
Trypanosomiasis [1,4]oxazepines derivatives
(compound 7a)
Leprosy Neobavaisoflavone
Inhibition of
peroxisomal import
matrix 14
Inhibition of
Molecular docking,
pharmacophore
modelling
Molecular docking
Preclinical; in vitro; IC
µM 4.0
Preclinical; in silico
=
[125]
50
[126]
dihydropteroate
synthase
97 Parasitic Diseases and Neglected Tropical Diseases (NTDs)
TABLE 4.3 (Continued)
Disease Compound Possible mechanism of Computational Stage Reference
action technique involved
Dapsone-thymol conjugate Inhibition of Molecular docking, Preclinical; in vivo; Bacilli [127]
dihydropteroate MD simulations, cell count before treatment
synthase MM-PBSA = 5 × 10
binding free energy after treatment = 2.6 × 10
5
; Bacilli cell count
4
calculations
Lymphatic Ursolic acid Inhibition of Molecular docking Preclinical; in vitro, IC
filariasis glutathione-s-transferase = 8.84 µM; in vivo, 54%
[128]
50
macrofilaricidal activity, 56%
female worm sterility
Moxidectin Inhibition of glutamate- Molecular docking Preclinical; in vitro, IC
gated chloride channels 0.242 µM (female parasite),
= [129]
50
µM (male worm),0.186
µM0.813 (microfilaria); in
vivo, 49% macrofilaricidal
activity, 54% female worm
sterility
Schistosomiasis GPQF-108 Inhibition of carbonic Target fishing, Preclinical; in vitro, IC
anhydrase and arginase molecular docking 29.4 µM; in vivo, 400 mg/kg
= [130]
50
single dose, 54% reduction
in total worm burden,
38.72% reduction in number
of immature eggs, 56.38%
reduction of eggs
98
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
TABLE 4.3 (Continued)
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Disease Compound Possible mechanism of Computational Stage Reference
action technique involved
(E)-2’-hydroxy-4- Inhibition of ATP Molecular docking Preclinical; in vitro [131]
methoxychalcone diphosphohydrolase enzymatic assay (ATPase),
IC
= 30.62 µM; in vivo,
50
32.8% reduction in total
worm burden
Mycetoma Olorofim (Phase II in clinical Inhibition of Molecular docking Preclinical; in vitro, MIC = [132]
trials for the treatment of dihydroorotate 0.004–0.125 mg/L
invasive fungal infections) dehydrogenase
99 Parasitic Diseases and Neglected Tropical Diseases (NTDs)
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