(2024) 18:132
Oudah et al. BMC Chemistry
https://doi.org/10.1186/s13065-024-01233-z

BMC Chemistry

RESEARCH

Open Access

Drug repurposing of pyrazolotriazine
derivatives as potential anti‑SARS‑CoV‑2 agents:
in vitro and in silico studies
Khulood H. Oudah1 , Mazin A. A. Najm1 , Reham F. Barghash2,3 , Omnia Kutkat4,7, Mohamed GabAllah4,
Amgad Albohy5* and Khaled A. M. Abouzid6*

Abstract
The search for new molecules targeting SARS-CoV-2 has been a priority since 2020. The continuous evolution of new
mutants increases the need for more research in the area. One way to find new leads is to repurpose existing drugs
and molecules against the required target. Here, we present the in vitro and in silico screening of ten previously
synthesized and reported compounds as anti-COVID 19 agents. The compounds were screened in vitro against VEROE6 cells to find their Cytotoxic Concentration ­(CC50) and their Inhibitory Concentration ­(IC50). Compounds 1, 2, and 5
revealed a promising anti-SARS-CoV-2 of ­(IC50 = 2.4, 11.2 and 2.8 µM), respectively while compounds 3 and 7 showed
moderate activity of ­(IC50 = 17.8 and 26.1 µM) compared to Chloroquine which showed an ­IC50 of 24.9 µM. Amongtested compounds, 1 showed the highest selectivity ­(CC50/IC50) of 192.8. Docking, molecular dynamics and ADME
studies were done to investigate potential interactions between compounds and SARS-CoV-2 targets as wellas to study the possibility of using them as lead compounds.
Keywords Pyrazolotriazine, COVID 19, Molecular docking, Drug repurposing, Main protease ­Mpro, SARS-CoV-2*Correspondence:
Amgad Albohy
albohy@ualberta.ca
Khaled A. M. Abouzidkhaled.abouzid@pharma.asu.edu.eg
1
Department of Pharmacy, Mazaya University Collage, Nasiriyah, Thi‑Qar,
Iraq
2
Institute of Chemical Industries Research, National Research Centre,
Dokki, Giza 12622, Egypt
3
Faculty of Biotechnology, October University for Modern Scienceand Arts (MSA University), Giza, Egypt
4Center of Scientifc Excellence for Infuenza Viruses, National ResearchCentre, Dokki, Giza 12622, Egypt
5
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The
British University in Egypt, El‑Sherouk City 11837, Cairo, Egypt
6
Pharmaceutical Chemistry Department, Faculty of Pharmacy, Ain Shams
University, Abbassia 11566, Cairo, Egypt
7
Department of microbiology, Faculty of pharmacy, Ahram Canadian
University, 6 th of October, Giza 12566, Egypt

Introduction
During the previous years, the world has been suffering from the newly evolving severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) [1–4]. COVID19 first appeared in Wuhan, China, in December 2019,
where the first pneumonia cases of unknown origin wererecorded [5–8]. The disease has spread swiftly over theworld, and on March 11th, 2020 [9], the World HealthOrganization (WHO) proclaimed a COVID-19 pandemic. To date, new mutants are still evolving which
creates an urge for more research to be done in area ofanti-SARS-CoV-2 agents.
Until now, a number of vaccinations have been developed to stop COVID-19 pandemic from global spreading[10]. In addition, more than 9000 clinical trials related to
COVID-19 are currently available on the ClinicalTrials.
gov database and subjected to off-label and repurposed
therapies including Chloroquine, Hydrochloroquine,© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Oudah et al. BMC Chemistry(2024) 18:132

Lopinavir-Ritonavir, Remdesivir, Molnupiravir, Favipiravir and Baricitinib [11–13] (Fig. 1). Additionally, significant and urgent efforts have been made to find effective
treatments/drugs as potential therapies for those who
had already contracted the disease.
Among drugs approved for COVID-19 treatment by
the U.S. Food and Drug Administration (FDA); is a drug
called Veklury (Remdesivir) [14–16], which approvedin October 2020, as the first drug for the treatment ofsevere COVID-19 cases requiring hospitalization [16].
In November 2020 [17], Olumiant (baricitinib) was also
FDA approved followed by Lagevrio (Molnupiravir) FDA
approval for emergency uses authorization in December
2021 [18, 19].
Typically, it takes many years to develop or generate
new drugs and evaluate them for safety and efficacy inclinical trials. The average cost of this process is close
to billion dollars per drug [20]. Recent approaches
towards drug discovery include using the computational structure-based drug discovery, molecular modelling and molecular simulation [13, 21–23]. Anotherstrategy involves utilizing already approved drugs in
new ways [24]. so-called drug repurposing which could

Fig. 1 Chemical structures of some drugs repurposed against COVID-19

Page 2 of 14

be considered as one of the fastest and most affordable
options to find COVID-19 treatments [25]. Compared
to de novo drug development, it results in faster drug
approval at a cheaper cost and shorter time [25–27]. To
find new uses for already-established drugs that havesuccessfully completed in-depth clinical trials, numerous experimental, computational drug repurposing
methodologies have been developed [28–31]. Recent
publications reported treating COVID-19 patients with
anticancer drugs [32–35] which inspired us for thiscurrent work.
Interestingly, the pyrazolopyrimidine and pyrazolotriazine scaffolds stand out for several pharmacological activities; among which are anticancer [36–41] andantiviral [42]. In this work, we decided to follow up
on ten pyrazolotriazine compounds have been previously prepared and showed a promising activity as targeted anticancer agents [41]. These compounds were
tested for their cytotoxicity and SARS-CoV-2 inhibition against a strain collected and confirmed in Egypt(hCoV-19/Egypt/NRC-03/2020 (Accession Number on
GSAID: EPI_ISL_430820). The potential target of theseOudah et al. BMC Chemistry(2024) 18:132

compounds was investigated using molecular modelling and dynamics studies.

Experimental
Chemistry

The synthesis of the target substituted pyrazolotriazine
derivatives (Fig. 2) was performed according to previously reported procedure [41].

Fig. 2 Chemical Structures of the tested compounds

Page 3 of 14

Antiviral activity
Compounds safety (MTT cytotoxicity assay)

The safety of the test compounds was assayed by testing
their cytotoxicity using MTT method as reported earlier [43]. In brief, cytotoxicity of the test compounds wastested against VERO-E6 cells in a standard 96 well-plate.
After incubation, treatment with serial concentrationof the test compound, then treatment with MTT, cells
were reincubated for 4 h at 37 °C. Media was collected,

Oudah et al. BMC Chemistry(2024) 18:132

prepared and absorbance was measured at 540 nm usinga plate reader. The absorbance obtained from treated cells
was compared to that obtained from non-treated cells to
calculate percent cytotoxicity. Half maximal cytotoxic
concentration ­(CC50) was obtained from a plot of percent
toxicity against compound concentration [44].
Compounds efficiency (Inhibitory concentration 50 (­ IC50)
determination)

Efficiency of test compounds was tested through thedetermination of their ­IC50 values against VERo-E6 cells
after treatment and virus absorption using SARS-CoV-2
virus obtained and confirmed in Egypt (hCoV-19/Egypt/
NRC-03/2020 (Accession Number on GSAID: EPI_
ISL_430820) as reported earlier [43]. Incubated cells were
fixed using paraformaldehyde (4%) then stained withcrystal violet. The color obtained from dissolving thedye in methanol was measured at λ max of 570 nm using
Anthos Zenyth 200rt plate reader (Anthos Labtec Instruments, Heerhugowaard, Netherlands). The ­IC50 of thecompound is that concentration required to reduce thevirus-induced cytopathic effect (CPE) by 50%, relative tothe virus control and were determined as reported [45].
Molecular modeling study
Molecular docking study

Docking was done following the same protocol reported
earlier [46]. In short, the proteins were downloaded fromthe protein data bank with their pdb codes (6LU7, 7BV2
and 6W4H) and prepared by removal of water and othersmall molecules. In addition, hydrogens were added andprepared proteins were saved using AutoDock tools.
Ligands were constructed, converted to 3D structure andminimized using Avogadro [47]. Docking was done using
Autodock Vina [48] within a cubic grid box with side of
25 Å centered on the co-crystalized ligand using exhaustiveness of 16.
Molecular dynamics

All atom molecular dynamics simulations were performed using GROMACS 2020.3 [49] for the selected
protein–ligand complexes as reported earlier [50]. In
brief, SwissParam server [51] was used for ligands parameterization while Charmm36 all-atom force field [52] wasused to generate topology files for the protein. Ligand
coordinates obtained from docking studies for compounds 1 and 5 were used to build complexes. Solvation
was done by surrounding these compleses with dodecahedron boxes and then filling them with explicit water(TIP3P) [53]. Neutralization of the final complexes wasdone by adding the required number of either sodium
or chloride ions. Energy minimization of the genrated
solvated neutralized complexes was done using steepest

Page 4 of 14

descent algorithm. The complexes were then equillibrated to reach the target templerature and pressureusing two successive rounds of 1 ns equillibration following NVT then NPT ensembles. One hundred nanosecond production run was performed during which theresulted trajectories were collected. Temperature was
kept at 300 K during equillibration steps and production
run using the V-rescale algorithm [54] while pressure wascontrolled using the Parrinello-Rahman barostat [55] asrequired. The LINear Constraint Solver (LINCS) algorithm [56] and Particle mesh Ewald (PME) method [57]were used for bond’s length constraints and long-range
electrostatics calculations, respectively. All simulations
were done using two femtosecond timesteps. Van der
Waals distance cut-off (rvdw) was set to 1.2 nm. Trajectories collected during the production run were analyzed
using the required GROMACS commands after correction of periodic boundary condition (PBC).
Physicochemical properties, drug likeness and ADMET
prediction

In order to find compounds with the best drug-like qualities that can be turned into safe and effective medications, evaluating pharmacokinetic and physicochemical
properties is a crucial stage in the drug development process. In this investigation, a number of factors including
molar refractivity, partition coefficient (Log P), rotatablebonds, hydrogen bond acceptor–donor (HBA-D), andtopological polar surface area (TPSA) were predictedusing the SwissADME server [58]. Additionally, the
ADMET properties were predicted using the pkCSM tool
[59].
This tool offers comprehensive details on a numberof characteristics that may affect the compound’s safety
and effectiveness as a prospective medication candidate.
The process was carried out in accordance with accepted
medicinal chemistry standards, assuring that the compounds chosen for additional research had the potential
to be turned into secure and efficient medicines.

Results and discussion
Antiviral activity

The antiviral activity of the tested compounds were
measured against SARS-CoV-2 using the standard
VERO-E6 cells as it is more permissive for SARS-CoV-2
infection than airway epithelial cells, allowing virus isolation and characterization [60]. All compounds were
tested on VERO-E6 without infection of the healthy cellline to detect cytotoxic concentration (­CC50) for eachcompound on the cells (Fig. 3) to detect Safety Index (SI)for each one equal ­CC50/IC50 (Table 1), using Hydroxychloroquine and Chloroquine and Favipiravir as standard
reference drug. Some of the tested compound showedOudah et al. BMC Chemistry(2024) 18:132

Page 5 of 14

Fig. 3 Dose–response curves for the tested compounds against SARS-CoV-2

good activity against SARS-CoV-2. These compounds
showed promising inhibition effect against the viralpropagation and infectivity of the virus compared withstandard references as compounds 1, 2, and 5 revealed
a promising anti-SARS-CoV-2 with I­C50 = 2.4, 11.2 and2.8 µM, respectively while compounds 3 and 7 showed
moderate activities ­(IC50 = 17.8 and 26.1 µM, respectively). Repurposed drugs tested (Hydroxychloroquine,
Chloroquine and Favipiravir) showed ­IC50 ranging from
24.9 to 1382 μM. The selectivity index (SI) was calculated to correlate the antiviral properties and cytotoxicity. Compound 1 showed the best SI value equal 192.8
followed by compounds 2 and 5 compared with tested
standards drug as chloroquine showed best SI of 15. In
general, ester derivatives at position 8 (1–7), showed
better activity compared to amide derivatives (8–10). In
addition, hydrophobic amino derivatives at position 2 did
not affect the activity to a large extent. Furthermore, halo
phenyl group at position 4 were generally more active
than the unsubstituted phenyl derivatives. These results
suggest that our compounds could be a promising leadas anti-COVID 19. Further investigations especially forpotential SARS-CoV-2 targets are required.
Molecular modelling studies
Molecular docking

Docking was done to investigate possible SARS-CoV-2
targets. Tested compounds were docked in the active sitesof three different viral proteins that include SARS-CoV-2

Main protease ­(Mpro), RNA dependent RNA polymerase(RdRp) and methyl transferase. In addition to these compounds, co-crystalized ligands (N3 inhibitor, remdesivir
and SAM) were docked to the active sites of their corresponding enzymes. The docking scores of each of thetested compounds in the active sites of these proteins areshown in Table 2. The most potent compounds in biological assays (1 and 5) showed the best docking scores withthe methyltransferase with docking scores of − 8.1 and− 8.5 kcal/mol, respectively. This docking score is comparable or better than the docking score of the co-crystalized ligand in the same pdb file (6W4H). The docking
poses of both compounds are shown in Fig. 4a and b,
respectively. Test compounds were generally docked
overlapped with the co-crystalized ligand forming similarinteractions. For example, hydrogen bond with residue
D6897 was maintained in both co-crystalized ligand andcompound 5. In addition, several other interactions areobserved for compound 5 including hydrogen bonds with
Y6930 and D6912. Also, hydrophobic interactions with
F6947 and L6898 are also observed. Several of the other
tested compounds also showed also docking scores better
than the co-crystalized ligand as can be seen in Table 2.
These results suggest that these two compounds (1 and5) might have a potential inhibitory effect on this target which might require further investigations. Docking
process was validated by redocking of the co-crystalized
ligand (SAM, Fig. 4e) in the active site of its corresponding protein and compare its docking pose to the crystal

Oudah et al. BMC Chemistry(2024) 18:132

Page 6 of 14

Table 1 Cytotoxicity and virus-inhibition effect of the tested pyrazolotriazine derivatives (1–10) against SARS-CoV-2

IC50, (µM)

CC50, (µM)

SIa

COOEt

2.4

462.9

192.8

Br

COOEt

11.2

445.0

39.7

3

Cl

COOEt

17.8

88.0

4.9

4

H

COOEt

61.8

366.4

5.9

5

Cl

COOEt

2.8

87.5

31.2

6

H

COOEt

359.3

929.6

2.5

7

Br

COOEt

26.1

278.9

10.6

8

H

CONHCH2Ph

55.0

399.6

7.2

9

H

CONH(CH2)2Ph

44.2

577.5

13.0

10

Cl

CONH(CH2)3Ph

60.7

342.9

5.6

Compound

X

R

1

H

2

R’

Hydroxy-chloroquine [61]

–

36.9

356.4

9.7

Chloroquine [61]

–

24.9

377.7

15.1

Favipiravir [61]

–

1382

5262

3.8

Remdesivir [62]

–

3.38

58.12

17.18

a

SI = ­CC50/IC50

Oudah et al. BMC Chemistry(2024) 18:132

Page 7 of 14

Table 2 Docking results of test compounds against SARS-CoV-2 targets
Compound #

6LU7

7BV2

6W4H

Mpro

RdRp-Mg

Methyltransferase

1

− 7.8

− 6.8

− 8.1

2

− 7.6

− 6.2

− 8.3

3

− 7.9

− 6.7

− 8.2

4

− 8.3

− 5.8

− 8.3

5

− 7.8

− 5.8

− 8.5

6

−8

− 7.1

− 8.2

7

−8

−6

− 7.6

8

− 8.6

− 7.8

− 8.8

9

− 8.3

− 7.5

− 8.6

10

− 8.5

− 7.6

− 8.9

Co-crystalized Ligand− 7.8 (N3 inhibitor)− 6.9 (Remdesivir)− 8.1 (SAM)

a.

b.

c.

d.

e.

Fig. 4 Docking results of test compounds against methyl transferase. a docking pose of compound 1 (dark blue) overlapped with co-crystalized
ligand (green). b docking pose of compound 5 (pink). c validation of docking procedure showing docked (blue) pose overlappedwith co-crystalized pose (green). d interactions of co-crystalized ligand in the active site methyl transferase. e structure of S-adenosylmethionine(SAM)

Oudah et al. BMC Chemistry(2024) 18:132

pose. The docking procedure was accepted since the
RMSD between both poses is less than 2 Å (Fig. 4c andd).
Molecular dynamics

Molecular dynamic study was performed to follow up
on the docking results and to investigate the stability of
1 and 5 in the methyl transferase active site of SARSCoV-2 (6W4H). Each complex along with the apoprotein
were enclosed in a dodecahedron boxe which were filledwith water and ions to simulate experimental condition.
After minimization, the systems were equilibrated, andtemperature was adjusted to 300 ˚K and pressure wasPage 8 of 14

adjusted for 1 atm. Then complexes were subjected to
100 ns of production run at the same temperature andpressure. Collected trajectories were analyzed to extract
information about these complexes compared to the apoprotein. The results are shown in Fig. 5 and Table 3.
Root mean square deviation (RMSD) of protein relative to first frame and to crystal structure (Fig. 5a andb) shows very similar results for the apoprotein as wellas the two studies complexes. The fluctuation of RMSD
value (Table 3) is less than 1 Å which indicates the stability of the protein during the production run. This also
shows that the insertion of these two inhibitors does
not affect the protein structure. Furthermore, radius ofFig. 5 Molecular Dynamics results for compounds 1 and 5 in the active site of SARS-CoV-2 methyl transferase. a RMSD of protein chain relative
to first frame of production run. b RMSD of protein chain relative to starting crystal structure. c Radius of gyration of protein. d average RMSF
of protein amino acids during the production run. e number of hydrogen bonds between ligands and protein. f RMSD of ligand heavy atoms
during production run

Oudah et al. BMC Chemistry(2024) 18:132

Page 9 of 14

Table 3 Results of molecular dynamics study
Apoprotein

1

5

RMSD (nm)0.327 ± 0.056

0.334 ± 0.073

0.344 ± 0.073

RMSD_Crystal (nm)0.327 ± 0.056

0.334 ± 0.072

0.343 ± 0.072

Radius of gyration (nm)1.916 ± 0.010

1.913 ± 0.010

1.925 ± 0.012

Number of H bonds–

1.97 ± 0.71

1.89 ± 0.79

RMSD of ligand (nm)–1.254 ± 0.436

2.133 ± 0.610

gyration (Fig. 5c), which is a measurement of protein
compactness, was very stable during the production run
which also support the above finding. Figure 5d shows aplot of the root mean square fluctuation (RMSF) of protein individual residues during the production run. The
high similarity between all the complexes as well as thesmall values (mostly less than 1 Ǻ except for terminal residues) supports the stability of protein and the protein–ligand complexes. The last 2 plots (Fig. 5e and f ) compare
the interactions of ligands 1 and 5 with target protein. as
seen in the figures as well as Table 3, compound 1 is predicted to have better interaction with the target protein
as it shows higher average of hydrogen bonds formed. In
addition, compound 1 showed less RMSD value (1.25 Åversus 2.13 Å for compound 5) as well as less fluctuation compared to compound 5 (0.44 Å for compound 1
versus 0.61 Å for compound 5). These results indicate
the higher stability and tighter binding of compound 1
compared to compound 5 in the active site of target protein. In addition, energy calculation for the interactionsof compounds 1 and 5 with the target protein shows thatboth compounds showed similar short range coulombinteraction with the target protein (− 216.111 ± 16 kJ/
mol for compound 1 versus − 214.82 ± 24 kJ/mol). On
the other hand, compound 1 showed significantly better short range Lennard–Jones (L-J) interaction withtarget (− 27.1232 ± 5 kJ/mol for compound 1 versus-15.3226 ± 4 kJ/mol for compound 5). These findings
suggest the importance of the side chain of compound
1 (phenyl methyl amine) in the formation of hydrophobic interactions with the target protein which reflects the
improved L-J interaction energies without affecting coulombic interaction. These results opens the door to further follow up studies to confirm these interactions anduse them to improve inhibition of this important target
against SARS-CoV-2 virus.
Physicochemical, drug‑likeness properties:

We also were interested in examining the drug-likeness ofcompounds by the SwissADME server [58]. When determining whether a compound has a chance to develop
into a drug, a set of criteria referred to as drug-likenessproperties are used. Molar refractivity, molecular weight,
lipophilicity (log P), HBA, HBD, the number of rotatable bonds, and TPSA are some of the descriptors used
to evaluate these qualities. One of these is Lipinski’s rule[63] of five, which emphasizes that a compound is more
likely to be orally bioavailable if it meets certain criteria,such as having a molecular weight of less than 500, a log
P of less than 5, no more than five H-bond donors and no
more than ten H-bond acceptors.
The physicochemical parameters acquired from the
SwissADME server (Table 4) are within the acceptable
ranges, indicating that the tested compounds 1–10
fully complied with Lipinski’s rules [63]. This indicates that they possess advantageous characteristics
for example optimal size, flexibility, as well as polarity that are associated with bioavailability. The ADME
prediction (Table 5) reveals that all of the compounds
respond to other essential drug-likeness rules, including those of Ghose et al. and Muegge et al. [64–67],
with the exception of compounds 9 and 10, which have

Table 4 Predicted physicochemical properties of compounds 1–10 using the SwissADME server
CompoundMolecular weight(< 500)TPSAa (< 140A˚2)

H-Bond Acceptor(< 10)

H-Bond Donnor(< 5)

LogP (< 5)Lipinski’sViolation

1

373.41

81.41

5

1

3.32

0

2

452.3

81.41

5

1

3.93

0

3

407.85

81.41

5

1

3.85

0

4

351.4

72.62

5

0

2.93

0

5

385.85

72.62

5

0

3.39

0

6

387.43

81.41

5

1

3.58

0

7

416.27

72.62

5

0

3.28

0

8

434.49

84.21

4

2

3.88

0

9

462.55

84.21

4

2

4.34

0

10

496.99

84.21

4

2

4.88

1

a

Topological polar surface area

Oudah et al. BMC Chemistry(2024) 18:132

Page 10 of 14

Table 5 Drug-likeness properties of compounds 1–10 by the SwissADME server
Compound

Lipinski

Ghose

Veber

Egan

MueggeBioavailability
score

1

Yes

Yes

Yes

Yes

Yes

0.55

2

Yes

Yes

Yes

Yes

Yes

0.55

3

Yes

Yes

Yes

Yes

Yes

0.55

4

Yes

Yes

Yes

Yes

Yes

0.55

5

Yes

Yes

Yes

Yes

Yes

0.55

6

Yes

Yes

Yes

Yes

Yes

0.55

7

Yes

Yes

Yes

Yes

Yes

0.55

8

Yes

Yes

Yes

Yes

Yes

0.55

9

Yes

No

Yes

Yes

No

0.55

10

Yes

No

Yes

Yes

No

0.55

only one variable for these two rules. In addition, theanalyzed substances exhibited excellent bioavailability
scores of 0.55. These results suggests that these previously synthesized compounds have significant potential

Fig. 6 Bioavailability radar for compounds 1 (a), 4 (b), 5 (c) and 7 (d)as drug candidates and could be investigated further for
their therapeutic properties, according to the findings.
In addition, the SwissADME server was used for plotting the bioavailability radar of compounds (Fig. 6) whichis also another way to represent parameters affecting

Oudah et al. BMC Chemistry(2024) 18:132

bioavailbility. Figure 6 shows representative examples forthe bioavailability radar for 4 compounds including 1, 4,
5 and 7. All parameters for all compounds are within theacceptable ranges except insaturation of compound 1,
which was slightly higher.
ADMET properties of the investigated compounds

ADMET (Absorption, distribution, metabolism, elimination and toxicity) properties are necessary to assess aprospective drug candidate’s compatibility for clinicalapplication. Along with being effective at low concentrations and having a low degree of toxicity, a drug candidate
must have sufficient pharmacokinetic properties which
maintain the drug molecules available in their active
state for the duration of action desired. The computational assessment of ADMET properties can substantiallyassist the prediction of the effects of a drug candidate in
the human system, thereby accelerating and streamlining
the process of developing drugs and reducing the risk ofcollapse during clinical trials. Therefore, a comprehensive
evaluation of the characteristics of ADMET is essential
for the successful development and commercialization of
new pharmaceuticals.[59].
According to data in Table 6, tested compounds
showed medium solubility in aqueous media as well as
significant oral absorption (92.206%–99.129%), ensuring exceptional absorption which is an essential requirement for oral drugs. In addition, this finding is supportedby the predicted Caco2 permeability values indicating
the suitability of the oral route of administration for all
tested compounds with the exception of compounds 8,9, and 10. Furthermore, these compounds also showedsuperior skin permeability. Moreover, tested compounds
are inhibitors of P-glycoproteins which could help with
efflux problem seen with some inhibitors leading to thedecrease in their bioavailability [68, 69]. In terms of distribution, the tested compounds have a low volume of
distribution at steady state (VDss) and most of drugs
were predicted to be in the bound state which meansthat these molecules have limited distribution whichindicates the need for lower loading doses. In addition,
their Blood–Brain Barrier (BBB) and the central nervous
system (CNS) permeability assessments are moderate topoor, indicating the lower potential to cause CNS adverse
effects [70].
The tested compounds were predicted to interact
with CYP3A4 which is one of the liver microsomal
enzymes involved in the metabolism of various drugs.
All compounds were predicted to be both substrates
and inhibitors except compounds 4, 5, and 7 which aresubstrates but not inhibitors. These compounds, however, are not substrates for another variant which isPage 11 of 14

CYP2D6. This might be an advantage, as this enzyme
is known to be involved in the metabolism of several
known drugs. In terms of elimination, tested substances
showed a medium clearance and are not predicted to berenal OCT2 substrates [71].
Furthermore, the compounds that were examined
showed signs of hepatotoxicity but did not cause skinsensitization. Moreover, the AMES toxicity test was
negative for the majority of the compounds, which suggests that the examined compounds are not mutagenic,
with the exception of compounds 4, 8, and 9. These
three compounds showed positive results. Tested compounds do not inhibit hERG I, but they exhibit a certain inhibitory effect on hERG II, known to be involved
in some cardiac arrhythmias [72]. This is in contrast tohERG I, which they were not predicted to inhibit. The
elevated human tolerated dose (0.271–0.697 log mg/kg/day) also serves as a good indication for the reasonably
safe profile of these compounds, with the exception ofcompounds 4, 5, and 7.
In general, these ADMET predictions are able toprovide information that can be helpful when further
assessing the potential of the compounds that havebeen investigated as therapeutic candidates. In addition to the predictions made using computer models,
the real-life pharmacokinetic characteristics of the drugcandidate need to be tested in vitro as well as in vivo inorder to validate the efficacy and safety of the pharmaceutical approach [72, 73].

Conclusion
In this work we report the screening of ten previously reported compounds to repurpose them against
SARS-CoV-2. Among tested compounds 3 compounds
showed promising results as viral inhibitors with ­IC50
values 2.4–11.2 μM. Compound 1 showed best inhibition and has the best selective index with ­CC50 190-foldlarger than I­ C50. It worth to mention that all top compounds have the smaller ethyl carboxylate substitution
at position 8 on the pyrazolotriazine ring. In addition,
two of the tested compounds had 4-halophenyl substitution which suggest the priority of these functiongroup. Docking studies suggests that these compounds
have a potential effect on methyl transferase. Follow up
molecular dynamics study was also performed to investigate the stability of compounds 1 and 5 in the active
site of SRS-CoV-2 methyl transferase and suggested
that compound 1 has the highest potential against
this important target. The results of this study suggestthat these compounds should be further investigatedas leads against SARS-CoV-2 targets especially methyltransferase.

Oudah et al. BMC Chemistry(2024) 18:132

Page 12 of 14

Table 6 In silico ADMET predictions of the compounds 1–10
Property and Model Name

1

2

3

4

5

6

7

8

9

10

Absorption
Water solubility (log mol/L)−4− 4.271 − 4.229 − 3.124 − 3.578 − 4.307 − 3.453 − 4.232 − 4.44

− 4.525

Caco2 permeability (log Papp in 10-6 cm/s)

1.116

1.125

1.129

1.408

1.42

1.172

1.408

0.613

0.64

0.661

Intestinal absorption in humans (% Ab)

95.65994.09894.16599.12997.63595.63197.95793.20893.7

92.206Skin permeability (log Kp)− 2.754 − 2.756 − 2.756 − 2.735 − 2.765 − 2.757 − 2.761 − 2.735 − 2.735 − 2.735

P-glycoprotein substrate (Yes/No)

No

No

No

No

No

No

No

Yes

Yes

Yes

P-glycoprotein I inhibitor (Yes/No)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

P-glycoprotein II inhibitor (Yes/No)

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Distribution
VDss in humans (log L/kg)

0.021

0.091

0.073

− 0.128 − 0.107 0.062− 0.146 0.09

0.107

0.158

Fraction unbound in humans (Fu)

0.042

0.045

0.047

0.123

0.139

0.096

0.067

0.061

0.113

− 0.059

0.13

0.003

BBB permeability (log BB)− 0.465 − 0.646 − 0.638 − 0.872 − 1.061 − 0.42

− 1.074 0.023

CNS permeability (log PS)− 2.477 − 2.335 − 2.358 − 2.539 − 2.975 − 2.49

− 2.992 − 2.083 − 2.208 − 2.097

Metabolism
CYP2D6 substrate (Yes/No)

No

No

No

No

No

No

No

No

No

No

CYP3A4 substrate (Yes/No)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

CYP1A2 inhibitor (Yes/No)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

CYP2C19 inhibitor (Yes/No)

No

No

No

No

No

Yes

No

Yes

Yes

Yes

CYP2C9 inhibitor (Yes/No)

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

CYP2D6 inhibitor (Yes/No)

No

No

No

No

No

No

No

No

No

No

CYP3A4 inhibitor (Yes/No)

Yes

Yes

Yes

No

No

Yes

No

Yes

Yes

Yes

Excretion
Total Clearance (log ml/min/kg)

0.358

0.185

0.092

0.387

0.101

0.388

0.242

0.128

0.191

− 0.078

Renal OCT2 Substrate (Yes/No)

No

No

No

No

No

No

No

No

No

No

No

No

No

Yes

Toxicity
AMES toxicity (Yes/No)

No

No

No

Yes

Yes

No

Max. tolerated dose in humans (log mg/kg/day)

0.35

0.272

0.271

− 0.044 − 0.047 0.324− 0.031 0.697

0.628

0.622

hERG I inhibitor (Yes/No)

No

No

No

No

No

No

No

No

No

No

hERG II inhibitor (Yes/No)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Oral Rat Acute Toxicity (LD50) (mol/kg)

3.318

3.268

3.264

2.542

2.534

3.189

2.5

3.098

2.917

2.894

Oral Rat Chronic Toxicity (LOAEL) (log mg/ky_bw/day) 1.207

1.021

1.031

0.89

1.041

1.259

1.014

1.271

1.354

1.291

Hepatotoxicity (Yes/No)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Skin sensitization (Yes/No)

No

No

No

No

No

No

No

No

No

No

T. Pyriformis toxicity (log ug/L)

0.295

0.295

0.295

0.311

0.31

0.301

0.311

0.285

0.285

0.285

Minnow toxicity (log mM)− 2.137 − 2.544 − 2.398 0.097

Acknowledgements
The authors gratefully acknowledge the College of Pharmacy, Ain-Shams
University, Cairo, Egypt for their support. Also, they gratefully acknowledge the
National Research Center, Cairo, Egypt for their support.
Author contributions
K.O. and M.N. synthesized the compounds in this study. R.B., O.K. and M.G.
tested the compounds and performed the biology. K.O. and A.A. carried out
the modelling section. K.O., A.A. and K.A. contributed in concept and ideaof the article. All authors contributed in writing the manuscript. All authors
reviewed the manuscript.
Funding
AA would like to acknowledge the British University in Egypt (BUE) for theirsupport through grant (YIRG-2021-13).

− 0.443 − 3.214 − 0.472 0.082− 0.953 − 1.2

Availability of data and materials
The datasets used and/or analysed during the current study are available fromthe corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.

Oudah et al. BMC Chemistry(2024) 18:132

Received: 25 September 2023 Accepted: 25 June 2024

References
1. Wang M, Cao R, Zhang L, Yang X, Liu J, Xu M, et al. Remdesivir andchloroquine effectively inhibit the recently emerged novel coronavirus(2019-nCoV) in vitro. Cell Res. 2020;30(3):269–71.
2. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features ofpatients infected with 2019 novel coronavirus in Wuhan. China Lancet.
2020;395(10223):497–506.
3. Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, et al. A familial clusterof pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet.
2020;395(10223):514–23.
4. Dennison Himmelfarb CR, Baptiste D. Coronavirus disease (COVID-19):
implications for cardiovascular and socially at-risk populations. J Cardiovasc Nurs. 2020;35:318–21.
5. Rothan HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun. 2020;109:102433.
6. da Silva PG, Mesquita JR, de São M, Ferreira VAM. Viral, host and environmental factors that favor anthropozoonotic spillover of coronaviruses: an
opinionated review, focusing on SARS-CoV, MERS-CoV and SARS-CoV-2.
Sci Total Environ. 2021;750:141483.
7. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. New England J Med. 2020;382:1708–20.
8. Jasim SA, Mahdi RS, Bokov DO, Najm MAA, Sobirova GN, Bafoyeva ZO,
et al. The deciphering of the immune cells and marker signature in
COVID-19 pathogenesis: an update. J Med Virol. 2022;94(11):5128–48.
9. Global research on coronavirus disease (COVID-19). https://​www.​who.​
int/​emerg​encies/​disea​ses/​novel-​coron​avirus-​2019/​global-​resea​rch-​on-​novel-​coron​avirus-​2019-​ncov. Accessed 29 Dec 2022
10. COVID-19 vaccines: everything you need to know | Gavi, the Vaccine Alliance. (2021). Accessed 29 Dec 2022
11. Home—ClinicalTrials.gov. https://​clini​caltr​ials.​gov/. Accessed 29 Dec
2022
12. Annunziata G, Sanduzzi Zamparelli M, Santoro C, Ciampaglia R, Stornaiuolo M, Tenore GC, et al. May Polyphenols have a role against coronavirus
infection? an overview of in vitro evidence. Front Med. 2020;7(May):1–7.
13. Mahmoud DB, Shitu Z, Mostafa A. Drug repurposing of nitazoxanide:
can it be an effective therapy for COVID-19? J Genet Eng Biotechnol.
2020;18(1):1–10.
14. World Health Organization. WHO recommends against the use of remdesivir in COVID-19 patients. (2020). https://​www.​who.​int/​news-​room/​featu​
re-​stori​es/​detail/​who-​recom​mends-​again​st-​the-​use-​of-​remde​sivir-​in-​covid-​19-​patie​nts. Accessed 30 Dec 2022
15. VEKLURY® (remdesivir) Mechanism of Action | HCP. https://​www.​veklu​
ryhcp.​com/​about/​about-​moa.​php. Accessed 30 Dec 2022
16. Food US. Drug Administration. FDA approves first treatment for COVID19. (2020). https://​www.​fda.​gov/​news-​events/​press-​annou​nceme​nts/​
fda-​appro​ves-​first-​treat​ment-​covid-​19.
17. Coronavirus (COVID-19) Update: FDA Authorizes drug combination fortreatment of COVID-19. FDA. (2020). https://​www.​fda.​gov/​news-​events/​
press-​annou​nceme​nts/​coron​avirus-​covid-​19-​update-​fda-​autho​rizes-​
drug-​combi​nation-​treat​ment-​covid-​19. Accessed 29 Dec 2022
18. Dimenhydrinate: Uses, interactions, mechanism of action | DrugBank
Online. DrugBank OnLine. https://​go.​drugb​ank.​com/​drugs/​DB156​61.
Accessed 30 Dec 2022
19. Lagevrio (molnupiravir) FDA Approval Status—Drugs.com. https://​www.​drugs.​com/​histo​ry/​lagev​rio.​html. Accessed 30 Dec 2022
20. Wouters OJ, McKee M, Luyten J. Estimated research and development
investment needed to bring a new medicine to market, 2009–2018.
JAMA. 2020;323(9):844–53.
21. Fayed MAA, El-Behairy MF, Abdallah IA, Abdel-Bar HM, Elimam H, Mostafa
A, et al. Structure- and ligand-based in silico studies towards the repurposing of marine bioactive compounds to target SARS-CoV-2. Arab J
Chem. 2021;14(4):103092.

Page 13 of 14

22. Abdel-Bar HM, Abdallah IA, Fayed MAA, Moatasim Y, Mostafa A, El-Behairy
MF, et al. Lipid polymer hybrid nanocarriers as a combinatory platform
for different anti-SARS-CoV-2 drugs supported by computational studies.
RSC Adv. 2021;11(46):28876–91.
23. Tam NM, Pham MQ, Ha NX, Nam PC, Phung HTT. Computational estimation of potential inhibitors from known drugs against the main proteaseof SARS-CoV-2. RSC Adv. 2021;11(28):17478–86.
24. Cavalla D. Therapeutic switching: a new strategic approach to enhance
R&D productivity. IDrugs Investig drugs J. 2005;8(11):914–8.
25. Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The
Drug Repurposing Hub: a next-generation drug library and information
resource. Nat Med. 2017;23(4):405–8.
26. Ghanem A, Emara HA, Muawia S, Abd EI Maksoud AI, Al-Karmalawy AA,
Elshal MF. Tanshinone IIA synergistically enhances the antitumor activityof doxorubicin by interfering with the PI3K/AKT/mTOR pathway and inhibition of topoisomerase II: in vitro and molecular docking studies. New J
Chem. 2020;44(40):17374–81.
27. Eliaa SG, Al-Karmalawy AA, Saleh RM, Elshal MF. Empagliflozin anddoxorubicin synergistically inhibit the survival of triple-negative breast
cancer cells via interfering with the mTOR pathway and inhibition of
calmodulin: in vitro and molecular docking studies. ACS Pharmacol Transl
Sci. 2020;3(6):1330–8.
28. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug
repurposing: progress, challenges and recommendations. Nat Rev Drug
Discov. 2019;18(1):41–58.
29. Barghash RF, Fawzy IM, Chandrasekar V, Singh AV, Katha U, Mandour
AA. In Silico modeling as a perspective in developing potential vaccine
candidates and therapeutics for COVID-19. Coatings. 2021;11(11):1273.
30. Gougis P, Fenioux C, Funck-Brentano C, Veyri M, Gligorov J, Solas C, et al.
Anticancer drugs and COVID-19 antiviral treatments in patients withcancer: what can we safely use? Eur J Cancer. 2020;136:1–3.
31. Wyman KA, Girgis AS, Surapaneni PS, Moore JM, Abo Shama NM,
Mahmoud SH, et al. Synthesis of potential antiviral agents for SARS-CoV-2
using molecular hybridization approach. Molecules. 2022;27(18):5923.
32. Aldea M, Michot J-M, Danlos F-X, Ribas A, Soria J-C. Repurposing ofanticancer drugs expands possibilities for antiviral and anti-inflammatory
discovery in COVID-19 anticancer drug repurposing in COVID-19. Cancer
Discov. 2021;11(6):1336–44.
33. Raya I, Chupradit S, Mustafa YF, Oudaha KH, Kadhim MM, Jalil AT, et al. Carboxymethyl chitosan nano-fibers for controlled releasing 5-fluorouracil
anticancer drug. J Nanostructures. 2022;12(1):136–43.
34. Abdalkareem Jasim S, Kzar HH, Haider Hamad M, Ahmad I, AI-Gazally
ME, Ziyadullaev S, et al. The emerging role of 27-hydroxycholesterol incancer development and progression: an update. Int Immunopharmacol.
2022;110:109074.
35. Fawazy NG, Panda SS, Mostafa A, Kariuki BM, Bekheit MS, Moatasim
Y, et al. Development of spiro-3-indolin-2-one containing compounds of antiproliferative and anti-SARS-CoV-2 properties. Sci Rep.
2022;12(1):13880.
36. Barghash RF, Eldehna WM, Kovalová M, Vojáčková V, Kryštof V, Abdel-Aziz
HA. One-pot three-component synthesis of novel pyrazolo [3, 4-b] pyridines as potent antileukemic agents. Eur J Med Chem. 2022;227:113952.
37. Najm MAA, Oudah KH, Hassan WNM, Roomi AB. An insight into pyrazolo
scaffold as anticancer. Syst Rev Pharm. 2020;11(11):254–63.
38. Noori SD, Kadhi MS, Najm MAAA, Oudah KH, Qasim QA, Al-Salman HNKK.
In-vitro evaluation of anticancer activity of natural flavonoids, apigenin
and hesperidin. Mater Today Proc. 2022;60(3):1840–3.
39. Oudah KH, Najm MAAA, Roomi AB, AAI-sa H, Awadallah FM, AI-Sa’idy HA,
et al. The recent progress of sulfonamide in medicinal chemistry. Syst Rev
Pharm. 2020;11(12):1473–7.
40. Oudah KH, Mahmoud WR, Awadallah FM, Taher AT, Abbas SES, Allam HA,
et al. Design and synthesis of some new benzoylthioureido benzenesulfonamide derivatives and their analogues as carbonic anhydrase inhibitors. J Enzyme Inhib Med Chem. 2023;38(1):12–23.
41. Oudah KH, Najm MAA, Samir N, Serya RAT, Abouzid KA. Design, synthesis
and molecular docking of novel pyrazolo [1,5-a] [1,3,5] triazine derivatives
as CDK2 inhibitors. Bioorg Chem. 2019;92:103239.
42. Gudmundsson KS, Johns BA, Weatherhead J. Pyrazolopyrimidines andpyrazolotriazines with potent activity against herpesviruses. Bioorg Med
Chem Lett. 2009;19(19):5689–92.

Oudah et al. BMC Chemistry(2024) 18:132

43. Abo Elmaaty A, Eldehna WM, Khattab M, Kutkat O, Alnajjar R, El-Taweel
AN, et al. Anticoagulants as potential SARS-CoV-2 Mpro inhibitors for
COVID-19 patients: in vitro, molecular docking, molecular dynamics, DFT,and SAR studies. Int J Mol Sci. 2022;23(20):12235.
44. Mosmann T. Rapid colorimetric assay for cellular growth and survival:
application to proliferation and cytotoxicity assays. J lmmunol Methods.
1983;65:55–63.
45. Feoktistova M, Geserick P, Leverkus M. Crystal violet assay for
determining viability of cultured cells. Cold Spring Harb Protoc.
2016;2016(4):pdb-prot087379.
46. Abdelmohsen UR, Albohy A, Abdulrazik BS, Bayoumi SAL, Malak LG,
Khallaf ISA, et al. Natural coumarins as potential anti-SARS-CoV-2 agents
supported by docking analysis. RSC Adv. 2021;11(28):16970–9.
47. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison
GR. Avogadro: an advanced semantic chemical editor, visualization, andanalysis platform. J Cheminform. 2012;4(1):1–17.
48. Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2. 0: new
docking methods, expanded force field, and python bindings. J Chem Inf
Model. 2021;61(8):3891–8.
49. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS:
high performance molecular simulations through multi-level parallelism
from laptops to supercomputers. SoftwareX. 2015;1(2):19–25.
50. Said MA, Albohy A, Abdelrahman MA, Ibrahim HS. Importance of glutamine 189 flexibility in SARS-CoV-2 main protease: lesson learned fromin silico virtual screening of ChEMBL database and molecular dynamics.
Eur J Pharm Sci. 2021;160:105744.
51. Zoete V, Cuendet MA, Grosdidier A, Michielin O. SwissParam: a fast force
field generation tool for small organic molecules. J Comput Chem.
2011;32(11):2359–68.
52. Huang J, MacKerell AD Jr. CHARMM36 all-atom additive protein force
field: Validation based on comparison to NMR data. J Comput Chem.
2013;34(25):2135–45.
53. Harrach MF, Drossel B. Structure and dynamics of TIP3P, TIP4P, and TIP5P
water near smooth and atomistic walls of different hydroaffinity. J Chem
Phys. 2014. https://​doi.​org/​10.​1063/1.​48722​39.
54. Bussi G, Donadio D, Parrinello M. Canonical sampling through velocity
rescaling. J Chem Phys. 2007. https://​doi.​org/​10.​1063/1.​24084​20.
55. Parrinello M, Rahman A. Polymorphic transitions in single crystals: a newmolecular dynamics method. J Appl Phys. 1981;52(12):7182–90.
56. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: a linear constraint
solver for molecular simulations. J Comput Chem. 1997;18(12):1463–72.
57. Darden T, York D, Pedersen L. Particle mesh Ewald: an N⋅ log (N) method
for Ewald sums in large systems. J Chem Phys. 1993;98(12):10089–92.
58. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate
pharmacokinetics, drug-likeness and medicinal chemistry friendliness of
small molecules. Sci Rep. 2017;7(1):42717.
59. Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule
pharmacokinetic and toxicity properties using graph-based signatures. J
Med Chem. 2015;58(9):4066–72.
60. Essaidi-Laziosi M, Rodriguez FJP, Hulo N, Jacquerioz F, Kaiser L, Eckerle
I. Estimating clinical SARS-CoV-2 infectiousness in Vero E6 and primary
airway epithelial cells. Lancet Microbe. 2021;2(11):e571.
61. Girgis AS, Panda SS, Srour AM, Abdelnaser A, Nasr S, Moatasim Y, et al.
3-Alkenyl-2-oxindoles: synthesis, antiproliferative and antiviral properties
against SARS-CoV-2. Bioorg Chem. 2021;114:105131.
62. Tan YL, Tan KSW, Chu JJH, Chow VT. Combination treatment withremdesivir and ivermectin exerts highly synergistic and potent antiviral
activity against murine coronavirus infection. Front Cell Infect Microbiol.
2021;11(11):700502.
63. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug
discovery and development settings. Adv Drug Deliv Rev. 2012;64:4–17.
64. Muegge I, Heald SL, Brittelli D. Simple selection criteria for drug-likechemical matter. J Med Chem. 2001;44(12):1841–6.
65. Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based
approach in designing combinatorial or medicinal chemistry libraries
for drug discovery. 1. A qualitative and quantitative characterization of
known drug databases. J Comb Chem. 1999;1(1):55–68.
66. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J
Med Chem. 2002;45(12):2615–23.

Page 14 of 14

67. Egan WJ, Merz KM, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem. 2000;43(21):3867–77.
68. Kalantzi L, Goumas K, Kalioras V, Abrahamsson B, Dressman JB, Reppas
C. Characterization of the human upper gastrointestinal contents underconditions simulating bioavailability/bioequivalence studies. Pharm Res.
2006;23:165–76.
69. Awortwe C, Fasinu PS, Rosenkranz B. Application of Caco-2 cell line
in herb-drug interaction studies: current approaches and challenges.
J Pharm Pharm Sci a Publ Can Soc Pharm Sci Soc Can des Sci Pharm.
2014;17(1):1.
70. Mishra SS, Gaur R, Sirvi G, Kumar N, Sharma S, Singh HP. Computational
analysis of pharmacokinetic, bioactivity and toxicity parameters of someselected oral-hypoglycaemic agents. Chem Res J. 2018;3(2):135–40.
71. Rodrigues-Junior VS, Villela AD, Abbadi BL, Sperotto NDM, Pissinate K,
Picada JN, et al. Nonclinical evaluation of IQG-607, an anti-tuberculosis
candidate with potential use in combination drug therapy. Regul Toxicol
Pharmacol. 2020;111:104553.
72. Garrido A, Lepailleur A, Mignani SM, Dallemagne P, Rochais C. hERG
toxicity assessment: useful guidelines for drug design. Eur J Med Chem.
2020;195:112290.
73. Han Y, Zhang J, Hu CQ, Zhang X, Ma B, Zhang P. In silico ADME andtoxicity prediction of ceftazidime and its impurities. Front Pharmacol.
2019;10:434.

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