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Chikodili, I.M., Chioma, I.I., Ukamaka, I.A., Nnenna, O.T., Ogechukwu, O.D., Mmesoma, E., Chikodi, E.C., IfedibaluChukwu, E.I. Phytocompound inhibitors of caspase 3 as beta-cell apoptosis treatment development option: An In-silico approach. Sciences of Phytochemistry 2023, 2(1), 17-37.

AMA Style

Chikodili, IM, Chioma, II, Ukamaka, IA, Nnenna, OT, Ogechukwu, OD, Mmesoma, E, Chikodi, EC, IfedibaluChukwu, EI. Phytocompound inhibitors of caspase 3 as beta-cell apoptosis treatment development option: An In-silico approach. Sciences of Phytochemistry. 2023; 2(1):17-37.

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Igbokwe Mariagoretti Chikodili, Ibe Ifeoma Chioma, Ilechukwu Augusta Ukamaka, Oju Theclar Nnenna, Okoye Delphine Ogechukwu, Ernest Eze Mmesoma, Ekeomodi Christabel Chikodi, Ejiofor InnocentMary IfedibaluChukwu. 2023. "Phytocompound inhibitors of caspase 3 as beta-cell apoptosis treatment development option: An In-silico approach" Sciences of Phytochemistry 2, no. 1:17-37.

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Home / Sciences of Phytochemistry / Volume 2 Issue 1 / 10.58920/sciphy02010017

Research Article

Phytocompound inhibitors of caspase 3 as beta-cell apoptosis treatment development option: An In-silico approach

by Igbokwe Mariagoretti Chikodili, Ibe Ifeoma Chioma, Ilechukwu Augusta Ukamaka, Oju Theclar Nnenna, Okoye Delphine Ogechukwu, Ernest Eze Mmesoma, Ekeomodi Christabel Chikodi, Ejiofor InnocentMary IfedibaluChukwu

Academic editor: James H. Zothantluanga
Sciences of Phytochemistry 2(1): 17-37 (2023); https://doi.org/10.58920/sciphy02010017
This article is licensed under the Creative Commons Attribution (CC BY) 4.0 International License.


Received
18 Jan 2023
Revised
28 Feb 2023
Accepted
08 Mar 2023
Published
09 Mar 2023

Abstract: The prevalence of Diabetes mellitus (DM) is continuously rising worldwide. Among its types, type I is characterized by the destruction of beta cells triggered by various mechanisms, including the activation of Caspase 3. Studies have demonstrated the crucial role of Caspase 3 in initiating the apoptosis of beta cells in DM. Our research aims to identify possible phytocompounds inhibitors of Caspase 3 using computational approach. We obtained 3D structures of Caspase 3 and 6511 phytocompounds from the Protein Data Bank and the African Natural Products Database, respectively. The phytocompounds were assessed for druglikeness properties, topological polar surface area, and preliminary toxicity using DataWarrior. The phytocompounds were subjected to molecular docking simulation (MDS) at Caspase 3 active site using AutoDock-Vina. The frontrunner phytocompounds obtained from the MDS were subjected to protease inhibition prediction on Molinspiration. The pharmacokinetics of the phytocompounds were assessed on SwissADME. The in-depth computational toxicity profile of the phytocompounds was evaluated on the pkCSM web. The binding interactions of the phytocompounds with Caspase 3 were assessed with Discovery Studio Visualizer and Maestro. Seventeen phytocompounds were found to have no violation of Lipinski's rule and had no toxicity based on the preliminary assessment, have better binding affinity and protease inhibitory prediction scores than the references, have optimistic bioactivity radar prediction and similar amino acids interaction, in comparison with the references. Further studies, which include in-vitro and in-vivo studies, will be carried out to validate the results of this study.

Keywords: Diabetesbeta-cellsapoptosisCaspase 3phytocompoundsin-silico


1.        Introduction

Beta-cell apoptosis is a critical event in the pathogenesis of type 1 diabetes mellitus (DM). Aside from being the primary mechanism by which cells are destroyed, beta-cell apoptosis has been linked to the onset of type 1 DM via antigen cross-presentation mechanisms that result in beta-cell-specific T-cell activation (1). Apoptosis can be activated via the extrinsic death receptor or intrinsic mitochondrial pathway, activating effector caspases (2). Apoptosis is also a critical process in the development of atherosclerosis (2).

Caspases are endoproteases and genes crucial for preserving homeostasis by controlling cell death and inflammation. A phylogenetically conserved death program that is essential for the homeostasis and growth of higher organisms carefully regulates their activation. Numerous human diseases are primarily pathogenetic due to the dysregulation of apoptosis. Caspases are potential therapeutic targets because they are part of the apoptotic machinery (3, 4).

Caspases are classified broadly according to their known roles in apoptosis (caspase-3, -6, -7, -8, and -9 in mammals) and inflammation (caspase-1, -4, -5, -12 in humans and caspase-1, -11, and -12 in mice). Caspase-2, -10, and -14 functions are more difficult to classify. Caspases involved in apoptosis have been divided into two groups based on their mechanism of action: initiator caspases (caspases - 8 and -9) and executioner caspases (caspase-3, -6, and -7) (3).

In a study titled "Caspase-3-Dependent -Cell Apoptosis in the Initiation of Autoimmune Diabetes Mellitus", the authors used a genetic approach to show that this process is necessary for cross-presentation of beta-cell antigen to activate beta-cell-specific T cells (1). They proved that mice lacking caspase-3 do not experience the onset of autoimmune diabetes, which is indicated by normal level of glucose concentration in the blood, unaffected beta-cells revealing high insulin content, and absence of beta-cell specific T-cell activation in the pancreatic draining lymph nodes. In a different study titled "Immunocytochemical localization of caspase-3 in pancreatic islets from type 2 diabetic subjects", the author reported finding more cleaved caspase-3 immunostained islets from type 2 diabetics, which may indicate an accelerated apoptotic cascade in the islets, along with increasing amyloid deposition before ultimate cell death (5).

The improper control of caspase-mediated cell death and inflammation is linked to various illnesses, including inflammatory, neurological, and other metabolic diseases and cancer. It may be necessary to therapeutically target caspase-3 activity in cells to stop the onset of autoimmune diabetes (1). Numerous natural and synthetic caspase inhibitors have been discovered and created to be used therapeutically. Only a few synthetic caspase inhibitors have progressed into clinical trials due to their lacklustre efficacy or harmful side effects. They have yet to prove compelling enough for patient use (6).

The aim of this study is to detect phytocompounds with drug like properties in African plants that could inhibit Caspase-3 through in-silico analysis.

2.        Materials and Methods

2.1     Materials

The materials used are personal computer, African Natural Compounds Database, PubChem (http://Pubchem.ncbi.nlm.nih.gov) (7), Linux operating system (Ubuntu desktop 18.04), Protein data bank (https://www.rcsb.org/) (8), DataWarrior software (9), PyMOL software (10), AutoDockTools-1.5.6 software (11), AutoDock Vina 1.1.2 software (12), on Ubuntu operating system, and Molinspiration Chemoinformatics web tool (https://www.molinspiration.com/cgi-bin/properties) (13).

2.2     Literature Mining

To find essential targets and receptors for apoptotic processes, literatures were explored. This was done to examine the role of the target and receptors in the pathophysiology and initiation of cell apoptosis. This provides more details regarding the receptor's characteristics, activities, and druggability.

2.3     Selection and Preparation of the Receptor

Caspase 3 in 3D format was retrieved from Protein Data Bank (PDB) with the PDB ID: 3KJF after various targets and receptors had been identified, literature had been mined, and the target and receptor had been analyzed. PyMOL program was initially used to prepare the pdb file by selecting the necessary chains and deleting multiple ligands. To understand how the ligands attach to receptors, PyMOL software was used. The AutoDockTools was used to get the receptor ready for molecular docking simulations. The receptors were prepared by adding polar hydrogens and Kollman's charges before storing them in the pdbqt file format, the structural format needed for performing molecular docking simulation on Autodock vina. As shown in Table 1, the electrostatic grid boxes and the three-dimensional affinity with various sizes and centers were formed around the protein's active region.

Table 1. Grid box parameters used for the molecular docking simulations

 

3KJF

Centres

Sizes

X

21.94

14

Y

-4.306

14

Z

10.718

14

2.4     Selection of the Ligands

In this study, 6511 phytocompounds were examined, which were obtained from the African Natural Products Database (African-compounds.org) (14, 15). The compounds were downloaded as 3D-structure data files for analysis. Various parameters such as partition coefficient (Log P), topological polar surface area (TPSA), molecular weight, hydrogen bond donor, and hydrogen bond acceptor were used to assess the phytocompounds. Some of the phytocompounds were found to infringe Lipinski's rule. Those that did not breach the rule underwent toxicological assessment for mutagenicity, carcinogenicity, tumorigenicity, and reproductive effect.

2.5     Preparation of the Ligands

Phytocompounds with no Lipinski’s rule of five infarction and no predicted toxicity (mutagenicity, carcinogenicity, tumorigenicity, and reproductive effect) in-silico were prepared for the molecular docking simulation. Reference ligands were identified from the literature, including the compound co-crystallized with the receptor/protein on the PDB database. In preparation for the ligands for molecular docking simulation, all rotatable bonds, torsions, and Gasteiger charges were assigned and saved in the pdbqt file format.

2.6     Validation of Docking Protocol

The PDB structure of the 3KJF (Caspase 3) protein, in association with a reference inhibitor as was downloaded from the PDB, was replicated in-silico to validate the molecular docking simulations procedure for this protein. Other known inhibitors of Caspase 3 were also used for the validation, including Flubendazole, Fenoprofen, Pranoprofen, and Diflunisal (16). The AutoDockTools-1.5.6 was used to calculate polar hydrogen, Kollman charges, grid box sizes, and centers at a grid space of 1.0 (11, 12). The protein was stored as a pdbqt file. AutoDockTools-1.5.6 was used to prepare the reference chemicals for molecular docking simulation. Torsion-free bonds, as well as any other rotatable bonds, were permitted. After that, files with the pdbqt extension were generated as output. On a Linux environment, a virtual screening shell script was used to locally implement the AutoDockVina® molecular docking simulation of the protein and reference chemical utilizing the centers and sizes (12). Co-crystal inhibitor binding interaction was compared with the re-docked co-crystalized compounds, Flubendazole, Fenoprofen, Pranoprofen, and Diflunisal using PyMol-1.4.1 software and Discovery studio visualizer.

2.7     Molecular Docking of the Phytocompounds on Caspase 3

The phytocompounds were prepared in batches for molecular docking simulations using virtual screening scripts against the Caspase 3. Following the validation of docking methods, four replicates of Molecular Docking Simulations were performed on a Linux platform using AutoDockVina® and related tools. To determine the leading phytocompounds, binding free energy values (kcal/mol SD) were ranked.

2.8     Bioactivity Prediction of Phytocompounds

The online Molinspiration web tool version 2011.06 (www.molinspiration.com) was supplied SMILES notations of the leading phytocompounds to forecast the bioactivity scores for protease inhibition.

2.9     Computational Pharmacokinetics of Frontrunner Phytocompounds

The top phytocompounds underwent a thorough pharmacokinetics evaluation using SwissADME, a web-based tool that assesses the druglikeness, physicochemical, ADME properties, and medicinal chemistry compatibility of small molecules (17). The assessment was conducted to examine the pharmacokinetics of the lead phytocompounds in detail.

2.10  In-depth Toxicity Assessment of Frontrunner Phytocompounds

An in-depth toxicity prediction of the frontrunner phytocompounds for AMES toxicity, Max. tolerated dose (human), hERG I inhibitor, hERG II inhibitor, Oral Rat Acute Toxicity (LD50), Oral Rat Chronic Toxicity (LOAEL), Hepatotoxicity, Skin Sensitization, T. Pyriformis toxicity and Minnow toxicity on the pkCSM platform (18).

2.11  Analysis of the Frontrunner Phytocompounds-Caspase 3 Binding Interactions

The amino acids of Caspase 3 binding interactions with each frontrunner phytocompounds were analyzed using Discovery Studio Visualizer v20.1.0.19295, and Maestro 13.3 aided the generation of 2D structures of the interaction for easy observation (19, 20).

3.        Results

3.1     Preliminary Drug-likeness and Toxicity Assessment of the Ligands (Phytocompounds)

The drug-likeness assessment of the 6511 phytocompounds was performed using Lipinski's rule of five to screen out phytocompounds that violated the guidelines on the DataWarrior application. Following the screening, 3814 phytocompounds had no infraction of Lipinski's rule, but 2697 phytocompounds did. Toxicity testing on the 3814 phytocompounds that did not violate Lipinski's criteria was performed using DataWarrior to discover phytocompounds that could be mutagenic, tumorigenic, irritating, or have reproductive implications. In-silico testing revealed that 1897 phytocompounds possessed none of the identified toxicities. The total polar surface area (TPSA) was also calculated for each phytocompounds.

3.2     Validation of docking protocol

The docking procedure was validated to assure the in-silico repeatability of the experimental protein-ligand interactions gathered from the protein data bank and to observe Caspase 3 amino-acids-conventional hydrogen bond interactions with the reference compounds known as inhibitors of caspase 3. Table 2 shows the binding energy of the docked co-crystalized ligand and that of the reference known inhibitors of caspase 3. Figure 2 is the 2D representation of the docked co-crystalized ligand and reference compounds with the specific Caspase 3 amino acids involved in the interaction. Table 3 shows each reference compound, docked co-crystalized ligand, and the docked co-crystalized ligand with the specific amino acids involved in their interaction with caspase 3.

Table 2. Mean binding energies of the docked co-crystalized ligand and reference compounds

No.

Reference compounds

Mean Binding Affinity

Standard Deviation

1

Flubendazole

-7.60

0.20

2

Diflunisal

-7.20

0.00

3

B92 (Co-crystalized)

-7.13

0.15

4

Pranoprofen

-6.50

0.00

5

Fenoprofen

-6.18

0.05

 


Figure 1. 2D representations of the docked co-crystalized ligand and reference compounds amino acids interaction

Table 3. Mean binding energies of the docked co-crystalized ligand and reference compounds

No.

Reference compounds

Amino acids

1

B92

ARG 207,SER 205, SER 209

2

B92 (Co-crystalized)

ARG 207, SER 209, PHE 250

3

Diflunisal

ASN 208, SER 209

4

Pranoprofen

ASN 208

5

Fenoprofen

SER 209

6

Flubendazole

ARG 207, ASN 208, PHE 250

 

3.3     Molecular Docking of the Phytocompounds against Caspase 3

To identify phytocompounds with greater in silico binding energies against Caspase 3 than the co-crystalized ligand and reference compounds, molecular docking of the phytocompounds was carried out on Caspase 3. The result is presented in table 4, showing phytocompounds with higher mean binding energies than the co-crystalized ligand and reference compounds. The table also contains Lipinski's rule parameters and TPSA values of the phytocompounds.

Table 4. Phytocompounds with better mean binding affinities than the reference compounds

No.

Compound name

Mean Binding Affinity

Standard Deviation (±)

Molecular Weight

Octanol-Water Coefficient

Hydrogen Bond Acceptors

Hydrogen

Donor

Topological

Polar Surface Area

1

Amataine

-9.20

0.00

493.65

-2.75

7.00

2.00

49.04

2

3'-epi-afroside

-9.20

0.00

534.64

1.07

9.00

4.00

134.91

3

Neoilexonol

-8.70

0.00

442.73

6.67

2.00

1.00

37.30

4

Chrysophanol-10,10'-bianthrone

-8.70

0.00

478.50

4.70

6.00

4.00

115.06

5

Hydroxyhopane

-8.50

0.00

426.73

7.16

1.00

1.00

20.23

6

Taraxast-20-ene-3beta,30-diol

-8.50

0.00

446.76

8.49

2.00

2.00

40.46

7

Caulindole A

-8.50

0.00

368.52

5.38

2.00

2.00

31.58

8

Acacic acid lactone

-8.50

0.00

470.69

4.79

4.00

2.00

66.76

9

3-oxo-12beta-hydroxy-

Oleanan-28,13beta-olide

-8.50

0.00

430.67

5.60

3.00

1.00

46.53

10

Lucidene

-8.50

0.00

416.60

8.00

2.00

0.00

18.46

11

Millettone

-8.40

0.00

382.41

1.24

6.00

0.00

77.05

12

Taraxasterol

-8.30

0.00

424.71

7.00

1.00

1.00

20.23

13

5,6-dehydrocalotropin

-8.30

0.00

532.63

0.79

9.00

3.00

131.75

14

Chrysophanol- isophyscion

Bianthrone

-8.30

0.00

508.53

4.63

7.00

4.00

124.29

15

Uguenensene

-8.30

0.00

484.59

2.99

7.00

0.00

87.50

16

Calotroproceryl acetate A

-8.10

0.00

466.75

7.74

2.00

0.00

26.30

17

Lupeol

-8.10

0.00

440.75

7.98

1.00

1.00

20.23

18

Beta-amyrin

-8.10

0.00

426.73

7.34

1.00

1.00

20.23

19

Anastatin B

-8.10

0.00

378.34

3.58

7.00

4.00

120.36

20

3-hydroxycycloart-24-one

-8.10

0.00

442.73

6.86

2.00

1.00

37.30

21

Diketo leucolactone

-8.10

0.00

468.68

5.04

4.00

1.00

63.60

22

Sigmoidin E

-8.08

0.22

406.48

5.55

5.00

2.00

75.99

23

Di-podocarpanoid hugonone A

-8.08

0.25

586.85

4.53

6.00

5.00

118.22

24

24-methylene cycloartanol

-8.05

0.06

440.75

8.34

1.00

1.00

20.23

25

Isojamaicin

-8.05

0.06

378.38

3.73

6.00

0.00

63.22

26

Seneganolide

-8.03

0.05

470.52

1.37

8.00

1.00

112.27

27

24-methylencycloartanol

-8.00

0.00

438.74

8.08

1.00

1.00

20.23

28

Scalarolide

-8.00

0.00

386.57

4.51

3.00

1.00

46.53

29

Citriquinochroman

-8.00

0.00

442.47

3.88

7.00

2.00

89.79

30

Matricolone

-8.00

0.00

286.41

3.36

2.00

1.00

37.30

31

Epi-lupeol

-8.00

0.00

426.73

7.65

1.00

1.00

20.23

32

20-epi-isoiguesterinol

-8.00

0.00

424.62

5.19

3.00

2.00

57.53

33

Melliferone

-8.00

0.00

452.68

5.64

3.00

0.00

43.37

34

Abyssinone I

-8.00

0.00

322.36

3.87

4.00

1.00

55.76

35

Argeloside O

-7.93

0.05

521.63

0.31

9.00

0.00

112.58

36

Calotropursenyl acetate B

-7.90

0.00

468.76

7.84

2.00

0.00

26.30

37

Beta-anhydroepidigitoxigenin

-7.90

0.00

356.50

3.47

3.00

1.00

46.53

38

3-acetyltaraxasterol

-7.90

0.00

468.76

7.84

2.00

0.00

26.30

39

Lupeol acetate

-7.90

0.00

480.77

8.20

2.00

0.00

26.30

40

Siphonellinol C

-7.90

0.00

490.72

5.04

5.00

4.00

90.15

41

Isoadiantol

-7.90

0.00

426.73

7.11

1.00

1.00

20.23

42

3-acetylsesterstatin 1

-7.90

0.00

446.63

4.07

5.00

1.00

72.83

43

1,5-di-O-caffeoylquinic acid

-7.90

0.00

426.73

7.59

1.00

0.00

17.07

44

Tingenin B

-7.90

0.00

438.61

4.55

4.00

2.00

74.60

45

Friedelane-3,7-dione

-7.90

0.00

440.71

6.88

2.00

0.00

34.14

46

Norisojamicin

-7.90

0.00

364.35

3.46

6.00

1.00

74.22

47

A-homo-3a-oxa-5beta-

Olean-12-en-3- one-28-oic acid

-7.90

0.00

471.70

3.58

4.00

0.00

66.43

48

Corosolic acid

-7.90

0.00

473.72

3.18

4.00

2.00

80.59

49

Lupenone

-7.88

0.05

424.71

7.79

1.00

0.00

17.07

50

Coladin

-7.88

0.17

424.54

4.92

5.00

0.00

61.83

51

Abyssinone III

-7.88

0.19

390.48

5.90

4.00

1.00

55.76

52

Neomacrotriol

-7.85

0.06

472.75

6.63

3.00

3.00

60.69

53

Abyssinoflavone V

-7.85

0.06

338.36

3.53

5.00

2.00

75.99

54

13-hydroxyfeselol

-7.85

0.06

400.51

3.53

5.00

2.00

75.99

55

Assafoetidnol A

-7.83

0.05

398.50

3.15

5.00

2.00

75.99

56

Demethoxyexcelsin

-7.83

0.05

384.38

3.15

7.00

0.00

64.61

57

3-taraxasterol

-7.80

0.00

430.76

9.48

1.00

1.00

20.23

58

Neoilexonol acetate

-7.80

0.00

484.76

7.16

3.00

0.00

43.37

59

Sipholenol I

-7.80

0.00

508.74

3.59

6.00

4.00

102.68

60

Cabralealactone

-7.80

0.00

412.61

5.00

3.00

0.00

43.37

61

Ursolic acid

-7.80

0.00

455.70

3.76

3.00

1.00

60.36

62

Stylopine

-7.80

0.00

328.39

0.46

5.00

2.00

44.66

63

Khayanolide D

-7.80

0.00

502.56

1.07

9.00

3.00

135.66

64

Olean-12-en-3-one

-7.80

0.00

426.73

7.59

1.00

0.00

17.07

65

Tribulus saponin aglycone 1

-7.80

0.00

350.54

4.75

3.00

2.00

49.69

66

Foetidin

-7.80

0.00

381.49

5.47

4.00

2.00

51.83

67

Samarcandin

-7.80

0.00

400.51

3.47

5.00

2.00

75.99

68

Resinone

-7.80

0.00

440.71

6.94

2.00

1.00

37.30

69

Uncinatone

-7.80

0.00

318.41

3.97

4.00

2.00

66.76

70

Urs-9(11),12-dien-3beta-ol

-7.80

0.14

424.71

7.17

1.00

1.00

20.23

71

Sesamin

-7.78

0.05

354.36

3.22

6.00

0.00

55.38

72

Euphornin C

-7.75

0.30

546.70

4.95

8.00

1.00

116.20

73

Salmahyrtisol B

-7.75

0.06

386.57

4.51

3.00

1.00

46.53

74

Isoferprenin

-7.75

0.06

362.47

6.42

3.00

0.00

35.53

75

(±)-paulownia

-7.75

0.06

370.36

2.40

7.00

1.00

75.61

76

Limonin

-7.75

0.06

470.52

1.03

8.00

0.00

104.57

77

Sablacaurin A

-7.73

0.15

482.79

9.30

2.00

0.00

26.30

78

Farnesiferol A

-7.73

0.05

384.51

3.58

4.00

1.00

55.76

79

Epilupeol

-7.70

0.00

426.73

7.65

1.00

1.00

20.23

80

Lupenone

-7.70

0.00

424.71

7.79

1.00

0.00

17.07

81

Sipholenol A

-7.70

0.00

478.76

5.38

4.00

3.00

69.92

82

Taraxasteryl acetate

-7.70

0.00

468.76

7.84

2.00

0.00

26.30

83

Retusolide B

-7.70

0.00

316.44

2.95

3.00

0.00

43.37

84

Cycloart-23Z-ene-3beta,25-diol

-7.70

0.00

456.75

7.32

2.00

1.00

29.46

85

7-deacetoxy-7-oxogedunin

-7.70

0.00

440.53

2.82

6.00

0.00

86.11

86

Tribulus saponin aglycone 2

-7.70

0.00

434.66

4.18

4.00

3.00

69.92

87

Lup-20(29)-ene-3beta,23-diol

-7.70

0.00

456.75

7.05

2.00

2.00

40.46

88

Beta-boswellic acid

-7.70

0.00

455.70

3.93

3.00

1.00

60.36

89

3-ketotirucall-8,24-dien-21-oic acid

-7.70

0.00

425.63

4.43

3.00

0.00

57.20

90

6-oxoisoiguesterin

-7.70

0.00

420.59

6.02

3.00

2.00

57.53

91

Friedelanol methyl ether

-7.70

0.00

470.82

8.44

1.00

0.00

9.23

92

Jamaicin

-7.70

0.00

378.38

3.73

6.00

0.00

63.22

93

Calopogonium isoflavone B

-7.70

0.00

348.35

3.80

5.00

0.00

53.99

94

Di-podocarpanoid hugonone B

-7.70

0.00

580.80

4.03

6.00

4.00

115.06

95

3-O-benzoylhosloquinone

-7.70

0.00

420.55

5.24

4.00

0.00

60.44

96

Isochamanetin

-7.68

0.05

364.40

3.80

5.00

3.00

86.99

97

Oleanolic acid

-7.68

0.05

457.72

4.09

3.00

1.00

60.36

98

Pectachol B

-7.68

0.05

442.55

4.29

6.00

1.00

74.22

99

3-O-benzoylhosloppone

-7.65

0.10

420.55

4.76

4.00

1.00

63.60

100

Lup-20(29)-ene-3-acetate

-7.63

0.05

467.76

5.87

2.00

0.00

40.13

101

Marmaricin

-7.63

0.05

384.51

3.93

4.00

1.00

55.76

102

Calactin

-7.60

0.00

532.63

0.79

9.00

3.00

131.75

103

Olibanumol H

-7.60

0.00

460.74

5.66

3.00

3.00

60.69

104

Botulin

-7.60

0.00

442.73

6.72

2.00

2.00

40.46

105

Proscillaridin

-7.60

0.00

532.67

2.09

8.00

4.00

125.68

106

Ottelione B

-7.60

0.00

312.41

3.93

3.00

1.00

46.53

107

Sesterstatin 7

-7.60

0.00

444.61

4.14

5.00

1.00

72.83

108

Sonchuside A

-7.60

0.00

416.51

1.08

8.00

4.00

125.68

109

3alpha-acetoxyolean-12-en-28-al

-7.60

0.00

499.75

4.58

4.00

0.00

66.43

110

Beta-amyrin acetate

-7.60

0.00

468.76

7.65

2.00

0.00

26.30

111

Isoiguesterin

-7.60

0.00

408.62

5.84

2.00

1.00

37.30

112

5beta,24-cyclofriedelan-3-one

-7.60

0.00

424.71

7.29

1.00

0.00

17.07

113

Sigmoidin B

-7.60

0.00

356.37

3.83

6.00

4.00

107.22

114

Sigmoidin F

-7.60

0.00

422.48

5.20

6.00

3.00

96.22

115

3'-prenylnaringenin

-7.60

0.00

338.36

4.36

5.00

3.00

86.99

116

Abyssinin I

-7.60

0.00

368.38

3.46

6.00

2.00

85.22

117

Durmillone

-7.60

0.00

378.38

3.73

6.00

0.00

63.22

118

Hugonone A

-7.60

0.00

584.84

4.64

6.00

4.00

115.06

119

3-oxo-12-oleanen-28-oic acid

-7.60

0.00

453.68

4.13

3.00

0.00

57.20

120

Limonyl acetate

-7.60

0.00

514.57

1.37

9.00

0.00

113.80

 

Flubendazole

-7.60

0.20

 

 

 

 

 

 

Diflunisal

-7.20

0.00

 

 

 

 

 

 

B92

-7.13

0.15

 

 

 

 

 

 

Pranoprofen

-6.50

0.00

 

 

 

 

 

 

Fenoprofen

-6.18

0.05

 

 

 

 

 

           3.4 Bioactivity Prediction of Phytocompounds

Because Caspase 3 is a protease, the phytocompounds were screened computationally on Molinspiration for their ability to inhibit protease. According to the results, 80 phytocompounds were discovered to have computational protease inhibitory scores below the co-crystalized ligand (B92) and above the four other reference compounds used (Flubendazole, Pranoprofen, Fenoprofen, and Diflunisal). Table 5 below shows the outcomes of the bioactivity prediction of the phytocompounds with higher binding energies than the co-crystalized ligand and reference compounds. One of the phytocompounds, as shown in table 5, has a bioactivity score above Pranoprofen, Fenoprofen, and Diflunisal but below B92 and Flubendazole.

Table 5 Bioactivity scores of the phytocompounds with their plant sources

No.

Phytochemical

Protease Inhibitory score

Plant source

1

B92

0.50

2

9R-hydroxysarcophine

0.41

Sarcophyton glaucum

3

Beta-boswellic acid

0.33

Boswellia species

4

Sipholenol I

0.3

Callyspongia siphonella

5

Olean-12-en-3- one-28-oic acid

0.28

Albizia gummifera

6

Tribulus saponin aglycone 2

0.26

Tribulus species

7

Urs-12-ene-1beta,3beta,11alpha,15alpha-tetraol

0.25

Salvia argentea var. aurasiaca

8

Neoilexonol

0.23

Boswellia carterii

9

Ursolic acid

0.23

Amaracus akhdarensis

10

3beta-hydroxy-11alpha-methoxyurs-12-ene

0.22

Launaea arborescens

11

1,5-di-O-caffeoylquinic acid

0.21

Cynara cardunculus

12

Olibanumol H

0.21

Boswellia carterii

13

Isoadiantol

0.18

Adiantum capillus-veneris

14

Lup-20(29)-ene-3beta,23-diol

0.18

Salvia palaestina

15

Uguenensene

0.17

Vepris uguenensis

16

(+)-7alpha,8beta-dihydroxydeepoxysarcophine

0.17

Sarcophyton auritum

17

Neoilexonol acetate

0.17

Boswellia carterii

18

Cycloart-23Z-ene-3beta,25-diol

0.17

Euphorbia bupleuroides

19

Taraxasterol

0.16

Calotropis procera

20

Lupeol

0.16

Salvia palaestina

21

Taraxasterol

0.16

Calotropis procera

22

Sonchuside A

0.16

Launaea arborescens

23

3-O-alpha-L-arabinopyranosyl-echinocystic acid

0.15

Dizygotheca kerchoveana

24

Epilupeol

0.15

Boswellia species

25

Oleanolic acid

0.15

Salia triloba

26

Abyssinoflavone V

0.14

Erythrina abyssinica

27

Isoferprenin

0.14

Ferula communis var. genuina

28

Limonyl acetate

0.14

Vepris uguenensis

29

3-hydroxycycloart-24-one

0.13

Euphorbia guyoniana

30

Sigmoidin E

0.13

Erythrina abyssinica

31

Tribulus saponin aglycone 1

0.13

Tribulus species

32

Isochamanetin

0.13

Uvaria lucida ssp. lucida

34

Hydroxyhopane

0.12

Azolla nilotica

35

Siphonellinol C

0.12

Callyspongia siphonella

36

Urs-9(11),12-dien-3beta-ol

0.12

Boswellia carterii

37

Sipholenol A

0.12

Callyspongia siphonella

38

Calactin

0.12

Pergularia tomentosa

39

3alpha-acetoxyolean-12-en-28-al

0.12

Salvia palaestina

40

Beta-amyrin

0.11

Trichodesma africanum

41

Abyssinone I

0.11

Erythrina abyssinica

42

Calotropursenyl acetate B

0.11

Calotropis procera

43

Lupeol acetate

0.11

Torilis radiata

44

Abyssinone III

0.11

Erythrina abyssinica

45

3-acetylsesterstatin 1

0.1

Hyrtios erecta

46

Sigmoidin F

0.1

Erythrina abyssinica

47

Resinone

0.09

Drypetes gerrardii

48

Euphornin C

0.09

Euphorbia helioscopia

49

Lucidene

0.08

Uvaria species

50

Calotroproceryl acetate A

0.08

Calotropis procera

51

Beta-anhydroepidigitoxigenin

0.08

Calotropis procera

52

3-taraxasterol

0.08

Pergularia tomentosa

53

3'-epi-afroside

0.07

Gomphocarpus sinaicus

54

Taraxast-20-ene-3beta,30-diol

0.07

Launaea arborescens

55

5,6-dehydrocalotropin

0.07

Gomphocarpus sinaicus

56

Argeloside O

0.07

Solenostemma argel

57

Khayanolide D

0.07

Khaya senegalensis

58

5beta,24-cyclofriedelan-3-one

0.07

Drypetes gerrardii

59

24-methylene cycloartanol

0.06

Euphorbia helioscopia

60

24-methylencycloartanol

0.06

Euphorbia bupleuroides

61

Limonin

0.06

Vepris glomerata

62

Sesterstatin 7

0.06

Hyrtios erecta

63

Beta-amyrin acetate

0.06

Scorzonera undulata

64

Anastatin B

0.05

Anastatica hierochuntica

65

Scalarolide

0.05

Hyrtios erecta

66

Retusolide B

0.05

Euphorbia retusa

67

3-O-benzoylhosloquinone

0.05

Hoslundia opposita

68

Lup-20(29)-ene-3-acetate

0.05

Euphorbia helioscopia

69

Neomacrotriol

0.04

Neoboutonia macrocalyx

70

3-acetyltaraxasterol

0.03

Pergularia tomentosa

71

Tingenin B

0.03

Elaeodendron schlechteranum

72

Friedelane-3,7-dione

0.03

Drypetes gerrardii

73

Taraxasteryl acetate

0.03

Achillea fragrantissima

74

Abyssinin I

0.03

Erythrina abyssinica

75

3-oxo-12-oleanen-28-oic acid

0.03

Ekebergia benguelensis

76

Di-podocarpanoid hugonone A

0.01

Hugonia busseana

77

20-epi-isoiguesterinol

0.01

Salacia madagascariensis

78

Lupenone

0.01

Diospyros mespiliformis

79

Sablacaurin A

0.01

Sabal causiarum

80

Lupenone

0.01

Diospyros mespiliformis

81

Hugonone A

0.01

Hugonia castaneifolia

Flubendazole

0.01

82

7-deacetoxy-7-oxogedunin

0.00

Swietenia mahogani

Pranoprofen

-0.05

Fenoprofen

-0.07

Diflunisal

-0.14

           3.5 Computation Pharmacokinetics of Frontrunner Phytocompounds

The results of the pharmacokinetic assessment of the frontrunner phytocompounds, reference compounds, and co-crystalized are presented below in figure 2. The results are shown in bioavailability radar graphics. The components of the pictures below include Lipophilicity (LIPO), size, polarity (POLAR), solubility (INSOLU), flexibility (FLEX), and saturation (INSATU). According to the result, the reference compounds and co-crystalized ligands failed the bioavailability radar test. Out of the 82 phytocompounds assessment, 18 were within the optimal range of the bioavailability radar test.