International Journal of Research and Innovation in Applied Science (IJRIAS)

Submission Deadline-10th October 2025
October Issue of 2025 : Publication Fee: 30$ USD Submit Now
Submission Deadline-04th November 2025
Special Issue on Economics, Management, Sociology, Communication, Psychology: Publication Fee: 30$ USD Submit Now
Submission Deadline-17th October 2025
Special Issue on Education, Public Health: Publication Fee: 30$ USD Submit Now

Structure-Based Discovery of Chromolaena odorata Phytochemicals Targeting Haemorrhage-Associated Proteins via Molecular Docking

  • Oshatuyi Olukayode
  • Dearsly, Emmanuel Markus
  • Janet Peter
  • Tennyson Manang Abraham
  • Eze, Kingsley Chijioke
  • Dada, Emmanuel Damilo
  • Emmanuel Ikegima
  • Adaji Princess Ojoma
  • 1205-1225
  • Sep 18, 2025
  • Chemistry

Structure-Based Discovery of Chromolaena odorata Phytochemicals Targeting Haemorrhage-Associated Proteins via Molecular Docking

Oshatuyi Olukayode2, Dearsly, Emmanuel Markus1, Janet Peter1, Tennyson Manang Abraham1, Eze, Kingsley Chijioke2, Dada, Emmanuel Damilo1, Emmanuel Ikegima1, Adaji Princess Ojoma1

1Department of Biochemistry, College of Natural and Applied Sciences, Salem University, Kogi State, Nigeria

2Department of Biochemistry, Faculty of Basic Medical Sciences, University of Calabar Nigeria

*Corresponding Author

DOI: https://doi.org/10.51584/IJRIAS.2025.100800104

Received: 26 July 2025; Accepted: 01 August 2025; Published: 18 September 2025

ABSTRACT

Haemorrhage remains a significant global health burden, particularly in trauma and maternal emergencies, with current pharmacological interventions often limited by side effects and cost. This study investigates the inhibitory potential of 68 bioactive compounds from Chromolaena odorata against two key haemorrhage-associated proteins—Matrix Metalloproteinase-2 (MMP2) and Glycogen Synthase Kinase 3 Beta (GSK-3β)—using a structure-based molecular docking approach. Protein structures were retrieved from the RCSB Protein Data Bank, while phytochemicals were sourced from PubChem. Molecular docking was performed via AutoDock Vina integrated in PyRx 0.8, and post-docking interactions were analyzed using Discovery Studio. Pharmacokinetics and drug-likeness properties were assessed using SwissADME and ADMETlab 2.0. Top compounds such as Naringin, Curcumin, Lunamarin, Catechin, and d-Ribalinidine exhibited superior binding affinities (up to -10.1 kcal/mol) compared to standard drugs like Atorvastatin and Nimodipine. These ligands formed stable interactions with critical active-site residues. ADMET profiling showed favorable absorption, metabolism, and low toxicity, while drug-likeness filters were satisfied. The findings demonstrate the promising potential of Chromolaena odorata phytochemicals as safe and effective anti-haemorrhagic agents. The study also underscores the value of computational drug discovery in accelerating natural product-based therapeutic development.

Keywords: Chromolaena odorata, haemorrhage, molecular docking, MMP2, GSK-3β, ADMET, bioactive compounds, phytotherapy, drug-likeness

INTRODUCTION

Haemorrhage, or excessive bleeding, is a life-threatening condition that contributes significantly to mortality worldwide, especially in trauma and postpartum cases. It results from blood vessel rupture or dysfunction and can be categorized as internal or external, depending on the site of bleeding. The management of haemorrhage involves pharmacological agents, blood transfusion, and surgical intervention. Despite current therapeutic options, challenges persist, including side effects, delayed action, and limited availability in low-resource settings (Duran et al., 2021).

The search for alternative therapeutic agents has intensified interest in natural products and medicinal plants. Among such plants is Chromolaena odorata (L.) R.M. King & H. Rob, commonly known as Siam weed. It has gained recognition for its diverse pharmacological properties including antimicrobial, anti-inflammatory, antioxidant, wound healing, and haemostatic activities (Odeyemi & Yakubu, 2022). Its phytochemical richness—containing flavonoids, alkaloids, terpenoids, and tannins—makes it a strong candidate for drug development against bleeding disorders.

Modern drug discovery is increasingly driven by computational methods such as molecular docking, which predicts the binding affinity and orientation of small molecules with target proteins. This approach allows rapid screening of bioactive compounds against proteins implicated in disease pathways, saving time and cost compared to conventional wet-lab methods (Tahir et al., 2023).

Key proteins such as Matrix Metalloproteinase-2 (MMP2) and Glycogen Synthase Kinase 3 Beta (GSK-3β) play significant roles in vascular integrity, inflammation, platelet function, and blood vessel repair. MMP2 degrades extracellular matrix components and contributes to vascular permeability, while GSK-3β is involved in platelet activation and haemostatic signaling (Yang et al., 2021; Liu et al., 2022). This study investigates 68 phytochemicals from Chromolaena odorata for their binding potentials against MMP2 and GSK-3β using structure-based molecular docking, ADMET prediction, and drug-likeness profiling. By identifying promising compounds with high affinity and desirable pharmacokinetics, this research contributes to the development of safer, plant-based therapeutics for haemorrhage management.

Statement of the Problem

Haemorrhage continues to be a critical cause of preventable death, particularly in trauma and maternal health emergencies. While pharmacological agents such as tranexamic acid and desmopressin have been deployed, their use is often limited by adverse effects, cost, and accessibility. There is a glaring need for safe, cost-effective, and easily accessible treatments, especially in low- and middle-income countries (Glover et al., 2022). Despite the ethnomedical use of Chromolaena odorata in wound care and bleeding control, its molecular interaction with haemorrhage-related proteins has not been adequately explored through modern computational drug discovery tools.

Aim of the study

To investigate the bioactive compounds of Chromolaena odorata for their potential inhibitory interactions with key proteins implicated in haemorrhage using molecular docking approaches.

MATERIALS AND METHODS

Software and Web Servers Utilized

The computational aspects of this study were carried out using a suite of advanced bioinformatics tools and web-based platforms. Key software included: Discovery Studio (DS) version 21.1 – for protein and ligand preparation, and post-docking analysis: AutoDock Vina integrated within PyRx version 0.8 – for molecular docking: PubChem (https://pubchem.ncbi.nlm.nih.gov) – for ligand retrieval: RCSB Protein Data Bank (https://www.rcsb.org) – for downloading target protein structures: PubMed (https://pubmed.ncbi.nlm.nih.gov) – for literature validation of compounds: SwissADME (http://www.swissadme.ch/) – for drug-likeness prediction: ADMETlab 2.0 (https://admetmesh.scbdd.com/) – for evaluating pharmacokinetic and toxicity profiles.

Preparation of Target Proteins

The three-dimensional (3D) crystallographic structures of the two selected haemorrhage-related proteins—Matrix Metalloproteinase-2 (MMP2) and Glycogen Synthase Kinase 3 Beta (GSK-3β)—were obtained in pdb format from the RCSB Protein Data Bank. Prior to docking: All native ligands and water molecules were removed. The protein structures were cleaned and energy minimized using Discovery Studio (DS v21.1). The binding sites were identified and recorded based on co-crystallized ligand positions and relevant literature. (Dearsly et al., 2025)

Preparation of Ligands

A total of 68 bioactive compounds derived from Chromolaena odorata were retrieved from the PubChem and PubMed databases in Structure Data File (SDF) format. These compounds were then:

  • Converted to PDBQT format using AutoDock Tools within PyRx.
  • Visualized and energy-minimized using Discovery Studio for structural optimization.
  • Saved in appropriate formats for subsequent molecular docking simulations. (Dearsly et al., 2025)

Molecular Docking Protocol

Molecular docking was carried out using AutoDock Vina through the PyRx interface, following the protocol described by Sharma et al. (2019). For each protein:

  • The docking grid was centered at the active site residues and dimensions were defined accordingly.
  • Each ligand was docked against both MMP2 and GSK-3β targets.
  • Binding affinities (in kcal/mol) were recorded for all protein-ligand interactions.

Post-Docking Analysis

Molecular Interaction Analysis

Post-docking interactions were visualized and interpreted using Discovery Studio. The nature of protein-ligand interactions—hydrogen bonds, hydrophobic contacts, π-π stacking, etc.—were evaluated and represented in both 2D and surface models.

ADMET Prediction

Pharmacokinetic profiling of top-scoring ligands was performed using ADMETlab 2.0. The following properties were assessed:

  • Absorption: Human intestinal absorption (HIA), Caco-2 permeability
  • Distribution: Blood-brain barrier (BBB) penetration
  • Metabolism: CYP450 substrate/inhibition profiles
  • Excretion: Predicted excretion rates
  • Toxicity: Ames test, carcinogenicity, acute oral toxicity

Drug-Likeness Evaluation

The drug-likeness of each top ligand was assessed using SwissADME by evaluating:

  • Lipinski’s Rule of Five
  • Ghose, Veber, Egan, and Muegge filters

Compounds that satisfied all major rules were considered to have favorable oral bioavailability

Preparation of Ligands

Table 1: List of bioactive compounds derived from Chromolaena odorata.

S/N. Compounds
1 1,8 Cineole
2 2,4 dihydroxy benzoic acid
3 2, 5-bis-(1-dimethyl) Phenol
4 2-Dodecanone
5 3-caffeoyl quinic acid
6  5-caffeoyl quinicacid
7  Alkaloids A
8  Astragalin
9  C15H24 copaene
10  Camphene
11  Carvone
12  Caryophyllene oxide
13  Catechin
14  Citronellyl acetate
15 Curcumin
16  Cymene
17  Diterpene derive
18  Dodecyl acetate
19  Epicatechin
20  Epihedrine
21 Ethylcaffeate
22  Eugenol
23  Flavan-3-ol
24  Flavone
25  Gallic acid
26  Germacrene D
27  Heptadecene
28  Hexadecanoic acid
29  Hexanoic acid
30  Hyperoside
31  Isopulegol
32  Isosakuranetin
33  Isotrifolin
34  Kaenpferol
35  Kaur-16-ene
36  Lauric Acid
37  Ledol
38  Limonene
39  Linalool
40  Lunamarin
41  Myristic acid
42  Myrtenol
43  Naringenin
44  Naringin
45  Octanoic acid
46  Oxalate
47  Patulein
48  Pentadecanoic acid
49  Quinine
50  Resveratrol
51  Rutin
52  Sapogenin
53  Steroid U
54  Taxifolin
55  Trifolin
56  Undecanal
57  Undecanone
58  Vanillin
59  Viridiflorene
60  Widdrol
61  cis-Linalool oxide (furanoid)
62  cis-Verbenol
63  d-Ribalinidine
64  decanoate ester
65  decanoic acid
66  m-cresol
67  methyl dodecanoate
68  methyl tetradecanoate

Preparation of protein

The crystal protein structures, and their co-crystallized compounds as presented in Table 3.2 were retrieved from the Protein Data Bank (https://www.rcsb.org). From the retrieved structures, the native ligands wereextracted, and water molecules removed using Autodock version 4.2 programs. (Dearsly et al., 2025)

Table 2. Target proteins of hemorrhage.

Sn. Protein PDB Code Amino Acid Residues Reference
1 Matrix metalloproteinase 2 (MMP2) 1QIB GLY162
LEU164
TRY223PRO221LEU222HIS201
https://www.rcsb.org/structure/1QIB
2 Glycogen synthase kinase 3 (GSK-3B)  1Q5K ASP133
LEU187
GLN185
TRY134LEU188ASP200ILE 62
https://www.rcsb.org/structure/1Q5K

RESULTS AND DISCUSSION

Results

Molecular Docking Results

Table 3: Molecular docking results

SN Compounds MMP2 GSK3-β
1 1,8-Cineole -5.3 -4.8
2 2,4 dihydroxy benzoic acid -6.9 -5.4
3 2, 5-bis-(1-dimethyl) Phenol 0 -6.6
4 2-Dodecanone -4.7 -5
5 3-caffeoyl quinic acid -7.8 -8
6  5-caffeoyl quinic acid -6.6 -6.7
7  Alkaloids A -7 -7
8  Astragalin -8.2 -8.1
9  Copaene -6.7 -7.6
10  Camphene -6.2 -5
11  Carvone -7.4 -6.5
12  Caryophyllene oxide -6.4 -6.7
13  Catechin -8 -7.9
14  Citronellyl acetate -7.1 -5.7
15 Curcumin -9.1 -8.3
16  Cymene -7.3 -6.4
17  Diterpene Deriv -7.5 -7.4
18  Dodecyl acetate -5.9 -5.3
19  Epicatechin -7.7 -8
20  Epihedrine -6.7 -5.7
21  Ethyl Caffeate -8.2 -8.6
22  Eugenol -6.8 -5.9
23  Flavan-3-ol -10.1 -7.5
24  Flavone -9.1 -8.3
25  Gallic acid -6 -5.5
26  Germacrene D -6.7 -7.7
27  Heptadecene -6.6 -5.5
28  Hexadecanoic acid -4.8 -5.2
29  Hexanoic acid -5.2 -4.4
30  Hyperoside -8.3 -8.4
31  Isopulegol -5.7 -6.1
32  Isosakuranetin -7.8 -8.3
33  Isotrifolin -8.2 -8.6
34  Kaenpferol -8.1 -8.1
35  Kaur-16-ene -8.4 -7.6
36  Lauric Acid -5.1 -5.1
37  Ledol -6.8 -6.4
38  Limonene -7 -6.4
39  Linalool -4.9 -5.4
40  Lunamarin -8.8 -9.1
41  Myristic acid -5 -5.1
42  Myrtenol -5.5 -5.1
43  Naringenin -7.8 -8.3
44  Naringin -9.5 -8.4
45  Octanoic acid -5.8 -4.7
46  Oxalate -4.4 -3.9
47  Patulein -6.4 -5.7
48  Pentadecanoic acid -7.1 -5.5
49  Quinine -7.2 -8.3
50  Resveratrol -8.3 -7.4
51  Rutin -8.9 -7.9
52  Sapogenin -8.9 -7.3
53  Steroid U -8.2 -8.3
54  Taxifolin -8.1 -8.2
55  Trifolin -8.4 -8.5
56  Undecanal -4.6 -4.9
57  Undecanone -6 -5.2
58  Vanillin -6.3 -5.5
59  Viridiflorene -7.5 -8.3
60  Widdrol -6.8 -6.8
61  cis-Linalool oxide (furanoid) -5.6 -5.4
62  cis-Verbenol -5.9 -4.9
63  d-Ribalinidine -8.3 -8.6
64  decanoate ester -4.8 -4.9
65  decanoic acid -4.8 -5
66  m-cresol -6.1 -5.1
67  methyl dodecanoate -6.4 -5.1
68  methyl tetradecanoate -6.3 -5.4

Dimension for molecular docking of each protein and the ligands

Table 4. Dimension

DESCRIPTION MMP2
CENTER X 65.773
CENTER Y 96.9469
CENTER Z 146.869
DIMENSION X 100.6768
DIMENSION Y 82.4441
DIMENSION Z 25

Table 5. Dimension

DESCRIPTION GSK-3
CENTER X 29.2806
CENTER Y 50.3856
CENTER Z 38.2354
DIMENSION X 80.4738
DIMENSION Y 78.3285
DIMENSION Z 25

 Docking of selected ligands with high binding affinity

Table 6: Docking results.

S/N Compounds MMP2 GSK3-β
1 3-caffeoylquinicacid -9.3 -7.9
2 Astragalin -8.5 -7.8
3 Catechin -9.3 -8.2
4 Curcumin -8.7 -8.3
5 d-Ribalinidine -7.7 -8.5
6 Ethyl caffeate -7.2 -6.5
7 Hyperoside -9 -7.9
8 Isosakuranetin -8.8 -8.6
9 Lunamarine -9.1 -9.3
10 Naringin -8 -9.6
11 Taxifolin -8.6 -7.2
S/N Co-Crystaline Compound MMP2 GSK3-β
1 Atovastatin -6.7 -8.2
S/N Drugs Used in Treating Hemorrhage MMP2 GSK3-β
1 Nimodipine -5.2 -6.3
2 Cyklokapron  -5.8 -7.4

 Dimension for molecular docking of top selected ligands and each protein

Table 7: Dimensions

DESCRIPTION MMP2
CENTER X 65.7593
CENTER Y 28.2985
CENTER Z 29.7313
DIMENSION X 14.7563
DIMENSION Y 18.9262
DIMENSION Z 18.7144s

Table 8: Dimensions

DESCRIPTION GSK-3
CENTER X 21.8103
CENTER Y 26.6686
CENTER Z 6.3104
DIMENSION X 19.3137
DIMENSION Y 23.5384
DIMENSION Z 20.9075

Interaction of proteins with selected ligands

Interaction of top ligands with (GSK3 1Q5K)

Table 9: Table of interaction of top ligands with (GSK3 1Q5K)

GSK3 1Q5K
Compounds Hydrogen bond interaction (Bond Distance A) Hydrophobic Interaction Other Interaction
No. Residues No. Residues No. Residues
 d-Ribalinidine 4 VAL135 (2.32)

ASN186 (2.33)

ASP133 (2.07)

CYS199 (3.83)

6 LEU188 (5.49)

VAL70 (5.05)

ALA83 (4.21)

CYS199 (4.34)

VAL70 (4.40)

LEU188 (3.92)

 Isosakuranetin 2 VAL135 (2.53)

ASP133 (2.36)

4 VAL70 (3.97)

ALA83 (4.50)

ILE62 (4.70)

LEU188 (3.97)

 Catechin 3 ASP200 (2.43)

ASN186 (2.15)

VAL135 (2.33)

6 VAL70 (5.15)

ALA83 (4.09)

ILE62 (5.30)

LEU188 (4.55)

CYS199 (5.08)

VAL70 (5.12)

1 ASP200 (2.33)
 Lunamarine 4 ASP200 (2.24)

GLY65 (3.73)

VAL135 (3.60)

CYS199 (3.73)

10 VAL70 (4.44)

ALA83 (4.15)

ILE62 (4.22)

LEU188 (5.05)

LEU188 (4.86)

CYS199 (4.28)

VAL70 (5.44)

LEU132 (5.46)

TYR134(4.77)

LYS85 (5.31)

1 ASP200 (3.61)
Curcumin GLN185 (3.52)

ASP200 (3.76)

  ILE62 (4.33)

LEU188 (5.27)

Ethyl caffeate ASP200 (2.42)

LYS85 (2.38)

VAL135 (2.16)

VAL70 (5.09)

ALA83 (4.91)

ILE62 (5.02)

LEU188 (3.67)

VAL70 (5.72)

Cyklokapron 3 ASN186 (2.77)
GLN185 (2.96)
CYS199 (2.99)
2 VAL70 (4.37)
LYS85 (4.58)

 Surface view of ligands interactions with protein

2D Molecular interaction post-docking of GSK3-β and ligands complexes

 image

Figure 1 Interaction with GSK3-β and d-Ribalinidine

image

Figure 2. Interaction with GSK3-β and Isosakuranetin

image

Figure 3. Interaction with GSK3-β and Catechin

image

Figure 4. Interaction with GSK3-β and Lunamarine

image

Figure 5. Interaction with GSK3-β and Curcumin

image

Figure 6 Interaction with GSK3-β and Ethyl caffeate

image

Figure 7. Interaction with GSK3-β and Cyklokapron

Drug likeness test of selected ligands

Table 10: Drug likeness results of compounds with high binding interaction with target proteins

Compounds Lipinski Ghose Veber Egan Muegge Reference
d-Ribalinidine Yes Yes Yes Yes Yes Passed
Isosakuranetin Yes Yes Yes Yes Yes Passed
Catechin Yes Yes Yes Yes Yes Passed
Lunamarine Yes Yes Yes Yes Yes Passed
Curcumin Yes Yes Yes Yes Yes Passed
Ethyl caffeate Yes Yes Yes Yes Yes Passed

ADMET analysis result of hemorrhage with top selected compounds

Table 11

d-Ribalinidine
Properties Value
water solubility -2.91
HIA  0.032
BBB  0.517
Caco-2 permeability  -4.692
P-Glycoprotein Substrate  0.19
CYP2C9 Substrate  0.886
CYP2D6 Substrate  0.553
CYP3A4 Substrate  0.336
CYP1A2 Inhibitor  0.898
CYP2C9 Inhibitor  0.103
CYP2D6 Inhibitor  0.077
CYP2C19 Inhibitor  0.121
CYP3A4 inhibitor  0.019
 Ames Toxicity  0.209
Carcinogenicity  0.442
Acute Oral Toxicity  0.041
Excretion  7.31

Table 12:

Isosakuranetin
Properties Value
water solubility -3.70
HIA 0.008
BBB 0.048
Caco-2 permeability -4.673
P-Glycoprotein Substrate 0.006
CYP2C9 Substrate 0.995
CYP2D6 Substrate 0.882
CYP3A4 Substrate 0.207
CYP1A2 Inhibitor 0.934
CYP2C9 Inhibitor 0.887
CYP2D6 Inhibitor 0.825
CYP2C19 Inhibitor 0.948
CYP3A4 inhibitor 0.827
 Ames Toxicity 0.474
Carcinogenicity 0.652
Acute Oral Toxicity 0.576
Excretion 13.625

Table 13:

Catechin
Properties Value
water solubility -3.70
HIA 0.096
BBB 0.025
Caco-2 permeability -5.971
P-Glycoprotein Substrate 0.007
CYP2C9 Substrate 0.838
CYP2D6 Substrate 0.41
CYP3A4 Substrate 0.215
CYP1A2 Inhibitor 0.219
CYP2C9 Inhibitor 0.218
CYP2D6 Inhibitor 0.173
CYP2C19 Inhibitor 0.037
CYP3A4 inhibitor 0.315
 Ames Toxicity 0.467
Carcinogenicity 0.09
Acute Oral Toxicity 0.467
Excretion 17.911

Table 14:

Curcumin
Properties Value
water solubility -3.94
HIA 0.06
BBB 0.103
Caco-2 permeability -4.834
P-Glycoprotein Substrate 0.014
CYP2C9 Substrate 0.096
CYP2D6 Substrate 0.895
CYP3A4 Substrate 0.517
CYP1A2 Inhibitor 0.593
CYP2C9 Inhibitor 0.661
CYP2D6 Inhibitor 0.037
CYP2C19 Inhibitor 0.287
CYP3A4 inhibitor 0.67
 Ames Toxicity 0.234
Carcinogenicity 0.706
Acute Oral Toxicity 0.896
Excretion 13.839

Table 15:

Ethyl caffeate
Properties Value
water solubility -2.78
HIA 0.009
BBB 0.197
Caco-2 permeability -4.62
P-Glycoprotein Substrate 0.019
CYP2C9 Substrate 0.868
CYP2D6 Substrate 0.671
CYP3A4 Substrate 0.346
CYP1A2 Inhibitor 0.964
CYP2C9 Inhibitor 0.612
CYP2D6 Inhibitor 0.424
CYP2C19 Inhibitor 0.510
CYP3A4 inhibitor 0.457

 Ames Toxicity

0.285
Carcinogenicity 0.540
Acute Oral Toxicity 0.741
Excretion 15.766

DISCUSSION AND CONCLUSION

Discussion

The study aimed to identify potential inhibitors of haemorrhage-implicated proteins—Matrix Metalloproteinase-2 (MMP2) and Glycogen Synthase Kinase 3 Beta (GSK-3β)—through structure-based virtual screening of 68 bioactive compounds from Chromolaena odorata. The discussion below integrates key findings with literature to provide scientific insight into the molecular basis of the observed interactions. Molecular docking has proven to be an efficient and predictive tool in virtual drug screening. It simulates the interaction between small molecules (ligands) and target proteins to predict binding affinity and stability of the complexes (Tahir et al., 2023). In this study, docking was executed using AutoDock Vina through PyRx, with post-docking visualizations in Discovery Studio. The results revealed several compounds with high binding affinities toward both MMP2 and GSK-3β. Notably, compounds such as flavan-3-ol (-10.1 kcal/mol for MMP2), curcumin, naringin, hyperoside, and lunamarin displayed superior interactions compared to the reference drug, Atorvastatin (-6.7 kcal/mol for MMP2) and known haemorrhage therapies like Nimodipine (-5.2 kcal/mol) and Cyklokapron (-5.8 kcal/mol). These results suggest a strong potential for these natural compounds to interfere with the activity of haemorrhage-related proteins, validating earlier reports on the therapeutic efficacy of Chromolaena odorata in bleeding control (Odeyemi & Yakubu, 2022).

Protein-Ligand Interactions

A comprehensive analysis of the protein-ligand interactions revealed that the top-performing phytochemicals from Chromolaena odorata formed robust hydrogen bonds, hydrophobic contacts, and π-interactions with amino acid residues located within or near the active sites of GSK-3β and MMP2, which are essential regulators of haemostatic and vascular processes. These interactions are not merely incidental but indicative of a deliberate binding mechanism likely to interfere with protein function.

d-Ribalinidine–GSK-3β Interaction

d-Ribalinidine, one of the top ligands, exhibited strong binding affinity with GSK-3β (-8.5 kcal/mol), forming four key hydrogen bonds with ASP133, CYS199, ASN186, and VAL135, and up to six hydrophobic interactions involving LEU188, VAL70, ALA83, and CYS199. The significance of these residues lies in their proximity to the ATP-binding site of GSK-3β, which is critical for its catalytic function. Notably, ASP133 and CYS199 have been reported to contribute to the regulation of substrate orientation and enzyme activation (Liu et al., 2022). The stable multi-point anchoring of d-Ribalinidine suggests it may act as a competitive inhibitor, thereby potentially disrupting the kinase’s role in downstream platelet activation and endothelial dysfunction, both of which are implicated in haemorrhage progression.

Lunamarin–MMP2 and GSK-3β Interaction

Lunamarin demonstrated an exceptional binding score of -9.1 kcal/mol (MMP2) and -9.3 kcal/mol (GSK-3β). Its binding was characterized by a rich array of hydrogen bonds, hydrophobic interactions, and van der Waals contacts. Particularly in GSK-3β, lunamarin formed hydrogen bonds with ASP200, GLY65, VAL135, and CYS199, residues vital for substrate stabilization and enzymatic turnover. Its interaction footprint extended across 10 hydrophobic residues, including ILE62, LEU188, VAL70, ALA83, and TYR134, forming a network of non-covalent forces that enhanced complex stability.

Structurally, lunamarin contains multiple fused aromatic and aliphatic rings, which allow it to snugly fit into the hydrophobic cleft of the target proteins. This kind of planar polycyclic structure is known to favor π-π stacking and hydrophobic enclosure within active sites, leading to improved binding enthalpy (Ganesan & Xu, 2020). The interaction pattern and its energetically favorable docking scores highlight lunamarin as a highly promising lead compound.

sosakuranetin and Catechin–GSK-3β Interactions

Isosakuranetin and Catechin, two flavonoid compounds with strong antioxidant profiles, also exhibited significant binding energies of -8.6 kcal/mol and -8.2 kcal/mol with GSK-3β, respectively. These compounds formed multiple hydrogen bonds with VAL135, ILE62, ALA83, and LEU188—residues that are frequently involved in binding regulatory ligands and allosteric inhibitors (Zhou et al., 2023). The hydrophobic anchoring with CYS199, VAL70, and LEU132 further stabilized the ligand within the ATP-binding pocket. The 3-ring backbone of flavonoids like catechin provides multiple sites for polar and non-polar interaction, enabling them to span the depth of the binding pocket and engage residues across different regions, effectively blocking substrate access and inhibiting kinase phosphorylation activity. Catechin’s interaction with ASP200 and ASN186, residues implicated in the activation loop of GSK-3β, may indicate potential to modulate conformational changes required for enzymatic activity. This makes it not just a binder but a functional disruptor of kinase signaling.

Biological Relevance of Targeted Residues

Importantly, the residues engaged by these ligands—ASP133, LEU188, CYS199, VAL135, and ILE62—are evolutionarily conserved and crucial for ATP coordination and catalysis in serine/threonine kinases (Feng et al., 2021). Similarly, in MMP2, residues like HIS201 and TYR223 are located in the zinc-dependent catalytic domain, and their occupation by ligands can potentially impair metalloprotease function, which is essential in ECM degradation and vascular breakdown during haemorrhage (Yang et al., 2021). These findings demonstrate that the binding of phytochemicals to key residues within the active pockets of MMP2 and GSK-3β is not arbitrary. Rather, it reflects a highly specific, stable, and potentially inhibitory interaction that could translate into significant biological effects—namely, the suppression of vascular degradation and bleeding cascades. The study supports the hypothesis that compounds from Chromolaena odorata can modulate haemorrhage-related proteins through precise molecular interactions, paving the way for their consideration in therapeutic development.

Comparison with Standard Drugs

When compared with conventional drugs: Curcumin had a docking score of -9.1 kcal/mol against MMP2 and -8.3 kcal/mol against GSK-3β, outperforming Atorvastatin and Cyklokapron in both cases. Curcumin is already recognized for its anti-inflammatory and vascular protective effects (Chakraborty et al., 2021). Naringin (from C. odorata) scored -9.5 kcal/mol and -9.6 kcal/mol respectively, higher than all standard drugs, reaffirming its role in promoting vascular integrity and haemostasis, possibly through flavonoid-mediated inhibition of MMPs (Ganesan & Xu, 2020). This superior performance of plant-derived compounds compared to known haemostatic agents supports further in vitro and in vivo validations for clinical development.

Drug-Likeness and Pharmacokinetics

An essential criterion in early drug discovery is the assessment of drug-likeness, which evaluates whether a compound possesses physicochemical properties consistent with known orally active drugs. In this study, the top-performing phytochemicals—including d-Ribalinidine, Isosakuranetin, Catechin, Curcumin, and Ethyl caffeate—were subjected to rigorous evaluation using five widely accepted drug-likeness filters: Lipinski’s Rule of Five, Veber, Egan, Ghose, and Muegge. All selected compounds successfully passed these filters, indicating favorable oral bioavailability, membrane permeability, and absorption potential, all of which are critical in determining a molecule’s “drug-likeness” (Feng et al., 2021). Lipinski’s Rule of Five, which assesses molecular weight (<500 Da), lipophilicity (LogP < 5), hydrogen bond donors (≤5), and hydrogen bond acceptors (≤10), was met by all compounds, suggesting strong potential for oral absorption. In addition, passing Veber’s filter (≤10 rotatable bonds and TPSA ≤140 Ų) and Egan’s rule (LogP ≤ 5.88, TPSA ≤ 131 Ų) implies good membrane permeability and intestinal uptake. This is particularly relevant for compounds aimed at treating non-central nervous system (non-CNS) conditions like haemorrhage, where peripheral bioavailability is prioritized. To complement drug-likeness profiling, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions were conducted using ADMETlab 2.0. The results revealed that the majority of the top compounds exhibited moderate to high Human Intestinal Absorption (HIA) values.

For instance: d-Ribalinidine had an HIA value of 0.032, coupled with a BBB penetration score of 0.517, indicating moderate systemic absorption and low CNS permeation. This is ideal for targeting peripheral systems while minimizing central toxicity. Furthermore, it exhibited non-carcinogenic potential (0.442) and low acute oral toxicity, with no significant Ames mutagenic effect. Isosakuranetin, another top ligand, showed minimal cytochrome P450 enzyme inhibition, with low interaction scores across major CYP isoforms (e.g., CYP2D6: 0.825, CYP3A4: 0.827). This reduces the risk of drug-drug interactions and metabolic liabilities, improving its suitability as a safe therapeutic agent (Gupta et al., 2022). Catechin and Curcumin displayed similar pharmacokinetic favorability, with high water solubility, low blood-brain barrier permeability (BBB < 0.1), and minimal inhibition of cytochrome P450 enzymes, suggesting efficient clearance and minimal metabolic complications. Catechin’s excretion value of 17.91 also suggests prompt elimination, decreasing the likelihood of systemic accumulation. Ethyl caffeate, while less potent in binding than others, maintained a balanced ADMET profile, including a non-carcinogenic profile (0.540) and Caco-2 permeability of -4.62, which indicates moderate passive diffusion, a key absorption mechanism in oral drugs.

In terms of toxicity prediction, all selected ligands demonstrated low risk of mutagenicity (Ames test) and non-carcinogenicity, meeting basic safety standards for early-stage drug development. Their acute oral toxicity levels were also within acceptable limits, affirming their potential to be developed further with minimal risk.

Collectively, these findings are consistent with accepted pharmacokinetic benchmarks in lead compound identification. As highlighted by Gupta et al. (2022), ideal drug candidates should strike a balance between potency, pharmacokinetics, and safety—attributes reflected in the current results. The top ligands from Chromolaena odorata not only demonstrated high binding affinities to haemorrhage-related protein targets but also met critical pharmacokinetic and drug-likeness requirements. These compounds show promising ADME profiles, low toxicity potential, and minimal metabolic interference, reinforcing their potential as safe, orally available therapeutic candidates for further experimental validation in anti-haemorrhagic drug development.

Implications for Anti-Haemorrhagic Therapy

The observed inhibitory potentials of C. odorata compounds against MMP2 and GSK-3β indicate that these phytochemicals may modulate blood vessel permeability, endothelial integrity, and inflammatory cascades that underpin haemorrhage. MMP2 is well known for degrading the extracellular matrix, which can exacerbate bleeding (Yang et al., 2021), while GSK-3β contributes to endothelial inflammation and platelet function (Liu et al., 2022). Thus, inhibiting these proteins with natural compounds can offer a dual advantage—reducing vascular leakage and modulating clotting pathways without the adverse effects commonly seen in synthetic drugs. Moreover, the flavonoid-rich profile of Chromolaena odorata is consistent with prior evidence of anti-inflammatory and vasoconstrictive effects, as seen in ethnomedicinal use across Africa and Asia (Ajayi et al., 2020). The study presents compelling evidence that phytochemicals from Chromolaena odorata, particularly naringin, curcumin, catechin, d-ribalinidine, and lunamarin, demonstrate strong binding affinity and favorable pharmacokinetic profiles against haemorrhage-associated targets MMP2 and GSK-3β.

CONCLUSION

This study successfully employed structure-based molecular docking to investigate the anti-haemorrhagic potential of bioactive compounds derived from Chromolaena odorata. Two critical protein targets—Matrix Metalloproteinase-2 (MMP2) and Glycogen Synthase Kinase 3 Beta (GSK-3β)—were selected based on their established roles in vascular degradation and haemostatic dysregulation. Out of 68 screened phytochemicals, several compounds, notably Naringin, Lunamarin, Curcumin, Catechin, Hyperoside, and d-Ribalinidine, demonstrated significantly strong binding affinities, even surpassing standard drugs such as Atorvastatin, Cyklokapron, and Nimodipine. These interactions involved essential amino acid residues within the active binding sites, suggesting possible inhibition of the target enzymes. Furthermore, ADMET and drug-likeness profiling confirmed that the top compounds possess acceptable pharmacokinetic and safety properties, qualifying them as potential lead molecules for the development of plant-based anti-haemorrhagic agents. The findings not only validate the traditional use of Chromolaena odorata in wound healing and bleeding management but also offer a computational foundation for further in vitro and in vivo studies.

Thus, the study highlights the relevance of integrating ethnopharmacology with modern computational biology in identifying novel therapeutic candidates for critical conditions like haemorrhage.

Conflict of interest

Authors declares no conflict of interest

REFERENCES

  1. Ajayi, I. A., Oderinde, R. A., & Ajayi, O. E. (2020). Ethnomedicinal use and pharmacological properties of Chromolaena odorata. African Journal of Traditional, Complementary and Alternative Medicines, 17(4), 63–74.
  2. Chakraborty, S., Bhattacharya, S., & Roy, P. (2021). Curcumin: An age-old anti-inflammatory molecule in modern medicine. Biomedicine & Pharmacotherapy, 138, 111580. https://doi.org/10.1016/j.biopha.2021.111580
  3. Dearsly, E. M., Eze, K. C., Chukwu, C. A., & Ofem, L. W. Insilico Studies and Pharmacokinetic Properties of Anti-Alzheirmer’s Disease Activities of Phytocompounds Derived from Lasianthera Africana.
  4. Dearsly, E. M., Eze, K. C., Olukayode, O., Thomas, I. F., Adaeze, C. C., John, I. M., … & Damilo, D. E. (2025). Chemo-Protective Effects of Anacardium Occidentale Nutshell Hexane Extract on Catalase and Tyrosinase Activities in UV-Exposed Skin in Wistar Rats. International Journal of Research and Innovation in Applied Science, 10(4), 942-950.
  5. Dearsly, E. M., Eze, K. C., Oshatuyi, O., Shaibu, A. O., Nwosu, U. A., & Mmadu, M. E. Insilico studies of therapeutic agents in Phytocompounds obtained from mondoro myristica (African nutmeg) against Mycobacterium tuberculosis.
  6. Duran, R., Balci, M., & Gulluoglu, M. (2021). Approaches in the management of acute hemorrhage. Turkish Journal of Trauma and Emergency Surgery, 27(2), 97–104. https://doi.org/10.14744/tjtes.2021.73378
  7. Feng, Y., Li, W., Chen, Y., & Wang, Y. (2021). Predictive ADMET evaluation and pharmacokinetic interactions of herbal flavonoids. Frontiers in Pharmacology, 12, 748888. https://doi.org/10.3389/fphar.2021.748888
  8. Feng, Y., Li, W., Chen, Y., & Wang, Y. (2021). Predictive ADMET evaluation and pharmacokinetic interactions of herbal flavonoids. Frontiers in Pharmacology, 12, 748888. https://doi.org/10.3389/fphar.2021.748888
  9. Feng, Y., Li, W., Chen, Y., & Wang, Y. (2021). Predictive ADMET evaluation and pharmacokinetic interactions of herbal flavonoids. Frontiers in Pharmacology, 12, 748888. https://doi.org/10.3389/fphar.2021.748888
  10. Ganesan, K., & Xu, B. (2020). Anti-hemorrhagic and vascular protective effects of dietary flavonoids: A review. Nutrients, 12(10), 2955. https://doi.org/10.3390/nu12102955
  11. Ganesan, K., & Xu, B. (2020). Anti-hemorrhagic and vascular protective effects of dietary flavonoids: A review. Nutrients, 12(10), 2955. https://doi.org/10.3390/nu12102955
  12. Glover, P., Khan, F., & Santarelli, A. (2022). Tranexamic acid in trauma: A review of current evidence. World Journal of Emergency Surgery, 17(1), 1–9. https://doi.org/10.1186/s13017-022-00427-z
  13. Gupta, R., Jain, A., & Chauhan, V. (2022). ADMET profiling in drug discovery: A practical approach. Drug Development Research, 83(2), 65–78. https://doi.org/10.1002/ddr.21933
  14. Gupta, R., Jain, A., & Chauhan, V. (2022). ADMET profiling in drug discovery: A practical approach. Drug Development Research, 83(2), 65–78. https://doi.org/10.1002/ddr.21933
  15. Liu, W., Zhang, C., & Li, Z. (2022). Glycogen Synthase Kinase-3β in vascular diseases: Mechanistic insights and therapeutic potential. Frontiers in Pharmacology, 13, 895234. https://doi.org/10.3389/fphar.2022.895234
  16. Liu, W., Zhang, C., & Li, Z. (2022). GSK-3β in vascular biology: A molecular perspective. Frontiers in Cell and Developmental Biology, 10, 876122. https://doi.org/10.3389/fcell.2022.876122
  17. Liu, W., Zhang, C., & Li, Z. (2022). GSK-3β in vascular biology: A molecular perspective. Frontiers in Cell and Developmental Biology, 10, 876122. https://doi.org/10.3389/fcell.2022.876122
  18. Odeyemi, S. W., & Yakubu, M. T. (2022). Medicinal potentials of Chromolaena odorata: A review. Journal of HerbMed Pharmacology, 11(2), 123–132. https://doi.org/10.34172/jhp.2022.17
  19. Odeyemi, S. W., & Yakubu, M. T. (2022). Pharmacological potential of Chromolaena odorata: A review. Journal of HerbMed Pharmacology, 11(2), 123–132.
  20. Tahir, M., Ali, M., & Hussain, A. (2023). Structure-based virtual screening: Insights into computational drug discovery. Computational Biology and Chemistry, 100, 107823. https://doi.org/10.1016/j.compbiolchem.2022.107823
  21. Tahir, M., Ali, M., & Hussain, A. (2023). Structure-based virtual screening and docking: An effective tool in drug discovery. Computational Biology and Chemistry, 100, 107823. https://doi.org/10.1016/j.compbiolchem.2022.107823
  22. Yang, J., Wang, J., & Zuo, Z. (2021). Matrix metalloproteinases in vascular inflammation and haemorrhage. Journal of Inflammation Research, 14, 3353–3366. https://doi.org/10.2147/JIR.S321790
  23. Yang, J., Wang, J., & Zuo, Z. (2021). MMPs in vascular injury and hemorrhage. Journal of Inflammation Research, 14, 3353–3366. https://doi.org/10.2147/JIR.S321790
  24. Yang, J., Wang, J., & Zuo, Z. (2021). MMPs in vascular injury and hemorrhage. Journal of Inflammation Research, 14, 3353–3366. https://doi.org/10.2147/JIR.S321790
  25. Zhou, X., Zhao, Y., & Sun, J. (2023). Advances in GSK-3β inhibitors for vascular diseases. Drug Discovery Today, 28(2), 103496. https://doi.org/10.1016/j.drudis.2022.103496
  26. Zhou, X., Zhao, Y., & Sun, J. (2023). Advances in GSK-3β inhibitors for vascular diseases. Drug Discovery Today, 28(2), 103496. https://doi.org/10.1016/j.drudis.2022.103496

Article Statistics

Track views and downloads to measure the impact and reach of your article.

0

PDF Downloads

[views]

Metrics

PlumX

Altmetrics

Paper Submission Deadline

Track Your Paper

Enter the following details to get the information about your paper

GET OUR MONTHLY NEWSLETTER