INTRODUCTION:

Natural products have been a rich source of compounds that have found many applications in the fields of medicine, pharmacy and biology. In the cancer field, a number of important new commercialized drugs have been obtained from natural sources, by structural modification of natural compounds, or by the synthesis of new compounds, designed following a natural compound as model.

The search for improved cytotoxic agents continues to be an important in the discovery of modern anticancer drugs. Hepatocellular carcinoma (HCC) ranks the fifth in frequency among common human solid tumors and the fourth leading cause of cancer-related death. And also major malignant tumor in humans and cause more than 25,00,000 deaths annually worldwide.(1)

The majority of HCC cases occur in Asia and Africa but incidence has been increasing in Western Europe and the United States in recent years. In China, HCC is now the second cancer killer. Numerous studies have been carried out in an effort to elucidate molecular mechanism of hepatocarcinogenesis (2). Hinokitiol, also known as 𝛽-thujaplicin, is a tropolone derivative found in the heartwood of cupressaceous plants. Hinokitiol (HIOL) is a naturally occurring tropolane derivative is a known biochemical target other than the fact that it induces apoptosis. As an iron-chelating compound, it triggers apoptosis via activation of caspase-3 and exerts a spectrum of biological effects including differentiation-inducing, anti inflammatory, antibacterial, antifungal, and antioxidant capacities, as well as antitumor activity. Hinokitiol has also been widely used in hair tonics, tooth pastes, cosmetics, and food as an antimicrobial agent. Hinokitiol has been shown to suppress tumor growth by inhibiting cell proliferation and inducing apoptosis in various carcinoma cell lines.(3,4) .

Computational Biology and bioinformatics have the potential not only of speeding up the drug discovery process thus reducing the costs, but also of changing the way drugs are designed.

Molecular docking plays an important role in the rational drug design. It predicts the binding orientation of small drug targets to their protein targets. Rational Drug Design (RDD) helps to facilitate and speedup the drug designing process, which involves variety of methods to identify novel compounds. One such method is the docking of the drug molecule with the receptor (target). The site of drug action, which is ultimately responsible for the pharmaceutical effect, is a receptor. The drug-likeness of plant derived compounds can be predicted by Lipinski’s rule of five which refers to the similarity of compounds to oral drugs. (5,6).

MATERIALS AND METHODS:

Preparation of Ligand:

The 3D structures of the constituents (Nilotinib, Sorafenib and Hinokitiol) are obtained from PubChem compound (7,8) and saved in .mol format. The ligands are imported to the workspace and preparation of them is done. The docking scores of the active constituents are compared against the standard drugs.

Preparation of Enzymes:

The target for docking studies is selected as Proto-oncogene tyrosine-protein kinase ABL1 and Mitogen-activated protein kinase 14. Docking analysis is done by initially selecting the target for the disease and followed by obtaining the 3D structure of Proto-oncogene tyrosine-protein kinase ABL1 and Mitogen-activated protein kinase 14 (PDB ID:2HZI) and (PDB ID: 3LFF) respectively from protein data bank in. pdb format (9-12). It is well known that PDB files often have poor or missing assignments of explicit hydrogens, and the PDB file format cannot accommodate bond order information. Therefore, proper bonds, bond orders, hybridization and charges were assigned using the MVD. The potential binding sites of both the targets were calculated using the built-in cavity detection algorithm implemented in MVD. (13-14)

Figure 1: Docked view of Nilotinib against 2HZI captured using Ligand Energy Inspector tool in MVD

Figure 2: Docked view of Hinokitol against 2HZI captured using Ligand Energy Inspector tool in MVD

Figure 3: Docked view of Sorafenib against 2HZI captured using Ligand Energy Inspector tool in MVD

Figure 6: Docked view of Sorafenib against 3LFF captured using Ligand Energy Inspector tool in MVD

Table 1.Energy overview of Nilotinib

Descriptors	Value	MolDock Score	Rerank Weight	Rerank Score
Total Energy		-83.813		-55.980
External Ligand interactions		-99.148		-74.310
Protein - Ligand interactions		-88.319		-63.612
Steric (by PLP)	-87.948	-87.948	0.686	-60.332
Steric (by LJ12-6)	-5.601		0.533	-2.986
Hydrogen bonds	-0.371	-0.371	0.792	-0.294
Hydrogen bonds (no directionality)	-1.253			0.000
Electrostatic (short range)	0.000	0.000	0.892	0.000
Electrostatic (long range)	0.000	0.000	0.156	0.000
Cofactor – Ligand		0.000	0.602	0.000
Steric (by PLP)	0.000	0.000
Steric (by LJ12-6)	0.000			0.000
Hydrogen bonds	0.000	0.000		0.000
Electrostatic	0.000	0.000		0.000
Water - Ligand interactions	-10.828	-10.828	0.988	-10.698
Internal Ligand interactions		15.335		18.330
Torsional strain	2.994	2.994	0.938	2.809
Torsional strain (sp2-sp2)	0.000		0.636	0.000
Hydrogen bonds	0.000			0.000
Steric (by PLP)	12.340	12.340	0.172	2.123
Steric (by LJ12-6)	96.394		0.139	13.399
Electrostatic	0.000	0.000	0.437	0.000
Soft Constraint Penalty	0.000	0.000
Search Space Penalty	0.000	0.000

Table 2.Energy overview of Sorafenib

Descriptors	Value	MolDock Score	Rerank Weight	Rerank Score
Total Energy		-97.446		-86.584
External Ligand interactions		-113.682		-99.179
Protein - Ligand interactions		-92.922		-78.668
Steric (by PLP)	-86.911	-86.911	0.686	-59.621
Steric (by LJ12-6)	-26.804		0.533	-14.286
Hydrogen bonds	-6.010	-6.010	0.792	-4.760
Hydrogen bonds (no directionality)	-7.087			0.000
Electrostatic (short range)	0.000	0.000	0.892	0.000
Electrostatic (long range)	0.000	0.000	0.156	0.000
Cofactor – Ligand		0.000	0.602	0.000
Steric (by PLP)	0.000	0.000
Steric (by LJ12-6)	0.000			0.000
Hydrogen bonds	0.000	0.000		0.000
Electrostatic	0.000	0.000		0.000
Water - Ligand interactions	-20.760	-20.760	0.988	-20.511
Internal Ligand interactions		16.235		12.594
Torsional strain	1.665	1.665	0.938	1.562
Torsional strain (sp2-sp2)	0.000		0.636	0.000
Hydrogen bonds	0.000			0.000
Steric (by PLP)	14.570	14.570	0.172	2.506
Steric (by LJ12-6)	61.342		0.139	8.527
Electrostatic	0.000	0.000	0.437	0.000
Soft Constraint Penalty	0.000	0.000
Search Space Penalty	0.000	0.000

Table 3. Energy overview of Hinokitiol

Descriptors	Value	MolDock Score	Rerank Weight	Rerank Score
Total Energy		-86.527		-78.055
External Ligand interactions		-93.113		-82.072
Protein - Ligand interactions		-63.543		-52.857
Steric (by PLP)	-58.620	-58.620	0.686	-40.213
Steric (by LJ12-6)	-16.406		0.533	-8.744
Hydrogen bonds	-4.923	-4.923	0.792	-3.899
Hydrogen bonds (no directionality)	-4.923			0.000
Electrostatic (short range)	0.000	0.000	0.892	0.000
Electrostatic (long range)	0.000	0.000	0.156	0.000
Cofactor – Ligand		0.000	0.602	0.000
Steric (by PLP)	0.000	0.000
Steric (by LJ12-6)	0.000			0.000
Hydrogen bonds	0.000	0.000		0.000
Electrostatic	0.000	0.000		0.000
Water - Ligand interactions	-29.570	-29.570	0.988	-29.215
Internal Ligand interactions		6.586		4.017
Torsional strain	0.909	0.909	0.938	0.853
Torsional strain (sp2-sp2)	0.000		0.636	0.000
Hydrogen bonds	0.000			0.000
Steric (by PLP)	5.677	5.677	0.172	0.976
Steric (by LJ12-6)	15.740		0.139	2.188
Electrostatic	0.000	0.000	0.437	0.000
Soft Constraint Penalty	0.000	0.000
Search Space Penalty	0.000	0.000

Table 4: In-silico docking analysis of Nilotinib, Sorafenib and Hinokitiol on Proto-oncogene tyrosine-protein kinase ABL1 (PDB ID: 2HZI) ranking based on MolDock Score

Name	Ligand	MolDock Score	Rerank Score	HBond
[00]Nilotinib	Nilotinib	-108.452	-80.7526	-0.371473
[01]Nilotinib	Nilotinib	-105.619	-85.4432	-2.5
[00]Sorafenib	Sorafenib	-97.4472	-86.8339	-6.01035
[01]Sorafenib	Sorafenib	-93.4136	-83.4473	-3.41271
[00]Hinokitiol	Hinokitiol	-84.103	-76.4898	-2.5
[01]Hinokitiol	Hinokitiol	-77.1306	-71.0033	0

Table 5: In-silico docking analysis of Nilotinib, Sorafenib and Hinokitiol on Mitogen-activated protein kinase 14 (PDB ID: 3LFF) ranking based on MolDock Score

Name	Ligand	MolDock Score	Rerank Score	HBond
[00]Sorafenib	Sorafenib	-123.578	-104.715	-0.824235
[00]Nilotinib	Nilotinib	-111.088	-82.5973	-8.56917
[01]Sorafenib	Sorafenib	-108.344	-79.6338	0
[01]Nilotinib	Nilotinib	-105.295	-81.79	-3.59979
[00]Hinokitiol	Hinokitiol	-86.4157	-77.0637	0
[01]Hinokitiol	Hinokitiol	-72.9071	-64.7239	0

Table 6.Based upon lipin rule

s.no	Compound	Hydrogen bond donor	Hydrogen bond acceptor	Molecular mass
1.	Hinokitiol	1	2	164.201
2.	Sorafenib	3	4	464.825
3.	Nilotinib	2	6	529.516

RESULTS AND DISCUSSION

Hepatocellular carcinoma treatment with new improved drugs is a high priority to addressing the global problem of resistance to existing anticancer drugs. The current study highlights the importance of analogue-based designing approaches in modelling anti-cancer compounds.(15-18)

The ability of the chemical constituents to bind with the targets is given in terms of MolDock Score, Rerank score and Hydrogen bond binding Energy. The poses are ranked according to their MolDock Score, Rerank score and Hydrogen bond binding Energy.

In-silico docking analysis of Nilotinib, Sorafenib and Hinokitiol on Proto-oncogene tyrosine-protein kinase ABL1 (PDB ID: 2HZI) (19) ranking based on MolDock Score is represented in table 4. Rerank score of Nilotinib, Sorafenib and Hinokitiol are -80.7526, -86.8339 and -76.4898, respectively and the Mol Dock score of Nilotinib, Sorafenib and Hinokitiol are -108.452, -97.4472 and -84.103, respectively against ABL1.

In-silico docking analysis of Nilotinib, Sorafenib and Hinokitiol on Mitogen-activated protein kinase 14 (PDB ID: 3LFF) (20) ranking based on MolDock Score is represented in table 5. Rerank score of Nilotinib, Sorafenib and Hinokitiol are -104.715, -82.5973 and -77.0637 respectively and the Mol Dock score of Nilotinib, Sorafenib and Hinokitiol are 123.578, -111.088, -and -86.4157, respectively against MAPK.

Based on the Mol Dock score, rerank score and lipin rule hinokitiol shows best results against the hepatocellular carcinoma and hinokitiol is the most recent potent drug target for hepatocellular carcinoma.According to literature survey till now no one didn’t reported that the hinokitiol docking with ABL1 and MAPK for liver cancer.This study may be the subject for further experimental validation and clinical trial to establish the hinokitiol derivatives or analogues as more potent drug for the treatment of liver cancer.

CONCLUSION:

The Protein-Ligand interaction plays a significant role in structural based drug designing. Analysis of these docking brought in focus on some important interactions operating at the molecular level. The seven-membered ring plays a vital role in holding the molecule at place (binding) of the active site. These studies are expected to provide useful insights into the roles of various substitution patterns on the hinikitiol derivative and also help to design more potent compounds. In the present work we have docked hinokitiol with ABL 1 and MAPK and compared with the standard drugs that were used against liver Cancer. From this we can conclude that some of the modified natural drugs are better than the commercial drugs available in the market. In future research work the ADME/T (Absorption, Distribution, Metabolism, Excretion / Toxicity) properties of hinokitiol derivatives can be calculated using the commercial ADME/T tools available thus reducing the time and cost in drug discovery process and go for further experimental validation and clinical trial to establish the hinokitiol derivatives or analogues as more potent drug for the treatment of liver cancer.

ACKNOWLEDGEMENT:

The authors are thankful to Vels University (VISTAS) and its management for providing research facilities, financial support and encouragement.

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Received on 18.08.2016 Modified on 26.11.2016

Research J. Pharm. and Tech. 2017; 10(1): 257-262.

DOI: 10.5958/0974-360X.2017.00053.1