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SN Applied Sciences

, 1:1533 | Cite as

Molecular docking analysis of triptoquinones from genus Tripterygium with iNOS and in silico ADMET prediction

  • Yulong Tao
  • Shengyan Yang
  • Honglei Xu
  • Xia TaoEmail author
Research Article
  • 91 Downloads
Part of the following topical collections:
  1. 6. Interdisciplinary (general)

Abstract

This paper presents an investigation on the binding interaction of triptoquinones identified from genus Tripterygium to iNOS. In silico methods are adopted to predict ADME parameters, pharmacokinetic properties, drug-likeliness and acute toxicity of these identified compounds. A total of 20 triptoquinones are currently identified from genus Tripterygium. Most of these triptoquinones are found to bind to the key human iNOS residues involved in inhibitor binding. All the compounds are considered having drug-likeliness properties with no violation against Lipinski's "rule of 5" and are under safe category when administered orally. Twelve out of the 20 triptoquinones are predicted as passively crossing the blood-brain barrier. Eight of the given compounds are predicted to be pumped out by the p-glycoprotein. CYP2C19 and CYP2C9 are the significant isoforms influenced by the investigated triptoquinones from genus Tripterygium. As a result, triptoquinone ingredients from genus Tripterygium may be promising candidates for the development of drugs preventing inflammatory diseases.

Keywords

Triptoquinone Tripterygium iNOS Molecular docking ADMET 

1 Introduction

Herbs of the genus Tripterygium have long been used in traditional Chinese medicine (TCM) for the treatment of autoimmune and inflammatory diseases like rheumatoid arthritis (RA) [1, 2, 3, 4]. The genus Tripterygium consists of three species, namely Tripterygium hypoglaucum (Levl.) Hutch (Kun Ming Shan Hai Tang in Chinese), Tripterygium regelii Sprague et Takeda (Dong Bei Lei Gong Teng in Chinese), and Tripterygium wilfordii Hook. f. (Lei Gong Teng in Chinese, also Thunder God Vine) [5].

Inducible nitric oxide synthase (iNOS) is one of the major mediators during inflammatory processes [6]. Nitric oxide (NO) is formed via iNOS activity mediates inflammation and has been implicated in many diseases, including rheumatoid arthritis (RA), inflammatory bowel disease (IBD), diabetes mellitus (DM), stroke, cancer, and Alzheimer’s disease (AD) [7, 8]. Besides, iNOS has been found to be associated with activation of another major inflammatory mediator cyclooxygenase-2 (COX-2) [6]. Therefore, the development of iNOS inhibitors is highly desirable. Research efforts have focused on natural products for the discovery of iNOS inhibitors [9]. Niwa et al. [10] and Moritoki et al. [11] reported that the triptoquinone A, an active constituent in Tripterygium wilfordii, could prevent iNOS induction by LPS or IL-1β. Chen et al. [12] recently found that some of the triptoquinone constituents from Tripterygium hypoglaucum exhibited inhibitory activity of lipopolysaccharide (LPS)-induced NO production in macrophages. These triptoquinone compounds identified from genus Tripterygium are characterized by a p-benzoquinone C-ring. However, it remains unclear whether triptoquinone constituents from genus Tripterygium can act directly on iNOS protein.

Given the importance of iNOS in inflammatory responses and the potential role of naturally occurring triptoquinone compounds from genus Tripterygium against iNOS, we set up to investigate the interaction of triptoquinones from plants in the genus of Tripterygium with iNOS protein using molecular docking method. Physicochemical descriptors, ADME parameters, pharmacokinetic properties, and drug-like nature of molecules were computed and predicted in silico through the free web tool in SwissADME. In silico acute rat toxicity for chemical compounds was predicted by a freely accessible web tool GUSAR software based on reliable quantitative-structure activity relationships (QSAR) modeling [13].

2 Materials and methods

2.1 Discovery of triptoquinone ingredients from Tripterygium

Triptoquinones from plants in the genus Tripterygium were discovered through literature retrieval and traditional Chinese medicine systems pharmacology database (TCMSP) search. Chemical structures were either obtained from the PubChem Compound Database or drawn using the software ChemBioDraw Ultra 14.0 and saved as SDF files.

2.2 Molecular docking studies

Molecular docking analysis was performed using Schrödinger Software (Maestro, version 10.2). Briefly, the 3D coordinate of the crystal structure of human iNOSox (PDB ID: 3E7G) [14] was downloaded from the RCSB Protein Data Bank (PDB) (https://www.rcsb.org/) in PDB format. The protein was prepared using the Protein Preparation Wizard panel. Water molecules were removed from the protein structure. The Receptor Grid Generation panel was then used to set up the grid generation job, which helps to show the active site of the receptor for Glide ligand docking jobs. The LigPrep panel was used for ligand preparation. Docking jobs were performed using the Glide Ligand Docking panel. Glide gscores were recorded. Ligand interactions with the protein domain were analyzed using PyMOL Molecular Graphics System Version 2.0 Schrödinger, LLC.

2.3 In silico ADME profile prediction

The ADME parameters (for absorption, distribution, metabolism, and excretion) of the triptoquinones from plants in the genus Tripterygium were predicted using a web tool SwissADME (http://www.swissadme.ch/). A rapid appraisal of drug likeness of each compound was conducted using Bioavailability Radar method [15]. The BOILED-Egg method was used to predict simultaneously two key ADME parameters, i.e., the passive gastrointestinal absorption (HIA) and brain access (BBB) [15]. For assessment of absorption for oral drug likeness, the number of free rotatable bonds and the so-called Lipinski’s “rule of 5” for the compounds were analyzed. The “rule of 5” states that drug-like compounds with good absorption or permeation are more likely to present molecular weight ≤ 500, number of H-bond acceptors ≤ 10, number of H-bond donors ≤ 5, and CLog P ≤ 5 [16]. The SwissADME web tool also predicts pharmacokinetic properties of a given compound, including P-glycoprotein substrate and inhibition of cytochrome P450 isoenzymes (CYP) 1A2, 2C19, 2C9, 2D6, and 3A4.

2.4 Acute rat toxicity prediction

In silico prediction of LD50 values for rats with four types of administration (intraperitoneal, intravenous, oral, and subcutaneous) was performed using GUSAR ONLINE software (http://www.way2drug.com/gusar/acutoxpredict.html). GUSAR software was developed to create QSAR models on the basis of the appropriate training sets. The QSAR models for rat LD50 values predictions include information about ~ 10,000 chemical structures with data on acute rat’s toxicity originated from SYMYX MDL Toxicity Database. The data of LD50 values were shown as mg/kg and acute rodent toxicity classification was also presented. The acute toxicity classification of the investigated triptoquinones from genus Tripterygium was reported in accordance with the Guidelines of Organisation for Economic Cooperation and Development (OECD) for the testing of chemicals.

3 Results and discussion

3.1 Triptoquinone constituents from genus Tripterygium

A total of 20 triptoquinones were currently identified from plants in the genus of Tripterygium, including 19 diterpene quinoides (Table 1). All these compounds were shown to possess a p-benzoquinone ring. Compounds triptoquinone A-G were first isolated from the stems of Tripterygium wilfordii var. regelii by Shishido et al. [17]. Six years after Shishido’s discovery, Fujita et al. [18] found triptoquinone H in the root bark of Tripterygium hypoglaucum. Triptoquinonide, also named quinone 21, was first reported as a natural product from the heartwood of the root of Tripterygium wilfordii by Morota et al. [19]. Thermophillin, also named 2, 5-dimethoxybenzoquinone, was collected in the online traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP, http://lsp.nwu.edu.cn/molecule.php?qn=3171). Compounds triregelin A–E were recently isolated and identified from the stems of Tripterygium regelii [20] and hypoglicin H–L were from the stems of Tripterygium hypoglaucum [12].
Table 1

Chemical structures of triptoquinones in plants of the genus Tripterygium

3.2 Molecular docking

It has been well documented that the key human iNOS residues involved in inhibitor binding include active site residue Glu377, first-shell residues (Gln263, Tyr347, Arg266, and Arg388), second-shell residue Asn283, and third-shell residues (Phe286 and Val305) [14]. To investigate the binding modes of triptoquinones from genus Tripterygium with iNOS enzyme, docking of these compounds utilizing a Schrödinger software Maestro 10.2 was performed. The docking scores ranged from 0.833 to − 3.779. Among 20 triptoquinones from plants in the genus of Tripterygium, only compounds thermophillin and hypoglicin L showed no interaction with key residues involved in iNOS inhibitor binding (Table 2). Triptoquinone D, F–G, triregelin A–B, D–E, and hypoglicin I showed hydrogen bonds to Glu377, the active site residue of human iNOS (Table 2, Fig. 1). The other 10 triptoquinones showed hydrogen bonding interactions restricted to the first-shell Gln263, Tyr347, Arg266, and Arg388 residues interacting directly with the iNOS inhibitor (Table 2, Fig. 1). These findings suggested that many of these naturally occurring triptoquinones from genus Tripterygium could bind to human iNOS and have a potential inhibitor effect against this enzyme.
Table 2

Glide score and binding interaction of triptoquinones in plants of the genus Tripterygium with human iNOS (PDB ID: 3E7G)

Compounds

Glide gscore

Interactions

Triptoquinone A

− 2.871

GLN263, GLN387, ASP382, ARG381

Triptoquinone B

− 3.194

GLN263, TYR373, ARG388

Triptoquinone C

− 3.713

ARG388, TYR373

Triptoquinone D

− 2.715

GLU377

Triptoquinone E

− 2.933

TYR347

Triptoquinone F

− 3.102

GLU377, GLN263, GLN387, ARG388

Triptoquinone G

− 3.077

GLU377, GLN263, GLN387, ARG388

Triptoquinone H

− 2.932

GLN263, TYR347

Triptoquinonide

− 2.837

GLN263, ASN354

Thermophillin

− 2.337

TRP372

Triregelin A

− 3.605

GLU377, GLN263, TYR373

Triregelin B

− 3.298

GLU377, GLN387, ASP382, ARG381

Triregelin C

− 3.455

GLN263, TYR491, ASN354

Triregelin D

− 3.536

GLU377, GLN263, TRY373, HEM901

Triregelin E

− 2.124

GLU377, ARG266, ARG388

Hypoglicin H

− 3.479

GLN263

Hypoglicin I

− 1.946

GLU377, ASP382, ARG388

Hypoglicin J

0.833

ASP382, ARG388

Hypoglicin K

− 3.779

GLN263, GLN387, TYR373, ARG381

Hypoglicin L

− 2.940

GLN387, ARG381

Fig. 1

3D diagrams showing the interactions between iNOS protein residues and the investigated triptoquinones in genus Tripterygium. The hydrogen bonds were shown as yellow dotted lines

3.3 Computer-aided ADME prediction

In the present study, the SwissADME web tool, developed to support drug discovery [15], has been used for the in silico prediction of ADME parameters of triptoquinones from plants in the genus Tripterygium.

Bioavailability and pharmacokinetics are two important factors involved in drug development. For oral bioavailability, six important properties (i.e., lipophilicity, size, polarity, solubility, flexibility, and saturation) should be taken into account [15]. Our bioavailability radar plot showed that all the triptoquinones were in the optimal range for each physicochemical property (lipophilicity: − 0.7 < XLOGP3 < 5.0, size: 150 g/mol < MV < 500 g/mol, polarity: 20 Å2 < TPSA < 130 Å2, solubility: 0 < Log S (ESOL) < 6, saturation: 0.25 < Fraction Csp3 < 1, and flexibility: 0 < Num. of rotatable bonds < 9), suggesting that these triptoquinone constituents from genus Tripterygium were orally bioavailable (Fig. 2).
Fig. 2

Bioavailability radar plots for rapid appraisal of the drug likeness of triptoquinones from plants in the genus of Tripterygium

In terms of pharmacokinetic behaviors, gastrointestinal absorption and brain access are crucial to make an estimation. The Brain Or IntestinaL EstimateD permeation method (BOILED-Egg) has been proposed as an accurate predictive model to predict gastrointestinal absorption and brain penetration of small molecules [21]. According to the readout of the BOILED-Egg model (Fig. 3), the investigated naturally occurring triptoquinones from genus Tripterygium were all located in the physicochemical space for highly probable HIA absorption, demonstrating that they were very likely to be passively absorbed by the gastrointestinal tract. More than half of these investigated triptoquinones were predicted to passively permeate through the blood–brain barrier (Fig. 3). The BOLIED-Egg also showed that compounds triptoquinone B–C and hypoglicin H were likely to be effluated from the central nervous system by the P-gp (Fig. 3).
Fig. 3

WLOGP versus tPSA plots for intuitive evaluation of passive gastrointestinal absorption and brain penetration using the Brain Or Intestinal EstimateD permeation method (BOILED-Egg)

Drug likeness assesses the probability of a molecule to become an oral drug. The SwissADME gives access to five different rule-based filters, including Lipinski, Ghose, Veber, Egan, and Muegge filter, with diverse ranges of properties inside of which the molecule is defined as drug like [15]. The Lipinski filter is the pioneer “rule of 5” widely implemented to estimate solubility and permeability in drug discovery and development [16]. Therefore, the Lipinski filter was selected in this study. As shown in Table 3, all the triptoquinones from plants in the genus of Tripterygium showed acceptable number of rotatable bonds (≤ 10) with no violation of the criteria stated by the Lipinski’s rule, suggesting that the identified triptoquinones from genus Tripterygium were drug-like molecules and thus possessed the potential to be considered oral drug candidates.
Table 3

ADME profile prediction of triptoquinones from genus Tripterygium

 

Triptoquinone

Triptoquinonide

Thermophillin

Triregelin

 

A

B

C

D

E

F

G

H

A

B

C

D

E

Lipinski chemical properties

Molecular weight (g/mol) (≤ 500)

328.40

330.42

344.44

316.43

314.42

330.42

346.42

314.42

314.42

168.15

342.39

344.40

346.42

360.44

372.45

Num. rotatable bonds (≤ 10)

2

2

2

2

2

2

2

1

1

2

1

3

2

3

4

Num. H-bond acceptors (≤ 10)

4

4

4

3

3

4

5

3

3

4

5

5

5

5

5

Num. H-bond donors (≤ 5)

1

1

2

1

0

1

2

0

0

0

1

2

2

1

0

Consensus log Po/w (≤ 5)

3.15

2.81

3.10

3.53

3.54

3.31

2.44

3.49

3.49

0.24

2.31

2.35

1.91

2.34

3.14

Absorption

GI absorption

High

High

High

High

High

High

High

High

High

High

High

High

High

High

High

BBB permeant

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

No

No

No

No

No

Yes

P-gp substrate

No

Yes

Yes

No

No

No

Yes

No

No

No

No

Yes

Yes

Yes

No

Log Kp (skin permeation) (cm/s)

− 6.17

− 6.41

− 6.36

− 5.53

− 5.62

− 5.67

− 6.72

− 5.83

− 5.83

− 7.37

− 7.39

− 7.13

− 7.43

− 7.13

− 6.65

Metabolism

CYP1A2 inhibitor

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

CYP2C19 inhibitor

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

No

No

No

No

No

Yes

CYP2C9 inhibitor

Yes

No

Yes

Yes

Yes

Yes

No

Yes

Yes

No

No

No

No

No

No

CYP2D6 inhibitor

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

CYP3A4 inhibitor

No

No

Yes

No

No

No

No

No

No

No

No

No

No

No

No

 

Hypoglicin

H

I

J

K

L

Lipinski chemical properties

Molecular weight (g/mol) (≤ 500)

314.42

314.42

314.42

358.39

358.39

Num. rotatable bonds (≤ 10)

2

2

1

1

1

Num. H-bond acceptors (≤ 10)

3

3

3

6

6

Num. H-bond donors (≤ 5)

1

1

1

1

1

Consensus log Po/w (≤ 5)

3.22

3.24

3.28

1.36

1.46

Absorption

GI absorption

High

High

High

High

High

BBB permeant

Yes

Yes

Yes

No

No

P-gp substrate

Yes

No

No

Yes

No

Log Kp (skin permeation) (cm/s)

− 6.49

− 6.14

− 6.29

− 8.32

− 8.32

Metabolism

CYP1A2 inhibitor

No

No

No

No

No

CYP2C19 inhibitor

Yes

Yes

Yes

No

No

CYP2C9 inhibitor

Yes

Yes

Yes

No

No

CYP2D6 inhibitor

No

No

No

No

No

CYP3A4 inhibitor

No

No

No

No

No

Num. number, GI gastrointestinal, BBB blood–brain barrier, P-gp p-glycoprotein

Cytochrome P450 (CYP) enzymes are a superfamily of monooxygenases, important proteins relevant to pharmacokinetics. In this study, five major CYP isoforms (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) were considered during in silico prediction. As shown in Table 3, all the compounds were non-inhibitors of CYP1A2 and CYP2D6. It was predicted that more than half of the investigated triptoquinones were CYP2C19 and CYP2C9 inhibitors, both of which are key mediators in the metabolizing of commonly prescribed drugs. For example, the CYP2C9 enzyme plays a major role in breaking down the anticlotting drug warfarin and assists in metabolizing the anti-inflammation drug ibuprofen [22]. Therefore, triptoquinone constituents of genus Tripterygium may help to strengthen the effect of warfarin and ibuprofen. It has been proved that more than 10 percent of commonly prescribed drugs, including the widely used antiplatelet drug clopidogrel, were processed or metabolized through CYP2C19 enzyme. This enzyme converts clopidogrel, a prodrug, to its active form, which is necessary for the drug to function in the body [23]. Thus, inhibition of CYP2C19 may reduce antiplatelet effect for clopidogrel. Triptoquinone C was the only compound among the 20 triptoquinones from genus Tripterygium predicted to inhibit CYP3A4. The enzyme CYP3A4, mainly located in the liver and small intestine, is responsible for the metabolism of more than 50% of medicines [24]. Thus, attention should be focused on interaction of triptoquinone C constituent in plants of genus Tripterygium with medicines metabolized by CYP3A4.

3.4 In silico prediction of acute toxicity

Toxicity estimation is important for drug development [13]. The free online GUSAR software has developed QSAR modeling of rat acute toxicity of compounds under investigation [13]. The values obtained for different routes of the administration of triptoquinones from plants in the genus Tripterygium were shown in Table 4. The LD50 values by intravenous and oral administration were found to be between 8.29–63.50 mg/kg and 397.50–2850.00 mg/kg, respectively. As can be seen in Table 4, most of the investigated triptoquinones from genus Tripterygium exhibited moderate toxicity (class 3) when considered intravenous route for administration. However, they turned out to be slightly toxic (class 4) or even nontoxic (class 5) when considered oral route.
Table 4

Acute rat toxicity of triptoquinones from Tripterygium by GUSAR software and toxicity classification by OECD project

Compounds

LD50 (mg/kg)

i.p.

i.v.

oral

s.c.

Triptoquinone A

402.50 (Class 4) in AD

48.67 (Class 4) in AD

2257.00 (Class 5) in AD

914.20 (Class 4) in AD

Triptoquinone B

745.40 (Class 5) in AD

24.65 (Class 3) in AD

2826.00 (Class 5) in AD

862.60 (Class 4) out of AD

Triptoquinone C

783.40 (Class 5) in AD

14.65 (Class 3) in AD

2850.00 (Class 5) in AD

103.90 (Class 3) in AD

Triptoquinone D

666.00 (Class 5) in AD

23.14 (Class 3) in AD

2843.00 (Class 5) in AD

414.10 (Class 4) in AD

Triptoquinone E

925.20 (Class 5) in AD

17.23 (Class 3) in AD

2607.00 (Class 5) in AD

1140.00 (Class 5) in AD

Triptoquinone F

933.30 (Class 5) in AD

33.96 (Class 3) in AD

2618.00 (Class 5) out of AD

1163.00 (Class 5) in AD

Triptoquinone G

1105.00 (Class 5) in AD

24.00 (Class 3) in AD

1743.00 (Class 4) in AD

397.70 (Class 4) out of AD

Triptoquinone H

781.30 (Class 5) in AD

25.40 (Class 3) in AD

1547.00 (Class 4) in AD

1982.00 (Class 4) in AD

Triptoquinonide

605.20 (Class 5) in AD

11.18 (Class 3) in AD

1204.00 (Class 5) in AD

834.40 (Class 4) in AD

Thermophillin

456.40 (Class 4) in AD

63.50 (Class 4) in AD

2604.00 (Class 5) in AD

1572.00 (Class 5) in AD

Triregelin A

353.90 (Class 4) in AD

27.20 (Class 3) in AD

625.90 (Class 4) in AD

379.50 (Class 4) in AD

Triregelin B

679.70 (Class 5) in AD

106.70 (Class 4) in AD

2103.00 (Class 5) in AD

797.50 (Class 4) in AD

Triregelin C

878.30 (Class 5) in AD

9.90 (Class 3) in AD

550.70 (Class 4) in AD

101.30 (Class 3) in AD

Triregelin D

413.90 (Class 4) in AD

12.09 (Class 3) in AD

987.30 (Class 4) in AD

87.91 (Class 3) in AD

Triregelin E

764.90 (Class 5) in AD

8.82 (Class 3) in AD

2011.00 (Class 5) in AD

1114.00 (Class 5) in AD

Hypoglicin H

732.30 (Class 5) in AD

28.40 (Class 3) in AD

2832.00 (Class 5) in AD

785.90 (Class 4) out of AD

Hypoglicin I

667.90 (Class 5) in AD

11.42 (Class 3) in AD

1574.00 (Class 4) in AD

227.90 (Class 4) in AD

Hypoglicin J

1170.00 (Class 5) in AD

14.23 (Class 3) in AD

817.90 (Class 4) in AD

1443.00 (Class 5) in AD

Hypoglicin K

298.60 (Class 4) in AD

8.29 (Class 3) in AD

397.50 (Class 4) in AD

63.54 (Class 3) in AD

Hypoglicin L

298.60 (Class 4) in AD

8.29 (Class 3) in AD

397.50 (Class 4) in AD

63.54 (Class 3) in AD

i.p. intraperitoneal, i.v. intravenous, s.c. subcutaneous, AD applicability domain; in AD, compound falls in applicability domain of models; out of AD, compound is out of applicability domain of models

4 Conclusion

In this study, we performed molecular docking, pharmacokinetic, and toxicity prediction of triptoquinones from plants in the genus of Tripterygium. The outcomes of this study concerning the interactions of these compounds with the iNOS were important for the discovery and development of novel drugs specific to iNOS enzyme. All the given triptoquinones were estimated as drug like for oral bioavailable, and most of them could cross the BBB. CYP2C19 and CYP2C9 were the main affected human cytochromes by the investigated triptoquinones, and consequently, their use may influence other medication. The investigated triptoquinones were of low toxicity with oral route of administration. These studies thus provide in silico evidence for understanding of triptoquinone constituents from genus Tripterygium as medicinal chemicals. Further, detailed experimental testing is indicated to confirm the role, safety, and efficacy of these naturally occurring triptoquinones found in plant species from the Tripterygium genus.

Notes

Acknowledgements

The authors would like to acknowledge Zhi Meng, Ph.D. in Medicinal Chemistry from Fudan University, for his instruction of molecular docking studies. This work was supported by the National Key Research and Development Plan (No. 2018YFC1707304).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

No animals were directly involved in the present study.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Pharmacy, Changzheng HospitalSecond Military Medical UniversityShanghaiChina
  2. 2.Department of PharmacyThe 983 Hospital of Joint Logistics Support Force of the Chinese People’s Liberation ArmyTianjinChina

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