Open Access

3-D structural interactions and quantitative structural toxicity studies of tyrosine derivatives intended for safe potent inflammation treatment

  • Ayarivan Puratchikody1Email author,
  • Dharmaraj Sriram2,
  • Appavoo Umamaheswari1 and
  • Navabshan Irfan1
Chemistry Central Journal201610:24

https://doi.org/10.1186/s13065-016-0169-9

Received: 30 November 2015

Accepted: 11 April 2016

Published: 30 April 2016

Abstract

Background

Drugs that inhibit cyclooxygenase-2 (COX-2) while sparing cyclooxygenase-1 (COX-1) represent a new attractive therapeutic development and offer new perspective for further use of COX-2 inhibitors. Intention of this work is to develop safer, selective COX-2 inhibitors that do not produce harmful effects.

Results

A series of 55 tyrosine derivatives were designed for evaluation as selective COX-2 inhibitors and investigated by in silico for their anti-inflammatory activities using C-Docker. The results of docking study showed that 35 molecules were found to selectively inhibit the enzyme COX-2. These molecules formed stable π hydrophobic and additional van der Waals interactions in the active site side pocket of COX-2. The molecules selected from docking studies were examined through ADMET descriptors and Osiris property explorer to find its safety profile as well. The tyrosine derivatives containing toxic fragments were eliminated.

Conclusion

The results conclude that out of 55, 19 molecules possessed best binding energy (< −3.333 kcal/mol) and these molecules had more selective and safer COX-2 inhibitor profile compared to the standard celecoxib.

Keywords

Anti-inflammatory Tyrosine derivatives Docking ADMET descriptors Osiris

Background

Cyclooxygenase-1 (COX-1) and Cyclooxygenase-2 (COX-2) are two discrete isoforms of cyclooxygenase enzyme. These enzymes play a catalytic role in transfiguration of arachidonic acid to prostaglandins in the cyclic pathway of arachidonic acid [1, 2]. Prostaglandins (PGs) are involved in various pathophysiological conditions such as inflammation, carcinogenesis, cardiovascular activity etc. Generally, COX-2 is not detectable in most normal tissues, but it is induced by pro-inflammatory cytokines, growth factors and carcinogens. This fact indicates the role of COX-2 in inflammation [3]. Rheumatoid synovium expression of COX-2 is up regulated in inflammatory tissues resulting in the production of prostaglandin precursors which ultimately gets converted into PGs [4].

Some of the coxib derivatives, Rofecoxib, Celecoxib, Etoricoxib and Valdecoxib are selective COX-2 inhibitors that act by blocking COX-2 enzyme responsible for inflammation and pain [5]. Most of these coxib derivatives have been voluntarily withdrawn from the worldwide market due to safety concerns of an increased risk of cardiovascular events in patients. Due to greater therapeutic effect, Celecoxib is remaining in the market, even though it have a risk of serious and potentially fatal adverse cardiovascular thrombotic events, myocardial infarction and stroke [6].

Importantly, design of agents with higher anti-inflammatory potential and less side effects is one of the most challenging areas in the inflammation. On review of literature, researchers have proved anti-inflammatory effects for dibromotyrosine derivatives [7]. In this concern, we searched for tyrosine scaffold from the natural sources since the biologically active natural compounds are composed of very complex structures. This complexity makes the compounds extremely novel. The marine sponges such as Psammaplysilla purpurea and Ianthella basta are known to produce biogenetically related bromotyrosine derived secondary metabolites [8, 9]. These observations prompted us to design and develop analogue(s) of bromotyrosine derivatives which specifically inhibits COX-2 with improved biological activity. As part of this drug development, an effort has been made to develop higher-quality drug candidates through computational techniques.

Methods

Ligand preparation

A library of novel 55 tyrosine molecules were designed based on the SAR studies of known anti-inflammatory drugs. These molecules were generated with tyrosine as a basic skeleton. The 15 (R1) and 16 (R2) position of aromatic ring hydrogen was substituted with different electronegative groups such us, –I, –Br, –Cl and –NO2. Further, one hydrogen atom of –NH2 group in 14 (R3) position was replaced by –SO2CH3 group. The eighth position (R4) of phenolic –OH group hydrogen was replaced by diverse heterocyclic fragments (Fig. 1). The structures of these molecules were drawn in Hyperchem molecular modeling and visualization tool (version 7.5) and the energies were minimized using ADS. The minimized ligands and proteins were saved in structure data (.sd) and.pdb format (Fig. 2) respectively for further studies.
Fig. 1

3D and 2D structure of energy minimized tyrosine derivatives

Fig. 2

Minimized secondary structure of a COX-2 (3NT1) b COX-1 (3KK6) c hERG protein (homology model)

Docking study

The docking study was performed using Accelyrs Discovery Studio client version 2.5 software (Accelyrs Inc., http://www.accelrys.com). The X-ray crystallographic structure of COX-2 (PDB ID 3NT1) protein bound with naproxen was acquired from the protein data bank (PDB) at a resolution of 1.73 Å (Table 1). The active site was defined with a 8.500 (Å) radius around the bound inhibitor which covered all the active site amino acids of the COX-2 protein. A grid-based molecular docking method, C-DOCKER algorithm was used to dock the small molecules into the protein active site. The designed structures were submitted to CHARMm (Chemistry at HARvard Macromolecular Mechanics) force field for structure refinement. All water molecules, bound inhibitor and other hetero atoms were removed from the macromolecule and polar hydrogen atoms were added. The designed structures were also verified for its valency, missing hydrogen and any structural disorders like connectivity and bond orders. Energy minimization was carried out for all compounds using CHARMm force field to make stable conformation of protein with an energy gradient of 0.01 kcal/mol/A°. A final minimization of the ligand in the rigid receptor using non-softened potential was performed. For each final pose, the CHARMm energy (interaction energy plus ligand strain) and the interaction energy alone were calculated. The poses were sorted by CHARMm energy and the top scoring (most negative, thus favorable to binding) poses. The energy minimized individual proteins and the designed structures along with the binding site sphere radius (Table 2; Fig. 3) and the X, Y and Z coordinates (Table 3) were submitted to the C-Docker job parameter. The docked conformation which had the lowest C-Docker energy was selected to analyze the mode of binding pattern. The C-Docker energy score, hydrogen bond and VDW interactions were visualized in C-Docker report and used for further analysis.
Table 1

Protein resolution and its stable conformational energy

PDB ID

Description

Resolution (Å)

Initial potential energy

(kcal/mol)

Final potential energy

(kcal/mol)

3NT1

High resolution structure of naproxen:COX-2 complex

1.73

−492,721

−500,025

3KK6

Crystal structure of COX-1 in complex with celecoxib

2.75

248,964,312.95

−34,200.97

HMa

hERG IFD S terfenadine model 1

−15,609

−21,445.6

a Homology modeling

Table 2

Binding sphere radius and X, Y and Z coordinate values of defined protein binding site

Protein PDB ID

Binding sphere radius (Å)

Coordinates (Å)

X

Y

Z

3NT1

8.50067

−40.406

−51.829

−22.502

3KK6

6.98804

−32.413

−51.829

−5.617

hERG_IFD_S−terfenadine_model_1

7.41161

189.526

−0.442

40.737

Fig. 3

Binding site representation of proteins a COX-2 b COX-1 c hERG_IFD_S- terfenadine_model_1

Table 3

C-Docker docking protocol parameters

Parameters

Inputs

Input receptor

../Input/3NT1.dsv

Input ligands

C:\Users\g\Desktop\all 55 new.sd

Input site sphere

−40.4058, −51.8288, −22.5019, 8.50067

Top hits

1

Random conformations

10

Random conformations dynamics steps

1000

Random conformations dynamics target temperature

1000

Include electrostatic interactions

True

Orientations to refine

10

Maximum bad orientations

800

Orientation VDW energy threshold

300

Simulated annealing

True

Heating steps

2000

Heating target temperature

700

Cooling steps

5000

Cooling target temperature

300

Force field

CHARMm

Use full potential

TRUE

Grid extension

8

Ligand partial charge method

CHARMm

Random number seed

314,159

Final minimization

Full potential

Random dynamics time step

0.002

The potential fatal adverse effects viz ulcerogenecity and cardiotoxicity were determined by C-Docker using the crystal structures of COX-1 in complex with celecoxib (3KK6:2.75 Å) and hERG_IFD_S-terfenadine_model_1 [Homology model (HM)] (Table 1) which were chosen from the PDB and Schrodinger website respectively. The binding sites of the COX-1 (3KK6) and hERG proteins were defined with the radii of 6.988 and 7.411 Ǻ respectively. The novelty of the final hits was confirmed using SciFinder [10] and PubChem [11] structure search tools.

Docking protocol validation

The validation of the docking protocol is essential to analyse the prediction ability of the proposed method [12]. In this study, validation is performed by two methods to verify whether our docking protocol is able to discriminate selective and non-selective COX-2 inhibitors. To start with, four native co-crystallised ligands of selective and non-selective COX-2 inhibitors were identified and kept as reference template. The structures of these ligands were drawn separately and its energies were minimized. RMSD values were calculated and analysed by redocking the energy minimised ligand on reference template by molecular overlay technique in ADS. In the second method, the structures of various selective and non-selective inhibitors were drawn and the potential energies of the molecules were minimized with the help of conjugated gradient algorithm. Further, these molecules were docked with the COX-2 (3NT1) protein to calculate the binding energies. The experimental IC50 activity values of these molecules were compared with its corresponding predicted C-Docker energy values and the point plot is graphed to identify the correlation between the IC50 and C-Docker energy.

Toxicity study

ADMET descriptors

Most of the failure of drug candidates during clinical trials is due to its poor pharmacokinetic and toxicity properties [13]. Hence, prediction of ADMET properties prior to expensive experimental procedures is considered to be essential for the selection of successful candidates. In this work, in silico ADMET studies were done using ADMET descriptors algorithm of ADS. This protocol uses the six pharmacokinetic parameters like Human Intestinal Absorption (HIA), Blood–Brain–Barrier (BBB) penetration, aqueous solubility, hepatotoxicity levels, cytochrome P450 2D6 inhibition and Plasma Protein Binding (PPB) to quantitatively predict the molecular properties of selected 35 ligands.

Osiris property explorer

Toxicity risks (mutagenicity, tumorigenicity, skin irritation, reproduction) and physicochemical properties (drug likeness and drug score) of the selected 35 tyrosine derivatives were calculated using OSIRIS Property Explorer (free web-based program). The drug likeness (d) was calculated with the following equation by summing up the scores of molecular fragments (Vi) and n indicates the number of molecular fragments [14].
$${\text{d}} = \frac{{\sum {\text{v}}_{\text{i}} }}{{\sqrt {\text{n}} }}.$$
(1)

The fragment list was created by shredding 3300 traded drug as well as 1500 commercially available chemicals.

The drug score (ds) combines drug-likeness, cLogP, logS, molecular weight and toxicity risks in one handy value that may be used to judge the compound’s overall potential to be qualified as a drug. This value was calculated by multiplying the contributions of individual properties with Eq. (1) [15].
$${\text{ds}} = \uppi \left( {\frac{1}{2} + \frac{1}{2}{\text{si}}} \right)\cdot\uppi {\text{ti}}$$
(2)
ds is the drug score. si are the contributions calculated directly from of cLogP, logS, molecular weight and drug-likeness ti is the contribution taken from the four toxicity risk types via the Eq. (2) which describes a spline curve.

Results and discussion

Docking

The results of C-Docker protocol run were analysed. These results have provided essential information relating to the orientation of the tyrosine derivatives in the active site of proteins (3NT1, 3KK6, hERG).

Molecular docking

In this study, 35 drug-like hit compounds were selected from the designed 55 tyrosine derivatives based on their better binding affinity (–C-Docker energy) compared to the standard celecoxib (Table 4). The active site was defined based on the bound inhibitor, naproxen, in a crystal structure of COX-2 (PDB code 3NT1). The important criteria considered in the selection of best hit compounds was binding modes, molecular interactions with the active site components and fitness scores. Evaluation of the interaction pattern of tyrosine derivatives makes clear that the molecule 8 (Fig. 4 ) have six folds higher affinity (−78.7003) in the COX-2 active site compared to standard celecoxib (17.3339). This interaction affinity is due to the 24th oxygen atom of the carboxylic group in tyrosine moiety has formed two site point interactions with the binding site residue of Arg120 and Tyr355 residue. The 25th oxygen atom of the molecule produced one ligand point interaction with Arg120 residue which allows major interaction impact of the tyrosine derivatives on catalytic domain of COX-2 protein. Besides, aromatic ring of the tyrosine skeleton make π-cationic interaction with Arg120. This created a stable conformation of the molecule 8 in the hydrophobic binding site of the COX-2 protein. This long hydrophobic channel creates cyclooxygenase active site that inhibits the inflammation via non-steroidal anti-inflammatory drugs. This active site lengthen from the membrane binding domain to the region where the catalyzed chemical reaction takes place [16, 17]. In addition, R1 and R2 bromine substitution had generated VDW interaction with Val523 and Phe518 that permitted the molecule 8 to access an additional side pocket which is a pre-requisite for COX-2 drug selectivity. This structural modification may be attributed to the interchange of valine at position of 523 in COX-2 for a relatively bulky isoleucine residue in COX-1 [5]. The substitution of 1, 3-thiazole ring at –OH (R4) position of tyrosine induced the VDW and electrostatic interactions with the active site amino acids. It created conducive chemical environment in the COX-2 binding site. Substitution of electronegative sulfonyl group at R3 position enhanced the binding potential of the molecule by interacting with Ser353 (Figs. 5, 6). It is confirmed from this study that the COX-2 selectivity of the molecule 8 is higher than the standard celecoxib. The rest of 34 molecules were examined and found to have more stability when compared to the standard.
Table 4

Interaction energy values of tyrosine derivatives and celecoxib with COX-2 protein

Name

C-Docker energya

–C-Docker interaction energya

Initial potential energya

Initial RMS gradient

Electrostatic energya

Potential energya

VDW energya

RMS gradient

Molecule_8

−78.7003

4.96727

−74.2658

16.3263

−199.774

−155.629

3.78158

0.09694

Molecule_54

−46.1094

3.80434

9.73689

40.9916

−161.106

−129.460

5.45173

0.09737

Molecule_23

−45.4158

1.08668

−2.98987

44.0659

−177.976

−139.880

5.17214

0.09761

Molecule_6

−40.1233

1.50834

339.920

91.3010

−131.124

−106.360

1.62197

0.09667

Molecule_14

−38.0308

9.72515

−93.3437

6.78104

−128.448

−98.3557

2.48238

0.08123

Molecule_50

−32.9449

−3.8949

25.7593

47.7124

−133.414

−112.472

−0.05903

0.08110

Molecule_25

−29.4798

14.5849

14.0717

43.5888

−142.506

−118.107

1.45489

0.07719

Molecule_51

−28.5191

0.90861

32.0508

46.2969

−130.255

−100.616

2.08610

0.08137

Molecule_24

−28.4505

16.3299

534.240

568.860

−140.619

−104.274

3.04089

0.09149

Molecule_11

−26.1386

6.71301

61.7373

44.7827

−151.439

−136.499

−1.89433

0.09066

Molecule_10

−23.4787

21.0787

71.8921

63.5300

−157.857

−126.759

6.24669

0.09716

Molecule_20

−21.3714

17.7833

−17.3987

40.1040

−120.253

−99.3152

4.00568

0.09156

Molecule_21

−20.4346

21.4014

−55.1410

4.32287

−79.2812

−58.7042

1.69521

0.08615

Molecule_57

−15.0159

13.1286

28.7444

53.0095

−162.501

−128.173

8.40306

0.09806

Molecule_58

−12.0458

3.82902

−56.6613

20.5821

−152.031

−129.655

3.88633

0.09311

Molecule_7

−5.28412

22.9306

55,568.4

75,666.6

−150.99

−121.105

1.02490

0.09610

Molecule_67

−3.39829

11.7573

26.7832

42.2915

−123.539

−103.737

2.94580

0.09285

Molecule_59

−1.19358

14.2210

−89.0706

14.9038

−132.811

−104.716

3.23357

0.08664

Molecule_13

0.274143

19.9477

73.5331

48.1590

−130.355

−111.012

0.14158

0.09153

Molecule_17

0.957257

13.0669

−13.8560

6.29743

−42.2671

−25.9434

−1.71694

0.08943

Molecule_15

0.961175

18.3100

−37.7612

6.14656

−75.5862

−50.066

3.31430

0.08757

Molecule_102

4.763580

38.6705

−1.47768

6.52181

−20.0264

−13.5243

−6.95490

0.08871

Molecule_52

7.997040

16.8484

487.293

105.229

−155.493

−119.589

−0.16776

0.09978

Molecule_26

8.272020

21.8179

42.0749

38.5993

−138.631

−94.2976

2.46568

0.08678

Molecule_146

8.494660

38.3012

13.8468

6.52572

−12.4255

−7.32723

−7.01223

0.09203

Molecule_103

9.218700

41.1444

−0.67757

6.08668

−21.515

−13.0472

−4.12813

0.09382

Molecule_12

9.37307

23.8593

599.169

124.448

−133.207

−95.3196

−0.06212

0.09771

Molecule_99

10.0093

38.4689

10.9431

4.96463

−5.95502

0.97673

−4.40147

0.09965

Molecule_154

10.8974

42.4735

10.9119

7.15111

−16.5170

−5.14193

−5.71648

0.86146

Molecule_9

11.5098

24.9470

65.3320

46.7416

−120.920

−71.2045

2.39190

0.09569

Molecule_113

12.1402

41.6382

28,289.50

39,402.2

−136.929

−83.7555

2.70303

0.09185

Molecule_60

12.5198

19.1621

−13.2592

6.10851

−41.6039

−25.2553

−0.89500

0.08769

Molecule_115

12.5673

46.3928

−11.8405

6.12586

−44.0638

−23.2031

1.68847

0.09377

Molecule_141

12.8093

32.4320

−5.41508

4.20617

−29.7202

−17.6498

−0.35700

0.09705

Molecule_117

17.0983

38.6338

2021.79

2299.10

59.3405

96.7284

3.50862

0.09529

Celecoxib

17.33395

33.9253

13.8933

42.5446

−139.661

−117.986

6.21732

0.09936

Molecule_142

17.3898

38.1553

−79.2244

15.0265

−128.565

−93.4084

2.66973

0.09101

Molecule_100

17.9025

29.9299

6222.05

6373.98

−160.241

−112.801

1.91017

0.08234

Molecule_105

17.9411

39.9044

3.14311

5.19254

−19.9248

−12.5248

−3.32516

0.09908

Molecule_110

18.7239

44.9821

6.43866

5.82078

−15.4753

−6.75722

−1.88785

0.09904

Molecule_107

20.4115

36.5229

18.9587

5.76753

−11.5264

0.057840

−2.43628

0.09055

Molecule_98

20.7154

30.5548

−1.67995

5.36936

−21.3184

−9.82350

−1.23215

0.09869

Molecule_104

23.4169

41.1073

6.86145

4.83158

−23.4274

−2.71648

−5.74037

0.0969

Molecule_114

24.2130

49.8248

5.38092

5.21023

−12.5330

−4.10493

−4.12701

0.08961

Molecule_101

24.4073

38.7888

9.88176

16.3253

−17.7871

−5.53674

−3.82927

0.08234

Molecule_143

25.1057

38.8955

4.73240

5.43985

−23.7807

−9.70408

−2.10607

0.08908

Molecule_159

25.8484

38.2027

6.37157

5.98037

−34.4232

−3.90721

−5.13228

0.09613

Molecule_122

26.6459

39.9598

2.21425

6.49133

−34.9642

−14.2581

−6.04252

0.08389

Molecule_111

29.4514

42.7783

38.9483

46.4718

−127.864

−82.6885

3.20745

0.08620

Molecule_118

30.2871

43.3848

13.8351

7.06563

−23.0209

−1.49548

−7.16945

0.08975

Molecule_144

31.0438

41.6663

16.4609

6.42745

−18.8412

4.78885

−6.92729

0.09901

Molecule_150

34.7730

46.4684

36.3206

7.23252

−7.94199

11.2902

−2.19893

0.08886

Molecule_112

35.1376

46.2887

8.39953

6.33198

−33.0042

−4.69838

−5.58198

0.09233

Molecule_151

35.3649

45.4588

33.7593

7.10073

−14.0113

18.3148

−3.51091

0.08832

Molecule_152

41.9392

45.3560

90.9266

30.9076

−12.7073

16.6751

−3.82475

0.09296

aThe energies of the molecules are indicated in kcal/mol unit

Fig. 4

Structure of molecule 8

Fig. 5

Interactions of molecule 8 with active site amino acids of COX-2 protein

Fig. 6

2D Interactions view of molecule 8 with active site amino acids of COX-2 protein

The COX-2 selectivity of the 55 tyrosine derivatives was compared with COX-1 enzyme. In this COX-1 docking study, the designed molecule had not created appropriate conformation inside the active site of COX-1 enzyme due to the bulky amino acid residue Ilu523 and non-polar moieties of the His513. The VDW space of the tyrosine molecules in COX-1 chemical space of the active site is in conflict with the receptor essential volume. This conflict creates steric repulsion between side chain amino acids of the COX-1 and designed molecules. It strongly evidenced that there is a large decrease in the affinity of the designed tyrosine derivatives with COX-1 when compared to the celecoxib. The above results proved that the tyrosine derivatives are more selective on COX-2 than COX-1.

Ulcerogenic interaction

The enzyme COX-1 played pivotal role in the maintenance of mucosal integrity in the gastrointestinal tract. It is believed that the ulcerogenic effects of non-steroidal anti-inflammatory drugs is owing to exclusive inhibition of COX-1 [18]. The interaction between the designed 55 tyrosine moiety and COX-1 protein aided to identify the ulcerogenicity level of designed molecules. The results of docking studies (C-Docker) revealed that the designed tyrosine derivatives exhibited more binding energy which was in contrast with the standard celecoxib (Table 5). The standard drug formed, one sigma-π, π-cationic and two hydrogen bond interaction with the Ile523, Arg120, Gln192 and Lue352 amino acids respectively (Fig. 7). These bonds support the celecoxib to fit into the cavity of COX-1 enzyme. On the other hand, the designed tyrosine derivatives formed hydrogen bonds with the Tyr385 and Ser530 (Fig. 8) and there is no other additional interaction with the active site amino acids of COX-1 receptor. Also, the electro negative groups (-Br, -I) of the designed molecules forms intermolecular bumps which disfavors the binding capability of the molecules. These unstable conformations of the designed molecule prove their negligible ulcerogenic side effect.
Table 5

C-Docker values for the tyrosine derivatives with COX-1 and hERG protein

Name of the molecule

COX-1

hERG

C-Docker energy

–C-Docker interaction energy

C-Docker energy

–C-Docker interaction energy

Molecule_11

11.1931

39.4964

25.6376

36.6014

Molecule_7

15.4566

35.6748

25.7715

38.8912

Molecule_102

18.612

45.7385

6.12591

32.7374

Molecule_99

22.7057

45.0127

13.8034

33.4137

Molecule_10

23.7368

49.2067

17.2666

37.8672

Molecule_113

25.291

51.6406

11.4752

37.2134

Molecule_14

25.5442

45.5213

27.0423

39.9337

Molecule_50

27.2685

42.4608

37.1187

35.2034

Molecule_54

27.9592

45.2948

30.1138

37.0948

Molecule_154

28.0068

48.3108

22.5487

37.1437

Molecule_103

28.5622

49.3350

12.6594

33.7937

Molecule_23

28.9051

51.4072

27.8294

36.9222

Molecule_146

29.7938

48.7906

12.7369

29.7554

Molecule_117

32.24

50.9400

17.502

35.4744

Molecule_122

32.4296

41.5757

23.1628

31.3608

Molecule_21

32.7128

54.7328

26.6071

40.1819

Molecule_8

33.0627

39.4668

36.4622

33.5932

Molecule_115

33.5042

53.2589

16.3142

35.3354

Molecule_105

33.9553

48.2822

20.7848

31.2565

Molecule_114

34.2029

54.9272

19.9584

37.415

Molecule_25

34.4117

51.3171

32.1083

41.2442

Molecule_100

34.7976

46.4333

21.2323

30.1093

Molecule_110

34.9249

53.1217

16.7253

34.8426

Molecule_6

35.033

45.8393

41.2549

36.2332

Molecule_26

35.2188

44.5275

43.3746

38.2527

Molecule_51

35.9835

42.5881

41.6024

38.3912

Molecule_107

36.0181

46.6087

21.6437

29.0359

Molecule_159

36.4927

43.0306

25.964

32.5955

Molecule_142

37.3162

49.1502

22.6982

30.9975

Molecule_141

37.8732

49.0256

23.9516

32.7221

Molecule_15

37.9714

43.6680

37.2577

34.7344

Molecule_58

38.0398

42.8592

43.586

36.3985

Molecule_13

39.4551

49.4952

35.9365

38.5156

Molecule_52

41.2608

41.016

48.0603

37.6181

Molecule_67

41.3861

43.4004

40.4953

37.1043

Molecule_59

41.5117

49.588

51.4686

44.477

Molecule_104

41.5637

48.3222

25.0942

32.4227

Molecule_101

42.6379

48.1987

25.8003

32.6898

Molecule_98

42.9202

48.7329

27.3695

33.4591

Molecule_143

43.1506

48.7602

26.4997

32.3527

Molecule_9

43.4413

49.4292

40.1671

35.6967

Molecule_20

44.4891

48.6384

45.1393

37.1212

Molecule_24

45.1278

54.9812

39.8631

37.5571

Molecule_12

45.21

49.8787

41.6676

37.8414

Molecule_111

45.9116

51.7703

27.1743

33.3161

Molecule_144

46.9694

46.4174

34.4717

35.0657

Molecule_112

47.1361

50.3979

32.8243

35.6348

Molecule_151

48.0494

53.3779

35.1662

37.0065

Molecule_150

48.0592

53.4856

29.3814

36.5024

Molecule_118

48.319

52.7029

29.5539

33.4975

Molecule_17

48.6628

48.8614

45.811

38.6698

Molecule_60

49.6967

49.4645

56.8447

44.3316

Molecule_152

52.4378

51.2126

40.7779

35.7427

Celecoxib

19.4457

51.7111

−0.642396

30.7255

Fig. 7

Celecoxib interaction map with the COX-1 protein a 2D view of non-bonded interactions b 3D interaction view

Fig. 8

Tyrosine derivatives interaction map with the COX-1 protein a 2D view of non-bonded interactions b 3D interaction view of hERG protein interaction

hERG protein interaction studies

The hERG is the most critical channel involved in drug induced Torsade de Pointes (TdP) arrhythmias. Extra cellular application of celecoxib causes rapid suppression of hERG channels which induces the cardiac disturbances [19]. Evaluation of spatial orientation of the designed molecule interactions with the hERG protein recognizes the cardiotoxicity level of molecules [20]. The results of docking studies indicated that among the 55 designed molecules, 52 molecules possessed more interaction energy against the standard (Table 5). It revealed that these molecules are having less binding affinity to the active site residues of the hERG protein. In standard celecoxib, the benzyl ring creates π-π interaction with the Tyr652 (Fig. 9). This enables the celecoxib to fit well into the hydrophobic pocket of COX-2 protein. On contrary, tyrosine derivatives did not form any π-π interactions and the extra volume of the electronegative group substitutions in the R1 and R2 positions which repulse the molecules to bind in the active site (Fig. 10). Hence, the cardiotoxicity of the designed molecules were less when compared to the celecoxib. The selected 35 tyrosine molecules demonstrated high COX-2 selectivity, less COX-1 (ulcerogenic) and hERG (cardiotoxicity) binding affinity. Further, these molecules were examined by ADMET descriptors calculation and OSIRIS properties explorer.
Fig. 9

Standard celecoxib interaction map with the hERG protein a 2D view of non-bonded interactions b 3D interaction view

Fig. 10

Tyrosine derivatives interaction map with the hERG protein a 2D view of non-bonded interactions b 3D interaction view

Docking protocol validation

The results of RMSD values of redocked native co-crystallized ligand of each PDB entry revealed that native ligand conformations including 3NT1 and best docked ligand conformation exactly binds in the experimental protein binding mode. In the docking study performed by first method, RMSD values of best docked conformations ranged from 0.8436 to 1.7674 Å. According to validation protocol, RMSD values of best docked conformation should be ≤2.0 Å [21]. It represents that this docking protocol is able to find an appropriate binding mode. The designed 55 molecules were redocked into the active site of the COX-2 (3NT1) receptor and confirms that these docked molecules followed the similar binding method as in native co-crystallised ligand (Table 6).
Table 6

Native co-crystallised ligands and its respective PDB ID with its redocked RMSD values

Co-crystallized ligand

PDB ID

RMSD (Ǻ)

CEL682

3LN1

1.7674

NPS5

3NT1

1.3330

DIF701

3N8Y

0.8436

IBP601

4PHA

1.0834

Molecule 8

3NT1

1.0810

In the second method, the selected docking protocol parameters accurately distinguished the selective and non-selective COX-2 inhibitors. It is illuminated by the docking results in which C-Docker energy of selective COX-2 inhibitors fall in the negative kcal/mol range and the non-selective inhibitors energies fall in the range of positive kcal/mol (Table 7). Additionally, the binding site (3NT1) analysis of the drug receptor complexes revealed that all the selective COX-2 inhibitors formed π interaction with the active site amino acids which are major force for molecular recognition and join with hydrophobic interaction [22]. But, non-selective COX inhibitors formed hydrogen bond, VDW and electrostatic interactions only (Fig. 11). It clearly proves that the selective COX-2 inhibitors and designed 55 molecules possessed more selectivity compared to the non-selective inhibitors. This proposed model predicted the correlation between C-Docker energy and the experimental IC50 value of the selective and non-selective inhibitors. The correlation coefficient was predicted to be 0.835 (r2) (Fig. 12). This correlation strongly indicates that the docking protocol of this study possessed good predicting ability as well as it distinguishes the selective and non-selective COX-2 inhibitors precisely.
Table 7

C-Docker energy values of the selective and non-selective inhibitors

Selective COX-2 inhibitors

C-Docker energy value (kcal/mol)

Non-selective COX-2 inhibitors

C-Docker energy value (kcal/mol)

Rofecoxib

−19.0343

Diclofenac

5.45905

Valdecoxib

−9.2766

Ketorolac

12.2429

Etoricoxib

−3.32262

Aspirin

29.113

  

Naproxen

32.0361

  

Ibuprofen

39.7383

Fig. 11

Interactions of selective and non-selective COX-2 inhibitors. a Rofecoxib b Aceclofenac

Fig. 12

Correlation point plot of C-Docker energy and the experimental activity (IC50) of the nonselective COX-2 inhibitors

Toxicity

ADMET descriptors

In the present work, we have assessed ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the 35 compounds which were selected from the docking report. ADMET descriptors were calculated to filter the poor tyrosine molecule with undesired pharmacokinetic and toxicity properties [23]. This step prevents wasting of time, chemicals as well as animal studies of tyrosine derivatives. The pharmacokinetic profile of all the molecules was predicted by means of six pre-calculated ADMET models provided by ADS 2.5 software. The ADMET plot shows the 95 and 99 % confidence ellipse for the HIA and BBB models (Fig. 13). The 95 % confidence ellipse represents the region of chemical space with molecules having excellent absorption through cell membrane. According to this model, for a designed molecule to have an optimal cell permeability, it should follow the criteria of PSA < 140 Å2 and AlogP98 < 5) [24]. The selected 35 molecules have shown PSA < 140 Å2 and AlogP98 < 5 which satisfied the criteria.
Fig. 13

The 95 and 99 % confidence limit ellipses corresponding to the BBB and HIA models for tyrosine derivatives

These selected molecules as well as standard celecoxib fall in the 95 and 99 % confidence ellipse for both HIA and BBB (Fig. 13). The HIA of the tyrosine derivatives ranges from 0 (good absorption) to 1 (moderate absorption) (Table 8). It indicates the good bioavailability of designed molecules to produce desired therapeutic effect. BBB penetration of the designed molecules indicated undefined to low penetration, except the molecule 141. On the other hand, celecoxib exhibited moderate penetration to the BBB (Table 8). The aqueous solubility plays a vital role in the bioavailability of the drug. The designed tyrosine derivatives have solubility in the range of 2 (low soluble) to 3 (soluble) as referred in Table 9. Further, the hepatotoxicity level of all the molecules were calculated, the molecules with liver toxic nature were filtered out. Similarly, all the molecules were found to be satisfactory with respect to CYP 450 2D6 liver enzyme, suggesting that the tyrosine derivatives were non inhibitors of the metabolic enzyme. Finally, the PPB prediction denotes that all the designed molecules have binding ≤90 % clearly revealing that the molecules have good bioavailability and are not likely to be highly bound to carrier proteins in the blood [25].
Table 8

ADMET predictions of 35 tyrosine molecules and celecoxib

Name of the molecule

Absorption level

AlogP98

PSA 2D

BBB level

Solubility

Solubility level

Hepatotoxicity level

CYP 2D6

PPB level

Molecule_6

0

2.843

109.513

4

−4.495

2

0

0

0

Molecule_8

0

2.634

105.719

4

−4.197

2

0

0

0

Molecule_9

0

1.862

118.273

4

−3.523

3

1

0

0

Molecule_10

1

1.317

129.534

4

−3.217

3

0

0

0

Molecule_11

0

2.402

120.689

4

−4.027

2

0

0

0

Molecule_12

0

1.852

120.774

4

−3.883

3

1

0

0

Molecule_13

0

2.419

118.273

4

−4.009

2

1

0

0

Molecule_14

1

1.804

132.035

4

−4.096

2

0

0

0

Molecule_15

0

3.047

94.458

3

−4.388

2

1

0

0

Molecule_17

0

2.503

109.513

4

−4.197

2

1

0

0

Molecule_20

0

1.522

118.273

4

−3.225

3

0

0

0

Molecule_21

1

0.976

129.534

4

−2.919

3

0

0

0

Molecule_23

0

2.068

120.774

4

−4.071

2

0

0

0

Molecule_24

0

2.078

118.273

4

−3.711

3

0

0

0

Molecule_25

1

1.464

132.035

4

−3.798

3

0

0

0

Molecule_26

0

2.707

94.458

3

−4.09

2

1

0

0

Molecule_50

0

2.599

105.719

4

−3.984

3

0

0

0

Molecule_51

0

2.186

116.98

4

−3.793

3

0

0

0

Molecule_54

0

0.613

116.198

4

−2.789

3

0

0

0

Molecule_58

0

2.259

105.719

3

−3.686

3

0

0

0

Molecule_67

0

1.354

116.98

4

−2.83

3

0

0

0

Molecule_99

0

3.245

90.972

3

−4.639

2

1

0

0

Molecule_102

0

1.505

108.662

3

−3.598

3

1

0

0

Molecule_103

0

2.59

99.817

3

−4.351

2

1

0

0

Molecule_113

0

1.164

108.662

3

−3.3

3

1

0

0

Molecule_115

0

2.256

99.902

3

−4.114

2

1

0

0

Molecule_117

0

1.652

111.163

4

−3.925

3

1

0

0

Molecule_141

0

3.937

73.586

2

−4.973

2

1

1

0

Molecule_146

0

0.801

95.326

3

−2.859

3

1

0

0

Molecule_154

0

2.18

99.817

3

−3.996

3

1

0

0

Molecule_7

0

2.193

120.774

4

−4.182

2

0

0

0

Molecule_52

1

1.753

128.241

4

−3.584

3

1

0

0

Molecule_57

0

3.409

94.458

3

−4.521

2

0

0

0

Molecule_59

0

1.846

116.98

4

−3.495

3

0

0

0

Molecule_60

1

1.413

128.241

4

−3.286

3

1

0

0

Celecoxib

0

4.428

77.75

2

−6.603

1

1

0

1

Table 9

ADMET descriptor models

Name of the ADMET model

Prediction levels

Human intestinal absorption

0 (Good absorption)

1 (Moderate absorption)

2 (Low absorption)

3 (Very low absorption)

Aqueous solubility

0 (Extremely low)

1 (No, very low, but possible)

2 (Yes, low)

3 (Yes, good)

4 (Yes, optimal)

5 (Too soluble)

Blood brain barrier (BBB)

0 (Very high penetration)

1 (High penetration)

2 (Medium penetration)

3 (Low penetration)

4 (Undefined penetration)

Cytochrome P450 2D6 (CYP 2D6)

0 (Non−inhibitor)

1 (Inhibitor)

Hepatotoxicity

0 (Nontoxic)

1 (Toxic)

Plasma protein binding (PBB)

0 (Binding is <90 %)

1 (Binding is >90 %)

2 (Binding is >95 %

Osiris property explorer

The result of toxicity analysis of designed molecules showed low toxicity tendency except the molecules 103 and 113. The drug-likeness value of standard and designed molecule exhibited the fragment content of the drug. If the drug-likeness value of designed molecules is increasing, then it has the same fragment content with existing drugs. Table 10 shows that the drug-likeness value of the tyrosine derivatives were higher than the standard celecoxib (−8.11), with the exception of 102, 103, 117, 141, 146 and 154 (−10.82 to −11.92). This results predict that among 35, 29 molecules exhibited same fragment content of the drugs. It confirms the drug likeness properties of these compounds.
Table 10

Toxicity of tyrosine derivatives and standard drug based on OSIRIS property explorer

Molecule

Mutagenicity

Tumorigenic

Irritant

Reproductive effect

Drug likeness

Drug score

Molecule_6

Green

Green

Green

Green

1.88

0.63

Molecule_8

Green

Green

Green

Green

2.25

0.62

Molecule_9

Green

Green

Green

Green

1.83

0.60

Molecule_10

Green

Green

Green

Green

2.46

0.66

Molecule_11

Green

Green

Green

Green

0.87

0.53

Molecule_12

Green

Green

Green

Green

2.46

0.67

Molecule_13

Green

Green

Green

Green

2.61

0.65

Molecule_14

Green

Green

Green

Green

−2.08

0.39

Molecule_15

Green

Green

Green

Green

2.03

0.54

Molecule_17

Green

Green

Green

Green

4.74

0.54

Molecule_20

Green

Green

Green

Green

4.69

0.50

Molecule_21

Green

Green

Green

Green

5.29

0.55

Molecule_23

Green

Green

Green

Green

5.54

0.57

Molecule_24

Green

Green

Green

Green

5.43

0.53

Molecule_25

Green

Green

Green

Green

0.74

0.48

Molecule_26

Green

Green

Green

Green

4.88

0.45

Molecule_50

Green

Green

Green

Green

2.34

0.45

Molecule_51

Green

Green

Green

Green

1.46

0.60

Molecule_54

Green

Green

Green

Green

1.77

0.59

Molecule_58

Green

Green

Green

Green

4.31

0.51

Molecule_67

Green

Green

Green

Green

2.39

0.46

Molecule_99

Green

Green

Green

Green

−0.06

0.46

Molecule_102

Green

Green

Green

Green

−10.82

0.39

Molecule_103

Green

Yellow

Red

Green

−15.1

0.18

Molecule_113

Green

Green

Red

Green

−7.79

0.32

Molecule_115

Green

Green

Green

Green

−7.28

0.33

Molecule_117

Green

Green

Green

Green

−11.92

0.33

Molecule_141

Green

Green

Green

Green

−17.18

0.34

Molecule_146

Green

Green

Green

Green

−11.29

0.35

Molecule_154

Green

Green

Green

Green

−8.91

0.31

Molecule_7

Green

Green

Green

Green

3.47

0.70

Molecule_52

Green

Green

Green

Green

−2.79

0.21

Molecule_57

Green

Green

Green

Green

−0.83

0.49

Molecule_59

Green

Green

Green

Green

4.36

0.52

Molecule_60

Green

Green

Green

Green

2.39

0.29

Celecoxib

Green

Green

Green

Green

−8.11

0.37

The drug score value is the combination of solubility, molecular weight, logP, drug likeness and toxicity risk. It is used for evaluating the potential of the drug candidate. When the drug score is better, then the compound is predictive to be a drug candidate [26]. The drug score value of standard celecoxib is found to contain 0.37. Finally 19 compounds which possessed drug score greater than the standard were shortlisted for further studies (Tables 11, 12).
Table 11

Details of shortlisted potent COX-2 inhibitors

Table 12

Details of shortlisted potent COX-2 inhibitors

Conclusion

In the current work, 55 tyrosine structural analogues on docking with COX-2, COX-1 and hERG revealed that 35 molecules have more affinity at active site residues of COX-2 enzyme and less interaction with the other two proteins (COX-1, hERG) than standard celecoxib. This information proved to exhibit potential of high selective, less ulcerogenic and cardiotoxicity of the designed novel anti-inflammatory molecules. Further, the result of ADMET and Osiris property explorer helped to eliminate 16 unwanted toxic fragments contained tyrosine molecules. Finally, 19 hits with good pharmacokinetic parameter and negligible toxicity was proceeded for synthesis. Hence, it is concluded that the predicted parameters are exclusively used as a basis for the further design of tyrosine derivatives and understand the mechanism of COX-2 related enzymatic inhibition reactions. The next step of the potent safe anti-inflammatory drug identification involves the synthesis and biological evaluation of the selected molecules which are in progress.

Abbreviations

ADS: 

accelyrs discovery studio

Arg: 

arginine

BBB: 

blood brain barrier

COX-1: 

cyclooxygenase-1

COX-2: 

cyclooxygenase-2

CYP 2D6: 

cytochrome P450 2D6

Gln: 

glutamine

HIA: 

human intestinal absorption

HM: 

homology modeling

His: 

histidine

Ile: 

isoleucine

Lue: 

leucine

PDB: 

Protein Data Bank

Phe: 

phenylalanine

PPB: 

plasma protein binding

PSA_2D: 

2D polar surface area

RMS: 

root mean square

SAR: 

structure activity relationship

Ser: 

serine

TdP: 

torsade de pointes

Tyr: 

tyrosine

Val: 

valine

VDW: 

van der Waals

Declarations

Authors’ contributions

It is certified that all authors have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each manuscript author certified that this material or similar material has not submitted to or published in any other publication. Dr. AP: A Conception, design of study and approval of final version of manuscript. Dr. DS: Participated in computational studies. Ms. AU: Contributed to design the study and Drafting of manuscript. Mr. NI: Carried out the computational studies and participated in the Data analysis. All authors read and approved the final manuscript.

Acknowledgements

The authors are thankful to the Department of Science and Technology (DST-SERB), New Delhi for their financial assistance provided for this research (SR/S1/OC-48/2011 Dt: 14-052013).

Competing interests

The authors declare that they have no competing interests.

Funding

The present project was supported by grants from the Department of Science and Technology (DST-SERB), Government of India, New Delhi (SR/S1/OC-48/2011 Dt: 14-05-2013).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Drug Discovery and Development Research Group, Department of Pharmaceutical Technology, Anna University Chennai, BIT Campus
(2)
Pharmacy Group, Birla Institute of Technology and Sciences, Pilani, Hyderabad Campus

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© Puratchikody et al. 2016