ADMET, Molecular docking studies and binding energy calculations of Pyrimidine-2-Thiol Derivatives as Cox Inhibitors

 

Deepthi D. Kodical, Jainey P. James*, Deepthi K,  Pankaj Kumar,

Chinchumol Cyriac, Gopika K.V.

NGSMIPS CADD Lab, Department of Pharmaceutical Chemistry, NGSM Institute of Pharmaceutical Sciences, Nitte (Deemed to be University), Paneer, Deralakatte, Mangalore-575 018, Karnataka, India.

 

ABSTRACT:

Cyclooxygenase (COX), is an enzyme that is responsible for the formation of prostanoids, including thromboxane and prostaglandins. Prostaglandins promote inflammation. Blockage of COX enzymes reduces prostaglandins and thus their effects are reduced. The present study focuses on In silico ADMET, molecular docking studies and binding energy calculations of ten pyrimidine derivatives as cox-1 and cox-2 inhibitors. Molecular docking studies were conducted on two target enzymes. Energy minimisation of the ligands was carried out using ligprep. They were docked to the active site of the proteins using the extra precision mode of glide module. The docked poses were ranked according to their docking score and binding energy with the enzymes. Compound PY4 showed better docking score with cox-1 (-6.081 kcal/mol) and compound PY5 showed good docking score with cox-2 (-8.602 kcal/mol). All compounds were found to have good docking and binding affinity scores when compared to the standards. Thus it reveals that the synthesised compounds act as anti-inflammatory agents by inhibiting cox-1 and cox-2 enzymes.

 

 

INTRODUCTION:

Inflammation is the tissue reaction against infection, irritation or foreign substance. It is a part of the host defense mechanisms that is known to be involved in the inflammatory reactions associated with the release of histamine, bradykinin and prostaglandins[1-2].

 

Cyclooxygenases (COX) or prostaglandin endoperoxide synthases (PGHS) are the key enzymes in the synthesis of prostaglandins, the main mediators of inflammation, pain and increased body temperature (hyperpyrexia). The body produces two main isoforms COX proteins i.e., cyclooxygenases −1 (cox-1) and cyclooxygenases-2 (cox-2). The cox-1 is responsible for formation of important biological mediators such as prostanoids, including prostaglandins, prostacyclin and thromboxane and involved in pain causing, blood clotting and protecting the stomach [3] whereas cox-2 involved in the pain by inflammation and plays a major role in prostaglandin biosynthesis in inflammatory cells and central nervous system [4] Blockage of COX enzymes reduces prostaglandins and thus their effects are also reduced [5-6].

 

The development of non-steroidal drugs for inflammation especially in overcoming Rheumatoid arthritis has evoked much interest in the extensive search for new drugs with anti-inflamatory property [7] Improved understanding of the molecular mechanisms has opened new avenues for the design of drugs that selectively interfere with key targets. The binding analysis will help us to understand the type of interactions and affinity of compounds with the targets [8].

 

The computational studies are being applied to select the possible best lead candidates based on the assessments of various important drug-relevant and biological properties of compounds with the targets through in silico methods to reduce the failure rate during the drug discovery process [ 9].

 

Molecular docking [10-13] is a computational tool to determine the binding affinity of the ligand and target molecule. Docking predicts the approved orientation of one molecule to the other forming a stable complex with minimum energy. Molecular docking programs utilize scoring functions to evaluate the binding property of protein-ligand complexes. Variation in energy of ligand-protein complex is due to various physicochemical properties involved in the ligand-protein complexes.

 

Pyrimidine is the most important member because this ring system occurs widely in living organisms with wide range of therapeutic uses like antiviral, anticancer, ulcerogenic, analgesic etc. which also include anti-inflammatory activity [14-16]. Another core which is widely present in the biological system is the pyridine ring which has many therapeutic uses such as, antibacterial, antioxidant, analgesic and anticancer [17-18]. Recent studies have demonstrated that chalcones have been authenticated with diverse biological efficiency including antibacterial [19], anti-inflammatory [20], antioxidant [20], anti-tumor effects [21-22]. On the other hand thiophene moiety is present in a large number of bioactive molecules having diverse biological activities such as anti-inflammatory [23], anticonvulsant [24] and antitumor [25].

 

In view of the above, the present investigation aims to find the binding interactions and energy between the synthesized heterocyclic derivatives with the enzyme cyclooxygenases. These compounds have been reported for their in vivo anti-inflammatory activities; hence proving their target mechanisms is an essential task. Therefore, this in silico study merits in understanding the imperative role of pyrimidine-pyridine-thiophene based heterocyclics’s anti-inflammatory properties against cox-1 and cox-2 enzymes, based on their binding and interaction patterns.

 

MATERIALS AND METHODS:

The scheme designed was based on the research paper by Shehab SW et al [26]. The chemical structures were drawn by ChemSketch and smiles were generated and given in the Table 1.

 

In silico studies:

Modeling platform:

All computational analysis was carried out on Maestro 11.9 version [27] (LigPrep, Glide XP docking, grid generation, free energy calculations and ADMET). This software package programmed on DELL Inc.27” workstation machine running on Intel Core i7-7700 CPU@ 3.60 GHz x8, processor with 8GB RAM and 1000 GB hard disk with Linux –x86_64 as the operating system. This program was used to study the physicochemical properties, druglikeness, interactions and ADMET properties of the compounds.

 

Physicochemical properties and Rule of Five properties:

To achieve good oral drugs, we have investigated a series of derivatives for the prediction of their molecular properties and Lipinski’s ‘Rule of Five’. High oral bioavailability is an essential factor for the development of bioactive molecules as therapeutic agents.

 

ADMET studies:

ADMET (absorption, distribution, metabolism, excretion and toxicity) predictions for the docking hits were calculated by using the QikProp program running in normal mode. QikProp (Schrödinger 2019c) [28] generates physically relevant descriptors, the toxicity a ligand is considered necessary for the ligand to act as an effectual drug discovery of new drug development.

 

Table 1: Chemical structures and SMILES of the synthesized compounds

Si No.	Ligand ID	Chemical structure	SMILES
1	PY1		O=CC1=CC=CS1
2	PY2		CC(C1=CC=CN=C1)=O
3	PY3		O=C(C1=CN=CC=C1)/C=C/C2=CC=CS2
4	PY4		S=C(N1)N=C(C2=CN=CC=C2)C=C1C3=CC=CS3
5	PY5		NNC1=NC(C2=CC=CS2)=CC(C3=CN=CC=C3)=N1
6	PY6		O=C1OC(C2=CN=CC=C2)=CC(C3=CC=CS3)=C1C#N
7	PY7		O=C1N(N)C(C2=CN=CC=C2)=CC(C3=CC=CS3)=C1C#N
8	PY8		CC1=NN(C2=NC(C3=CC=CS3)=CC(C4=CN =CC=C4)=N2)C(O5)=C1C(C)=CC5=O
9	PY9		CC1=NN(C2=NC(C3=CC=CS3)=CC(C4= CN=CC=C4)=N2)C(C1)=O
10	PY10		CC1=NN(C2=NC(C3=CC=CS3)=CC(C4=CN= CC=C4)=N2)C(C)=C1

 

Molecular Docking:

Ligand Preparation:

The ligands used in this study were prepared using LigPrep (Schrödinger) module [29]. Ligand preparation was done to minimize ligands energy. The smiles were imported to maestro and the ligand structures were generated. 2D structures were converted to 3D and their energy was minimized. The moiety was desalted and the charged structures were neutralized. At this stage the chiralities, conformation and ionization condition was all optimized.

 

Protein preparation:

Protein preparation was carried out using protein preparation wizard. X-ray crystal structures for cox-1 (1PRH) [30] and cox-2 (1CX2) [31] were obtained from protein data bank. Preprocessing is done in import and process. Optimization, water removal and minimization were carried out. The protein structure was refined. The ionization structure and most stable state were chosen.

 

Receptor grid generation:

Grid is generated in receptor grid generation wizard. The protein may contain lots of active sites but one of the sites is picked up. Initially an atom in the ligand is chosen and length of the ligand is selected.

 

Glide extra precision docking [32]:

The ligand was docked with the protein and the interactions were observed. The scoring function gives scores based on the best ligand- protein interaction. The docking poses were evaluated in the extra- precision mode. The poses were filtered to obtain the best ligand protein complex. The algorithm identifies the hydrogen bonding, hydrophobic, metal-ligation interactions, and steric clashes. The further step involves the evaluation and minimization of ligand-receptor interaction energy. Finally, the poses were scored using Glide Score scoring function.

 

Prime MM-GBSA Binding Free Energy:

The receptor binding free energy and a set of ligands were predicted using the calculation of prime module in Schrodinger [33]. The software estimates the total free energy of binding, dGbind (kcal/mol) as:

ΔG bind = Gcomplex - (Gprotein+Gligand), where G = MME + GSGB + GNP

MME (molecular mechanics energies) + GSGB (SGB solvation model for polar solvation) + GNP (nonpolar solvation)

 

RESULTS AND DISCUSSION:

Physicochemical properties and Rule of Five properties:

Based on the experimental values, it was inferred that all the compounds successfully satisfied all the parameters of Lipinski’s Rule of Five and possessed desired physicochemical properties with no violations from the standard ranges and are tabulated in Table 2. Compounds with log P values less than 5, mean that they can readily get pass ester/phosphate groups in skin membranes and the calculated values of log P for the derivatives ranged from 0.01 to 4.49. And, all of the compounds have their molecular weight below 500 ranging from 112- 401. The series under investigation has not only the most of the compounds possessing less number of hydrogen bond donors (<5) but also does possess considerable number of acceptors (<10). Thus, it proves that it these compounds would not have problem with oral bioavailability.

 

ADMET studies:

The results reveal that all the compounds had better human oral absorption score, Caco-2 permeability score, human serum binding and QPlogKhsa scores within the recommended range (table 3).

 

Table 2: Physicochemical properties

Ligand ID	Molecular weight	Log P	Donor HB	acceptHB	PSA	Volume	rotor
PY1	112.15	1.76	0	2.000	37.998	418.142	1
PY2	121.14	0.01	0	3.500	41.732	487.496	1
PY3	215.28	2.23	0	3.500	39.928	738.141	4
PY4	271.37	2.91	1	4.500	43.682	846.537	1
PY5	269.33	2.17	3	5.000	75.181	851.017	3
PY6	280.31	2.23	0	5.500	75.077	854.477	1
PY7	294.34	1.3	2	6.000	93.077	893.712	3
PY8	401.45	4.31	0	7.000	85.961	1198.065	1
PY9	335.39	2.58	0	7.000	75.001	1045.488	1
PY10	333.42	4.49	0	4.500	46.122	1073.975	1
Diclofenac	296.152	1.85	2	2.5	58.496	854.486	4
Celecoxib	381.372	4.34	2	5.5	81.113	1081.169	2

 

Table 3: Predicted ADMET properties

Ligand ID	QPPCaco	% Human oral absorption	QPlogKhsa	SASA	Rule of five	Rule of three
PY1	1737.613	91.753	0.49	286.304	0	0
PY2	1757.507	87.868	-0.262	321.991	0	0
PY3	2010.162	100.00	0.181	455.777	0	0
PY4	2355.786	100.00	-0.19	515.727	0	0
PY5	123.583	72.191	-0.415	511.921	0	0
PY6	392.873	83.165	-0.197	508.191	0	0
PY7	207.989	77.708	0.023	526.311	0	0
PY8	830.496	100.000	-0.345	673.501	0	0
PY9	1209.869	100.000	-0.946	609.242	0	0
PY10	3188.502	100.000	-0.746	622.196	0	0
Diclofenac	381.962	100	0.373	495.412	0	0
Celecoxib	362.565	92.377	0.375	624.560	0	1

 

Table 4: Extra precision glide docking results with interacting ligands in the active site of 1PRH

Ligand ID	Glide XP docking score	MMGBSA dG Bind	Polar interaction with ligand	HB	pi-pi stackings
PY1	-2.739	-46.05	Gln 203, His 388, Hid 388, His 386 Hid 386, His 207, Hid 207, Thr 206	-	His 388 Hid 388
PY2	-2.642	-35.44	Gln 203, His 386, Hid 386, Hid 207 Hid 388, His 388, His 207, Thr 206	-	Trp 387
PY3	-4.134	-12.96	Gln 203, His 388, Hid 386, His 207 Thr 206	-	-
PY4	-6.081	-52.52	Hid 388, His 388, Hid 386, His 386 His 207, Asn 382, Thr 206, Hid 207 Gln 289, Thr 212	Gln 289	Hid 386, His 386, Hem 601
PY5	-4.225	-41.19	His 388, Hid 388, Gln 203, His 207 Hid 207, Hie 446, Hid 446	-	Hie 446, Hid 446
PY6	-4.764	-31.73	Gln 203, His 388, Hid 388, Hid 443 Hie 446	-	-
PY7	-5.864	-36.37	His 388, Hid 388, Hid 386, His 386 Asn 382, Thr 212, Hid 207, His 207 Thr 206, Gln 203	Asn 382 Thr 212	Hem 601, His 20, Hid 207 Hid 386, His 386
PY8	-4.834	-41.92	Thr 212, Asn 382, HIS 386, Hid 386 His 388, His 207, Thr 206, Gln 206 HIE 446	Asn 382 Thr 212	His 386, His 207, Hem 601 Hid 386, Hid 207
PY9	-4.865	-31.78	Hid 446, Hie 446, His 388, Hid 388 Hid 386, His 386, Thr 212, Hid 207 His 207, Thr 206, Gln 203	Thr 212	Hem 601, Hid 386, His 386
PY10	-3.966	-30.45	Gln 203, His 388, Hid 388, Hie 446 Hid 446, Hid 207, His 207	-	Hid 446, Hie 446, Hem 601
Diclofenac sodium	-4.688	-71.27	Hie 446, His 388, Gln 203, His 207	-	-

 

Figure 1: 2D Interactions of PY4 and PY7 with 1PRH

 

Figure 2: 3D Conformations of PY7 and PY4 with 1PRH 

 

Table 5: Extra precision glide docking results with interacting ligands in the active site of 1CX2

Ligand ID	Glide XP docking score	MMGBSA dG Bind	Polar interaction with ligand	HB	Pi-Pi stackings
PY1	-4.709	-91.45	Ser 530, Ser 353	Ser 530	-
PY2	-5.255	-81.16	Ser 530, Ser 353, His 90, Gln 192	Tyr 355	-
PY3	-6.993	-66.29	Ser 530, Ser 353, His 90	Leu 352	-
PY4	-7.162	-67.71	Ser 530, Ser 353, His 90, Gln 192	Tyr 355, Ser 530	-
PY5	-8.602	-71.63	Ser 530, Ser 353, His 90	Leu 352	-
PY6	-7.400	-72.74	Ser 530, Ser 353, His 90, Gln 192	His 90	-
PY7	-6.569	-63.25	Ser 530, Ser R 353 , His 90, Gln 192	-	-
PY8	-3.953	-35.27	Ser 530, Ser 353, His 90, Gln 192	-	Tyr 355
PY9	-8.046	-42.59	Ser 530, Ser 353, His 90	-	Trp 387
PY10	-7.599	-36.99	Ser 530, Ser 353 , His 90, Gln 192	-	-
celecoxib	-11.195	-94.07	Ser 530, Ser 353 , His 90, Gln 192	Arg 513, Leu 352, Ser 353	-

 

Figure 3: 2D Interactions of PY5 and PY9 with 1CX2

 

Figure 4: 3D Conformations of PY5 and PY9 with 1CX2

 

Bindings of 1PRH:

The active amino acids in cox-1 (1PRH) was found to be Hid 388, His 388, His 207, Thr 206, Gln 203, His 386, Hid 386, Hid 207, Hid 446, Hem 661, Asn 382, Hid 386 and Gln 286. PY4 shows hydrogen bonding with Gln 289 through nitrogen group of pyridine ring. It also shows pi-pi stacking with Hid 386, Hem 601 and His 386 through aromatic rings. PY7 shows hydrogen bonding with Asn 382 and Thr 212 through nitrogen atom. It also shows pi-pi stacking with Hem 601, His 207, Hid 207, Hed 386 and His 386 through pyridine, pyrimidine and thiophene rings. PY8 shows hydrogen bonding with Thr 212 and Asn 382 through carbonyl group. It also shows pi-pi stacking with His 386, His 207, Hem 60, Hid 386 and Hid 207 through pyrazolone ring. The compound PY4 have shown highest docking scores of -6.081 kcal/mol, when compared with the standard diclofenac sodium (-4.688 kcal/mol) (Table 4, Figures 1,2).

 

Bindings of 1CX2:

The active amino acids in cox-2 (1CX2) were found to be Arg 120, Arg 513, His 90, Gln 192, Ser 530, Ser 353, Arg 313, Tyr 355, Arg 320 and Leu 352. PY5 showed hydrogen bonding with Leu 352 through amino group in the ligand molecule. It also showed pi cation interaction towards ARG120 through pyridine ring. PY9 showed pi-pi stacking towards Trp 387 through pyridine ring. PY10 showed pi cation interaction with Arg 120 through thiophene ring. The compound PY5 has shown highest docking score of --8.602 kcal/mol, when compared with the standard celecoxib (-11.195 kcal/mol) (Table 5, Figures 3,4).

 

MM-GBSA Calculation:

Prime MM-GBSA approach was used to predict ligand binding energy (ΔG bind ) of docked compounds and reported in the respective tables 4-5. The binding affinity score observed for the compound PY4 and PY7 with the target 1PRH is -52.52 23 kcal/mol and -36.37 kcal/mol, respectively. For the compounds, PY5 and PY9 with the receptor, 1CX2, the binding affinity score was found to be -71.63 kcal/mol and -42.59 kcal/mol for respectively.

 

CONCLUSION:

The effect of the different heterocyclic compounds was studied on cox-1 and cox-2 targets for anti-inflammatory activity. All the compounds obeyed the Lipinski’s rule of five, rule of three; and the physicochemical and ADMET properties was found to be within the standard range. Thus, it is evident from the fact that these compounds would not have a problem with oral bioavailability. From the docking score analysis, it was found that compound PY4 showed good docking score with cox-1 with -6.08 kcal/mol and compound PY5 with cox-2 with -8.60 kcal/mol. In addition, from their binding energy scores, it is known that these compounds obtained good affinity with the targets. The responsible forces for their interactions are hydrogen bond, polar, hydrophobic interactions, and pi-pi stackings. The report described here is supporting these compounds for their anti-inflammatory activity which was proved in their in-vivo studies. Further, it infers that the mechanism of these compounds is through inhibiting cox-1 and cox-2 inhibitors. Thus this study has achieved the goal of exhibiting the binding interaction and patterns of the ligand with their respective ligands, which can aid the researchers to carry further formulations studies.

 

ACKNOWLEDGEMENT:

We would like to thank Nitte (Deemed to be University), NGSMIPS CADD Lab and NGSM Institute of Pharmaceutical Sciences, Mangaluru, Karnataka for providing the financial support and facilities for performing this work.

 

CONFLICT OF INTEREST:

The authors declare no conflict of interests.

 

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Accepted on 18.03.2020           © RJPT All right reserved

Research J. Pharm. and Tech 2020; 13(9):4200-4206.