INTRODUCTION:

Matrix metalloproteinase:

Matrix metalloproteinases (MMPs) has a multigene family of zinc-dependent extracellular matrix (ECM) remodelling endopeptidases. They are involved in several pathological processes among which cancer progression is very important¹. MMP‐2, ‐9, and ‐13 are reported to be highly associated with the aggressiveness of cancers². Clinical trials were conducted to identify a better MMP inhibitor (MMPIs) but were spectacularly unsuccessful in a variety of tumor types. This was due to many reasons and were detailed by Paylaki et al., 2003; Fingleton 2008; Passwell et al., 2011 and Dufour et al., 2013^3-6.

Two major reasons for failure of drugs are (i) non-specific inhibitor activity; and (ii) side effects induced by the drugs tested⁷. Hemopexin domain of MMPs are crucial when compared with the catalytic domain of MMPs. Hence this domain was used for the current study.

Azadirachta indica:

To treat various ailments, A. indica (neem) has been used extensively since long time. This plant is available commonly throughout India. The taxonomic classification of this plant is as follows:

Domain: Eukaryota

Kingdom: Plantae

Phylum: Spermatophyta

Subphylum: Angiospermae

Class: Dicotyledonae

Order: Rutales

Family: Meliaceae

Genus: Azadirachta

Species: Azadirachta indica

In this work, the phytochemicals from plant A. indica was tested to determine their anti-neoplastic properties. Intermolecular interaction analysis was carried out with the anticancer target MMP’s hemopexin domain. The result was compared with the scores of already available drugs in market.

MATERIALS AND METHODS:

Uncovering phytochemicals and structure retrieval:

The phytochemicals from Azadiracta indica was identified by review of literature⁸. Similarly, Dr. Dukes Phytochemical and Ethnobotanical Database⁹ of United States Department of Agriculture and the entire set of reported phytochemicals from Neem Metabolite Structure Database¹⁰ was also used to determine the phytochemicals present in the plant. The three dimensional structure of all the reported secondary metabolites from A. indica was retrieved from PubChem of National Centre for Biotechnology Information¹¹ and ChemSpider database of Royal Society of Chemistry¹². These structures were then used as leads for docking analysis. Additionally, the structure of three drugs, one extensively used in treating cancer (5-Flurouracil) and a two other MMPIs clinically tested (Doxycycline and Metastat) was also retrieved for a comparative analysis.

Tissue Drug Exposure Kinetics:

The drug exposure kinetic properties of the lead molecule help in predicting the capability of the drug for absorption, distribution, metabolism and excretion by body and toxicity (ADMET) of the phytochemicals. It was determined using Swiss ADME¹³ based on Lipinski rule¹⁴. Consequently, other important parameters like blood brain barrier penetration, human intestinal absorption, plasma protein binding and hepatotoxicity were also predicted to ensure the quality of the phytochemicals to be used as drug.

Target structure retrieval:

The target structure for carrying out docking was retrieved from Protein Data Bank¹⁵. The crystallised structure of hemopexin domain of protein MMP14 (PDB ID: 3C7X) was retrieved¹⁶. The structure was determined by X-Ray diffraction method. The structure is with a resolution of 1.70A⁰. The quality of the structure was analysed using Ramachandran plot¹⁷. The presence of aminoacids in allowed and disallowed regions were carefully validated.

Preparation of Target:

The target structure was loaded into the window and was prepared for docking by removing the heteroatoms, alternative conformers and co-crystallised water molecules present in the structure. The result was manually validated. CHARMM forcefield¹⁸ was then applied to enhance the stability of the structure for docking. Similarly, the lead structures were also loaded one by one.

Active site prediction and docking:

The active site of the target molecule was determined from its cavity based on eraser and fold filling algorithm of Discovery studio. Furthermore, Metapocket 2.0¹⁹ which works based on Fpocket, POCASA, LIGSITE, Q-Site Finder, PASS, ConCavity, GHECOM and SURFNET with improved prediction rate was also used to identify, analyse and compare the predicted active site of the target and the amino acid residues associated with it. Comments to carryout molecular docking was also given by specifying each of the active sites created separately and with each ligands in the list. Ligand Fit algorithm was adapted for predicting the orientation of leads and the target when bound to each other.

Solvation and Dynamic Simulation:

The top scored ligands were further validated using molecular dynamic simulation studies to ensure its stability and activity. This was followed after solvating the molecule by adding sodium and chloride ions along with water molecule to surround the structure. For simulation, Steepest Descendent and Adapted basis NR algorithms were used. Simulation was ran for 10000 pico seconds.

RESULT:

Structure quality analysis:

The Ramachandran plot for the structure was created and analysed to determine the quality of the retrieved structure. The allowed and disallowed energetic regions of the amino acid residues in protein were predicted. The result was positive as no residues were found in the disallowed region. A total number of 163 amino acids except glycine and proline was present in the structure. Furthermore, there was no evidence regarding the presence of any amino acid mutations in the target.

Pharmacokinetics of lead compounds:

The pharmacokinetic properties and ADMET for the leads were first screened in order to elucidate the effect of drug when exposed to the organism in terms of absorption, distribution, metabolism, excretion and toxicity after administered. Only those leads that qualified these properties were further selected for virtual screening. The phytochemicals were validated based on their A_logP98, Absorption 95 and 98 as well as blood brain barrier (BBB) 95 and 98 respectively. Table 1 represents the physiochemical properties of the ligands that passed ADMET screening.

Table 1: The pharmacokinetic and physiochemical properties of the leads that passed ADMET screening from A. indica.

Lig-and No:	PubChem ID	IUPAC Name	H-Accep-tor	H-Donor	Rotat-able Bond	Log P	Mole. Weight
1.	739004	N-[(5-chloro-2-pyridyl)carbamothioyl]thiophene-2-carboxamide	4	2	2	3.4	297.775
2.	5280460	7-hydroxy-6-methoxy-chromen-2-one	4	1	1	1.5	192.17
3.	101529197	(4aS,10aR)-6-hydroxy-7-(2-hydroxy-1-methyl-ethyl)-1,1,4a-trimethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione	4	2	2	2.6	330.424
4.	21769963	2-ethyl-7-methyl-6,7-dihydro-5H-cyclopenta[c]pyridin-2-ium-4-carbaldehyde	1	0	2	1.9	190.266
5.	11369675	(4aS,10aS)-6-methoxy-1,1,4a,7-tetramethyl-3,4,10,10a-tetrahydro-2H-phenanthren-9-one	2	0	1	5.2	286.415
6.	177090	1-[(4bS,8aS)-3-methoxy-4b,8,8-trimethyl-5,6,7,8a,9,10-hexahydrophenanthren-2-yl]ethanone	2	0	2	5.6	300.442
7.	94162	(4aS,10aS)-6-hydroxy-7-isopropyl-1,1,4a-trimethyl-3,4,10,10a-tetrahydro-2H-phenanthren-9-one	2	1	1	5.6	300.442
8.	11119228	(4aS,10aS)-6-hydroxy-1,1,4a,7-tetramethyl-3,4,10,10a-tetrahydro-2H-phenanthren-9-one	2	1	0	4.9	272.388
9.	21632833	(4aS,10aR)-7-isopropyl-1,1,4a-trimethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione	2	0	1	4.1	298.426
10.	189660	(4aS,10aR)-6,7-dimethoxy-1,1,4a-trimethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione	4	0	2	2.9	316.397
11.	189403	(4aS,10aR)-6-hydroxy-1,1,4a,7-tetramethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione	3	1	0	3	286.371
12.	189404	(4aS,10aR)-7-hydroxy-1,1,4a,6-tetramethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione	3	1	0	3	286.371
13.	11334829	(4aS,10aS)-6,7-dihydroxy-1,1,4a-trimethyl-3,4,10,10a-tetrahydro-2H-phenanthren-9-one	3	2	0	4.1	274.36
14.	180429	(4aR,10aR)-6,10a-dihydroxy-7-isopropyl-1,1,4a-trimethyl-2,3,4,10-tetrahydrophenanthren-9-one	3	2	1	4.2	316.441
15.	189728	(4bS,8aS)-3,4b,8,8-tetramethyl-10-oxo-6,7,8a,9-tetrahydro-5H-phenanthrene-2-carboxylic acid	3	1	1	4.7	300.398
16.	10906239	[(5R,7R,8R,9R,10R,13S,17R)-17-(3-furyl)-4,4,8,10,13-pentamethyl-3-oxo-5,6,7,9,11,12,16,17-octahydrocyclopenta[a]phenanthren-7-yl] acetate	4	0	3	5.7	436.592
17.	13875741	(5R,7R,8R,9R,10R,13S,17R)-17-(3-furyl)-7-hydroxy-4,4,8,10,13-pentamethyl-6,7,9,11,12,17-hexahydro-5H-cyclopenta[a]phenanthrene-3,16-dione	4	1	1	4.3	408.538
18.	101289833	[(5R,6R,7S,8R,9R,10R,13S,17R)-17-(3-furyl)-7-hydroxy-4,4,8,10,13-pentamethyl-3-oxo-1,2,5,6,7,9,11,12,16,17-decahydrocyclopenta[a]phenanthren-6-yl] acetate	5	1	3	4.6	454.607
19.	73356511	[(5R,6R,7S,8R,9R,10R,13S,17R)-17-(3-furyl)-6-hydroxy-4,4,8,10,13-pentamethyl-3-oxo-5,6,7,9,11,12,16,17-octahydrocyclopenta[a]phenanthren-7-yl] acetate	5	1	3	4.7	452.591
20.	76316558	[(5R,6R,8R,9R,10R,13S,17R)-17-(3-furyl)-6-hydroxy-4,4,8,10,13-pentamethyl-3-oxo-1,2,5,6,7,9,11,12,16,17-decahydrocyclopenta[a]phenanthren-7-yl] acetate	5	1	3	4.6	454.607
21.	12313376	methyl 2-[(1R,2S,4R,6R,9R,10S,11R,15R,18R)-6-(furan-3-yl)-7,9,11,15-tetramethyl-12,16-dioxo-3,17-dioxapentacyclo[9.6.1.02,9.04,8.015,18]octadeca-7,13-dien-10-yl]acetate	7	0	4	2.2	466.53
22.	102285347	methyl 2-[(1S,2R,8R,9S,10R,13R)-13-(furan-3-yl)-2-hydroxy-4,8,10,12-tetramethyl-7-oxo-16-oxatetracyclo[8.6.0.03,8.011,15]hexadeca-4,11-dien-9-yl]acetate	6	1	4	1.5	440.536
23.	3034112	(1S,2R,4S,7S,8S,11R,12R,17R,19R)-7-(furan-3-yl)-19-hydroxy-1,8,12,16,16-pentamethyl-3,6-dioxapentacyclo[9.8.0.02,4.02,8.012,17]nonadec-13-ene-5,15-dione	6	1	1	3.6	440.536
24.	49863985	[(1S,2R,4S,6S,7S,10R,11R,16R,18R)-6-(furan-3-yl)-1,7,11,15,15-pentamethyl-5,14-dioxo-3-oxapentacyclo[8.8.0.02,4.02,7.011,16]octadec-12-en-18-yl] acetate	6	0	3	4.2	466.574
25.	14467538	methyl 2-[(1R,2S,4R,6R,9R,10S,11R,15R,18S)-6-(furan-3-yl)-7,9,11,15-tetramethyl-12-oxo-3,17-dioxapentacyclo[9.6.1.02,9.04,8.015,18]octadeca-7,13-dien-10-yl]acetate	6	0	4	2.4	452.547
26.	189704	(2S,4aS,10aR)-2,6-dihydroxy-7-methoxy-1,1,4a-trimethyl-3,4,10,10a-tetrahydro-2H-phenanthren-9-one	4	2	1	2.9	304.386
27.	189726	(4bS,8aR)-3,4b,8,8-tetramethyl-7,10-dioxo-5,6,8a,9-tetrahydrophenanthrene-2-carboxylic acid	4	1	1	2.9	314.381
28.	102090424	(1R,2R,5S,6R,10R,11S,12R,15R,16R,18S,19R)-6-(furan-3-yl)-1,5,10,15-tetramethyl-13-oxapentacyclo[10.6.1.02,10.05,9.015,19]nonadec-8-ene-11,16,18-triol	5	3	1	3.1	428.491
29.	23675950	sodium;2-[(1S,2R,3S,8R,10R,15S)-13-(furan-3-yl)-2-hydroxy-4,8,10,12-tetramethyl-7-oxo-16-oxatetracyclo[8.6.0.03,8.011,15]hexadeca-4,11-dien-9-yl]acetate	6	1	3		448.491

Table 2: Amino acids forming the active site residues of the target protein’s hemopexin domain analysed using MetaPocket.

Binding Site : 1
MET_328	GLY_331	MET_468	GLY_469	SER_470
ASP_471	PHE_474	TYR_476	VAL_473	LYS_485
TRP_505	MET_333	ARG_345	ASN_346	GLU_332
VAL_344	ASN_347	ASP_504	PHE_467	GLN_348
MET_350	ARG_343	ARG_330
Binding Site : 2
LYS_404	GLY_407	ARG_408	GLY_409	ARG_443
GLU_405	ASP_385	ILE_403	PRO_411	THR_412
ASP_413	LEU_410	HIS_402
Binding Site : 3
GLU_373	PHE_420	MET_422	MET_328	ARG_374
VAL_326	ALA_327	ALA_371	TYR_372	ALA_418
SER_466	PHE_467	LEU_419	MET_468	ARG_330
GLY_331	SER_470	LEU_329	THR_325	ASN_369
THR_370	ASP_416	GLY_465	GLY_469

Molecule preparation and active site allocation:

The target was loaded into window and CHARMM forcefield was applied. Based on the grid points, the active site of the target was predicted using Discovery studio. Three sites were predicted among which the bigger site is the first one with 462 points covering 57.750A^{0^3}. Followed by this the second site is of 188 points and 23.500A^{0^3} in area. The third and the final site is the smallest with 120 points and 15.00A0^{0^3}.

Cross validation with MetaPocket:

The significance of identified active site by discovery studio was compared with the results obtained from MetaPocket. Based on the z-score calculated separately for each pockets, the top three sites were finalized and are given in Table 2 along with the functional residues occurring around the active site. These residues play a vital role in lead interaction.

Analysis of predominant binding mode of ligand with protein:

Molecular docking was carried out for each phytochemical leads separately and their binding affinities were calculated based on possible conformations of each ligands and generating candidate conformations. The permutation sampling of the ligand and site along with its orientation inside the active site was also considered for scoring. Based on the dock score, the potential phytochemicals from plant Azadirachta indica with possible antineoplastic effect by inhibiting MMPs were predicted.

On the basis of intermolecular force between the ligand and the active site of the target, the affinity of ligand binding was calculated.

Table 3: Docking scores of the phytochemicals from A. indica

Ligand No.	PubCheem ID	Lig Score 1	Lig Score 2	-PLP1	-PLP2	Jain	-PMF	Dock Score
1.	739004	1.8	3.02	69.12	57.53	3.25	-22.38	79.874
2.	5280460	3.5	4.43	63.86	58.37	2.98	-8.27	66.214
3.	101529197	-3.33	4.63	71.43	80.43	3.01	-6.98	73.789
4.	21769963	-999.9	-999.9	40.28	46.98	2.32	5.48	64.211
5.	11369675	-0.8	0.09	57.45	68	6.49	-45.49	48.919
6.	177090	2.53	4.26	63.3	67.9	4.77	-20.6	52.784
7.	94162	2.59	4.39	65.28	71.6	3.1	-14.68	62.421
8.	11119228	-999.9	-999.9	57.35	55.94	1.35	19.63	48.952
9.	21632833	1.92	4.08	70.94	74.42	3.77	-17.91	62.513
10.	189660	1.7	4.13	57.4	55.57	2.64	-11.02	55.788
11.	189403	-999.9	-999.9	52.94	54.13	1.32	13.38	50.232
12.	189404	1.18	1.44	54.63	68.87	6.36	-40.73	47.653
13.	11334829	2.27	3.9	58.73	63.81	2.38	-19.59	55.64
14.	180429	2.11	4.1	58.17	63.33	2.37	-19.39	57.936
15.	189728	3.19	4.44	60.95	63.49	3.41	-26.91	61.514
16.	10906239	-999.9	-999.9	43.84	52.58	3.69	-24.8	36.604
17.	13875741	-999.9	-999.9	46.43	47.6	3.38	-0.8	30.215
18.	101289833	-999.9	-999.9	51.1	55.95	4.47	-7.35	48.386
19.	73356511	-999.9	-999.9	49.18	59.32	6.97	-34.12	42.449
20.	76316558	0.34	-0.36	72.75	77.55	8.26	-36.11	59.576
21.	12313376	-999.9	-999.9	89.69	83.21	4.47	-5.79	48.186
22.	102285347	-999.9	-999.9	97.55	89.73	5.28	9.12	62.357
23.	3034112	-999.9	-999.9	40.07	36.62	3.13	5.99	25.373
24.	49863985	-999.9	-999.9	48.54	60.33	6.18	-29.52	35.877
25.	14467538	-999.9	-999.9	40.06	36.62	0.86	1.87	50.859
26.	189704	1.18	1.44	54.63	68.87	6.36	-40.73	47.653
27.	189726	3.13	4.39	60.76	62.87	3.17	-22.33	62.346
28.	102090424	-999.9	-999.9	52.32	58.04	4.24	-2.59	48.437
29.	23675950	Docked at no sites.
30.	Doxycyclin	2.7	3.04	46.98	57.38	4.58	-18.04	30.729
31.	Metastat	4.49	5.01	61.7	66.12	3.32	-5.57	56.089
32.	5-Flurouracil	-999.9	-999.9	23.97	17.29	-0.32	-4.76	42.853

Most of the leads showed better scores of docking at site 1 when compared with the other. Table 3 shows the scores generated for docking of each ligands with phytochemicals. Comparative analysis of the dock scores were done with 5-Flurouracil, Doxycycline and Metastat. The above-mentioned compounds from the plant Azadirachta indica exhibited higher docking score than that of these drugs. Furthermore, they also passed the ADMET test.

Intermolecular interactions of antineoplastic compounds:

The compounds N-[(5-chloro-2-pyridyl)carbamothioyl]thiophene-2-carboxamide scored the top score of 79.874, It is followed with (4aS,10aR)-6-hydroxy-7-(2-hydroxy-1-methyl-ethyl)-1,1,4a-trimethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione scoring 73.789. 7-hydroxy-6-methoxy-chromen-2-one scored 66.214 and 2-ethyl-7-methyl-6,7-dihydro-5H-cyclopenta[c]pyridin-2-ium-4-carbaldehyde scored 64.211. These scores surpassed the scores of 5-Flurouracil with 42.853. Similarly, the scores of Doxycycline and Metastat was also 30.729 and 56.089 which was lower than that of the scores of phytochemicals from neem. The intermolecular interaction between the best scored ligand with the target was analysed and visualized. Figure 1 to 4 is the representation of the molecular bonding between the phytochemical and the protein. The two-dimensional representation clearly gives idea on the position of the leads inside the active site of the target along with the intermolecular interactions they make.

Dotted lines represent the type of intermolecular interactions occurring between the target and ligand.

Figure 1 : Interaction of N-[(5-Chloropyridin-2-yl)carbamothioyl] thiophene-2-carboxamide with the amino acids in the active site of target.

Dotted lines represent the type of intermolecular interactions occurring between the target and ligand.

Figure 2: Intermolecular interaction of 7-hydroxy-6-methoxychromen-2-one with aminoacids at active site of hemopexin domain

Dotted lines represent the type of intermolecular interactions occurring between the target and ligand.

Figure 3: Intermolecular interaction of (4aS,10aR)-6-hydroxy-7-(2-hydroxy-1-methyl-ethyl)-1,1,4a-trimethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione with amino acids at active site.

Molecular Dynamic Simulation:

Solvation of the target lead complex helped to assess the stability of the drug inside the system when exposed to water and salts. It was carried out with a model of explicit periodic boundary within an orthorhombic shaped cell. For the ligands, from one to four, the number of water molecules added were 11132, 11129, 11385 and 11130 respectively.

Dotted lines represent the type of intermolecular interactions occurring between the target and ligand.

Figure 4: Intermolecular interaction of 2-ethyl-7-methyl-6,7-dihydro-5H-cyclopenta[c]pyridin-2-ium-4-carbaldehyde with amino acids at active site.

Furthermore, the pattern, strength and the properties of drug receptor interaction was understood by molecular dynamic simulation. This ensured the efficiency of the drug in performing its antineoplastic effect when administered into the system. For electrostatics, Spherical cut-off method was applied with minimization in 3000 steps with RMS gradient 0.1. After heating the complex, production was set for 10000 picoseconds. Dynamic integrator is applied using Leapfrog Varlet after the application of shake constraints to the target lead complex.

DISCUSSION:

Matrix metallopeptidases are one among the most widely targeted protein in treating cancer. Batimastat, prinomastat, marimastat are some among the well-studied MMPIs. Batimastat (BB-94), Marimastat (BB-516) are reported to possess strong Zn2+ chelating group called hydroxamate. They induce higher level of drug toxicity and hence were not approved by FDA²⁰. Some of the major reasons for failure of these drugs include poor solubility, low oral bioavailability, improper metabolism and poor selectivity. Furthermore, it also exhibited certain side effects like musculoskeletal pain and inflammation. These complications and the risk of increased drug toxicity are the major reasons for elimination of MMPIs from clinical trials^{3, 21-22}.

Researchers apply natural source as alternative for curing diseases^23-24. Identification of a better MMPI from plant based compounds is of prime need. In this work, the binding affinity scores of the active phytochemicals from A. indica was compared with the commonly used 5-FU and two other clinically tested MMPIs. These analyses lead to the identification of potential leads capable of performing better than that of the other reported drugs. It is evident from the binding affinity scores. Furthermore, the sores were better than that of the scores of already available drugs in the market. When the position of intermolecular interaction happened between the aminoacids and the lead molecules were considered, they fall under those which were predicted as the promising binding site residues by meta-pocket. The bonds occurred between the ligand and target that favoured inter molecular binding are van-der-Waals force, conventional Hydrogen bond, carbon-hydrogen bond, pi-donor-hydrogen bond, pi-alkyl, alkyl, pi-anion, pi-sigma and pi-lone pair bonds. This further ensures the potentiality of the lead compound in inhibiting MMPs. Other than these bonds, attractive charges were also visible between the target-ligand complex after binding.

The total energy of the target lead molecule progressively fell down as the time passed from sixteen to thousand picoseconds. Among the four ligands, ligand_4 was more suggested as the drop in the energy level attained equilibrium and followed a straight path. Though the energy level of other ligands also fell to lower levels, fluctuations were seen.

Figure 5 shows the time vs total energy changes of the target ligand complexes. The root mean square deviation values were also considerable for the fourth ligand which was evident from the RMSD plot. Additionally, it became clear that much RMS fluctuations were not reported for the ligand_4 which further ensures the compounds efficiency to be used as an MMPI.

CONCLUSION:

Comparative analysis of the dock scores of phytochemicals from A. indica with the already available drugs in market put light on N-[(5-chloro-2-pyridyl)carbamothioyl]thiophene-2-carboxamide; (4aS,10aR)-6-hydroxy-7-(2-hydroxy-1-methyl-ethyl)-1,1,4a-trimethyl-3,4,10,10a-tetrahydrophenanthrene-2,9-dione; 7-hydroxy-6-methoxy-chromen-2-one and 2-ethyl-7-methyl-6,7-dihydro-5H-cyclopenta[c]pyridin-2-ium-4-carbaldehyde as the potential MMPIs. The bonds formed and the affinity of binding was also better for these drugs. The efficiency of these drugs can further tested and proved invitro and invivo to ensure their antineoplastic effect.

CONFLICT OF INTEREST:

None.

REFERENCES:

1. Gialeli, C, Theocharis, A.D, Karamanos, N.K, Roles of matrix metalloproteinases in cancer progression and their pharmacological targeting. FEBS J. 278:1 (2011) 16-27.

2. Chien‐Yi Chan, Tzu‐Yuan Lin, Jim Jinn‐Chyuan Sheu, Wen‐Chieh Wu and Chun‐Yin Huang. Matrix metalloproteinase‐13 is a target gene of high‐mobility group box‐containing protein 1 in modulating oral cancer cell invasion. Journal of Cellular Physiology. 234:4 (2019) 4375-84.

3. M. Pavlaki, S. Zucker. Matrix metalloproteinase inhibitors (MMPIs): the beginning of phase I or the termination of phase III clinical trials. Cancer Metastasis Rev. 22: (2003) 177-203.

4. Fingleton,. MMPs as therapeutic targets—still a viable option?. Semin Cell Dev Biol. 19: (2008) 61-68.

5. N. Sela-Passwell, A. Trahtenherts, A. Kruger, I. Sagi. New opportunities in drug design of metalloproteinase inhibitors: combination between structure–function experimental approaches and systems biology. Expert Opin Drug Discov. 6: (2011), 527-542.

6. A. Dufour, C.M. Overall. Missing the target: matrix metalloproteinase antitargets in inflammation and cancer. Trends Pharmacol Sci. 34: (2013) 233-242.

7. Gemma Shay, Conor C.Lync h, Barbara Fingleton. Moving targets: Emerging roles for MMPs in cancer progression and metastasis. Matrix Biology. 44 (2015) 200-206.

8. Mohammad A. Alzohairy. Therapeutics Role of Azadirachta indica (Neem) and Their Active Constituents in Diseases Prevention and Treatment. Evidence-Based Complementary and Alternative Medicine. 7382506 (2016) 1- 11.

9. Duke, James A. Dr. Duke's Phytochemical and Ethnobotanical Databases" (Data Set). Ag Data Commons. (2016).

10. Kaushik S Hatti, Lakshmi Muralitharan, Rajendra Hegde and Anil Kush. NeeMDB: Convenient Database for Neem Secondary Metabolites. Bioinformation 10:5 (2014) 314-315.

11. Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A.,Bryant, S. H. PubChem Substance and Compound databases. Nucleic acids research. 44: (2016) D1202–D1213.

12. Harry E. Pence, Antony Williams. ChemSpider: An Online Chemical Information Resource. J. Chem. Educ. 87:11; (2010) 1123-24.

13. Antoine Daina, Olivier Michielin and Vincent Zoete. SwissADME: a free web tool to evaluate pharmacokinetics, druglikeness and medicinal chemistry friendliness of small molecules. Scientific reports. 7: (2017) 1-13.

14. Lipinski CA, Lombardo F, Dominy BW, Feeney P. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46: (2001) 3–26.

15. Berman,H.M. The Protein Data Bank: a historical perspective. Acta Crystallographica. 64: (2008) 88-95.

16. Tochowicz, A., Goettig, P., Evans, R., Visse, R., Shitomi, Y., Palmisano, R., Ito, N., Richter, K., Maskos, K., Franke, D., Svergun, D., Nagase, H., Bode, W., Itoh, Y. The Dimer Interface of the Membrane Type 1 Matrix Metalloproteinase Hemopexin Domain crystal structure and biological functions. J.Biol.Chem. 286: (2011) 7587-7600.

17. BoscoK Ho and Robert Brasseur. The Ramachandran plots of glycine and pre-proline. BMC Structural Biology. 5: (2005) 1-4.

18. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M, CHARMM: A program for macromolecular energy, minimization and dynamic calculations. J Comput Chem. 4:2 (1983) 187-217.

19. Bingding Huang. MetaPocket: A Meta Approach to Improve Protein Ligand Binding Site Prediction. OMICS A Journal of Integrative Biology 13:4 (2009) 325-330.

20. Noel Vinay Thomas and Se-Kwon Kim. Metalloproteinase Inhibitors: Status and Scope from Marine Organisms. Biochemistry Research International. 845975: (2010) 1-10.

21. L. M. Coussens, B. Fingleton, and L. M. Matrisian. Matrix metalloproteinase inhibitors and cancer: trials and tribulations, Science. 295: (2002) 2387–2392.

22. Zhang and S.-K. Kim. Matrix metalloproteinase inhibitors (MMPIs) from marine natural products: the current situation and future prospects. Marine Drugs. 7: (2009) 71–84.

23. Nikeherpianti Lolok, Harlyanti Muthmainnah Mashar, Itma Annah, Ahmad Saleh, Wa Ode Yuliastri and Muhammad Isrul. Antidiabetic effect of the Combination of Garlic peel extract Allium sativum) and onion peel (Allium cepa) in rats with oral glucose tolerance method. RJPT. 12:5(2019)0974-3618.

24. Ariya SS, Baby Joseph, S Vijayasrei, W Hopper. Computational analysis of compounds from Ocimum sanctum for anticancer activity against oral squamous cell carcinoma. AJPCR. 12:1(2019) 168-172.

Received on 21.06.2019 Modified on 28.08.2019

Research J. Pharm. and Tech 2020; 13(3):1100-1106.

DOI: 10.5958/0974-360X.2020.00202.4