IN-SILICO PHARMACOPHORE MAPPING AND DOCKING STUDIES OF INDOLE/BENZOXIMIDAZOLE-5-CARBOXIMIDINE DERIVATIVES AS ANTI-CANCER AGENTS

IN-SILICO PHARMACOPHORE MAPPING AND DOCKING STUDIES OF INDOLE/BENZOXIMIDAZOLE-5-CARBOXIMIDINE DERIVATIVES AS ANTI-CANCER AGENTS

Shivangi Agarwal¹, Mitali Mishra¹, Vikash K. Mishra ¹, D. Vinod ², Ekta Verma ¹ and Sushil K. Kashaw ^*1

Department of Pharmaceutical Sciences ¹, Dr. H.S. Gour Central University, Sagar, Madhya Pradesh, India

Madras Medical College ², Chennai, Tamil Nadu, India

ABSTRACT: The present research has been focused on the development of pharmacophore mapping and docking studies of indole/benzoximidazole-5-carboximidine derivatives as anti-cancer agents that can explore basic pharmacophore responsible for biological activity of structurally diverse compounds and also their binding affinity to the urokinase-type plasminogen activator (uPA). For pharmacophore mapping, a highly predictive pharmacophore based 3D-QSAR model was generated. Molecular docking experiments were carried out by means of the Glide module of the Schrodinger. A cubing receptor grid was centred around the co-crystallized ligand where the active binding site is present. The XP (extra precision) scoring function of GLIDE 6.0 was used. The scoring function of GLIDE docking program is presented in the G-score form which indicates the binding affinity of the designed compound to the receptor. A five point pharmacophore (APRRR) with one acceptor atom, one positively charged group and three aromatic rings as pharmacophore was developed. The generated best pharmacophore hypothesis yielded a statistically significant QSAR model, with a correlation coefficient of R2 = 0.8548 for training set molecules. The same sets of molecules were docked with urokinase-type plasminogen activator as target protein. The Gscore of the ligand 25 was found to be -11.89 as comparable with the G-score of reference drug (132: 6-CHLORO-2-(2-HYDROXY-BIPHENYL-3-YL)-1H-INDOLE-5-CARBOXAMIDINE) i.e.-11.626. The present study aimed to develop ligand based pharmacophore hypothesis and an interaction pattern by docking. Both studies rendered significant information which highlights important binding features of uPA inhibitors which can be utilized further in the successful designing of novel highly active analogues against uPA.

Pharmacophore Mapping,

Docking, Indole/benzoximidazole-5-carboximidine, Anti-cancer,

Glide scores and Urokinase type plasminogen activator (uPA)

INTRODUCTION: The involvement of urokinase‐type plasminogen activator receptor in the pathology of human cancers is well documented ^{1, 2, 3}. High levels of uPAR in tumour tissues and plasma from patients with various human cancers are associated with poor prognosis and increased risk of tumour recurrence and metastasis. The uPAS (urokinase plasminogen activating system) is involved in the extracellular conversion of the ubiquitous inactive plasminogen to the broad-spectrum serine protease plasmin, implicated in numerous pathophysiological process requiring the remodelling of extracellular matrix (ECM) and basement membranes (BM) ⁴. Human uPAR (urokinase-type plasminogen activating receptor) is a glycolipid‐anchored modular protein having a single‐chain polypeptide (283 amino acids) organized into three extracellular domains ⁵. These three domains form a concave shape with a central cone-shaped cavity where the urokinase fragment inserts ⁶. Inhibition of expression of these components leads to a reduction in the invasive and metastatic capacity of many tumors ⁷. The causes of cancer are diverse, complex and only partially understood. Risk to cancer can be increase by tobacco use, dietary factors, certain infections, exposure to radiations, environmental factors, obesity etc ⁸.

These factors can either directly damage the genes or combine with genetic faults within the cells to cause cancerous mutations ⁹.  Only about 5% to 10% of all cancers are inherited – resulting directly from gene defects (called mutations) inherited from a parent ¹⁰. Cancer is the second leading cause of death in the developed world. Cancer accounted 7.9 million deaths (around 13% of all deaths) in 2008 ¹¹. Regular use of some established screening tests can prevent the development of cancer through identification and removal or treatment of premalignant abnormalities ¹². Currently in spite of intensive research and some major advances in treatment, cancer claims the life of nearly one out of four Americans.

It is thus second to heart diseases responsible for 35% of deaths in US ¹³. These numbers are projected to rise to 15 million new cases and 9.0 million deaths in 2015 and 21.4 million new cases and 13.1 million deaths in 2030, respectively. Increased consumption of coffee may reduce the risk of liver cancer ¹⁴. Urokinase-type plasminogen activator (uPA), a trypsin-like serine protease is strongly associated with tumor cells plays a vital role in several biological processes including tissue remodeling, cell migration, and matrix degradation¹⁵. Over expression of uPA (urokinase-type plasminogen activator) or uPAR (urokinase-type plasminogen activator receptor) is a feature of malignancy and is correlated with tumour progression and metastasis ⁷. Because of its involvement, uPA (urokinase-type plasminogen activator) has emerged as a drug target for development of therapeutics for various types of cancer. This has resulted in an immense clinical interest in developing potent and orally bioavailable inhibitors of uPA (urokinase-type plasminogen activator) that can serve as therapeutic agents in the treatment of cancer ¹⁵. Quantitative structure-activity relationships (QSARs) are based on the assumption that the structure of a molecule (i.e. geometric, steric and electronic properties) must contain the features responsible for its physical, chemical and biological properties and on the ability to represent the chemical by one or more numerical descriptors ¹⁶.

It also borders into various other areas of chemoinformatics and bioinformatics ¹⁷. Pharmacophore approaches have become one of the major tools in drug discovery after the past century’s development. The concept of pharmacophore was first introduced in 1909 by Ehrlich who defined the pharmacophore as ‘a molecular framework that carries (phoros) the essential features responsible for a drug’s (pharmacon) biological activity’. A Pharmacophore model can be established either in a ligand manner, by superposing a set of active molecules and extracting common chemical features that are essential for their bioactivity, or in a structure-based manner, by probing possible interaction points between the macromolecular target and ligands. Pharmacophore approaches have been used extensively in virtual screening, de novo design and other applications such as lead optimization and multitarget drug design ¹⁸.

Pharmacophore modeling is one of the most powerful techniques to classify and identify key features from a group of molecules such as active and inactive compounds ¹⁹. Phase is a versatile product for pharmacophore perception, structure alignment, activity prediction, and 3D database searching ²⁰. In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex²¹. Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an important role in the rational design of drugs ²². The aim of molecular docking is to achieve an optimized conformation for both the protein and ligand and relative orientation between protein and ligand such that the free energy of the overall system is minimized ^{23, 24}. To perform a docking screen, the first requirement is a structure of the protein of interest. Usually the structure has been determined using a biophysical technique such as x-ray crystallography, or NMR spectroscopy. This protein structure and a database of potential ligands serve as inputs to a docking program ²³.

In the present study, we have carried out pharmacophore mapping ^{25, 26} and docking studies²⁷ employing PHASE and GLIDE modules of Schrödinger's software program ‘MAESTRO’ respectively in order to explore the correlation between structure and biological activity of indole/benzoimidazole-5-carboxamidines as uPA (urokinase-type plasminogen activator) inhibitors and the docking studies were carried out to explore the binding interaction mechanism between analogues and the receptor. The final evaluation is done with glide score (docking score) and best pose is generated as the output. We have developed 3D-QSAR models for the series of indole/ benzoimidazole-5-carboxamidines and the contour maps derived revealed the significance of hydrogen bond donor, hydrophobic/non-polar (B) and electron withdrawing (C) properties. The blue contours represent the regions where the substitution of groups with the particular property may enhance the biological activity whereas red cubes represent the depreciating biological activity. The 3D-QSAR models generated in the present study are consistent with the binding site and can be used as putative pharmacophore. The structural requirements identified in the present study can be utilized strategically in the design of novel, potent, and selective urokinase plasminogen activator inhibitors as anticancer agents.

MATERIALS AND METHODS:

Selection of molecules and data set:

A dataset of thirty nine indole/benzoximidazole-5-carboximidine derivatives ¹⁵ reported to have inhibitory activity on urokinase plasminogen activator (uPA) was used in the present Pharmacophore Mapping and Docking Studies. The inhibitory concentration values reported ¹⁵ as _PIC₅₀ (-logIC₅₀) were used for the studies. The structures along with their _PIC₅₀ values are represented in Table 1.

TABLE 1: CHEMICAL STRUCTURES OF DATA SET USED FOR PHARMACOPHORE MAPPING AND DOCKING STUDIES WITH ACTUAL AND PREDICTED ACTIVITY FROM THE BEST MODEL

The set of molecules considered in this study was sketched and geometrically refined using Lig Prep module implemented in the “Maestro suite” program (version 9.5) ²⁰. After the sketching of all molecules, cleaning and conformational search was performed in “develop pharmacophore” module of PHASE. The conformations were generated by the Monte Carlo (MCMM) method as implemented in Macro Model version 9.5 using a maximum of 1000 steps with a distance-dependent dielectric solvent model and an OPLS-2005 force field. These conformers were employed for the development of pharmacophore model ^{28, 29}.Processing of molecules:

Pharmacophore mapping:

Phase methodology:

Pharmacophore mapping was carried out using PHASE: a module of Schrödinger's software program ‘MAESTRO’ [9.5] ³⁴. This method gives a series of possible common pharmacophore for consideration and hypothesises the imaginary points to be collections of pharmacophoric chemical features i.e (hydrogen-bond donors, hydrogen-bond acceptors, hydrophobic groups, aromatic rings, positive and negative ionic and ionisable centres) contained in the dataset molecules.

Pharmacophore hypothesis generation:

By using “Developing a pharmacophore model” of Phase module, a set of pharmacophore features to create pharmacophore sites (site points) for all the ligands was used. Once a feature has been mapped to a specific location in a conformation, it is referred to as a pharmacophore site. Common pharmacophoric features were then identified from a set of variants-a set of feature types that define a possible pharmacophore. In the next step, common pharmacophore hypothesis were examined using a scoring function i.e. survival scores of actives and inactives ²⁸. These pharmacophores were scored efficiently using scoring techniques like survival, survival minus inactives and post-hoc, to identify common pharmacophore hypothesis. Each hypothesis was accompanied by a set of aligned conformations that was further used for the alignment of study molecules for 3D- QSAR analysis. PHASE identifies the 3D pharmacophores as ‘common feature hypotheses’ and ranks them on the basis of how well the alignment of molecules correlates the activities of active and inactive molecules by using vector score , volume score and site score ³⁰. The regression analysis was performed by constructing a series of models with an increasing number of PLS factors ³¹.

Docking studies:

The molecular docking tool, Glide (Schrödinger, LLC, New York) software was used for studying binding modes of the compounds in to the binding pocket of caspase. All structures were prepared for docking using ‘protein preparation wizard’ in Maestro wizard 9.5 ³². Owing to the increase in computer power and algorithm performance, it is now possible to dock thousands of ligands in a timeline which is useful to the pharmaceutical industry ³³.

Compound dataset:

The series of compounds of iodole/ benzoximidazole-5-carboximidine derivatives was docked with the protein urokinase plasminogen activator. The ligands (molecules of dataset) were subjected to Lig Prep wizard work flow, their energies were minimized using OPLS 2005. Ionization of ligand was done between pH 5-9 using Epik ionizer and the stereoisomers were generated at most 32 per ligand. This is an automatic preparation process, performed with the LigPrep tool of the Schrödinger package.

Protein preparation and Receptor grid generation:

Protein structure (PDB ID-1GJ7) was subjected to the Protein Preparation Wizard workflow implemented in the Schrodinger package ²⁹. The hydrogens were added and water molecules were deleted. Then receptor grid was generated around the binding site. For receptor grid generation, centroid of the workspace ligand was selected by picking and excluding the co-crystallized ligand from the binding site.

Molecular docking:

Molecular docking experiments were carried out by means of the Glide, as implemented in the Schrodinger ³⁴. A cubing receptor grid was centered around the co-crystallized ligand where the active binding site is present. The XP (extra precision) scoring function of GLIDE 6.0 was used and lastly the docking job was run.

The scoring function of GLIDE docking program is presented in the G-score form. G-score indicates the binding affinity of the designed compound to the receptor/enzyme.

G Score = 0.05*vd W + 0.15*Coul + Lipo + H bond + Metal + Rewards + Rot B + Site.

where, vdW, Vander Waal energy; Coul, Coulomb energy; Lipo, lipophilic contact term; H Bond, hydrogen-bonding term; Metal, metal-binding term; Bury P, penalty for buried polar groups; RotB, penalty for freezing rotatable bonds; Site, polar interactions at the active site ³².

RESULTS:

Pharmacophore mapping and docking studies were performed on the series of indole/benzoximidazole-5-carboximidine derivatives against uPA to identify common structural features required for the biological activity. These studies were performed with the PHASE module of Schrodinger software.

Pharmacophore mapping:

A total of 5 different variant hypothesis were generated upon completion of common pharmacophore identification process. A maximum of five features were allowed to develop hypothesis. The result of top five hypothesis high gradient score is recorded in Table 2.

TABLE 2: SCORING RESULTS OF THE DIFFERENT HYPOTHESES GENERATED

The top model was found to be associated with the five point hypotheses (APRRR.83) which consist of one acceptor group (A), one positive ionic group (P), three aromatic rings (R). This is denoted as A2P8R9R11R12.The best hypothesis showed the survival score as 3.941. The common pharmacophoric features are then scored with reference to the volume occupied by training set molecules. These large numbers of independent variables are then correlated with dependent variables using Partial Least Squares (PLS) analysis ³⁵. The special disposition of the sites showing distance between pharmacophoric sites is shown in Fig.1 and the angle between pharmacophoric sites is shown in Fig.2.

FIG.1: SELECTED HYPOTHESIS: APRRR.83

FIG. 2: ANGLES BETWEEN DIFFERENT SITES OF SELECTED HYPOTHESIS: APRRR.83

The pharmacophoric sites mapping over all the molecules of dataset as well as active molecule is shown in Fig. 3 and Fig. 4 respectively.

FIG.3: PHARMACOPHORE MAPPED OVER ALL THE MOLECULES OF DATA SET

FIG.4: PHARMACOPHORE MAPPED OVER ACTIVE MOLECULE

The pharmacophore hypothesis yielded a 3D-QSAR model with good PLS statistics. Among various PLS factors, PLS factor 5 was selected on the basis of statistical parameters. The training set correlation is characterized by PLS factor 5 (r2 = 0.8548, SD = 0.4479, F = 25.9, stability=0.3965). The distances and angles between different sites of ADHHR.515 are given in Tables 3 and Table 4 respectively.

TABLE 3: DISTANCES BETWEEN DIFFERENT SITES OF MODEL APRRR.83

TABLE 4: ANGLES BETWEEN DIFFERENT SITES OF MODEL APRRR.83

 PLS regression analysis, a chemometric technique, was utilized to correlate dependent variables (biological activity) with independent variables (binary values) to derive a 3D-QSAR model [30].It is gaining importance in many fields of chemistry; analytical, physical, clinical chemistry. The pioneering work in PLS was done by H. Wold [36]. PLS creates a series of regression models with PLS factors not larger than 1/5 of the training set molecules. The model was selected on the basis of value of Q² and R².The generated best model was further validated for its external predictability. For model generation and validation, the total molecules were divided into Training and Test set molecules with training set=28 and test set=11.Differerated models were generated with 5 PLS factors and the best one was selected. The results for the 5 PLS factors are listed in Table 5.

TABLE 5: STATISTICAL RESULTS OF GENERATED 3D QSAR MODELS

 The model generated with PLS 5 was selected as the best model with correlation coefficient R²=O.8548. The reliability of the model can be judged based on the external prediction. The model showed very good correlation coefficient of 0.8244 with the test set molecules. The predicted activity of training and test set molecules are presented in the Table 1 and the fitness scores are shown of all the molecules of dataset is shown in Table 6.

TABLE 6: FITNESS SCORES OF ALL THE TRAINING AND TEST SET COMPOUNDS.

The correlation scatter plot between actual and predicted values of biological activity of training and test set is presented in Fig.5.

FIG.5: CORRELATION SCATTER PLOT ACTUAL AND PREDICTED ACTIVITY OF TRAINING AND TEST SET MOLECULES.

Contour analysis:

Contour plots generated from the best 3D QSAR model are represented as positive and negative activity coefficient of different properties, namely hydrogen bond donor (A), hydrophobic/non-polar (B) and electron withdrawing (C) properties are given in Fig. 6. The blue contours represent the regions where the substitution of groups with the particular property may enhance the biological activity whereas red cubes represent the depreciating biological activity.

FIG. 6: STREREOVIEWS OF CONTOUR MAPS FOR 3D-QSAR MODELS HYDROGEN BOND DONOR (A), HYDROPHOBIC (B) AND ELECTRON WITHDRAWING PROPERTY (C).

1. A) Hydrogen bond donor property contour:

As shown in the Fig. 6A, a large blue contour at 5^th position of indole moiety signifies the importance of H-bond donor group at this position. This H-bond donor group (carboximidine) is important for the anti-cancer activity of all the compounds of dataset. The blue region at Nitrogen of Indole and Hydroxyl group of biphenyl ring indicates the importance of indole ring and hydroxyl group for the activity. Compounds s16,s 17, s18, s19, s20, s25, s26, s27, s28, s34, s35, s36, s37with indole ring and hydroxyl group of biphenyl ring showed comparative activity as s25.

1. B) Hydrophobic property contour:

Contour map for hydrophobic property displayed in Fig. (6B) displays the most active compound as orienting itself into the favourable blue region. The blue region at position 1,3,4,5,6,7 and around biphenyl are in favourable region of positive activity coefficient thereby increasing the activity whereas in other most compounds less hydrophobic hydrogen group at position 6^th is present in favourable region thereby reducing the activity compared to s25.

1. C) Electron withdrawing property contour:

A large blue contour at 5^th position of indole moiety Fig. (6C)  signifies the importance of electron withdrawing group at this position. This electron withdrawing group (carboximidine) is important for the anti-cancer activity of all the compounds of dataset. The blue region at 1^st and 2^nd position of Indole and Hydroxyl group of biphenyl ring indicates the importance of these region for the activity.

Docking studies:

The docking studies were carried out to explore the interaction mechanism between inhibitors and the receptor. The final evaluation is done with glide score (docking score) and best pose is generated as the output.

 G score = a x vdw + b* cow þ Lipo +H bond+ Metal + BuryP + Rot B + Site

where, vdW, Vander Waal energy; Coul, Coulomb energy; Lipo, lipophilic contact term; H Bond, hydrogen-bonding term; Metal, metal-binding term; Bury P, penalty for buried polar groups; RotB, penalty for freezing rotatable bonds; Site, polar interactions at the active site ²⁶. The 3D view of uPA is shown in Fig.7.

FIG.7: 3D VIEW OF UROKINASE-TYPE PLASMINOGEN ACTIVATOR (PDB ID-1GJ7)

The most active compound in the training set has scored a best docking score value of -11.89. It has formed an interaction with HIS57, ASP219, GLY189, SER 190. The Glide scores and other solutions of protein-ligand complexes obtained from docking calculations for selected compound are listed in Table 7.

TABLE 7: DOCKING SCORES AND OTHER SOLUTIONS OBTAINED FOR THE MOST ACTIVE LIGAND

The ligand protein interaction in 2D view is shown in Fig.8 and Fig. 9 shows the 3D view of docking of most active ligand into binding pockets of uPA.

FIG. 8: DOCKING INTERACTION POSE OF MOST ACTIVE LIGAND (25) WITH UROKINASE TYPE PLASMINOGEN ACTIVATOR.

FIG.9: THE BINDING POCKET OF uPA (PDB ID: 1GJ7) WITH THE MOST ACTIVE COMPOUND S25.

To validate the docking protocol, co-crystallized ligand (132) was docked into the active site of urokinase plasminogen activator (1GJ7). The Glide scores and other solutions of protein-ligand complexes obtained from docking calculations for selected compound are listed in Table 8.

TABLE 8: DOCKING SCORES AND OTHER SOLUTIONS OBTAINED FOR THE CO-CRYSTALLIZED LIGAND

The co-crystallized ligand protein interaction in 2D view is shown in Fig.10 and Fig.11 shows the 3D view of docking of co-crystallized ligand into binding pockets of uPA (urokinase-type plasminogen activator).

FIG 10: DOCKING INTERACTION POSE OF CO-CRYSTALLIZED LIGAND WITH UROKINASE TYPE PLASMINOGEN ACTIVATOR

FIG.11: THE BINDING POCKET OF uPA (PDB ID: 1GJ7) WITH THE CO-CRYSTALLIZED LIGAND OF THE PROTEIN

Interactions in Docking:

1. A) Ligand s25:

The hydroxyl group (OH) of phenyl showed the H-bond interaction (distance=2.11Å) with the nitrogen atom of imidazole ring of HIS57 amino acid of the protein residue. The positively charged NH2 group of the indole ring showed a H-bond interaction (distance=1.91659Å) with the oxygen of carbonyl group of GLY219 and a H-bond interaction (distance=1.89 Å) with the oxygen of hydroxyl group of ASP 189 3amino acids and the other NH2 showed a H-bond interaction (distance=1.86 Å) with the oxygen of carbonyl group ASP 189 and H-bond interaction (distance=2.00 Å) with the oxygen of hydroxyl group of SER190  amino acids of the protein. Also the terminal phenyl group of ligand showed a pi-pi stacking (distance=4.81689Å) with the imidazole ring of HIS57.

1. B) Co-crystallized Ligand:

The hydroxyl group (OH) of phenyl showed the H-bond interaction (distance=2.2Å) with the nitrogen atom of imidazole ring of HIS57 amino acid of the protein residue. The positively charged NH2 group of the indole ring showed a H-bond interaction (distance=1.90873Å) with the oxygen of carbonyl group of the GLY219 and H-bond interaction (distance=1.94Å) with the oxygen of hydroxyl group of ASP 189 amino acids and the other NH2 showed a H-bond interaction (distance=2.02609Å) with the oxygen of hydroxyl group of SER190 amino acid of the protein. Also the terminal phenyl group of ligand showed a pi-pi stacking (distance=4.84263 Å) with the imidazole ring of HIS57.

DISCUSSION: In the present studies, PHASE and GLIDE modules were employed to a series of indole/ benzoimidazole-5-carboxamidines as uPA inhibitors. PHASE identifies the 3D pharmacophores as ‘common feature hypotheses’ and ranks them on the basis of how well the alignment of molecules correlates the activities of active and inactive molecules by using vector score, volume score and site score. The regression analysis was performed by constructing a series of models with an increasing number of PLS factors. PLS creates a series of regression models with PLS factors not larger than 1/5 of the training set molecules. The pharmacophore hypothesis yielded a 3D-QSAR model with good PLS statistics. The model was selected on the basis of value regression coefficient and co-relation coefficient.

Contour plots generated from the best 3D QSAR model are represented as positive and negative activity coefficient of different properties, namely hydrogen bond donor (A), hydrophobic/non-polar (B) and electron withdrawing (C) properties The blue contours represent the regions where the substitution of groups with the particular property may enhance the biological activity whereas red cubes represent the depreciating biological activity. The series of compounds of iodole/ benzoximidazole-5-carboximidine derivatives was then docked with the protein urokinase plasminogen activator. The ligands (molecules of dataset) were subjected to LigPrep wizard work flow, their energies were minimized using OPLS 2005. Ionization of ligand was done and the stereoisomers were generated at most 32 per ligand.

This is an automatic preparation process, performed with the LigPrep tool of the Schrödinger package. The XP (extra precision) scoring function of GLIDE 6.0 was used. The scoring function of GLIDE docking program is presented in the G-score form. G-score indicates the binding affinity of the designed compound to the receptor/enzyme. The docking studies were carried out to explore the interaction mechanism between inhibitors and the receptor. To validate the docking protocol, co-crystallized ligand (132) was docked into the active site of urokinase plasminogen activator (1GJ7).

CONCLUSION: Pharmacophore mapping studies were performed on indole/benzoimidazole-5-carboxamidine derivatives to determine the structural requirements for potency against uPA (urokinase-type plasminogen activator) for their anticancer activity. A highly predictive pharmacophore based 3D-QSAR model was generated with five point hypotheses (APRRR) with one acceptor atom, one positively charged group and three aromatic rings as pharmacophore features.

To explore the lead optimization options of the Indole / benzoimidazole – 5 - carboxamidine derivatives, this dataset was used to build a QSAR model where the model with best statistics found was with PLS factor 5 with best correlation coefficient (R2=0.8548), standard deviation (0.4479) and variance ratio (F) (25.9). This model showed correlation coefficient (Q2) 0.5732 and Pearson R (0.8244) with test set molecules. Contour analysis from our model gave us the following vital information about our core molecule. Hydrogen bond donor and electron withdrawing group at 5^th position of indole ring (carboximidine) is important for the activity. Indole ring itself plays a significant role in anticancer activity. Biphenyl substituent at 2^nd position of indole ring is in favourable region of positive activity coefficient.

Electron withdrawing group (hydroxyl) at 2’ position of biphenyl ring leads to increase in activity as in compound s25. More hydrophobic group than hydrogen at 6^th position of indole ring as fluorine, chlorine leads to increase in activity. The binding interactions of the database uPA inhibitors in the active site were studied by molecular docking. The scoring function of GLIDE docking program is presented in the G-score form which   indicates the binding affinity of the designed compound to the receptor/enzyme. The Gscore of the ligand no. s25 was found to be -11.89 as comparable with the G-score of reference drug i.e. -11.626. The present study aimed to develop ligand based pharmacophore hypothesis and a interaction pattern by docking. Both studies rendered significant information which gives detailed structural insights as well as highlights important binding features of uPA inhibitors which can provide crucial clues and guidance that can be used in the successful designing of novel highly active analogues against uPA.

ACKNOWLEDGEMENT: One of the author wish to acknowledge to the AICTE (All India Council of Technical Education) for granting of the fund for the completion of this project work.

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How to cite this article:

Agarwal S, Mishra M, Mishra VK, Vinod D, Verma E and Kashaw SK: In-silico Pharmacophore Mapping and Docking Studies of Indole/Benzoximidazole-5-Carboximidine Derivatives as Anti-Cancer Agents. Int J Pharm Sci Res 2016; 7(8): 3243-61.doi: 10.13040/IJPSR.0975-8232.7(8).3243-61.

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