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Application of Ligand Based Pharmacophore Modeling and Chemical Database Mining

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Application of Ligand Based Pharmacophore Modeling and Chemical Database Mining Available online a t www.derpharmachemica.com Scholars Research Library Der Pharma Chemica, 2015, 7(4):123-148 (http://derpharmachemica.com/archive.html) ISSN 0975-413X CODEN (USA): PCHHAX In-silico identification of novel Topoisomerase-I inhibitors: Application of ligand based pharmacophore modeling and chemical database mining Supriya Singh 1, Sarvesh Paliwal 1, Anubhuti Pandey 1, Sucheta Das 1 and Rajeev Singh 2* 1Department of Pharmacy, Banasthali University, Tonk, Rajasthan, India 2Material/ Organometallics Research Laboratory, Room No. 15, Department of Chemistry, ARSD, University of Delhi, New Delhi, India _____________________________________________________________________________________________ ABSTRACT In recent year’s topoisomerase I inhibitors like indenoisoquinolines have become important new lead for rational design of anticancer drugs due to their greater physiological and DNA-enzyme cleavage complexes stabilities. As a starting point a complete pharmacophore based 3D-QSAR study was performed on a series of 104 indenoisoquinolines and their derivatives. The best pharmacophore model consisted of one Hydrophobe (HY), one Positive Ionizable (PI) and one Ring Aromatic (RA) charecterstics which are a necessary requirement for good topoisomerase I inhibitory activity. The model was validated using Fischer randomization test and by internal and external data set of 38 and 27 compounds, respectively exhibiting r2 of 0.663 and 0.66. The validated pharmacophore model was used to screen NCI and Maybridge database resulting in identification of 21 novel topoisomerase I inhibitors. Since all the 21 compounds obeyed Lipinski’s rule of five, it is envisaged that these structurally diverse compounds have great potential for their development as anti-cancer agents. Keywords: DNA, Enzyme, QSAR, Database, NCI, Maybridge _____________________________________________________________________________________________ INTRODUCTION Cancer is the leading cause of mortality in most countries after cardiovascular disease. There is no other disease which parallels cancer in diversity of its origin, nature and treatments [1]. It is likely to become the major reason of the death in the upcoming years. In spite of the progress made, the index of cancer therapy remains low and its treatment is a challenge. Out of various types of cancers, lung cancer remains the leading cause of death among men and women in the civilized world. There will be an estimated 160,340 deaths caused by lung cancer (87,750 among men and 72,590 among women) in 2012, accounting for around 28 % of all cancer deaths [2]. As classified by the World Health Organization, there are four major types of lung cancer: squamous cell (epidermoid) carcinoma, small-cell (oat cell) carcinoma, adenocarcinoma, and large cell carcinoma. Conventional treatment of either form of lung cancer is quite ineffective. Therefore, there is a need to develop more effective chemotherapeutic agents against lung cancer. DNA Topoisomerase-I (TOP-I) is an effective molecular target for the development of clinically based anticancer agents. They show excellent activity against various types of tumors, especially lung cancer and colon cancer. Camptothecin (CPT) is the first agent identified as a TOP-I inhibitors. Camptothecins and its derivatives bind the interface of the TOP-I–DNA complex and exert their pharmacological activity. They are effective in S-phase rather than in the G1 or G2/M phases of the cell cycle [3-5]. Although CPTs are very potent but often show dose related toxicities. The indenoisoquinolines as a class of cytotoxic TOP-I inhibitors provide greater stabilities of the compounds as well as drug-enzyme-DNA cleavage complexes in comparison to the CPTs [6]. 123 www.scholarsresearchlibrary.com Rajeev Singh et al Der Pharma Chemica, 2015, 7 (4):123-148 ___________________ __________________________________________________________ Indenoisoquinoline compounds and their derivatives are found to possess antitumor and other biological activities like antimalarial, however, the relationships of their structure and activity are still not well understood [7, 8]. Therefore, correlating the physicochemical properties or structural features of compounds with their cytotoxicities in GI 50 will surely provide useful information for the identification of new antitumor drugs. Pharmacophore modeling is a powerful computational approach used for the study of biological activities with properties or molecular structures, which is helpful to explore the relationship between the structures of ligands and their biological activities. Also, it offers the advantages of higher speed and lower costs for bioactivity evaluation, especially compared to experimental testing. In present research work we have focused on two aspects. One is generation of ligand-based pharmacophore models with the help of Catalyst 4.8 (available from Accelrys Inc.), one of the leading software products for the automated generation of pharmacophore models and second is database mining using validated pharmacophore model to identify novel, structurally diverse and druggable chemical entities with high TOP-I inhibitory activity. The large number of successful applications of pharmacophore based virtual screening has been clearly demonstrated [9-11]. MATERIALS AND METHODS Biological activity and data set To build a dynamic 3D model four different datasets exhibiting TOP-I inhibitory activity were selected from the literature with experimentally determined cytotoxicity GI 50 values on human lung cancer cell lines. The homogeneity of the biological assays is one of the important aspects in pharmacophore based QSAR studies; therefore data set of 104 compounds belonging to the indenoisoquinolines was collected from the same research lab and group (Andrew Morrell and Mark Cushman et. al. ) with the same biological assay method. The dataset spanned a 3.8 order magnitude with the activity range from 0.012 µM to 89.1 µM. The structures of indenoisoquinolines along with their biological activity are given in Table 1 [12-15]. Table 1 Structures of 104 indenoisoquinoline derivatives as topoisomerase-I inhibitors R4 O R1 N R3 R2 O 1 2 3 4 Name of compound R R R R Activity (GI 50 in µ М) 1_3 -H -NO 2 Cl -OCH 3 0.295 1_35 -H -NO 2 Br O 5.75 1_36 -H -NO 2 Br 21.4 1_37 -H -NO 2 Br -CH 3 24.5 S 1_38 -H -NO 2 Br 1.05 1_39 -H -NO 2 Br 18.2 1_40 -H -NO 2 Cl -H 43.6 1_41 -H -NO 2 Cl -F 2.29 1_42 -H -NO 2 Cl -Cl 2.45 124 www.scholarsresearchlibrary.com Rajeev Singh et al Der Pharma Chemica, 2015, 7 (4):123-148 ___________________ __________________________________________________________ 1_43 -H -NO 2 Cl -Br 3.63 O 1_45 -H -NO 2 Cl 43.7 O Cl 1_46 -H -NO 2 N 2.57 O 1_47 -H -NO 2 Br 27.5 O O 1_48 -H -NO 2 Br -OTos 7.59 O O 1_49 -H -NO 2 Br 8.13 S N O 1_50 -H -NO 2 3 3.02 N 1_52 -H -NO 2 3 -CH 3 72.4 N 1_53 -H -NO 2 3 S 0.603 N 1_56 -H -NO 2 3 -F 0.251 1_58 -H -NO2 N3 -Br 56.2 1_59 -H -NO2 N3 -I 1.82 O 1_60 -H -NO2 N3 25.7 O 1_61 -H -NO2 N3 N 15.1 O 1_62 -H -NO 2 NH2 0.012 1_63 -H -NO 2 NH2 -C2H5 1.38 1_64 -H -NO 2 NH2 -CH 3 0.191 1_66 -H -NO 2 NH2 0.186 1_70 -H -NO 2 NH2 -Br 0.033 1_71 -H -NO 2 NH2 -I 0.021 H 1_86 -OCH 3 -OCH 3 Br N 1.29 O 1_87 -OCH 3 -OCH 3 Cl O 4.07 1_88 -OCH 3 -OCH 3 Br -C2H5 4.47 1_90 -OCH 3 -OCH 3 Br -F 4.17 Cl 1_92 -OCH 3 -OCH 3 N 14.8 125 www.scholarsresearchlibrary.com Rajeev Singh et al Der Pharma Chemica, 2015, 7 (4):123-148 ___________________ __________________________________________________________ H N N 1_94 -OCH 3 -OCH 3 3 50.1 O N 1_95 -OCH 3 -OCH 3 3 -NH 2 0.051 N 1_97 -OCH 3 -OCH 3 3 -OCH 3 11.5 N 1_98 -OCH 3 -OCH 3 3 56.2 N 1_99 -OCH 3 -OCH 3 3 -H 3.55 N 1_100 -OCH 3 -OCH 3 3 -F 4.17 N 1_102 -OCH 3 -OCH 3 3 N 14.1 N 1_103 -OCH 3 -OCH 3 3 -NO 2 89.1 1_105 -OCH 3 -OCH 3 NH2 -N(CH 3)2 17.0 1_107 -OCH 3 -OCH 3 NH2 -C2H5 1.82 1_108 -OCH 3 -OCH 3 NH2 -H 0.151 1_109 -OCH 3 -OCH 3 NH2 -F 0.603 O 1_110 -OCH 3 -OCH 3 NH2 3.72 O NH 1_111 -OCH 3 -OCH 3 2 N 3.98 1_112 -OCH 3 -OCH 3 NH2 -NO 2 0.617 2_6 -H -H Br -H 4.59 2_7 -H -H NH2 -H 0.20 2_8 -H -H N -H 1.74 N 2_9 -H -H -H 2.69 N 2_11 -H -NO 2 NH2 -H 0.275 2_12 -H -NO 2 N -H 5.62 N 2_13 -H -NO -H 0.19 2 N N 2_22 -H -H 3 -OCH 3 33.9 O 2_27 -H -H N -H 3.72 2_28 -H -H N3 -H 7.59 126 www.scholarsresearchlibrary.com Rajeev Singh et al Der Pharma Chemica, 2015, 7 (4):123-148 ___________________ __________________________________________________________ O 2_29 -H -H N -NO 2 0.021 O 2_31 -H -H N -OCH 3 1.41 H 2_32 -H -NO 2 N -H 0.031 OH H 2_34 -H -H N -OCH 3 0.026 OH H 2_35 -H -H N -H 0.195 OH 2_37 -H -H N -OCH 3 0.078 N 2_39 -H -H -OCH 0.056 N 3 O H N N 3_20 -H -H -H 0.794 O O O HN O N N 3_31 -H -H -H 22.4 N NH O O O O O HN N O 3_32 -H -H N -H 20 N NH O O O O O O HN N O N 3_34 -H -H -H 11 N NH O O O H N N 3_37 -H -H -H 0.977 O 127 www.scholarsresearchlibrary.com Rajeev Singh et al Der Pharma Chemica, 2015, 7 (4):123-148 ___________________ __________________________________________________________ O N N 3_38 -H -H H -H 0.028 O O H N N N 3_39 -H -H H -H 0.032 O O H H N N N 3_40 -H -H -H 0.339 O O N N N 3_42 -H -H -H 12.9 O O H N N N 3_44 -H -H H -H 0.525 O O H H N N N N 3_45 -H -H H -H 0.048 O O H H N N N N N 3_46 -H -H H H -H 0.977 O O H H N N N 3_49 -OCH3 -OCH3 -H 0.068 O O N N N 3_50 -OCH3 -OCH3 H H -H 1.51 O O H H N N N 3_51 -H -NO2 -H 0.631 O O 3_52 -H -NO2 -H 3.02 N N N H H O O O O 3_55 -OCH3 -OCH3 0.191 N N N H H O 128 www.scholarsresearchlibrary.com Rajeev Singh et al Der Pharma Chemica, 2015, 7 (4):123-148 ___________________ __________________________________________________________
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