Pharmacophore and QSAR modeling of some structurally diverse azaaurones derivatives as anti-malarial activity

Quantitative structure–activity relationship (QSAR) and Pharmacophore studies were performed on a series of 35 azaaurones derivatives to find out the structural requirements of their antimalarial activities. The compounds in the selected series were characterized by spatial, molecular and electrotopological descriptors using QSAR module of molecular design suite (V-Life MDSTM 3.5). Correlations between inhibitory activities and calculated predictor variables were established through partial least square regression method. The developed QSAR models were found to be statistically significant with respect to training (r2 > 0.6), and cross-validation (q2 > 0.6). Simulated annealing (SA) and stepwise (SW) regression are applied as variable selection methods for an effective comparison and model development. This study was performed with 35 compounds (data set) using the sphere exclusion (SE) algorithm method for the division of the data set into training and test sets. The statistically significant 2D-QSAR model having r2 = 0.9061 and q2 = 0.8150 with pred_r2 = 0.8719 was developed by SW-PLS confirms a positive contribution of T_O_Cl_6, T_C_Cl_1, SsOHcount, T_C_F_2 and SsCH3E index to the anti-malarial activity and best Group-based QSAR (GQSAR) model having r2 = 0.7624 and q2 = 0.7341 with pred_r2 = 0.7461 was developed by SA-PLS. The result two-dimensional QSAR and GQSAR clearly explained that substitution of topological indices, hydrophobic properties and auto-correlation descriptors of different atomic properties at R1 and R2 on aurone ring is essential for the activity. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by SA-partial least square method leading to anti-malarial activity prediction. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbor method, showing good correlative and predictive capabilities in terms of q2 = 0.6906 and pred_r2 = 0.7454. Additionally the pharmacophore model well corroborated with k-nearest neighbor studies as the contours of later were in good agreement with the 3D orientation of the pharmacophoric features. The distances between the pharmacophore sites were measured in order to confirm their significance to the activities. The results reveal that the acceptor, donor, and aromatic/hydrophobic pharmacophore properties are favorable contours sites for the activities. The results of 2D-QSAR, GQSAR and k-nearest neighbor-based 3D-QSAR studies give detailed structural insights as well as highlights important binding features of these substituted azaaurones derivatives as anti-malarial activity, which can provide guidance for the rational design of novel potent azaaurones derivatives.

[1]  M. Karelson,et al.  Structurally diverse quantitative structure--property relationship correlations of technologically relevant physical properties , 2000, Journal of chemical information and computer sciences.

[2]  M. Sharma,et al.  2D Qsar Study of 7-Methyljuglone Derivatives: An Approach to Design Anti Tubercular Agents , 2011 .

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  Nidhi Mishra,et al.  Synthesis of novel substituted 1,3-diaryl propenone derivatives and their antimalarial activity in vitro. , 2008, European journal of medicinal chemistry.

[5]  F. Souard,et al.  1-Azaaurones derived from the naturally occurring aurones as potential antimalarial drugs. , 2010, Bioorganic & medicinal chemistry.

[6]  Mukesh C. Sharma.  WITHDRAWN: Structural insight for (6-oxo-3-pyridazinyl)-benzimidazoles derivatives as angiotensin II receptor antagonists: QSAR, pharmacophore identification and kNNMFA approach , 2012 .

[7]  Sudhir A. Kulkarni,et al.  Application of GQSAR for Scaffold Hopping and Lead Optimization in Multitarget Inhibitors , 2012, Molecular informatics.

[8]  Mukesh C. Sharma,et al.  An approach to design potent anti-Alzheimer’s agents by 3D-QSAR studies on fused 5,6-bicyclic heterocycles as γ-secretase modulators using kNN–MFA methodology , 2014 .

[9]  S. Chaturvedi,et al.  3D- QSAR STUDIES, BIOLOGICAL EVALUATION STUDIES ON SOME SUBSTITUTED 3-CHLORO-1-(5-(5-CHLORO-2-PHENYL-BENZIMIDAZOLE-1- YLMETHYL)-(1, 3, 4) THIADIAZOLE-2-YL)-AZETIDIN-2-ONE AS POTENTIAL ANTIMICROBIAL ACTIVITY , 2009 .

[10]  Mukesh C. Sharma,et al.  WITHDRAWN: An approach to design antihypertensive agents by 2D QSAR studies on series of substituted benzimidazoles derivatives as angiotensin II receptor antagonists , 2011 .

[11]  T. Prentice.  World Health Report , 2013 .

[12]  Mukesh C. Sharma,et al.  WITHDRAWN: QSAR and pharmacophore modeling of some benzimidazole derivatives as Protein Kinase CK2 Inhibitors , 2013 .

[13]  Alexander Tropsha,et al.  Application of validated QSAR models of D1 dopaminergic antagonists for database mining. , 2005, Journal of medicinal chemistry.

[14]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[15]  P. Rosenthal.  Antimalarial chemotherapy: mechanisms of action, resistance, and new directions in drug discovery. , 2002 .

[16]  Mukesh C. Sharma,et al.  WITHDRAWN: Study of physicochemical properties-inducible nitric oxide synthase relationship of substituted quinazolinamines analogs: Pharmacophore identification and QSAR studies , 2013 .

[17]  Alexander Golbraikh,et al.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection , 2004, Molecular Diversity.

[18]  D. Warhurst,et al.  Drug‐resistant Malaria: Laboratory and Field Investigations * , 1997 .

[19]  V. Kulkarni,et al.  PHARMACOPHORE MODELING AND ATOM-BASED 3D-QSAR STUDIES OF ANTIFUNGAL BENZOFURANS , 2015 .

[20]  Aalt Bast,et al.  Comprehensive medicinal chemistry , 1991 .

[21]  D. V. Kohli,et al.  TWO DIMENSIONAL- QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS -2, 3 DIARYLTHIOPHENES AS SELECTIVE COX-1/-2 INHIBITORS , 2009 .

[22]  Mukesh C. Sharma,et al.  WITHDRAWN: QSAR studies of a series of angiotensin II receptor substituted benzimidazole bearing acidic heterocycles derivatives , 2011 .

[23]  Mukesh C. Sharma,et al.  Insight into the structural requirement of substituted quinazolinone biphenyl acylsulfonamides derivatives as Angiotensin II AT1 receptor antagonist: 2D and 3D QSAR approach , 2014 .

[24]  A. Tropsha,et al.  Beware of q 2 , 2002 .

[25]  Mukesh C. Sharma,et al.  Comprehensive two and three-dimensional QSAR studies of 3-substituted 6-butyl-1,2dihydropyridin-2-ones derivatives as angiotensin II receptor antagonists , 2012, Medicinal Chemistry Research.

[26]  Mukesh C. Sharma,et al.  WITHDRAWN: Predicting substituted 2-butylbenzimidazoles derivatives as angiotensin II receptor antagonists: 3D-QSAR and pharmacophore modeling , 2011 .

[27]  N. Sahu,et al.  SYNTHESIS , CHARACTERIZATION AND BIOLOGICAL ACTIVITIES OF SOME 1-( NICOTINYLAMINO )-2 SUBSTITUTED AZETIDINE-4-ONES AS POTENTIAL ANTIBACTERIAL AGENTS , 2009 .

[28]  Tommy Liljefors,et al.  A textbook of drug design and development , 1996 .

[29]  Mukesh C. Sharma,et al.  Molecular Modeling Studies of Substituted 2,4,5-Trisubstituted Triazolinones Aryl and Nonaryl Derivatives as Angiotensin II AT1 Receptor Antagonists , 2013 .

[30]  N. White.  Why is it that antimalarial drug treatments do not always work , 1998 .

[31]  H. Kubinyi.  QSAR : Hansch analysis and related approaches , 1993 .

[32]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[33]  P. Winstanley.  Chemotherapy for falciparum malaria: the armoury, the problems and the prospects. , 2000, Parasitology today.

[34]  Thomas A. Halgren.  Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94 , 1996, J. Comput. Chem..

[35]  Mukesh C. Sharma,et al.  QSAR, SYNTHESIS AND BIOLOGICAL ACTIVITY STUDIES OF SOME THIAZOLIDINONES DERIVATIVES , 2009 .

[36]  J. Durbin,et al.  Testing for serial correlation in least squares regression. II. , 1950, Biometrika.

[37]  Mukesh C. Sharma,et al.  WITHDRAWN: Comprehensive structure–activity relationship analysis of isoxazolinyl and isoxazolidinyl substituted quinazolinone derivatives as angiotensin II receptor antagonists , 2012 .

[38]  P. Choudhari,et al.  3D QSAR, pharmacophore indentification studies on series of 1-(2-ethoxyethyl)-1H-pyrazolo [4,3-d]pyrimidines as phosphodiesterase V inhibitors , 2015 .

[39]  Sudhir A. Kulkarni,et al.  A comprehensive structure-activity analysis of protein kinase B-alpha (Akt1) inhibitors. , 2010, Journal of molecular graphics & modelling.

[40]  Mukesh C. Sharma,et al.  Qsar study of some substituted 4-quinolinyl and 9-acridinyl hydrazones as antimalarial agents. , 2012, Acta poloniae pharmaceutica.

[41]  Alexander Tropsha,et al.  Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle , 2000, J. Chem. Inf. Comput. Sci..

[42]  Sudhir A. Kulkarni,et al.  A Dual‐Response Partial Least Squares Regression QSAR Model and its Application in Design of Dual Activators of PPARα and PPARγ , 2008 .

[43]  Mukesh C. Sharma,et al.  WITHDRAWN: QSAR analysis and 3D QSAR kNN-MFA approach on a series of substituted quinolines derivatives as angiotensin II receptor antagonists , 2011 .

[44]  Mukesh C. Sharma,et al.  A comprehensive structure–activity analysis 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives as angiotensin II receptor antagonists: using 2D- and 3D-QSAR approach , 2013, Medicinal Chemistry Research.

[45]  Mukesh C. Sharma,et al.  QSAR studies on imidazopyridazine derivatives as PfPK7 inhibitors , 2011 .

[46]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[47]  W. Wernsdorfer,et al.  Chemotherapy of malaria , 1987 .

[48]  J. Topliss,et al.  CHANCE FACTORS IN STUDIES OF QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS , 1980 .

[49]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[50]  Mukesh C. Sharma,et al.  WITHDRAWN: Structural insights for 5-β-Ketosulfoxide Imidazolyl Biphenyl Sulfonylureas derivatives as angiotensin II AT1 receptor antagonists using kNN-MFA with genetic algorithm , 2012 .

[51]  Mukesh C. Sharma,et al.  QSAR studies of some side chain modified 7-chloro-4-aminoquinolines as antimalarial agents , 2014 .

[52]  Mukesh C. Sharma,et al.  WITHDRAWN: QSAR analysis of imidazo[4,5-b]pyridine substituted α-Phenoxyphenylacetic acids as angiotensin II AT1 receptor antagonists , 2011 .

[53]  Knut Baumann,et al.  An Alignment-Independent Versatile Structure Descriptor for QSAR and QSPR Based on the Distribution of Molecular Features , 2002, J. Chem. Inf. Comput. Sci..

[54]  A. Jain,et al.  QSAR analysis of furanone derivatives as potential COX-2 inhibitors: kNN MFA approach , 2014 .

[55]  Mukesh C. Sharma,et al.  WITHDRAWN: QSAR studies on substituted benzimidazoles as angiotensin II receptor antagonists: kNN-MFA approach , 2011 .

[56]  C. G. Mohan,et al.  Molecular modelling and QSAR analysis of some structurally diverse N-type calcium channel blockers , 2010, Journal of molecular modeling.

[57]  R. Darlington,et al.  Regression and Linear Models , 1990 .

[58]  Mukesh C. Sharma,et al.  Comprehensive structure–activity relationship analysis of substituted 5-(biphenyl-4-ylmethyl) pyrazoles derivatives as AT1 selective angiotensin II receptor antagonists: 2D and kNNMFA QSAR approach , 2013, Medicinal Chemistry Research.

[59]  Kunal Roy,et al.  QSAR by LFER model of HIV protease inhibitor mannitol derivatives using FA-MLR, PCRA, and PLS techniques. , 2006, Bioorganic & medicinal chemistry.

[60]  R. Cramer,et al.  Validation of the general purpose tripos 5.2 force field , 1989 .

[61]  J. May,et al.  Chemoresistance in falciparum malaria. , 2003, Trends in parasitology.

[62]  Mukesh C. Sharma,et al.  WITHDRAWN: A comprehensive structure–activity analysis of 5-Carboxyl Imidazolyl biphenyl Sulfonylureas derivatives angiotensin AT1 receptor antagonists: 2D- and 3D-QSAR approach , 2012 .

[63]  Dan C. Fara,et al.  The Present Utility and Future Potential for Medicinal Chemistry of QSAR/QSPR with Whole Molecule Descriptors , 2003 .

[64]  M. Foley,et al.  Quinoline antimalarials: mechanisms of action and resistance. , 1997, International journal for parasitology.

[65]  Mukesh C. Sharma,et al.  3D QSAR kNN-MFA studies on 6-substituted benzimidazoles derivatives as Nonpeptide Angiotensin II Receptor Antagonists: A rational approach to antihypertensive agents , 2013 .

[66]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[67]  Mukesh C. Sharma,et al.  2D-QSAR STUDIES OF SOME 1, 3, 4-THIDIAZOLE-2YL AZETIDINE 2-ONE AS ANTIMICROBIAL ACTIVITY , 2009 .

[68]  Alexander Tropsha,et al.  Antitumor Agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents , 2007, J. Comput. Aided Mol. Des..

[69]  Juan M. Luco,et al.  QSAR Based on Multiple Linear Regression and PLS Methods for the Anti-HIV Activity of a Large Group of HEPT Derivatives , 1997, J. Chem. Inf. Comput. Sci..

[70]  Smita Sharma,et al.  QSAR studies of some substituted imidazolinones angiotensin II receptor antagonists using Partial Least Squares Regression (PLSR) method based feature selection , 2013 .

[71]  M. P. Freitas,et al.  Simple and highly predictive QSAR method: application to a series of (S)-N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides. , 2005, Talanta.

[72]  J. Durbin,et al.  Testing for serial correlation in least squares regression. I. , 1950, Biometrika.

[73]  J. Gasteiger,et al.  ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES , 1980 .

[74]  Mukesh C. Sharma,et al.  WITHDRAWN: Quantitative structure–activity analysis studies on triazolinone aryl and nonaryl substituents as angiotensin II receptor antagonists , 2012 .

[75]  Mukesh C. Sharma,et al.  Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis and molecular docking-based combined in silico rational approach to design potent and novel TRPV1 antagonists , 2012, Medicinal Chemistry Research.

[76]  Sudhir A. Kulkarni,et al.  Group‐Based QSAR (G‐QSAR): Mitigating Interpretation Challenges in QSAR , 2009 .

[77]  H. Kubinyi.  QSAR: Hansch Analysis and Related Approaches: Kubinyi/QSAR , 1993 .

[78]  Mukesh C. Sharma,et al.  WITHDRAWN: Two dimensional and k-nearest neighbor molecular field analysis approach on substituted Triazolone derivatives: An insight into the structural requirement for the angiotensin II receptor antagonist , 2011 .

[79]  P Ghosh,et al.  QSAR modeling for quinoxaline derivatives using genetic algorithm and simulated annealing based feature selection. , 2009, Current medicinal chemistry.

[80]  Sudhir A. Kulkarni,et al.  Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation , 2006, J. Chem. Inf. Model..

[81]  H. G. Vishwavidyalaya.  SYNTHESIS, CHARACTERIZATION AND BIOLOGICAL ACTIVITIES OF SOME 1-(NICOTINYLAMINO) -2 SUBSTITUTED AZETIDINE-4 -ONES AS POTENTIAL ANTIBACTERIAL AGENTS , 2009 .

[82]  J. Durbin,et al.  Corrections to Part I: Testing for Serial Correlation in Least Squares Regression: I , 1951 .

[83]  Mukesh C. Sharma,et al.  Insight into the structural requirement of aryltriazolinone derivatives as angiotensin II AT1 receptor: 2D and 3D-QSAR k-Nearest Neighbor Molecular Field Analysis approach , 2012, Medicinal Chemistry Research.

[84]  Smita Sharma,et al.  Comparative QSAR and pharmacophore modeling of substituted 2-[2′-(dimethylamino) ethyl]-1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives as anti-tumor activity , 2013, Medicinal Chemistry Research.

[85]  Mukesh C. Sharma,et al.  Molecular modeling and pharmacophore approach for structural requirements of some 2-substituted-1-naphthols derivatives as potent 5-lipoxygenase inhibitors , 2013, Medicinal Chemistry Research.

[86]  P. Fernandes,et al.  Structural analysis of α-glucosidase inhibitors by validated QSAR models using topological and hydrophobicity based descriptors , 2011 .

[87]  Alexander Golbraikh,et al.  QSAR Modeling Using Chirality Descriptors Derived from Molecular Topology , 2003, J. Chem. Inf. Comput. Sci..

[88]  S. Croft.  Antimalarial Chemotherapy: Mechanisms of Action, Resistance and New Directions in Drug Discovery. , 2001, Drug discovery today.

[89]  M. Perkins,et al.  Chapter 15. Chemotherapy of Malaria , 1996 .