Integrating GUSAR and QSAR analyses for antimalarial activity of synthetic prodiginines against multi drug resistant str
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Med Chem Res (2013) 22:2284–2292 DOI 10.1007/s00044-012-0223-7
ORIGINAL RESEARCH
Integrating GUSAR and QSAR analyses for antimalarial activity of synthetic prodiginines against multi drug resistant strain Devidas T. Mahajan • Vijay H. Masand Komalsing N. Patil • Taibi Ben Hadda • Vesna Rastija
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Received: 9 March 2012 / Accepted: 1 September 2012 / Published online: 22 September 2012 Ó Springer Science+Business Media, LLC 2012
Abstract In the present study, we have carried out extensive GUSAR and conventional 3D QSAR analyses of 49 synthetic prodiginines possessing moderate to high activities against multi drug resistant strain of Plasmodium falciparum. 2D and 3D descriptors, various statistical parameters, viz. R2, R2adj, standard error, Y-randomization, etc., were checked to build successful QSAR model. The best four parametric GA-MLR 3D-QSAR model was found to have R2train = 0.84; R2adj = 0.83. GUSAR analysis was performed to vindicate the QSAR results and get additional results. The consensus GUSAR model based on QNA descriptor is found to have R2train = 0.80 and Q2train = 0.76. The analyses reveal that certain groups/atoms like –F, benzylic –CH2– and –OCH3 play crucial role in deciding the antimalarial activity of prodiginines. The analyses could be useful to improve the antimalarial activity of these biologically privileged molecules. Keywords GUSAR QSAR Prodiginines Antimalarial activity Abbreviations GUSAR General unrestricted structure activity relationships
D. T. Mahajan V. H. Masand (&) K. N. Patil Department of Chemistry, Vidya Bharati College, Camp, Amravati, Maharashtra, India e-mail: [email protected]; [email protected] T. B. Hadda Laboratoire Chimie des Mate´riaux, FSO Universite´ Mohammed Premier, 60000 Oujda, Morocco V. Rastija Department of Chemistry, Faculty of Agriculture, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
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QSAR GA-MLR MDR
Quantitative structure activity relationships Genetic algorithm multi linear regression Multi drug resistant
Introduction Malaria, responsible for more than two million deaths every year (WHO, 2010), is becoming a global health challenge due to the emergence of multi drug resistant race of causative agent (Papireddy et al., 2011). The necessity to modify the available marketed drugs or discover new therapeutics is essential to provide better medicines against malaria. In the last two decades, various chemicals, viz. xanthones, artimisinins, prodiginines, etc., have been synthetically modified and tested to discover new therapeutic agents against malaria (Masand et al., 2010a, b; Mahajan et al., 2010; Wang et al., 2008; Pinto et al., 2005). Among the various promising agents, prodiginines (Fig. 1) have gained considerable attention due to moderate to high potency against Plasmodium falciparum, ease of synthesis, etc. (Papireddy et al., 2011; Williamson et al., 2006). Prodiginines, the oligopyrrole derivatives with two pyrrole rings directly coupled in tandem array, can be administered orally and result in effectiv
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