QSAR, Docking and ADMET studies of Artemisinin derivatives for Antimalarial activity targeting Plasmepsin II, a hemoglobin-degrading enzyme from P. falciparum.
This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54 and A62 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r2) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV2) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice.
Available from: Joachim Müller
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ABSTRACT: Antiparasitic chemotherapy is an important issue for drug development. Traditionally, novel compounds with antiprotozoan activities have been identified by screening of compound libraries in high-throughput systems. More recently developed approaches employ target-based drug design supported by genomics and proteomics of protozoan parasites. In this chapter, the drug targets in protozoan parasites are reviewed. The gene-expression machinery has been among the first targets for antiparasitic drugs and is still under investigation as a target for novel compounds. Other targets include cytoskeletal proteins, proteins involved in intracellular signaling, membranes, and enzymes participating in intermediary metabolism. In apicomplexan parasites, the apicoplast is a suitable target for established and novel drugs. Some drugs act on multiple subcellular targets. Drugs with nitro groups generate free radicals under anaerobic growth conditions, and drugs with peroxide groups generate radicals under aerobic growth conditions, both affecting multiple cellular pathways. Mefloquine and thiazolides are presented as examples for antiprotozoan compounds with multiple (side) effects. The classic approach of drug discovery employing high-throughput physiological screenings followed by identification of drug targets has yielded the mainstream of current antiprotozoal drugs. Target-based drug design supported by genomics and proteomics of protozoan parasites has not produced any antiparasitic drug so far. The reason for this is discussed and a synthesis of both methods is proposed.
International review of cell and molecular biology 01/2013; 301:359-401. DOI:10.1016/B978-0-12-407704-1.00007-5 · 3.42 Impact Factor
Available from: Feroz Khan
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ABSTRACT: Due to the high mortality rate in India, the identification of novel molecules is important in the development of novel and potent anticancer drugs. Xanthones are natural constituents of plants in the families Bonnetiaceae and Clusiaceae, and comprise oxygenated heterocycles with a variety of biological activities along with an anticancer effect. To explore the anticancer compounds from xanthone derivatives, a quantitative structure activity relationship (QSAR) model was developed by the multiple linear regression method. The structure-activity relationship represented by the QSAR model yielded a high activity-descriptors relationship accuracy (84%) referred by regression coefficient (r (2)=0.84) and a high activity prediction accuracy (82%). Five molecular descriptors - dielectric energy, group count (hydroxyl), LogP (the logarithm of the partition coefficient between n-octanol and water), shape index basic (order 3), and the solvent-accessible surface area - were significantly correlated with anticancer activity. Using this QSAR model, a set of virtually designed xanthone derivatives was screened out. A molecular docking study was also carried out to predict the molecular interaction between proposed compounds and deoxyribonucleic acid (DNA) topoisomerase IIα. The pharmacokinetics parameters, such as absorption, distribution, metabolism, excretion, and toxicity, were also calculated, and later an appraisal of synthetic accessibility of organic compounds was carried out. The strategy used in this study may provide understanding in designing novel DNA topoisomerase IIα inhibitors, as well as for other cancer targets.
Drug Design, Development and Therapy 01/2014; 8:183-95. DOI:10.2147/DDDT.S51577 · 3.03 Impact Factor
Available from: Prashant Murumkar
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ABSTRACT: Among the various parasitic diseases, malaria is the deadliest one. Due to the emergence of high drug resistance to the existing drug candidates there is a global need for development of new drug candidates which will be effective against resistant strains of malaria parasite. In silico molecular modeling approaches have been playing an important role in the discovery of novel lead molecules having antimalarial activity. Present review is an effort to cover all the developments related to the application of computational techniques for the design and discovery of novel antimalarial compounds since the year 2011 onwards.
Combinatorial Chemistry & High Throughput Screening 12/2014; 18(2). DOI:10.2174/1386207318666141229125852 · 1.22 Impact Factor
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