Abstract
There are very few studies for combinatorial library design and high throughput screening of 4-anilinoquinoline antimalarial compounds having activities against parasitic strain of P. falciparum. Therefore, an attempt has been made in the present paper to design potent lead compounds in this congener utilizing quantitative structure activity relationship utilizing theoretical molecular descriptors. QSAR models for a series of 4-anilinoquinolines considering various theoretical molecular descriptors including topological, constitutional, geometrical, functional group and atom-centered fragments has been carried out by stepwise forward–backward variable selections assimilating multiple linear regression (MLR) methods showing the topological indices contribute maximum impact on parasitic P. falciparum strain. A combinatorial library of 2160 compounds has been generated and finally, 16 compounds were screened through high throughput screening as promising 4-anilinoquinoline antimalarial hits based on their predicted activities utilizing topological descriptor based validated QSAR model. Highly predicted active compounds were then undergone for pharmacophore modeling to predict mode of binding and to optimize leads having greater affinity towards malarial P. falciparum parasitic strain.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-1593-3) contains supplementary material, which is available to authorized users.
Highlights
Malaria is an Anopheles mosquito borne parasitic disease triggered by four species of genus plasmodium including P. falciparum, P. vivax, P. ovale, and P. malariae
These IC50 values were converted into their negative logarithms which are taken into consideration in the present calculation as dependent variables whereas computed descriptors calculated by using optimized 3D-structure of 4-anilinoquinoline compounds are considered as independent variables for statistical multivariate regression modeling
Impact of different types of descriptors on antimalarial activities is focused in terms of R2 and its validation is done by calculating cross-validated R2 (Rc2v) while treating the data set using multiple linear regression (MLR) coupled with stepwise forward–backward selection methods
Summary
Malaria is an Anopheles mosquito borne parasitic disease triggered by four species of genus plasmodium including P. falciparum, P. vivax, P. ovale, and P. malariae. P. falciparum is the most dangerous species because it can penetrate into deeper tissues and infect red blood corpuscles leading to its breakdown and rupture, forming sticky lump like mass structure in the blood capillary which may ground circulatory arrest such as cerebral attack causing death of the individual (Tham and Kennedy 2015). Brutal death of more than 1 million people globally cries to develop new antimalarial chemotherapeutics. Due to drug resistance and lack of knowledge of exact mechanism of action of these series of compounds, it is really urgent to design and develop new congeneric leads utilizing structure activity-property relationship studies. In the present paper QSAR modelling has been carried out for antimalarial 4-anilinoquinolines based on the computed structure–propertyactivity correlations
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