Development of Triazine-4 , 6-diamines derivatives as potential Antimalarials : In-Silico Analysis

Development of 2, N-disubstituted 1,2dihydro-1,3,5-trizine-4,6-diamines derivatives were carried out by quantitative structure–activity relationship (QSAR) analyses. The nature of the substituent(s) on C-2; the nature of the substituent(s) on the distal aryl ring; as well as the nature and length of the flexible tether between the rings, to find out the structural requirements of their antimalarial activities against cycloguanil resistant (FCR-3) Plasmodium falciparum strain and sensitive to pyrimethamine. The statistically significant best 2D QSAR models for FCR-3, having correlation coefficient (r) = 0.9821 and cross validated squared correlation coefficient (q) = 0.6471 were developed by multiple linear regression stepwise (SW– MLR) forward algorithm. The results of the present study may be useful on the designing of more potent analogues as antimalarial agents.


Introduction
Malaria is one of the most widespread diseases in the world.According to WHO estimates 40% of the world's populations presently live under malarial threat [1].
Around 300 and 500 million cases of malaria occur annually, leading to 1-3 million deaths [1].Its control is globally a high priority task.Although effective antimalarial agents have been known for a long time, the alarming spread of drug resistant strains of Plasmodium falciparum, which is the most lethal parasite species, undergoes the urgency and continuous need for the discovery of new therapeutics.A major initiative in this direction is to find enzyme targets that are critical to the disease process or essential for the survival of the parasite.Identification and design of novel chemical entities specifically affecting these targets could lead to better drugs for the treatment of malaria [2].
Pyrimethamine, trimethoprim and cycloguanil inhibit malarial dihydrofolate reductase (DHFR), one of the few welldefined, validated targets in malarial chemotherapy [3].These antimalarials inhibit DHFR by competing with the natural substrate dihydrofolic acid.Unfortunately, point mutations at certain amino acid residues surrounding the P. falciparum DHFR active site have resulted in resistance, compromising the clinical effectiveness of pyrimethamine and cycloguanil [4,5].
Despite this, the folate pathway remains a good target for malarial chemotherapy because the enzyme is limited in its mutational capability, owing to loss in enzyme function [6][7][8].

Data set
A data set of 28 compounds of side chain modified 1,2-dihydro-1,3,5-triazine-4,6diamine heterocycle for antimalarial activities against pyrimethamine and cycloguanil sensitive and resistant (FCR-3) P. falciparum strains was used for the present 2D QSAR study [10].There is high structural diversity and a sufficient range of the biological activity in the selected series of these derivatives (    Pune, India) and then the structure was converted to three-dimensional space for further analysis.All molecules were batch optimized for the minimization of energies using Universal force field (UFF) followed by considering distance-dependent dielectric constant of 1.0, convergence criterion or root-mean-square (RMS) gradient at 0.01 kcal/mol A˚ and the interaction limit to 10,000 [11].The manual data selection method [12][13][14][15] was  building QSAR models and can explain the situation more effectively [14][15][16].
In the selected equations, the crosscorrelation limit was set at 0. 'stepping criteria' (in this case F= 4 for inclusion; F= 3.99 for exclusion for the forward-backward selection method).In GA method, population and number of generations were set as 10 and 1000, respectively and speed of 9999.

Model quality and validation
The developed QSAR models are evaluated using the following statistical measures: n, that the model is statistically significant.The low standard error of r 2 (r 2 _se), q 2 (q 2 _se) and pred_r 2 (Pred_r 2 se) shows absolute quality of fitness of the model.
Internal validation was carried out using 'leave-one-out' (q 2 , LOO) method [17].The cross-validated coefficient, q 2 , was calculated using the following equation: Where where yi, and ŷi are the actual and predicted activity of the i th molecule in the test set, respectively, and ymean is the average activity of all molecules in the training set.

Results and discussion
The QSAR study of 28 new side chain modified 1,2-dihydro-1,3,5-triazine-4,6diamine heterocycle derivatives for antimalarial activities (Table 1) through MLR methodology, based on various feature selection methods viz.SW using VLife MDS 3.5 software resulted in the following statistically significant models (Table 2), considering the term selection criterion as r 2 , q 2 and pred_r  3).
first attribute was T to characterize the topology of the molecule.The second attribute was the atom type, and the third attribute was assigned to atoms taking part in the double or triple bond.The preprocessing of the independent variables (i.e., 2D descriptors) was done by removing invariable (constant column), which resulted in total 289 descriptors to be used for QSAR analysis.

2 .
Feature selection and model development Feature selection is a key step in QSAR analysis.An integral aspect of any modelbuilding exercise is the selection of an appropriate set of features with low complexity and good predictive accuracy.This process forms the basis of a technique known as feature selection or variable selection.Among several search algorithms, stepwise (SW) forward-backward variable selection method, genetic algorithms (GA) and simulated annealing (SA) based feature selection procedures are most popular for

(
the number of compounds in regression); k, (number of variables); DF, (degree of freedom); optimum component, (number of optimum PLS components in the model); r 2 , (the squared correlation coefficient); r 2 se, (standard error of squared correlation coefficient); F test, (Fischer's value) for statistical significance; q 2 , (cross-validated correlation coefficient); q 2 _se, (standard error of cross-validated square correlation co-efficient); pred_r 2 , (r 2 for external test set); pred_r 2 se, (standard error of predicted squared regression); Z score, (Z score calculated by the randomization test); best_ran_q 2 , (highest q 2 value in the randomization test); best_ran_r 2 , (highest r 2 value in the randomization test).The regression coefficient r 2 is a relative measure of fit by the regression equation.It represents the part of the variation in the observed data that is explained by the regression.However, a QSAR model is considered to be predictive, if the following conditions are satisfied: r 2 > 0.6, q 2 > 0.6 and pred_r 2 >0.5 [13].The F-test reflects the ratio of the variance explained by the model and the variance due to the error in the regression.High values of the F-test indicate yi, and ŷi are the actual and predicted activity of the i th molecule in the training set, respectively, and ymean is the average activity of all molecules in the training set.However, a high q 2 value does not necessarily give a suitable representation of the real predictive power of the model for antimalarial ligands.So, an external validation was also carried out in the present study.The external predictive power of the model was assessed by predicting pIC50 value of the nine test set molecules, which were not included in the QSAR model development.The predictive ability of the The 21 st International Electronic Conference on Synthetic Organic Chemistry Pritam N. Dube et.al.

2 .
The training and test sets were selected by manual selection method and the models were validated by both internal and external validation procedure.To ensure a fair comparison, the same training and test sets were used for each model's development.A Uni-column statistics for training set and test set was generated to check correctness of selection criteria for trainings and test set molecules.results as compare to two other models (PCR and PLR).Therefore the activity predicted by MLR is only considered in this study (Table
ligand structures design with appropriate features, and for the explanation of the way in which these features affect the biological data upon binding to the respective receptor target.The results derived may be useful in further designing more novel antimalarial agents in series.

NH N N NH H 2 N R 1 R 2
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Table 2 .
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The 21 st International Electronic Conference on Synthetic Organic ChemistryPritam N. Dube et.al.

Table 4 : Percent Contribution of Descriptors. Sr.No.
The 21 st International Electronic Conference on Synthetic Organic ChemistryPritam N. Dube et.al.