Chemometric Studies on Potential Larvicidal Compounds Against Aedes Aegypti

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Chemometric Studies on Potential Larvicidal Compounds Against Aedes Aegypti ARTICLE in MEDICINAL CHEMISTRY (SHĀRIQAH (UNITED ARAB EMIRATES)) · MAY 2013 Impact Factor: 1.36 · DOI: 10.2174/15734064113099990005 · Source: PubMed

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Chemometric Studies on Potential Larvicidal Compounds Against Aedes Aegypti Luciana Scotti*a, Marcus Tullius Scottib, Viviane Barros aSilvac, Sandra Regina Lima Santosc, Sócrac tes C.H. Cavalcanti and Francisco J.B. Mendonça Junior a

State University of Paraíba, Biological Science Department, Laboratory of Synthesis and Drug Delivery, 58070-450, João Pessoa, PB, Brazil; bFederal University of Paraíba, Department of Engineering and the Environment, Campus IV; 58297-000, Rio Tinto, PB, Brazil; cFederal University of Sergipe, Pharmacy Department, Medicinal Chemistry Laboratory, Av. Marechal Rondon S/N, Rosa Elze, 49100-000, São Cristóvão, Sergipe, Brazil Abstract: The mosquito Aedes aegypti (Diptera, Culicidae) is the vector of yellow and dengue fever. In this study, chemometric tools, such as, Principal Component Analysis (PCA), Consensus PCA (CPCA), and Partial Least Squares Regression (PLS), were applied to a set of fifty five active compounds against Ae. aegypti larvae, which includes terpenes, cyclic alcohols, phenolic compounds, and their synthetic derivatives. The calculations were performed using the VolSurf+ program. CPCA analysis suggests that the higher weight blocks of descriptors were SIZE/SHAPE, DRY, and H2O. The PCA was generated with 48 descriptors selected from the previous blocks. The scores plot showed good separation between more and less potent compounds. The first two PCs accounted for over 60% of the data variance. The best model obtained in PLS, after validation leave-one-out, exhibited q2 = 0.679 and r2 = 0.714. External prediction model was R2 = 0.623. The independent variables having a hydrophobic profile were strongly correlated to the biological data. The interaction maps generated with the GRID force field showed that the most active compounds exhibit more interaction with the DRY probe.

Keywords: Aedes aegypti, chemometry, CPCA, descriptors, PLS, VolSurf. 1. INTRODUCTION Dengue is a viral disease caused by a Flavivirus transmitted by the mosquito Aedes aegypti (Diptera:Culicidae). Its symptoms vary from mild fever, to life-threatening severe dengue [1] which may progress to a shock syndrome [2]. The propagation of dengue is currently a public health threat, particularly in tropical and subtropical countries [3]. The disease is caused by the dengue virus, of which four serologically different serotypes are described (DV-1, DV-2, DV-3, and DV-4). The World Health Organization (WHO) reports that dengue is a leading cause of illness and death in the tropics and subtropics, causing a flu-like infection, which affects as many as 70 million people every year [4-6]. Since there are no effective treatments for this disease, the most effective way to control the virus outbreak is to avoid vector spreading, mainly by the use of larvicides. Organophosphates, such as, temephos, have been used as larvicides in several countries since the 1960’s. However, resistance to pesticides [7] has guided research to find new methods intended to control Ae. aegypti propagation. Several approaches have been employed to find new larvicidal candidates. Plant derived products are undoubtedly

*Address correspondence to this author at the State University of Paraíba, Biological Science Department, Laboratory of Synthesis and Drug Delivery, 58070-450, João Pessoa, PB, Brazil; Tel: 55-83-3291-1805; Fax: 55-83-3291-1528; E-mail: [email protected]

1573-4064/13 $58.00+.00

the most evaluated larvicidal substances against Ae. aegypti to date. Plant extracts provide a number of secondary products synthesized by the plants to act as natural insecticides or repellent. Phenolic acids [8], spinosyns [9], coumarins [10], essential oil monoterpenoids [11], vegetable oil [12], polyacetylenes, phytosterols, flavonoids, sesquiterpenoids, triterpenoids [13], are within the classes of natural products with larvicidal activities. As part of our effort to find natural products with larvicidal activity we have evaluated a number of compounds derived from natural substances [14-16]. The essential oils of Hyptis fruticosa (Lamiaceae) Salzm., H. pectinata (Lamiaceae) Poit., and Lippia gracilis (Verbenaceae) were characterized and evaluated against third-instar Ae. aegypti larvae [17]. Carvacrol was found to be the major compound in the essential oil of L. gracilis responsible for the observed activity. Additionally, other minor compounds, such as thymol, !-terpinene, and limonene contributed to the observed larvicidal activity [17]. These results motivated us to undertake a more detailed investigation of larvicidal compounds with the goal to study the Structure-Activity Relationships between the larvicidal activity and a set of compounds mostly found in plants [14, 15]. Subsets of aromatic, alicyclic and bicyclic compounds with a variety of substituents were previously evaluated [1416]. The presence of lipophilic groups in aromatic rings or in hydroxyls resulted in increased potency [14]. Additionally, the presence of hydroxyls in aromatic or aliphatic rings re© 2013 Bentham Science Publishers

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sulted in decreased potency. Conjugated and exo double bonds in aliphatic rings appeared to increase larvicidal potency [15]. Moreover, replacement of double bonds by epoxides decreased the larvicidal potency. Stereochemistry of selected compounds has as well been found to play an important role on modulating the potency. Generally, lipophilicity appears to play an important role on the larvicidal activity of selected compounds [14, 15]. However, no quantitative studies have been performed to corroborate the previous findings. Theoretical studies accomplished by applying computeraided drug design have significantly assisted the drug discovery of new bioactive compounds. Technological advances in the areas of structural characterization of the biological macromolecules, of computational science and of molecular biology contributed to a faster and more feasible design of new molecules. Among many computational tools, chemometric studies are popular methods to model allowing the analysis of large multivariable collinear data sets [18-23].

Scotti et al.

quently, a new geometry optimization process, based on the semi-empirical method AM1 (Austin Model 1) was performed [34, 35]. The optimized structures were subjected to conformational analysis using the random search method with 1,000 interactions, 100 cycles of optimization, and 10 conformers of lowest minimum energy. The selected dihedrals were evaluated by rotation in accordance with the standard (default) conditions of the program, which the number of simultaneous variations was 1 to 8. Acyclic chains were submitted to rotations from 60 to 180°, torsion rings were in the range of 30 to 120° [36, 37]. 2.3. Maps The lowest energy conformers were saved in .sdf format and imported into VolSurf+ for Windows [38, 39]. Each structure was subjected to GRID force field and interaction maps with the generation of the following "probes": H2O (light blue), O (red), N (dark blue) and DRY (green).

Principal Component Analysis (PCA), Consensus PCA (CPCA), and Partial Least Squares (PLS) regression are chemometric tools used for extracting and rationalizing the information from any multivariate description of a biological system. CPCA and PCA are part of an exploratory data analysis where graphical techniques provide a maximization of insights into a data set, pointing out important variables, detecting outliers and anomalies, and developing parsimonious models [22, 23].

2.4. Chemometrics

As part of our ongoing work to study the SAR of potential larvicidal agents, we investigated the molecular interactions of 55 selected compounds in various media (hydrophilic, hydrophobic, etc.) with the aid of chemometrics methods. These included Principal Component Analysis (PCA), Consensus PCA (CPCA), and Partial Least Squares Regression (PLS) using the VolSurf+ program. These predictions provide direction with regard to the syntheses of new derivatives with improved biological activities, which can be used as insecticides or repellent with larvicidal activity against the Ae. aegypti.

A preliminary exploratory analysis, CPCA, was developed by considering 128 independent variables or descriptors. Pre-processing (autoscaling) of the data was performed and 13 blocks of descriptors were calculated.

2. MATERIALS AND METHODS 2.1. Compounds and Larvicidal Assay Compounds 1-5, 21-24, 27, 28, 30-33, 36, 38, 40, 41, 46, 48-55 were purchased from Sigma-Aldrich. Isopulegol (37) and neoisopulegol (43) were separated and obtained by silica gel 60 column chromatography (hexanes) from technical grade isopulegol (Dierberger – Brazil). 1,2-Carvone oxide (44) [24], RS-menthone (39) [25], limonene oxide, mixture of cis and trans (45) [26], carvacrol, thymol [27-31], and eugenol derivatives [30] were synthesized according to the literature [32]. Structures of the evaluated compounds are shown in (Fig. 1). LC50 were previously obtained by a larvicidal assay, followed by Probit analysis of three replicates and are reported elsewhere [14,15,17]. 2.2. Molecular Modelling The three-dimensional structures were drawn using HyperChem 8.0 software [33] and energy-minimized employing the MM+ force field without any restriction. Subse-

The structures, modelled as described formerly, were used as the initial structures to calculate the molecular descriptors via the VolSurf+ program [38-45]. PCA, CPCA, and PLS are popular methods to model and analyze large multivariable collinear data sets. These methodologies were applied to the set of interest using the VolSurf+ software. 2.4.1. CPCA (Consensus PCA)

2.4.2. PCA With regard to the interaction of 3D structures and a GRID force field, PCA results were obtained using the descriptors of the high weight blocks obtained in CPCA. Forty eight molecular descriptors were selected of SHAPE, DRY and H2O blocks. The preprocessing was performed (autoscaling). 2.4.3. PLS The training set was composed of forty-one compounds and the test set was constituted of 14 rationally selected compound (3, 7, 11, 14, 17, 23, 31, 38, 40, 45, 48, 52 and 55) [45]. The autoscaling preprocess was further applied. The PLS analysis uses the VolSurf+ descriptors as the Xblock data and the larvicidal activity as the dependent variable. The internal validation (cross-validation test) of PLS model was performed by the leave-one-out (LOO) technique. 3. RESULTS 3.1. Experimental Larvicidal Activity (Table 1) shows the 50% lethal concentration (LC50) and confidence interval (CI) of the investigated compounds (1– 55). The LC50 values were converted to molar units and then expressed in negative logarithmic units, pLC50 (-log LC50 ).

Chemometric Studies on Potential Larvicidal Compounds Against Aedes Aegypti

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R1 O

3

R3

O

R2

R4

2 4

3

11 - R3 = OH, R4 = CHO 5 - R1 = OH 1 19 - R3 = CHO, R4 = OH 6 - R = OCOCH3 7 - R1 = OCOCH2Cl 8 - R1 = OCOCCl3 9 - R1 = OCOCH2CH3 10 - R1 = OCOPh OH 12 - R1 = OCH2COOH R6

H3CO R5 25 - R5 = OCOCH3 26 - R5 = OCH2COOH 29 - R5 = OCH2CH3 30 - R5 = OH 42 - R5 = OCH3 47 - R5 = OCOCH2CH3

H3CO R7

O

OH

32

34 - R7 = OCOPh 35 - R7 = OH

39

43

O

44

O

46

45 OH

OH O

49

40 - n = 6 41 - n = 8

HO

O 37

(CH2)n

28 - R6 = OH 31 - R6 = H 33 - R6 = OCH3 52 - R6 = CHO

O HO

38 - R2 = OH 13 - R2 = OCOCH3 14 - R2 = OCOCH2Cl 15 - R2 = OCOCCl3 16- R2 = OCOCH2CH3 17 - R2 = OCOPh 18 - R2 = OCH2CH3 20 - R2 = OCH2COOH

O

OCH3

OH CHO 55

51

50 53 54 48 (-)-Camphene (1), (±)-camphor (2), 1,4-cineole (3), 1,8-cineole (4), carvacrol (5), carvacryl acetate (6), carvacryl chloroacetate (7), carvacryl trichloroacetate (8), carvacryl propionate (9), carvacryl benzoate (10), 2-Hydroxy-3-methyl-6-(1-methylethyl)-benzaldehyde (11), carvacrylglycolic acid (12), thymyl acetate (13), thymyl chloroacetate (14), thymyl trichloroacetate (15), thymyl propionate (16), thymyl benzoate (17), thymyl ethyl ether (18), 2-hydroxy-6-methyl-3-(1methylethyl)-benzaldehyde (19), thymoxyacetic acid (20), 5-norbornene-2-ol (21), 5-norbornene-2,2-dimethanol (22), 5-norbornene-2-endo-3-endodimethanol (23), 5-norbornene-2-exo-3-exo-dimethanol (24), eugenyl acetate (25), 2-[2-methoxy-4-(2-propen-1-yl)phenoxy] acetic acid (26), borneol (27), catechol (28), 1-ethoxy-2-methoxy-4-(2-propen-1-yl)-benzene (29), eugenol (30), phenol (31), g-terpinene (32), guaiacol (33), 1-benzoate-2-methoxy-4-(3hydroxypropyl)-phenol (34), 4-hydroxy-3-methoxy-benzenepropanol (35), isoborneol (36), isopulegol (37), thymol (38), menthone (39), nonan-2-one (40), undecan-2-one (41), 1,2-dimethoxy-4-(2-propen-1-yl)-benzene (42), neoisopulegol (43), 1,2-carvone oxide (44), limonene oxide, mixture of cis and trans (45), p-cymene (46), eugenyl propionate (47), R-carvone (48), resorcinol (49), R-limonene (50), R/S-carvone (51), salicylaldehyde (52), S-carvone (53), S-limonene (54), vanillin (55).

Fig. (1). Structures of the investigated compounds.

3.2. CPCA Thirteen blocks of descriptors with higher weights are shown in (Fig. 2). This figure was obtained by taking into account the orthogonal properties of the CPCA, which was developed on the basis of 128 independent variables or descriptors. (Table 2) represents the total variance explained using the block of descriptor with higher weight: SIZE/SHAPE, H2O and DRY. 3.3. PCA (Table 3) shows that PC1 and PC2 accounted for more than 62% of total variance from the original data. The scores

plot exhibits satisfactory discrimination between more potent (dark grey), averaged (grey) and less potent (light grey) compounds, as presented in (Fig. 3). 3.4. PLS The model generated in the PLS analysis, with two LVs, explained more than 72% of the total variance from the original data (Table 4). Significant statistical measures (leave-one-out cross-validation correlation coefficient, qcv2=0.679; and regression correlation coefficient, r2=0.714) were obtained when the interactions fields were calculated by using a hydrophobic probe and two latent variables (LV) (see Fig. 4).

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Table 1. Larvicidal Activity of the Investigated Compounds Compound Number

pLC50 (CI)* Mol/L

Compound Number

pLC50 (CI)* Mol/L

1

2.79 (2.66 to 2.92)

29

3.40 (3.38 to 3.44)

2

2.36 (2.34 to 2.41)

30

3.35 (3.30 to 3.42)

3

2.31 (2.29 to 2.33)

31

2.69 (2.66 to 2.72)

4

2.04 (2.00 to 2.07)

32

3.39 (3.29 to 3.50)

5

3.47 (3.44 to 3.50)

33

2.84 (2.81 to 2.88)

6

3.32 (3.27 to 3.36)

34

3.28 (3.20 to 3.35)

7

3.64 (3.59 to 3.71)

35

2.05 (2.00 to 2.13)

8

3.59 (3.54 to 3.64)

36

2.41(2.46 to 2.57)

9

3.49 (3.43 to 3.56)

37

2.71 (2.68 to 2.75)

10

3.66 (3.57 to 3.72)

38

2.59 (2.38 to 2.64)

11

3.43 (3.38 to 3.49)

39

2.48 (2.44 to 2.52)

12

3.09 (3.06 to 3.12)

40

2.85 (2.80 to 2.91)

13

3.32 (3.27 to 3.37)

41

3.51 (3.43 to 3.61)

14

3.66 (3.63 to 3.69)

42

3.24 (3.22 to 3.28)

15

3.85 (3.82 to 3.90)

43

2.44 (2.39 to 2.52)

16

3.49 (3.45 to 3.53)

44

2.88 (2.85 to 2.89)

17

3.46 (3.41 to 3.49)

45

2.47 (2.43 to 2.52)

18

3.16 (2.82 to 3.79)

46

3.42 (3.38 to 3.45)

19

3.72 (3.68 to 3.75)

47

3.55 (3.50 to 3.61)

20

2.65 (2.61 to 2.69)

48

3.00 (2.94 to 3.04)

21

2.16 (2.14 to 2.18)

49

2.28 (2.24 to 2.33)

22

2.29 (2.24 to 2.34)

50

3.70 (3.64 to 3.77)

23

2.04 (1.99 to 2.09)

51

3.11 (3.09 to 3.13)

24

2.33 (2.24 to 2.42)

52

2.95 (2.90 to 3.02)

25

3.28 (3.25 to 3.32)

53

3.08 (3.06 to 3.11)

26

3.04 (2.94 to 3.09)

54

3.64 (3.62 to 3.72)

27

2.40 (2.36 to 2.46)

55

2.47 (2.41 to 2.53)

28

2.66 (2.61 to 2.70)

* CI = Confidence interval.

Table 2. Explained Variance by SIZE/SHAPE, H2O, and DRY Blocks % Explained Variance from Original Data

PC SIZE/SHAPE block

H2O block

DRY block

1

25.222

59.493

37.954

2

51.560

7.366

24.762

3

-3.115

2.444

8.913

4

0.511

4.658

-1.464

5

4.668

7.656

0.054

Chemometric Studies on Potential Larvicidal Compounds Against Aedes Aegypti

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Fig. (2). Plot of block weights considering PC1 and PC2. Block of descriptors: DDRY, OH2, SHAPE, Mixed descriptors (MISC), O, N1, Charge State descriptors (FU), ADME model descriptors (LOGD, LOGS), 3D pharmacophoric descriptors (TOPP), MODEL and STRUCT.

Fig. (3). Scores plot from PCA with representation of more potent (dark grey), averaged (grey) and less potent (light grey) compounds.

Fig. (4). PLS predictions found for larvicidal activity: dark grey balls represent more active compounds and light grey ball represents compounds that are less active compounds. The test set compounds are the small circles.

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Fig. (5). Coefficients plot: influence of descriptors in the PLS model. Table 3. Explained Variance by PCA PC

% Explained Variance from Original Data

1

36.784

2

25.240

3

14.149

4

5.823

5

3.444

Fig. (6). Predictive power of the test set: scores plot in the space Y CALCULATED X Y OBSERVED.

Table 4. Explained Variance by PLS LV

% Explained Variance from Original Data

1

45.160

2

27.450

3

19.910

4

7.480

5

1.538

The PLS scores plot of the resulting model is shown in (Fig. 4). The selected model provides a good discrimination between more potent (dark grey balls) and less potent (light grey balls) compounds. The coefficient plot indicates the greater influence of descriptors: D1-D4, WO2-3 (Fig. 5). The results obtained by the external predictions may be considered as acceptable. The compounds used as the test set for external prediction generated a suitable model with R2 = 0.623. The scores plot is shown in (Fig. 6).

DISCUSSION Our first study explored the computational properties contained in the orthogonal algorithm of the Consensus PCA. The method was applied to fifty-five compounds (Fig. 1) and 128 descriptors, distributed in 13 blocks. (Fig. 2) shows the weight of the blocks on the graphic and the equivalent numeric values, calculated considering PC1 and PC2. The blocks of descriptors with the highest weight are highlighted in black rectangles: H2O, DRY and SIZE/SHAPE. (Table 2) represents the variance explained by each of these blocks, in the first 5 PCs. (Table 2) shows that the variance explained by the SIZE/SHAPE block is higher than 76% and approximately 66% and 62% for the blocks H2O and DRY, respectively by the first two PCs. This first analysis guided the application of the PCA. Thus, forty-eight descriptor, contained in the block of greater weight, were selected. The first two principal components explained over than 62% of the total variance (see Table 3). This percentage value is satisfactory if compared with the findings referred in the literature [47-49]. The score plot in (Fig. 3) shows that more active compounds (dark grey balls) are tendentially located on the right side of the graph.

Chemometric Studies on Potential Larvicidal Compounds Against Aedes Aegypti

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Fig. (7). Maps of interaction with the probes H2O (light blue), DRY (green), O (red) and N (dark blue) using GRID force field: compounds 8, 15 (more potent) and 21, 22 (less potent).

Since multicollinearity among the descriptor variables may affect the regression analysis detrimentally, PLS is frequently used as the regression method in 3D QSAR (Quantitative structure–activity relationship) [18-21]. The PLS was applied to forty-one compounds as training set and fourteen compounds as test set (3, 7, 11, 14, 17, 23, 29, 31, 38, 40, 45, 48, 52, 55), selected following three conditions [46]: - All compounds of the test set in the multidimensional descriptor space must be close to those of the training set. -All compounds of the training set must be close to those of the test set. -The representative points of the training set must be distributed within the entire dataset. The best model after LOO cross-validation exhibited qcv2 = 0.679 and regression correlation coefficient r2 = 0.714 in the LV1. (Table 4) shows that the model is able to explain more than 72% of the total variance, considering LV1 and LV2. The PLS demonstrate very satisfactory results, especially if compared with those reached in other studies [5052]. The score plot shows the arrangement of more active and less active objects. (Fig. 4) presents the excellent separation in predicting the activities of the objects. Through the interaction maps, using the GRIG force field, the differences of interactions between the compounds and the probes H2O (light blue), DRY (green), O (red) and N (dark blue) could be compared. In (Fig. 7), we present some examples of the

differences observed between more potent (8 and 15) and less potent (21 and 22) compounds. The interaction of molecules with biological membranes is mediated by surface properties such as shape, electrostatic forces, H-bonds and hydrophobicity. Therefore, the GRID force field was chosen to characterize potential polar and hydrophobic interaction sites around target molecules by the water (OH2), the hydrophobic (DRY), and the carbonyl oxygen (O) and amide nitrogen (N1) probe. The information contained in the Molecular Interaction Fields (MIF) is transformed into a quantitative scale by calculating the volume or the surface of the interaction contours. The VolSurf+ procedure is as follows: i) in the first step, the 3D molecular field is generated from the interactions of the OH2, the DRY, O and N1 probe around a target molecule; ii) the second step consists in the calculation of descriptors from the 3D maps obtained in the first step. The molecular descriptors obtained, called VolSurf+ descriptors, refer to molecular size and shape, to hydrophilic and hydrophobic regions and to the balance between them [38]. Maps generated with the GRID force field show great interactions with the O and N probes, and less interactions with the DRY probes; in less potent compounds. Hydrophobic regions are more present in more potent compounds, which leads us to believe that the hydrophobic profile is an important factor for increasing the larvicidal activity. The coefficients of the descriptors selected from the best PLS model corroborate these observations (see Fig. 5). We

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observed positive influence of descriptors D1-D4, which are generated with hydrophobic interactions (DRY probe). Volfsurf computes these hydrophobic descriptors at eight different energy levels adapted to the usual energy range of hydrophobic interactions (from 0.2 to 1.6 kcal/mol). GRIDa uses a probe called O (carbonylic oxygen) to generate 3D Hbond donor fields. H-bond donor regions may be defined to the molecular envelope generating attractive H-donor interactions. Volfsurf computes H-bond donor descriptor at six different energy levels. Two of these levels (WO2 and WO3) were observed in the present study and confirm that donor H-bonds regions influence negatively the larvicidal activity.

PLS

=

Partial Least Regression

Squares

PC

=

Principal Component

GRID

=

Force field

MIF

=

Molecular Fields

Interaction

DV-1, DV-2, DV-3, and DV-4 =

Serotypes of the dengue virus

WHO

=

World Health Organization

The external validation employed fourteen compounds as test set. The results show a good predictive power of the PLS model, with R2 = 0.623 (Fig. 6).

Ae. aegypti

=

Aedes aegypti

SAR

=

Structure–Activity Relationship

CONCLUSION

AM1

=

Austin Model 1

This work used chemometric tools to investigate a set of fifty-five compounds with activity against Aedes aegypti larvae. The results were satisfactory, with good predictive power of the PLS model. The descriptors and the maps calculated with GRID force field showed that hydrophobicity is strongly correlated with the larvicidal activity.

H2O, DRY, O, and N

=

Probes of VolSurf+ program

SHAPE, DRY, and H2O

=

Blocks of descriptors

3D

=

Three-dimensional

LOO

=

Leave-one-out technique

pLC50

=

!log LC50

LC50

=

50% lethal concentration

LV

=

Latent variable

D1-D4 and WO2-3

=

Variables of VolSurf+ program

Compounds 34 and 41 exhibited reasonable larvicidal activity when compared to similar derivatives such as 35 and 40 (Table 1). The improvement in potency is herein attributed to an increase in lipophilicity. However, in some cases, an increase in potency is not always followed by an increase in lipophilicity, which may be attributed to the diverse structural templates encountered in this set, resulting in different mechanisms of action for each related subset. This finding encourages further theoretical and experimental researchers to continue performing studies, aiming to reveal structural groups of these compounds responsible for the larvicidal activity. Dengue vector control is a constant social and government concern. In order to reduce the spreading of this disease, public health agencies in tropical countries use larvicides with the goal to eliminate Ae. aegypti larvae growing in breeding sites, therefore preventing virus transmission. In this work, we extract important structural features of the investigated compounds, allowing future use of the method and/or directing research to the synthesis of new substances with larvicidal activity.

REFERENCES [1] [2]

[3]

[4] [5] [6]

CONFLICT OF INTEREST The author(s) confirm that this article content has no conflicts of interest. ACKNOWLEDGEMENTS

[7]

[8]

The authors are grateful to Prof. Gabriele Cruciani from Università di Perugia - Italy for the VolSurf+ license and to CNPq and UEPB (Research and Post-Graduation Incentive Program/PROPESQ-PRPGP) for financial support.

[9]

ABBREVIATIONS CPCA

=

Consensus PCA

PCA

=

Principal Analysis

Component

[10]

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Received: December 28, 2012

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McNally, R.C.; Akdeniz, M.B.; Calantone, R.J. New Product Development Processes and New Product Profitability: Exploring the Mediating Role of Speed to Market and Product Quality. J. Prod. Innov. Manag., 2011, 28, 63-.77.

Accepted: May 08, 2013