Investigations on the antimalarial activity of alkoxylated and hydroxylated chalcones 2

SECTION FIVE STRUCTURE ACTIVITY RELATIONSHIPS 5. STRUCTURE ACTIVITY RELATIONSHIPS 5.1 Introduction In the 1st part of this section, the selection and determination of the physicochemical parameters used to characterize the chalcones for quantitative structure-activity relationship studies are presented. This is followed by a discussion of the multivariate analysis of these properties and their relationship to antimalarial activity. The 2’-hydroxychalcones are not included in this part of the analysis. In the 2nd part of this section, the structure activity relationships for antimalarial and antileishmanial activities of the chalcones are compared in qualitative terms and by comparative molecular field analysis (CoMFA) with the aim of determining if structure-activity correlations for these two activities are mutually exclusive or share some elements of similarity. All the chalcones (including the 2’-hydroxychalcones) are included in this analysis. 5.2. Selection and Determination of Physicochemical Parameters 5.2.1 Selection of parameters Parameters for quantitative structure-activity relationships can be classified in several ways. They can be categorized as global or substituent parameters according to whether they describe the properties of the whole molecule (global) or that of a substructure (substituent). The parameters may describe various aspects of the molecule or substituent, namely its steric (size and shape) properties, lipophilicity (affinity for a lipophilic environment) and electronic (charge distribution, electron withdrawing/donating tendencies) characteristics. These properties are important in determining how a compound interacts with its chemical or biological environment. In 58 this investigation, eleven descriptors representing size, lipophilicity and electronic properties are used to characterize the chalcones (Table 2, Appendix) Lipophilicity is a property that has attracted more attention than other descriptors used in structure-correlation studies. This is because lipophilicity is an important determinant of a compound’s ability to transverse biological membranes and influences the rate and extent to which it interacts with its site of action. Lipophilicity is most commonly measured as partition coefficient (P), which is experimentally determined by a variety of methods such as the traditional shake flask method using octanol and buffer, chromatographic methods and potentiometry. 119 It can also be determined in silico. Most molecular modeling programs are capable of assigning “log P” values to energy-minimized structures in a fraction of a second. In this investigation, the lipophilicity was evaluated by experimental and in silico methods. The experimental method involves determining the retention times of a chalcone as it elutes out from a hydrophobic stationary phase using a mobile phase comprising of methanol-water in different proportions. The retention time reflects the partitioning of the compound between the non-polar stationary phase and the polar mobile phase. A lipophilic compound will be preferentially adsorbed onto the nonpolar column and exhibits a longer retention time than a less lipophilic compound for the same mobile phase. Retention time is also influenced by the proportion of methanol (less polar than water) in the mobile phase: as the proportion of methanol in the mobile phase increases, the retention time decreases. Retention times are corrected for the dead-space in the column and given as a logarithmic term (log k’ or capacity factor). Plotting capacity factors on the y-axis against % composition of methanol (x axis) gives a straight line, from which extrapolation to the y-axis gives the capacity factor of the chalcone at 0% methanol (= 100% water or buffer solution). This value 59 (log kw) is taken as a measure of the lipophilicity of the compound at pH 7.0, which is the pH of the buffer. There are some missing log kw values, notably among the hydroxylated chalcones due to difficulties in the experimental determination. Capacity factors were not determined at other pH values besides pH 7. A determination at pH may be warranted in view of the hypothesis that the chalcones interfere with hemoglobin degradation, a process which occurs within the acidic food vacuole of the parasite. However, the chalcones investigated in this study are neither acids nor bases. Therefore their ionization characteristics should be similar at either pH or 5, and the choice was to carry out determinations at physiological pH. The in silico determination of lipophilicity is made using the Sybyl software which gives the ClogP of the chalcone in question. The correlation between the experimentally determined and in silico lipophilicity values is examined using Pearson correlation. The two values are significantly correlated to one another at the level of p < 0.05 (n = 80, r = 0.256) (Table 3, Appendix) despite the fact that the experimentally determined log kw values were determined at pH 7.0 while the ClogP values are for the non-ionized compounds. Noting that the chalcones are essentially neutral compounds [with the exception of the quinolinyl (e.g. 27, 28) and pyridinyl (e.g. 207, 209) chalcones and members carrying aromatic amino substituents on ring B (e.g. 4, 103)], ionization differences may not matter in this case. Size parameters are generally intrinsic descriptors of molecular shape and bulk. Typical examples are molecular weight, volume and surface area. In this investigation, Connolly surface area and volume parameters (log V, log A), molecular refractivity (MR), and a “dummy” parameter to indicate the occurrence of disubstitution in ring A are used to model the bulk and shape of the compounds. Except for the “dummy” parameter, the other parameters are determined in silico. The Connolly parameters 60 measure the solvent accessible molecular surface. Molar refractivity is obtained from the Lorentz-Lorenz equation: Molar refractivity = [(n2-1) × molecular weight] / [(n2+1) × density] Since n (refractive index) does not vary significantly for organic compounds and molecular weight / density is equivalent to volume, molar refractivity is often used as a crude steric parameter characterizing bulk (but not shape) of the molecule. The inclusion of the “dummy” parameter is prompted by the observation that about ¼ of the chalcones are disubstituted on ring A, which may influence the overall size and shape of the ring. The presence of disubstitution is assigned a value of 1, while monosubstituted or unsubstituted rings are assigned “zero”. As seen from Table in Appendix, the size parameters (with the exception of the dummy parameter) are significantly correlated to one another (p > N and where collinearity is likely to exist among the x variables, multivariate data analysis is the preferred option. In this investigation, conventional Hansch analysis was initially employed to derive structure activity relationships for the antimalarial chalcones. This was prompted by the fact that the available data set has a “long and thin” rather than “short and fat” appearance, as there are only 11 x-variables and more than 100 compounds being considered. However, no reasonable correlation could be obtained. The presence of correlated physicochemical parameters (Table 3, Appendix) and missing descriptors for some compounds are probable reasons for the failure to adequately represent activity by a single regression equation. In fact, the prevailing conditions would make analysis by multivariate tools more appropriate. In this investigation, two multivariate data analytical methods are used. Principal component analysis (PCA) is a multivariate projection method designed to give an overview of the dominant patterns and trends in the x-data matrix. PCA is 64 essentially a pattern recognition technique. The x-variables (physicochemical descriptors) are reduced to a smaller number of latent variables (or principal components) that retain the maximum information from the original data matrix. Compounds that are characterized by common principal components will cluster together, indicating that they are “similar” with respect to the variables considered. 120 The 2nd technique employed is that of partial least squares projection to latent structures (PLS). PLS is a regression extension of PCA widely employed when there is the need to connect the information in blocks of variables, which in this case comprises the x-variables (summarized as principal components) and biological activity (y-variable). The usefulness of PLS lies in its ability to analyze data with several noisy, collinear and incomplete variables in both x and y data matrices. 120 PCA and PLS are carried out using the SIMCA-P (Umetrics AB, version 8.0, Umea, Sweden, 1999) software. 5.3.2 Statistical methods Multiple linear regression and Pearson correlation analyses were carried out using SPSS 10 (SPSS Inc., Chicago, IL). The following statistical parameters were determined for each regression equation: 95% confidence interval variables, measure of explained variance r2, Fischer significance ratio F at P = 0.05 and standard error SE. Cross-validated r2 and SE were determined using QSAR module of SYBYL 6.6. Multivariate data analyses were performed with SIMCA-P (Umetrics AB, version 8.0, Umea, Sweden, 1999) using default settings. 5.3.3 Results and discussion 5.3.3.1 Principal component analysis (PCA) 65 Principal component analysis (PCA) was carried out separately on the alkoxylated and hydroxylated chalcones. The outcome of each analysis was viewed in a score plot which provides a summary of the relationships among the compounds. Compounds close to each other have similar physicochemical properties (which have been summarized as principal components) while compounds far away from each other have dissimilar profiles. The Hotelling eclipse is also depicted in the score plot. Compounds outside the eclipse are outliers, that is, their location in the score plot has arisen by chance (> of out 100 times: p > 0.05). The score plot of alkoxylated chalcones is given in Figure 5.1. It can be seen that the trimethoxychalcones (∆) are clustered in the upper right quadrant of the Hotelling eclipse. In contrast, most of the methoxychalcones are found in the lower left quadrant while members of the ethoxy and dimethoxychalcones are distributed over the upper left and lower right quadrants. The clustering of specific ring B alkoxylated chalcones suggests that these compounds share similar physicochemical properties that are encoded in the principal components. Such a pattern implies that QSAR modeling of the alkoxylated chalcones as a whole is be of little value. An appropriate approach is to distinguish the chalcones according to their clustering patterns and to investigate the QSAR of these clusters separately. Such an approach has been applied by others 121 with good results. Thus, the relationship between activity and physicochemical properties is explored separately for the methoxy and trimethoxychalcones, and jointly for the ethoxy and dimethoxychalcones. In the case of the hydroxylated chalcones, the score plot shows a homogenous distribution of the hydroxy and dihydroxy members throughout the quadrants (Figure 5.2). Therefore, it is reasonable to consider both series together as they appear to share common characteristics. 66 5.4.2.5 Comparative molecular field analysis (COMFA) To obtain the CoMFA for antimalarial chalcones, the energy-minimized conformations of the chalcones were aligned using the geometry of the α,β-unsaturated carbonyl linkage of the most potent member for antimalarial activity (27). In the case of antileishmanial activity, the process was repeated using the most active antileishmanial chalcone (212). Both compounds have a trans orientation of rings A and B, and comparable torsion angles (9-11 o) for the carbon-carbon single bond in the -C(=O)-CH=CH- linkage. CoMFA analysis was carried out using the QSAR module of SYBYL 6.6 with the following settings: Å grid spacing, Å extension of the region beyond the van der waals volume of the molecules, sp3 carbon probe atom with +1 charge and a distance dependent (1/r2) dielectric constant. Computation of atomic charge was carried out using the Gasteiger Huckel method. The steric and electrostatic CoMFA fields were scaled using the default settings, which gives identical weight to CoMFA fields and additional variables. Column filtering was set at 2.0 kcal and steric/electrostatic cutoff values at 30 kcal / mol. The CoMFA QSAR equations were derived using partial least squares “leave-one-out” cross validation procedure and the models were evaluated on the basis of cross validated r2 (q2) optimum number of components and standard error of prediction. 5.4.3 Results and discussion 5.4.3.1 Structural requirements for antileishmanial and antimalarial activities As anticipated, many of the chalcones demonstrated activity against L. donovani amastigotes. Table 5.2 summarizes the IC50 and ED50 values for antimalarial and antileishmanial activities. 83 Table 5.2 In order to have an overview of the distribution of activity among the chalcones, the following classifications are made. Firstly, the chalcones are classified according to their ring B substitution patterns as before, namely 2’,3’,4’-trimethoxy (n = 18), 2’,4’-dimethoxy (n = 16), 4’-methoxy (n = 14), 4’-ethoxy (n = 13), 2’,4’dihydroxy (n = 12), 4’-hydroxy (n = 16) and 2’-hydroxy (n =13). Secondly, activities are arbitrarily classified into classes (A – E) according to ED50 or IC50 values to facilitate comparison. In the case of antileishmanial activities, the cut-off points are as follows: A (very good) < µM, B (good) 5-10 µM, C (fair) 10-20 µM, D (poor) 20-30 µM, and E (very poor) > 30 µM. IC50 values for antimalarial activities were similarly classified: A (very good) < 10 µM, B (good) 10-20 µM, C (fair) 20-50 µM, D (poor) 50-100 µM and E (very poor) > 100 µM. The distribution of the chalcones according to their activity and class is shown in Figure 5.7. Figure 5.7 The following observations can be made from Figure 5.7. Firstly, methoxylated chalcones (ring B substituted with trimethoxy, dimethoxy or methoxy substituents) have members found in almost every category (A-D) of antimalarial and antileishmanial activities. The ethoxychalcones are an exception, being notable in having very poor antimalarial activity. The trimethoxychalcones have the best representation of members in Class A for both activities, although not necessarily the same compounds are found in the two classes. Secondly, hydroxylated chalcones are better antileishmanial than antimalarial agents. There is no hydroxylated chalcone with category A antimalarial activity but several members have category A antileishmanial activity. Most notably, nearly half of the sixteen 4-hydroxychalcones have ED50 values < µM. 84 It is clear that different structural requirements exist for antimalarial and antileishmanial activities. There is no correlation between the IC50 and ED50 values of the chalcones. Lead compounds with dual activities are more likely to be found among methoxylated chalcones than hydroxylated chalcones. Indeed, only two compounds are found to have category A antimalarial and antileishmanial activities and both are methoxylated derivatives, namely, 2’,4’-dimethoxy- 4-ethylchalcone (8) and 4’methoxy-4- hydroxychalcone (19). The dimethoxychalcone is of particular interest because it is one of two compounds found to increase the survivability of P. berghei ANKA infected mice at 100 mg/kg (Section 4.3.2). In the course of the antileishmanial tests, the toxicities of the test compounds on mice macrophages were also monitored. Toxicities to macrophages were detected at 10 µM or 30 µM for many of the active chalcones, including and 19 (Table 5.2). It should be noted that both and 19 were found to be relatively non-toxic at 20 µM when evaluated in an MTT assay using a KB cell line (Section 4.3.3). Furthermore, dosing of malaria-infected mice with (100 mg/kg × days, intraperitoneal) did not kill the animals (Section 4.3.2). It might be timely to carry out an MTT assay using mouse macrophages to establish the toxic dose of these compounds. Previous structure-activity studies on the leishmanicidal activity of chalcones have emphasized the importance of the B ring over the A ring for activity. In particular, p-substitution of ring B with oxygenated and non-bulky substituents are preferred for optimal activity. 64, 123 The good activity of the 4’-hydroxychalcones seen here lends support to this conclusion. The largest number of category A antileishmanial compounds (n = 8) is found among the 4’-hydroxychalcones. The present results show that the role of the A ring should not be overlooked either. Not withstanding the nature of Ring B, very good antileishmanial activity is 85 observed when ring A is 1-napthalenyl (110, 205, 212, 242), 2-pyridyl (207, 214, 241) and 4-quinolinyl (28, 30, 34, 215). Activity is also influenced by positional isomerism as can be seen from the poorer activities of the 3-quinolinyl (213; 29) and 2napthalenyl (216; 243) derivatives. This is in contrast to antimalarial activity where the 3-quinolinylchalcones are consistently more active than the 4-quinolinyl derivatives. A PCA analysis of antileishmanial activity was carried using SIMCA-P (Version 8) as before. Score plots for alkoxylated and hydroxylated chalcones did not show any clustering of chalcones according to ring B substitution pattern. PLS analysis was carried out separately on the alkoxylated and hydroxylated chalcones but no significant model could be derived. This suggests that the present set of descriptors was appropriate for describing activity. Conventional Hansch analysis using multiple linear regression also failed to reveal any significant correlation pattern. 5.4.3.2 Comparative molecular field analysis (CoMFA) of antimalarial and antileishmanial chalcones CoMFA is an example of a 3D-QSAR technique which is widely used for designing, explaining and predicting activities of molecules. 124 To a CoMFA study, one starts with a database of test compounds with known biological properties (preferably in vitro data). The structures of these compounds are drawn and minimized using a commercial molecular modeling program. They are then suitably aligned in 3D space according to various methodologies such as superimposition based on maximizing steric overlap, crystallographic data, and pharmacophore mapping programs among others. Having arrived at what is considered to be an alignment of choice, charges are then calculated for each molecule. This is followed by the 86 construction of steric and electrostatic fields for each molecule by assessing its interaction with a probe atom (like a sp3 hybridized carbon atom) at a series of grid points surrounding the aligned database in 3D space. The resulting field energy terms are then correlated with a property of interest by the use of partial least squares with cross validation, in order to give a measure of the predictive power of the model. One of the most attractive features of CoMFA is the ability to view the steric and electrostatic contours surrounding each molecule, which can be used to explain why certain features increase or decrease biological potency. On the other hand, one has to be cautious as these near perfect 3D graphical representations tend to encourage over-interpretation of results. The geometrical alignment of the test compounds /molecules is critical to a successful CoMFA. Ideally, the geometry should reflect the conformation at the binding site. In situations (like the present) where the nature of the binding site is not known and the requirement for maintaining interaction geometry is not so important, CoMFA is used primarily as a tool to evaluate structure-activity data. The importance of the α,β-unsaturated carbonyl linkage to antimalarial activity has been reported in the literature. 72 Its importance to antileishmanial activity is less definite as reports indicate that saturation of the double bond causes some but no drastic reduction to activity. 79 The carbon-carbon double bond can be cis or trans but is found to be trans in all the synthesized chalcones. Since there are considerable variations in the nature and substitution pattern of rings A and B, it was decided that the alignment of the chalcones would be based on the α,β-unsaturated carbonyl linkage as it is the most consistent feature in all the chalcones. In the case of antimalarial activity, the most active chalcone (27) was used as a template for alignment. Similarly, the most active member (212) was used as a template for antileishmanial 87 activity. Alignment was based on the following four atoms (in bold) of the α,βunsaturated linkage: O H C C C H The torsion angle for the C-C single bond in the -C(O)-CH=CH- linkage was found to be 9-11o for both 27 and 212. In preliminary runs, various CoMFA models were generated using compounds, classified according to B ring substitution pattern or activity levels (A-E). The most acceptable model to emerge was one in which only active chalcones (n = 19, IC 50 < 10 µM for alkoxylated chalcones and < 20 µM for hydroxylated chalcones) were considered. This was a 3-component model with q2 of 0.547 and cross-validated standard error of prediction (SEPcv) of 0.218. Noting that these same compounds have been successfully analyzed using multivariate analysis (Section 5.3.3.2.5) and multiple regression (-log IC50 = 6.4 log A -10.6; n =19, r2 = 0.690, r2cv = 0.616), it was decided that the CoMFA should be re-done with the inclusion of the log A parameter. A 3-component model was again derived, but with slightly improved predictability (q2 = 0.655; SEPcv = 0.227). Contributions by the steric, electrostatic and log A parameters were 33.6%, 40.1% and 26.3 % respectively. The best CoMFA model for antileishmanial activity was one involving chalcones with hydroxylated B rings (n = 40) covering the full range of antileishmanial activity (A to E). However, the model had low predictability (r2cv = 0.358, SEPcv = 0.383, components), with balanced steric (48.8%) and electrostatic (51.2%) contributions. Attempts were made to improve the model by including a lipophilicity parameter together with the CoMFA steric and 88 electrostatic fields, but it was not successful. Thus the original model was retained as the final CoMFA model for antileishmanial activity. Visualization of the steric and electrostatic contours of the two CoMFA models was carried out using 1-(4’-hydroxy)-3-(2-pyridinyl)-2-propen-1-one (214), a representative compound with an ED50 of 1.48 µM and IC50 of 16.25 µM for antileishmanial and antimalarial activities respectively. Steric contours in the CoMFA model are color-coded green (where bulk is acceptable) and yellow (where bulk is undesirable). The electrostatic contours are color-coded red (where negative charge is desirable) and blue (where positive charge is preferred). The orientation of 214 in the steric (Figure 5.8a) contours of the CoMFA model for antimalarial activity shows a concentration of green zones around ring B, indicating that more bulk is tolerated or even preferred in this region. Unfortunately, the solitary 4-hydroxyl substituent on ring B of 214 did not project into the green zones. An alkoxylated B ring would be better placed in the green zones and this was indeed observed when the steric contours around the 2’,4’-dihydroxy chalcone (202) and its 2’,4’-dimethoxy analogue (29) were compared (Figure 5.9). The distribution of steric contours in this model would explain why alkoxylated chalcones are generally better antimalarials than their hydroxylated counterparts. Unlike the preference for more bulk around ring B, the large yellow zone in the vicinity of ring A suggests the presence of steric restriction in this region (Figure 5.8a). But this does not seem to be the case since the small-size pyridine ring in 214 is as well accommodated in this region as the bigger size quinoline and naphthalene rings. The blue contours (preference for positive-charged groups) around ring A suggest that the electronic characteristics of this ring would be important for activity (Figure 5.8b). Thus, the π deficient carbon atoms of the pyridine ring of 214 would be suitably placed in this region, as would the carbon atoms of the π deficient quinoline ring. 89 In summary, the CoMFA model for antimalarial activity indicates a preference for a large size (alkoxylated) ring B and an electron deficient ring A. These conclusions compare favorably with those derived from multivariate and multiple linear regression analyses which propose a large size ring B and ring A with suitable “polar characteristics”. It is noted that the nature of ring A was not adequately defined in these studies, beyond a suggestion that it should have optimal log kw values. CoMFA suggests that ring A should be electron deficient. Examination of the electrostatic and steric contours around 214 in the CoMFA model for antileishmanial activity shows that both contours are concentrated around ring A (Figures 5.8a, b). Ring A may have a greater influence than ring B in the activity of hydroxylated chalcones. The concentration of green contours around ring A suggests that bulk is well tolerated in this region (Figure 5.8c). The interspersing of red and blue zones around ring A suggests that its electronic character (electron-deficient or electron-rich) is less important. (Figure 5.8d) Smaller electrostatic contours are found around Ring B and a careful examination suggests that the good activity of 4’-hydroxylated chalcones may be traced to the favorable orientation of the 4’-hydroxyl group away from the blue zone (preference for positive charge) and towards the negative charge-seeking red zone. This is illustrated in Figure 5.8d for 214. On the other hand, when ring B carries 2’,4’-dihydroxyl substituents, the 2’-hydroxyl group projects into the blue zone (undesirable) although some members (205, 207) have their B rings twisted so as to avoid this interaction. Similarly, some 2’-hydroxychalcones (241, 242) orient their hydroxyl groups away from this blue zone. These members manifest better antileishmanial activity than those that not adopt a twisted B ring conformation (235, 238, 239). This is shown in Figure 5.10, using 207, 241 and 238 as examples. Thus, the CoMFA electrostatic contours indicates that differences in antileishmanial activity may be traced to the 90 orientation of the hydroxyl groups on ring B (twisted versus non-twisted B ring conformation) but more examples would be required to validate this hypothesis. Figure 5.8a-d Figure 5.9 Figure 5.10 5.4.4 Summary of findings The present study shows that antimalarial and antileishmanial activities have different physicochemical and structural requirements. A cursory survey of structure activity correlations indicate that antileishmanial activity is favored by chalcones with more hydrophilic character, with the most active members found among 4’-hydroxychalcones. In contrast, good antimalarial activity is found among alkoxylated chalcones with polar A rings, in particular those substituted with electron withdrawing groups or replaced by quinoline rings. The main contribution of CoMFA is to assess the relative contributions of rings A and B to these two types of activities. For antileishmanial activity, the steric and electronic characteristics of ring A appear to be more important than that of ring B. Ring A could be electron-rich or electron poor but should preferably be large in size. For antimalarial activity, both rings A and B are important but have different roles, with size characteristics predominating for ring B (large, alkoxylated) and electronic properties (electron deficiency) for ring A. However, one shortcoming of these CoMFA models is that they have been derived from different subsets of compounds. The antimalarial model was obtained using alkoxylated and hydroxylated chalcones with category A activities, while the antileishmanial model was based only on hydroxylated chalcones exhibiting the full range (A-E) of activity. This may create some bias in the interpretation of the results. Despite this limitation, CoMFA confirms the differing requirements for antimalarial and antileishmanial activities. In addition, two 91 chalcones (8, 19) have been identified which have good in vitro antimalarial and antileishmania activities (< 10 µM) and moderate toxicities (observed at 10 µM) against mice macrophages. is of particular interest as it has been shown to increase the survivability of P. berghei ANKA infected mice at a dose of 100 mg/kg. 5.5 Conclusions The structural requirements for antimalarial activity of chalcones have been explored in this section using multivariate data analytical tools (PCA, PLS), conventional multiple linear regression and CoMFA. It is seen the structural requirements differ according to the nature of the ring B. Table 5.3 summarizes the findings obtained from this study. Table 5.3 Summary of SAR Findings Class of chalcones Structural requirements for antimalarial activity Trimethoxychalcones Electron withdrawing ring A Dimethoxychalcones Large size ring A Methoxychalcones Low lipophilicity Ethoxychalcones Cannot be determined Dihydroxy and hydroxychalcones Polar and small substituent(s) on ring A “Actives” from above classes Large size ring B, polar ring A with electron withdrawing groups One of the hypotheses proposed in this thesis is that similar structural requirements exist for antimalarial and antileishmanial activities. This is not supported by the present findings. Antileishmanial activity is found mainly among the hydroxylated chalcones which have weaker antimalarial activity. A large size ring A is preferable for antileishmanial activity. In contrast, good antimalarial activity is found mainly among the alkoxylated chalcones and the influence of ring A is primarily electrostatic. 92 Table 5.2 Antileishmanial, antimalarial activities and toxicity against mice macrophages of chalcones O Ring A Ring B Compound number 11 12 13 27d 28d 35 36 40 128 129 130 131 132 133 134 29d 30d 101 102 93 Ring B Ring A 2',3',4'trimethoxy 2,4-dichloro 4-dimethylamino 4-trifluoromethyl 2,4-dimethoxy 4-methyl 4-ethyl 3-quinolinyl 4-quinolinyl 4-methoxy 4-fluoro 4-phenyl 2,4-difluoro 4-nitro 3,4-dichloro 4-chloro 2-chloro 3-chloro H 2,4-dichloro 4-trifluoromethyl 2,4-difluoro 2,4-dimethoxy 4-ethyl 3-quinolinyl 4-quinolinyl 4-methyl 4-methoxy 2’,4’dimethoxy ED50 (µM) a 13.24 7.81 6.82 13.15 14.15 6.98 7.95 4.38 13.57 8.13 21.52 4.38 >30 12.33 4.89 9.09 4.39 6.82 >30 >30 13.16 >30 4.38 12.46 1.5 6.82 12.23 IC50 (µM) b 5.4 18.0 3.0 16.5 25.6 16.5 2.0 60.0 25.0 9.5 26.2 18.5 22.5 14.5 14.5 41.5 24.4 15.8 18.75 5.85 6.23 2.10 2.42 2.16 27.00 93.75 128.50 Toxicity to macrophages (µM) c 30 10 10 30 30 10 10 10 30 10 30 10 30 10 30 10 10 30 10 30 10 30 Compound number 39 121 122 123 124 125 126 127 136 41f 201 202d 203 204 205d 206 207d 208d 209d 210d 211 245 212d 213d 214d 215d 216d Ring B 4’-ethoxy 4’-butoxy 2’,4’dihydroxy 4’-hydroxy Ring A 4-fluoro 2,4-dichloro 4-trifluoromethyl 2,4-dimethoxy 4-methyl 4-nitro 4-dimethylamino 4-cyano H 2,4-dimethoxy 2,4-dichloro 3-quinolinyl 2,4-difluoro 2,4-dimethoxy 1-naphthalenyl 4-trifluoromethyl 2-pyridinyl 2-naphthalenyl 4-pyridinyl 4-quinolinyl 4-chloro 4-methyl 1-naphthalenyl 3-quinolinyl 2-pyridinyl 4-quinolinyl 2-naphthalenyl ED50 (µM) a >30 >30 >30 24.17 >30 25.23 29.93 13.92 11.23 >30 23.66 >30 >30 12.12 4.03 8.07 2.23 >30 10.99 ND 4.36 8.02 1.38 >30 1.48 3.83 >30 IC50 (µM) b 24.1 96.0 24.0 30.0 38.0 39.0 30.0 540.0 43.0 108.0 68.5 16.1 16.0 56.4 24.8 26.5 19.7 20.0 121.6 92.8 12.30 ND 39.9 41.0 16.3 51.0 27.5 Toxicity to macrophages (µM) c 30 30 30 30 30 10 30 10 10 30 ND 10 10 3 10 - Compound number 104 105 106 107 108 109 110d 19 22 23 31d 32d 38 111 112 113 114 115 116 117 135 25 26 33d 34d Ring B Ring A 2’,4’dimethoxy 4-fluoro 4-chloro 4-bromo 2-chloro-4-fluoro 3,4-dichloro 4-nitro 1-naphthalenyl 4-hydroxy 2,4-difluoro 4-methoxy 3-quinolinyl 4-quinolinyl 4-fluoro 2,4-dichloro 4-trifluoromethyl 2,4-dimethoxy 4-methyl 4-nitro 4-dimethylamino 4-cyano H 2,4-difluoro 4-methoxy 3-quinolinyl 4-quinolinyl 4’methoxy 4’-ethoxy ED50 (µM) a >30 >30 23.38 11.89 11.27 >30 4.37 4.18 12.51 13.13 6.82 8.13 ND 12.38 >30 23.38 ND >30 >30 6.91 12.2 >30 29.37 9.99 4.39 IC50 (µM) b 322.00 342.00 542.50 600.00 297.50 415.00 320.00 7.00 26.75 21.70 4.83 43.00 14.40 16.00 19.00 6.40 70.00 100.00 70.00 94.50 55.50 28.10 33.00 24.85 100.00 Toxicity to macrophages (µM) c 30 30 30 10 10 30 30 10 10 ND 30 30 ND 10 30 30 10e Compound number 217 220 221 222 224 225 226 227 228 229 230 231 232 233d 234 235 236 237 238 239 241d 242d 243d 244d Ring B 4’-hydroxy 2’-hydroxy Ring A 4-chloro 4-methoxy 4-methyl 3-methyl 4-trifluoromethyl 4-nitro 4-fluoro 3,4-dichloro 4-dimethylamino 2,4-dichloro H 2,4-dichloro 4-dimethylamino 3-quinolinyl 4-chloro 4-methyl 4-methoxy 2,4-dimethoxy 4-trifluoromethyl 4-fluoro 2-pyridinyl 1-naphthalenyl 2-naphthalenyl 4-quinolinyl ED50 (µM) a 6.98 5.77 4.4 4.19 4.18 >30 2.6 >30 8.13 4.39 4.41 11.74 >30 13.16 6.82 12.05 13.16 10.85 29.31 29.31 1.57 4.07 11.73 12.63 IC50 (µM) b 38.0 32.2 25.4 25.8 30.4 20.4 21.7 18.4 17.70 24.50 29.6 35.45 188.00 28.00 12.85 62.50 61.50 25.50 35.50 47.00 31.00 32.50 29.50 ND Toxicity to macrophages (µM) c 10 10 10 10 10 10 10 10 30 30 10 30 30 30 30 10 30 30 a. In vitro sensitivity against L. donovani (L82) amastigotes determined in a mouse peritoneal macrophage model. 86 Compounds with ED50 values > 30 µM are considered inactive. ED50 for Pentosam® (sodium stilbogluconate) is 0.6 µM. Compounds were tested in triplicates. b. Inhibition of [3H] hypoxanthine uptake into P.falciparum K1 (Section 4.2.1). IC50 for chloroquine =0.27 µM. c. Concentration at which toxicity to mouse macrophages was observed. (-) : no toxicity observed at 30 µM. ND: Not done. d. Ring A = heterocyclic or polyaromatic. e. Compound 34 is toxic at 10 µM, but parasites are present at this concentration. f. Compound 41 (2’,4’-dimethoxy-4’-butoxychalcone) has been reported to have antimalarial and antileishmanial activities.68, 84 Low levels of activities observed here may be due to different strains of parasites being used. 94 Figure 5.7 Distribution of antimalarial and antileishmanial activities of chalcones classified according to nature of Ring B. Antileishmanial activities J ED50 values: < µM (A), 5-10 µM (B), 10-20 µM (C), 20-30 µM (D), > 30 µM (E). Antimalarial activities   IC50 values: < 10 µM (A), 10-20 µM (B), 20-50 µM (C), 50-100 µM (D), > 100 µM (E). 2',4'-dihydroxychalcones (n=12) 4'-hydroxychalcones (n=16) 100 90 90 80 80 80 % of Compounds 70 60 50 40 30 20 70 60 50 40 30 70 60 50 40 30 20 20 10 10 10 B C D Level of activity E A B C D Level of activity 2',4'-dim e thoxychalcone s (n=16) 2',3',4'-t r im e t h o x ych alco n e s ( n =18) 100 100 80 80 80 40 20 60 40 20 A B C D L e ve l o f act ivit y E 60 40 20 A B C D Le ve l of activity E A B C D Level of activity E 4'-ethoxychalcones (n=13) % of Compounds 60 B C D Level of activity 100 % of Compounds 80 A E 4'-methoxychalcones (n=14) % of Compounds 100 % of Compounds % of Compounds 100 90 % of Compounds 100 A 95 2'-hydroxychalcones (n=13) E 60 40 20 A B C D Level of activity E Figure 5.8 a-d Steric and electrostatic contour maps of CoMFA models for antimalarial and antileishmanial activities, showing the orientation of 1(4’-hydroxy)-3-(2-pyridinyl)-2-propen-1-one (214) in the antimalarial model (a, b) and antileishmanial model (c, d). a b c d 1. Steric contours are colour-coded yellow (where bulk is undesirable) and green (where bulk is acceptable). 2. The electrostatic contours are colour-coded blue (where positive charge is desirable) and red (where negative charge is preferred). 3. In (a), the steric contours show that a large sized B ring (green) and a smaller sized A ring (yellow) is preferable. 4. In (b), the blue contours indicate that an electron-deficient A ring is preferred. 5. The steric (c) and electrostatic (d) contours in the antileishmanial model are concentrated around ring A, indicating the relative importance of this ring compared to ring B. 96 Figure 5.9 Orientation of 1-(2’,4’-dihydroxy)-3-(3-quinolinyl)-2-propen-1-one (202) and its dimethoxy analogue (29) in the steric contours of the CoMFA model for antimalarial acitivty. 202 29 The methoxy groups on ring B of 29 are directed towards the green zone (where bulk is desirable), unlike the hydroxyl groups of 202. This may account for the generally better antimalarial activities of alkoxylated chalcones. 97 Figure 5.10 Orientation of 1-(2’,4’-dihydroxy)-3-(2-pyridinyl)-2-propen-1-one (207), 1-(2’-hydroxy)-3-(2-pyridinyl)-2-propen-1-one (241) and 2’-hydroxy-4trifluoromethylchalcone (238) in the electrostatic contours of the CoMFA model for antileishmanial activity. 207 241 238 1. The B rings of 207 and 241 are ‘twisted’, and their 2’-hydroxyl groups (electron rich oxygen) not point towards the blue zone. 2. In 238, the B ring is not twisted and the 2’-hydroxyl group points towards the blue zone which is undesirable. 3. ED50 of 238 is 29.3 µM compared to 2.23 and 1.57 µM for 207 and 241, respectively. 98 [...]... On the other hand, there might be reasons to focus on only one activity to the exclusion of the other Regardless of the objective, defining structure -activity relationships for each activity is a necessary starting point Thus, this section considers the structural requirements for antileishmanial and antimalarial activities of the present series of chalcones The 2- hydroxychalcones which were not considered... plot of principal components t1 against t2 for alkoxylated chalcones 4’-ethoxychalcones (∆); 2 ,4’-dimethoxychalcones (O); 4’-methoxychalcones (×); 2 ,3’,4’trimethoxychalcones (∆) The location of 2 ,4’-dimethoxy-4’-butoxychalcone (41) is indicated in the score plot The ellipse corresponds to the confidence region based on Hotelling T2 (0.05) Figure 5 .2 Score plot of principal components t1 against t2... of the trimethoxychalcones, the relationship is inverse, i.e lower lipophilicity in the compound favors good antimalarial activity The direct correlation between activity and HOMO suggests that the electron donating ability of the chalcone is important for good activity 5.3.3 .2. 3 Dimethoxychalcones and ethoxychalcones The overlapping distribution of dimethoxy and ethoxychalcones in the PCA score plot... antileishmanial and antimalarial activities As anticipated, many of the chalcones demonstrated activity against L donovani amastigotes Table 5 .2 summarizes the IC50 and ED50 values for antimalarial and antileishmanial activities 83 Table 5 .2 In order to have an overview of the distribution of activity among the chalcones, the following classifications are made Firstly, the chalcones are classified according to their... Structure -Activity Relationships of Antileishmanial and Antimalarial Chalcones 5.4.1 Introduction Antimalarial and antileishmanial activities have been found in licochalcone A 64, 68 and 2, 4-dimethoxy-4’-butoxychalcone (41) 70 Of the large number of antimalarial and antileishmanial chalcones that have been reported in the literature, most have been evaluated 80 for one or the other activity 72, 123 There are no... dose of these compounds Previous structure -activity studies on the leishmanicidal activity of chalcones have emphasized the importance of the B ring over the A ring for activity In particular, p-substitution of ring B with oxygenated and non-bulky substituents are preferred for optimal activity 64, 123 The good activity of the 4’-hydroxychalcones seen here lends support to this conclusion The largest... nature and substitution pattern of rings A and B, it was decided that the alignment of the chalcones would be based on the α,β-unsaturated carbonyl linkage as it is the most consistent feature in all the chalcones In the case of antimalarial activity, the most active chalcone (27 ) was used as a template for alignment Similarly, the most active member (21 2) was used as a template for antileishmanial 87 activity. .. largest number of category A antileishmanial compounds (n = 8) is found among the 4’-hydroxychalcones The present results show that the role of the A ring should not be overlooked either Not withstanding the nature of Ring B, very good antileishmanial activity is 85 observed when ring A is 1-napthalenyl (110, 20 5, 21 2, 24 2), 2- pyridyl (20 7, 21 4, 24 1) and 4-quinolinyl (28 , 30, 34, 21 5) Activity is also... primary function of PLS is to relate two data matrices to each other In this case, the two data matrices are the biological activity (Y) and the principal components summarizing the descriptors of the compounds under study Following the lead given by PCA, the chalcones are divided into groups (trimethoxychalcones, methoxychalcones, dimethoxy- and ethoxychalcones, hydroxylated chalcones) and PLS is carried... the solitary 4-hydroxyl substituent on ring B of 21 4 did not project into the green zones An alkoxylated B ring would be better placed in the green zones and this was indeed observed when the steric contours around the 2 ,4’-dihydroxy chalcone (20 2) and its 2 ,4’-dimethoxy analogue (29 ) were compared (Figure 5.9) The distribution of steric contours in this model would explain why alkoxylated chalcones . analysis of these properties and their relationship to antimalarial activity. The 2 -hydroxychalcones are not included in this part of the analysis. In the 2 nd part of this section, the structure. gave log k w of the compound at pH 7.0. 5 .2. 2 .2 Determination of the chemical shift of the carbonyl carbon The method is described in Section 3.3.3. 5 .2. 2.3 Determination of physicochemical. alkoxylated chalcones 4’-ethoxychalcones ( ∆ ); 2 ,4’-dimethoxychalcones (O); 4’-methoxychalcones ( × ); 2 ,3’,4’- trimethoxychalcones (∆). The location of 2 ,4’-dimethoxy-4’-butoxychalcone (41)