Shaheen Begum et al. : 3D QSAR Studies Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents 557

Research Paper 3D QSAR Studies on Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents

Shaheen Begum1, Satya Parameshwar K2,*, Ravindra G K3, Achaiah G4,* 1Institute of Pharmaceutical Technology, Sri Padmavati Mahila Visvavidyalayam, Tirupathi, Andhra Pradesh. 2L.B. College, Warangal. 3National institute of Pharmaceutical Education and Research, Hyderabad. 4University College of Pharmaceutical Sciences, Kakatiya University, Warangal.

ABSTRACT: Benzoxazoles and Oxazolo-[4,5-b]pyridines have been reported as potent anti-fungal agents. 3D QSAR tools including CoMFA and CoMSIA have been known to be a promising approaches is to correlate structures and activity which further enable the medicinal chemists to design more potent molecules thus curtailing the cost and time in drug research. CoMFA and CoMSIA studies have been carried out on 31 molecules of benzoxazole and oxazolopyridines in order to determine the structural properties required for effective antifungal activity. 26 compounds were evaluated for establishing QSAR model, which was then validated by predicting the activities of five test set molecules. All the molecules were aligned by SYBYL database alignment which led to a best model with q2 value of 0.835, r2=0.976 and r2

pred=0.773. This model was further employed to derive CoMSIA models, a best model with steric, electrostatic, hydrophobic and hydrogen bond acceptor indices exhibited q2 = 0.812, r2=0.971 and r2

pred=0.81. The models thus obtained from this study can be useful for the design and development of new potential anti-fungal agents.

Introduction Azoles have been one of the extensively studied scaffolds in medicinal chemistry as anti-microbial agents in particular as anti-fungal agents [Cavelleri et al., 1978, Cuckler et al., 1966, Elamine et al., 1981]. In the last two decades there has been shift in the development of anti-fungal drugs. Inspite of expansion in anti-fungal drug discovery there remains a wide gap in the range and scope of current anti-fungal chemotherapy (Graybill et al., 1996). Benzoxazoles have been extensively studied for their antibacterial and anti-fungal activity [Oren et al., 1999, Temiz et al., 1998], anticancer activity [Kumar D et al., 2002], and also as new non-nucleoside topoisomerase I poisons [Kim et al., 1996] and HIV-1 reverse transcriptase inhibitors [Perrin et al., 1996].

Quantitative structure–activity relationships (QSARs) are computational statistical methods which reveal explainable difference in the observed biological

activity. It is imperative to improve predictability of QSAR model of test compounds by considering the structural and physicochemical features. Three dimensional quantitative structure–activity relationship (3D-QSAR) techniques including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) could facilitate in designing unsynthesized agents for therapeutic purpose [Lepper et al., 2004, Ravindra et al., 2007, Sperandio da Silva et al., 2004]. Both the techniques predict the activities based on the existing set of molecules, CoMFA develops the 3D QSAR model based on the relationship between the steric/electrostatic properties and biological activities, while CoMSIA develops the models using steric, electrostatic, hydrophobic and hydrogen bonding properties. The addition of other fields in CoMSIA resulted in augmented significance and predictive power of model and also interpretation of the resultant correlation of the model. CoMSIA is known to be less sensitive to molecular alignments/ conformations than CoMFA [Bandyopadhyaya et al., 2005].

Inumerable reports are available containing 2D QSAR on various benzoxazoles as anti-fungal agents and very few 3D QSAR on the same scaffolds are available. In this connection, it is aimed is to analyze 3D structural

International Journal of Pharmaceutical Sciences and Nanotechnology

Volume 2 • Issue 2 • July – September 2009

* For correspondence: Satya Parameshwar K, Achaiah G,

E-mail: spkonda@yahoo.co.in ; achaiah_g@yahoo.co.in

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558 International Journal of Pharmaceutical Sciences and Nanotechnology Volume 2 • Issue 2 • July - September 2009

requirements of benzoxazoles and Oxazolo-(4,5-b)pyridines as anti-fungal agents and also to understand the structural basis for their affinity for the activity and to design still better potent molecules.

Materials and methods

A series, containing 31 benzoxazole and oxazolopyridines have been obtained from the literature to establish CoMFA

and CoMSIA models [Oren et al., 2004, Yalcin et al., 2000]. The structures and their anti-fungal activities have been mentioned in Table 1. The anti-fungal activity of the data set has been converted into pIC50 using the formula pIC50 = -log IC50 and which was in the range of 3.8-4.4. The inhibitors were randomly sorted into training set and test set containing 26 and 5 molecules respectively.

Table1 Structure and in vitro anti-fungal activity of training set compounds

X

O

NR1

R

Comp. No X R R1 pIC50 1 CH -H -H 3.892 2 CH -C(CH3)3 -H 4.001 3 CH -NH2 -H 3.924 4* CH -NHCH3 -H 3.952 5 CH -CH2CH3 -Cl 4.013 6 CH -NHCOCH3 -Cl 4.059 7 CH -NHCH3 -Cl 4.015 8 CH -Cl -Cl 4.024 9 CH -NO2 -Cl 4.04

10 CH -H -Cl 4.282 11 CH -CH3 -NO2 4.308 12 CH -C(CH3)3 -NO2 4.375 13* CH -NH2 -NO2 4.31 14 CH -Cl -NO2 4.342 15 CH -Br -NO2 4.406 16 CH -CH2CH3 -NH2 3.976 17 CH -F -NH2 3.96 18 CH -N(CH3)2 -NH2 4.005 19* CH -CH3 -CH3 3.95 20 CH -CH2CH3 -CH3 3.977 21 CH -CH2CH3 -CH3 3.98 22 CH -F -CH3 3.958 23 CH -NHCOCH3 -CH3 4.027 24* CH -NHCH3 -CH3 3.979 25 CH -N(CH3)2 -CH3 4.004 26 N -CH3 -H 4.225 27 N -CH2CH3 -H 4.253 28 N -OCH3 -H 4.257 29 N -OCH2CH3 -H 4.283 30* N -NH2 -H 4.227 31 N -NO2 -H 4.285

Shaheen Begum et al. : 3D QSAR Studies Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents 559

All the molecular modeling studies were performed using Sybyl 6.8 version [Sybyl 6.8, Tripos Inc.] on silicon graphics O2 workstation with IRIX 6.5 operating system. All the structures were constructed using sketch module of SYBYL using standard library models. The modeled structure was minimized using Tripos force field with distance dependent dielectric function and applying Gasteiger-Hückel partial atomic charges. These structures were further optimized by semi-empirical MOPAC calculations using AM1 method. These structures were considered for generating CoMFA and CoMSIA calculations.

Alignment

The important aspect of 3DQSAR is the alignment of the inhibitors that defines the presumed pharmacophore of the series under the study. As no structural information is available about ligand-target complex, the lowest energy conformation obtained after MOPAC of most active molecule has been employed as templet of alignment using SYBYL database alignment (Fig.1).

Fig.1 Database alignment of anti-fungal agents.

Methodology of CoMFA and CoMSIA

A 3D cubic lattice was defined in CoMFA automatically by extending at least 4 Å beyond all the aligned molecules in all the three axes (X, Y, Z directions) with a grid spacing of 2.0 Å. Lennard-Jones and Columbic potentials were used to calculate the steric and electrostatic interaction fields respectively at each lattice intersection. Tripos force field with distant dependent dielectric constant e = e0 Rij with e0 =1.0 was considered for the calculation of interaction energies considering an sp3

carbon atom with a radius of 1.52 Å bearing +1 charge as a probe atom. Steric and electrostatic cutoffs were applied to truncate the contribution up to ±30 kcal/mol [Cramer et al., 1988].

Klebe et al introduced CoMSIA analysis which calculates five similarity descriptors namely steric, electrostatic, hydrophobic, hydrogen bond donor and acceptor [Klebe et al., 1994, Klebe et al., 1999)]. Gaussian type function was employed to gauge similarity indices at all the grid points, a default attenuation factor (α) of 0.3 was employed as a smoothening function. A probe atom

having charge +1, hydrophobicity +1, and hydrogen bond donor and acceptor property of +1 was assigned to calculate similarity indices. Atom based parameters were employed to compute hydrophobic descriptors [Viswanadhan et al., 1989] and rule based method was used to get the hydrogen bond donor and acceptor fields.

Partial least square analysis Partial least square analysis (PLS) is the best statistical tool to derive the correlation between biological activity and descriptors calculated by CoMFA and CoMSIA [Ciubotariu et al., 1993]. Leave one out (LOO) method was carried out as a rudimentary step to ascertain the predictivity of the model and also to determine the optimum number of components with minimum standard error of prediction (SEP). The optimum number of components thus obtained from the previous LOO method was further applied to derive the final non-cross validated correlation r2. Group cross validation and bootstrapping were performed to get the confidence intervals (mean and standard deviation) and also to assess the robustness of the model. In the group cross validation, which was conducted for 50 iterations in which the training set molecules were

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randomly divided into groups and subjected to the correlation. The mean of 50 cross validation runs was considered as correlation coefficient, rcv

2. The bootstrapping (rbs

2) analysis for 100 runs was performed with optimum number of components, which provides an estimate of the variability of the parameters in a final model [Bunce D et al., 1988].

Result and discussions

CoMFA model resulted from SYBYL database alignment (Fig.1) has yielded a crossvalidated q2 value of 0.835, non-

cross validated r2 value of 0.976 with six components, and standard error of estimation (SEE) of 0.106 as listed in Table 2. The contributions of steric and electrostatic were 60.5% and 39.5%, respectively. Group cross validation was carried out for 50 iterations to prove for the confidence of the model that resulted in a mean group cross-validated r2 value of 0.81. Bootstrapping analysis of 100 runs was led to an r2

bs of 0.943 with deviation of 0.03 (Table 3) and the CoMSIA predicted activities of both training and test sets are presented in Fig 2.

Table 2 CoMFA and CoMSIA statistics.

Parameters CoMFA CoMSIA

I II

r2 0.976 0.971

q2 0.835 0.812

SEE 0.029 0.023

SEP 0.075 0.032

Number of components 6 6

F value 129.92 105.45

% Contribution

Steric 60.5 38.7

Electrostatic 39.5 14.3

Hydrophobic -- 34.4

H acceptor -- 12.6

r2pred 0.773 0.81

Table 3 Bootstrapping results of CoMFA and CoMSIA.

r2

bsa r2

cvb

CoMFAc CoMSIAd CoMFAc CoMSIAd

Mean 0.945 0.98 0.81 0.785

SD 0.03 0.019 0.059 0.07

a From 100 run boot strapping b From mean of 50 run group cross validation c CoMFA with atom fit based alignment d CoMSIA model generated by the combination of steric, hydrophobic and hydrogen bond

acceptor fields

Shaheen Begum et al. : 3D QSAR Studies Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents 561

Fig. 2 Predicted and observed activities of training and test sets using CoMFA.

CoMSIA results

CoMFA model obtained as explained in the previous section, has been considered to derive CoMSIA model and five different models with different CoMSIA field combination were generated. One best model containing steric, electrostatic, hydrophobic and hydrogen bond acceptor fields demonstrated q2 and r2 value of 0.812 and 0.971, respectively with acceptable F-value of 105.52 from six components. The contributions of all fields were found to be 32.4 % Steric, 12.3 % Electrostatic, 28.2 % Hydrophobic, 16.5 % Hydrogen bond acceptor and 10.6 % Hydrogen bond donor. The aforementioned model was further subjected to group cross validation for 50 runs and correlation of 0.785 was obtained and the boot strapping for 100 runs was found to be 0.98 with 0.19 SD as summarized in Table 3. The predicted activity of both test set and training set molecules by CoMFA and CoMSIA models are listed in Table 5 and the graphs of experimental activity against the CoMSIA predicted activities of both training and test sets are presented in Fig 3.

Table 5 actual and predicted (CoMFA and CoMSIA) activities of test set molecules.

Comp. No Actual pIC50 Predicted pIC50 CoMFA

Residuals CoMFA

Predicted pIC50

CoMSIA Residuals CoMSIA

4 3.952 4.027 -0.077 3.988 -0.038 13 4.31 4.252 0.058 4.315 -0.005 19 3.95 3.962 -0.012 3.952 -0.002 24 3.979 4.004 -0.024 3.965 0.015 30 4.227 4.157 0.073 4.165 0.065

Fig. 3 Predicted and observed activities of training and test sets using CoMSI.

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CoMFA and CoMSIA contour map analysis

The steric and electrostatic contour plots of CoMFA revealed that three green colored plots at the 5th position of benzoxazole, 3′ or 4′ of 2-phenyl infer the sterically favorable groups required for anti-fungal activity (Fig. 4). A small red contour plot was also noticed at 5th position along with green. From the SAR it is found that unsubstituted molecule 1 exhibited least anti-fungal activity against Candida albicans. Substitution at 5th position by chlorine, nitro, amino and methyl groups has led to molecules with greater potency, however nitro group

was found to be the most influential for the antifungal activity. Further insight into the contour plots at this position led to the conclusion that not only steric property but also electronic effect guides the activity. Comparatively, electron releasing groups demonstrated reduced activity, whereas groups with electron withdrawing nature such as chlorine and nitro groups showed better activity as noticed in molecule 10 and 15 respectively. A big blue contour map which covered the benzoxazole was found which indicates that the electron withdrawing groups would create lesser electron cloud over the aromatic ring by withdrawing the electrons.

Fig. 4 CoMFA steric and electrostatic contour maps. Green contour favors steric bulk, whereas yellow color disfavors steric bulk. Blue contour indicates electropositive charge and red contour electronegative charge.

Two more green colored plots have been spotted at the 4′ of 2-phenyl ring which suggests that substitution with bulky groups at this position could influence positively for the antifungal activity. Activity of compounds was found increasing from fluorine to bromine as in molecules 22, 14 and 15. Compound 12 has also exhibited potent activity having t-butyl group at the same place. Moderate increase in the potency of the compounds was also observed in the molecules bearing substituted amino groups.

CoMSIA steric and electrostatic contour plots are quite similar however one big yellow plot was found at the 5th position of heterocyclic ring (Fig 5). A careful observation of the data set considered for the model development revealed that a few molecules (26-29, 31) which are unsubstituted found to be potent This indicates that

substitution at 5th position might not be essential for the activity however bulky groups alone at the 4′ position on 2-phenyl ring is vital for the activity.

Four yellow isopleths could be sighted one at the 5th position, one on the 2-phenyl ring and two across the 4′ position of 2-phenyl ring infer about the requirement of hydrophobicity (Fig. 6). From the previous explanation, substitution at the 5th position might not be key requirement for potent activity, hence yellow isopleth at this position can not be explained. However two yellow contours across the 4′-position indicates that the sustituents that increase the lipophilicity contribute positively for the activity. Compounds like 11 and 12 bearing methyl and t-butyl demonstrated potent anti-fungal activity.

Shaheen Begum et al. : 3D QSAR Studies Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents 563

Fig. 5 CoMSIA steric and electrostatic fields are represented by green, yellow, blue and red colour regions.

Fig. 6 CoMSIA hydrophobic contour map.

Validation of CoMFA and CoMSIA models

The most critical and important task of 3D-QSAR is model validation in which the predictive ability of the model established by reproducing the activity of test set compounds which are not included in training set. The best CoMFA and CoMSIA models thus resulted were subjected to external validation by predicting the activity of test set compounds. All the test set compounds were constructed and aligned similarly. Table 4 contains the predicted activity of test set compounds which are found to be in

greater agreement with the observed activities with marginal error range with predictive correlation coefficient of r2

pred of 0.771 and 0.81 for CoMFA and CoMSIA models respectively. The predicted activities of both test set and training set compounds by both CoMFA and CoMSIA verses actual activities has been shown in Fig. These results indicate that the CoMFA and CoMSIA models could be consistently employed in the design for developing inhibitors against fungal infections caused by candida albicans.

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Table 4 actual and predicted activities of training set molecules.

Comp. No

Actual pIC50 Predicted pIC50 CoMFA

Residuals CoMFA

Predicted pIC50

CoMSIA Residuals CoMSIA

1 3.892 3.977 -0.087 3.882 0.010

2 4.001 4.016 -0.016 3.970 0.030

3 3.924 3.912 0.008 3.934 -0.010

5 4.013 3.996 0.014 4.049 -0.036

6 4.059 4.043 0.017 4.071 -0.012

7 4.015 4.018 -0.008 4.020 -0.005

8 4.024 3.980 0.040 4.013 0.011

9 4.04 4.016 0.024 4.081 -0.040

10 4.282 4.317 -0.037 4.263 0.019

11 4.308 4.334 -0.024 4.349 -0.041

12 4.375 4.356 0.014 4.377 -0.002

14 4.342 4.341 -0.001 4.346 -0.004

15 4.406 4.345 0.065 4.356 0.050

16 3.976 3.977 0.003 3.960 0.19

17 3.96 3.957 0.003 3.919 0.040

18 4.005 3.983 0.027 3.978 0.027

20 3.977 3.983 -0.003 3.972 0.005

21 3.98 3.984 -0.004 4.001 -0.021

22 3.958 3.990 -0.030 3.992 -0.034

23 4.027 4.026 0.004 4.039 -0.012

25 4.004 3.983 0.017 4.030 -0.027

26 4.225 4.240 -0.010 4.202 0.024

27 4.253 4.263 -0.013 4.238 0.016

28 4.257 4.263 -0.003 4.266 -0.008

29 4.283 4.286 -0.006 4.295 -0.012

31 4.285 4.284 -0.004 4.165 0.065

Conclusion

CoMFA and CoMSIA methods could derive best models employing twenty six anti-fungal agents. These models were successfully validated by predicting the anti-fungal activity of five test set molecules. In comparison, the predictivity of the CoMFA model was found to be superior to that of the CoMSIA model. However, a combined use of both the CoMFA and the CoMSIA methods would further enhance the predictivity of the derived models.

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