|Iranian Journal of Pharmaceutical Research
(2009), 8 (3): 163-167
Received: May 2008
Accepted: October 2008
Copyright ? 2009 by School of Pharmacy
3D QSAR Studies of
as antimycobacterial agents
Krishna Kant Jhaa*, Abdul Samadc, Yatendra
Kumarb, Mohd Shaharyare,
RatanLal Khosad, Jainendra Jaina and Sandeep Bansald
aDepartment of Pharmaceutical Technology, MIET, Meerut. (U.P.) India. bI.T.S. Paramedical College, Muradnagar, Ghaziabad, (U.P.) India. cD.J. College of Pharmacy Modinagar, Ghaziabad, (U.P.) India. dB.I.T Meerut. (U.P.) India. e Faculty of Pharmacy, Jamia Hamdard, New Delhi, India.
Recently several 1,3,4-oxadiazole derivatives were identified as potentially active antimycobacterial agents. Various 5-aryl-2-thio-1,3,4-oxadiazoles have been reported having good antimycobacterial activity against Mycobacterium tuberculosis H37Rv (ATCC 27294). In this paper we report 3D QSAR studies for the 41 molecules of 1,3,4-oxadiazoles by using k-Nearest Neighbor Molecular Field Analysis (kNN-MFA) combined with various selection procedures. Using kNN-MFA approach 52 3D-QSAR models were generated; one of these models was selected on the basis of q2 and pred_r2 values. The selected model had shown good internal and external predictivity for the training set of 33 molecules and test set of 8 molecules with validation (q2) and cross validation (pred_r2) values of 0.5022 and 0.2898, respectively.
This model can be used for preliminary screening of large diversified compound libraries. The model has shown that presence of sulphur is must for activity, however the larger bulky substituents reduce the activity. The presence of halogen and other non-halogen groups have also contributed to the activity.
Hence the future schemes with smaller groups on sulphur and electronegative groups in the molecule would result in potentially active molecules.
Recently W.H.O has reported a significant rise in drug resistant tuberculosis (1, 2). Tuberculosis (T B) is one of the leading causes of death and suffering world wide among the infectious diseases. The ever increasing drug resistance, toxicity and side effect of currently used anti-tuberculosis drugs and the absence of their bactericidal activity highlight the need for new, safer and more effective antituberculosis drugs (3). The computer-aided prediction of biological activity in relation to the chemical structure of a compound is now a commonly used technique in drug discovery (4-7). Modern drug discovery also relies on the interface of chemical and biological diversity through high throughput screening (8). Generation of functional molecular diversity for probing the biological activity space requires robust molecular scaffolds that are low in molecular weight and are easily modified to create a variety of chemically diverse, biologically active potential drugs. We do report our efforts to relate the dependence of the antimycobacterial activity of new compounds on the nature of substitution in the 5-aryl-2-thio-1, 3, 4-oxadiazoles.
The present 3D QSAR study was carried out by using k-Nearest Neighbor Molecular Field Analysis (K-NNMFA) method for predicting the antitubercular activity. The better the description of a molecule in terms of structural parameters representing its activity, the better the results of pattern recognition and separation of molecules by activity.
The in vitro percentage inhibition values for anti mycobacterial activities of 5-aryl-2-thio-1, 3, 4-oxadiazoles against M . tuberculosis H37 Rv were taken from the literature (9). To these values, we have applied one of the modest kNN-MFA with various variable selection methods. Similar to many 3D QSAR methods (10, 11) kNN-MFA requires suitable alignment of set of molecule. This is followed by generation of common rectangular grid around the molecules. The steric and electrostatic energies are computed at the lattice point of grid using methyl probe of charge +1, these interaction energy values at the grid point are considered for relationship generation using kNN method and utilized as descriptors to decide nearness between molecules (12).
All the 41 molecules taken in the study (Figure 1) were drawn in Vlife QSAR Plus. They were optimized by using ?Merck Molecular Force Field (MMFF)? and were also batch optimized. After this all the 41 molecules were aligned (Figure 2) using template based alignment method by choosing a minimum common structure as ?Template? (Figure 3) and the most effective one as the ?Reference Molecule? (Figure 4). From the 41 molecules taken in the study, a training set of 33 molecules and test set of 8 molecules were generated using the various selection procedures. After the selection of the test and training sets, kNN methodology was applied to the descriptors generated over the grid as shown in the ?Show Point? (Figure 5).
Evaluation of QSAR models
The QSAR models were evaluated using following statistical measures: n-number of descriptors; k-number of nearest neighbors; q2-cross validated r2 (by leave-one-out method); pred-r2 - predicted r2 for the external test.
Results and Discussion
The importance and utility of the new 3D QSAR method discussed has been established by applying it to known sets of molecules as described above. We report that 52, 3D QSAR models were generated by kNN-MFA in conjunction with Simulated Annealing (SA), Genetic Algorithms and Stepwise (SW) Forward Backward selection methods. From these models, two of them were having good q2 and pred-r2 values, one of which was selected having good internal and external predictivity. For this model training and test sets were selected using random selection method and the descriptors were selected using simulated annealing method. The summary of the selected model (Table 1) can be given as: k=5; q2=0.5022; pred-r2 = 0.2898; Descriptor range: S-954 -0.4392 to -0.2215; E-1177 -1.267 to -0.4091; S-1678 -0.0060 to -0.0005; E-318 -0.3709 to 0.9445.
The selected model has shown good internal and external predictivity with q2=0.5022 and pred-r2=0.2898 for the training and test set molecules. The model had indicated that the presence of sulphur is required for optimum antimycobacterial activity, where as the large bulky substituents on sulphur i.e. R4 reduces the activity, as indicated by increase in negative value of descriptor S-954. The presence of electronegative groups on R6 with less bulky substituents on other positions specifically R2 and R3 has shown better activity as indicated by increase in positive value of electronic descriptor E-318.
Thus, it would be worth while to synthesize a novel 1,3,4-oxadiazole analogue with less bulky groups on sulphur and more electronegative group at R6 along with less bulky substituents at R2 and R3 positions.
(1) World Health Organization: Anti-Tuberculosis Drug
Resistance in the World: The WHO/IUATLD Global Project on Anti-Tuberculosis Drug
Resistance Surveillance. World Health Organization (1997):WHO/TB/97.229:1-227.
(2) Butler D. New fronts in an old war. Nature (2000) 406: 670-672.
(3) Gazdic A. Correlation between increased incidence of pulmonary tuberculosis and AIDS Med. Arch. (1998) 52: 207-209.
(4) Stepanchikova AV, Lagunin AA, Filimonov DA and Poroikov VV. Prediction of biological activity spectra for substances: evaluation on the diverse sets of drug-like structures. Curr. Med. Chem. (2003) 10: 225-233.
(5) Geronikaki A, Deraden JC, Filimonov DA, Garibova TL and Macaev FZ. Computer-aided creation of new effective and safe medicine. J. Med. Chem. (2004) 47: 2870-2876.
(6) Geronikaki A, Babaev E and Dehaen W. Design, synthesis, computational and biological evaluation of new anxiolytics. Bioorg. Med. Chem. (2004) 12: 6559-6568.
(7) Oruc E, Rollas S, Kandemirili F, Shvets N and Dimoglo AS. 1,3,4-Thiadiazole derivatives, synthesis, structure elucidation, and structure-antituberculosis activity relationship investigation. J. Med. Chem. (2004) 47: 6760-6767.
(8) Collins L and Franzblau SG. Microplate alamar blue assay versus BACTEC 460 system for high- throughput screening of compounds against Mycobacterium tuberculosis and Mycobacterium avium. Antimicrob. Agents Chemother. (1997) 41: 1004-1009.
(9) Macaev F, Rusu G, Pogrebnoi S, Nathaly S and Reynolds R. Synthesis of novel 5-aryl-2-thio-1,3,4-oxadiazoles and the study of their structure-anti-mycobacterial activities. Bioorg. Med. Chem. (2005) 13: 4842-4850.
(10) Cramer RD, Patterson DE and Bunce JD. Comparative molecular field analysis (CoMFA). Effect of shape on binding of steroids to carrier proteins J. Am. Chem. Soc. (1998) 110: 5959-5967.
(11) Klebe G, Abraham U and Meitzner T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. (1994) 37: 24-30.
(12) Ajmani S, Kamalakar J and Kulkarni SA. Three-dimensional QSAR using the k-nearest neighbor method and its interpretation J. Chem. Inf. Model. (2006) 46: 24-31.