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  • br Materials and methods br Results


    Materials and methods
    Results and discussion In the present work, we have applied two methodologies, CoMFA and CoMSIA to build 3D-QSAR models. There is no crystal structure available; therefore, an indirect method i.e., a ligand-based approach is the method of choice. The highly active molecule among the dataset was selected as template and its bioactive conformation was TAK-285 clinical generated using systematic search and simulated annealing. The obtained conformation was used for alignment where the common scaffold was used to align the compounds. We segregated the dataset (65 compounds) into test (15 compounds) and training set (50 compounds). Based on leave several out cross validation, 80% of the compounds were randomly selected for the generation of the model and the remaining 20% were taken as test set. It is also reported that the predictive ability of a 3D-QSAR model is strongly dependent on the structural similarity between the training and test set molecules. Test set should be chosen such that it covers the same descriptor space as utilized by the training set. This operation must be repeated numerous times in order to obtain reliable statistical results (Puzyn et al., 2010). Hence, 10 different combinations of training and test set TAK-285 clinical were identified. Based on these, we have generated 20 models for CoMFA analysis and by using the combinations of steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor parameters a total of 100 models was generated for CoMSIA analysis. For the generated CoMFA and CoMSIA models the statistical values like q2, r2, SEE, F value, r2pred, steric, electrostatic, hydrophobic, donor and acceptor field contributions was calculated. Each model was validated with statistical cut off values such as q2>0.4, r2>0.5 and r2pred>0.5. For two different alignments, the best predictions were obtained for CoMFA model (q2=0.488, r2=0.718), and CoMSIA model (steric, electrostatic, hydrophobic, H-bond donor and acceptor) (q2=0.525, r2=0.680) and CoMFA model (q2=0.471, r2=0.768), and CoMSIA model (steric, electrostatic, hydrophobic) (q2=0.534, r2=0.947), using systematic search conformation and simulated annealing based alignment, respectively. The alignment using systematic search conformation gives better statistical results in terms of r2pred than simulated annealing and therefore it was used for further analysis. Finally, the CoMFA and CoMISA results were then graphically interpreted by field contribution maps.
    Conflict of interest
    Prostaglandin D2 (PGD) is the major prostanoid released by mast cells during allergic attacks., In human patients, allergen challenge leads to a rapid increase in the production of PGD in the airway of asthmatics, in the nasal mucosa of allergic rhinitics and in the skin of patients suffering from atopic dermatitis. Two high affinity binding G-protein-coupled receptors, the DP1 receptor and the chemoattractant receptor-homologous expressed on Th2 cells (CRTH2 or DP2), have been shown to mediate the effects of PGD. The later receptor is expressed on cell types associated with allergic inflammation, such as Th2 cells, basophils and eosinophils and its activation by PGD triggers the migration, and prevents the apoptosis of these cell types. In vivo reports in rodents have highlighted the role of CRTH2 in promoting chronic allergic skin inflammation and eosinophilic airway inflammation. CRTH2 antagonists have shown efficacy in a murine model of allergic rhinitis. More recently, an orally bioavailable CRTH2 antagonist (OC000459) successfully completed Phase IIa trials demonstrating efficacy in asthma and allergic rhinoconjunctivitis thus unambiguously establishing CRTH2 as a central player of airway inflammation. A number of medicinal chemistry programs started after the identification of non-steroidal anti-inflammatory drug indomethacin (, ) as an agonist of the CRTH2 receptor,, and that of thromboxane receptor antagonist ramatroban (, ) as an antagonist of the CRTH2 receptor. As a result a large number of CRTH2 antagonists known to date are indole acetic acids, bearing the acetic acid moiety either at position 3, exemplified with compound () or at position 1, exemplified with compound ().