Association of two 3D QSAR analyses. application to the study of partial agonist serotonin-3 ligands

Ligand-Based Molecular Modeling Study on a Chemically Diverse Series of Cholecystokinin-B/Gastrin Receptor Antagonists: Generation of Predictive Model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.

Characterization of β3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

The beta3-adrenoreceptor (beta3-AR) has been shown to mediate various pharmacological and physiological effects such as lipolysis, thermogenesis, and intestinal smooth muscle relaxation. It also plays an important role in glucose homeostasis and energy balance. Molecular modeling studies were undertaken to develop predictive pharmacophoric hypothesis and 3D-QSAR model, which may explain variations in beta3-AR agonistic activity in terms of chemical features and physicochemical properties. The two softwares, CATALYST for pharmacophoric alignment and APEX-3D for 3D-QSAR modeling were used to establish the structure activity relationships for beta3-AR agonistic activity. Among the several statistically significant models, the selection of the best pharmacophore and 3D-QSAR model was based on its ability to estimate the activity of external test sets of similar and different structural types along with the reasonable consistency of the model with the limited information of the active site of beta3-AR. The final 3D-QSAR model was derived using the pharmacophoric alignments from the hypothesis which consisted of four chemical features: basic or positive ionizable feature on the nitrogen of the aryloxypropylamino group, two ring aromatic features corresponding to the phenyl ring of the phenoxide and the benzenesulphonamido groups and a hydrogen-bond donor (HBD) in the vicinity of the nitrogen atom of the benzenesulphonamido group with the most active molecule mapping in an energetically favorable extended conformation. This hypothesis was in agreement with the site directed mutagenesis studies on human beta3-AR and correlated well the observed and estimated activity both in, training and both the external test sets. It also mapped reasonably well to six beta3-AR agonists of different structural classes under clinical development and thus this hypothesis may have a universal applicability in providing a powerful template for virtual screening and also for designing new chemical entities (NCEs) as beta3-AR agonists.

CoMFA and Docking Studies on Glycogen Phosphorylase a Inhibitors as Antidiabetic Agents

Glycogen phosphorylase (GP(a)) is a specific target for the design of inhibitors and may prevent glycogenolysis under high glucose conditions in type II diabetes. The carboxamides first reported by Hoover D. J. et al. (J. Med. Chem. 1998, 41, 2934-2938) are one of the major classes of GP(a) inhibitors other than glucose derivatives. The recent, X-ray crystallographic analyses (Oikonomakos et al. Biochim. Biophys. Acta 2003, 1647, 325-332) have revealed a distinct mechanism of action for these inhibitors, which bind at a new allosteric site away from the inhibitory and catalytic sites. To elucidate the essential structural and physicochemical requirements responsible for binding to the GP(a) enzyme and to develop predictive models, CoMFA and docking studies have been carried out on a series of indole-2-carboxamide derivates. The CoMFA model developed using pharmacophoric alignments and hydrogen-bonding fields demonstrated high predictive ability against the training (r2 = 0.98, q2 = 0.68) and the test set (r2pred = 0.85). Further the superimposition of PLS coefficient contour maps from CoMFA with the GP(a) active site (PDB: 1lwo) has shown a high level of compatibility.

Arylguanidine and arylbiguanide binding at 5-HT3 serotonin receptors: a QSAR study

For a series of monosubstituted arylguanidines, 5-HT3 receptor affinity was found generally related to the electron withdrawing nature of the substituent at the aryl 3-position and the lipophilicity of the 4-position substituent. A broader examination of 35 arylguanidines and arylbiguanides revealed that affinity could be described by molecular polarizability, a Chi index term (8chiP), and the sum of all (-Cl) E-State values (SsCl) in the molecule.

Topomer CoMFA: a design methodology for rapid lead optimization

To provide an objective QSAR methodology that might accelerate lead optimization, the CoMFA and topomer technologies have been merged, with surprisingly good results. A series of input structures are each broken into two or more fragments at central acyclic single bonds, while removing any core fragment structurally common to the entire series. Standard topomer 3D models are automatically constructed for each fragment, and a set of steric and electrostatic fields ("CoMFA column") is generated for each set of topomers. Application of "topomer CoMFA" to 15 3D-QSAR analyses taken from the literature (847 structures) were all successful, with an average q(2) of 0.520 (literature average q(2) = 0.636) and an average standard deviation of true prediction (SDEP) of 0.688 (literature average SDEP = 0.553) for 133 structures. Topomer CoMFA results are particularly promising as queries into virtual libraries already composed of topomer structures, to directly seek structures having increased potency. Accordingly, in 13 of the 15 such "topomer CoMFA searches" attempted, combinations of commercially offered fragments were retrieved that were predicted to be more potent than any structure described in the original publication (average predicted potency increase = 20 x), showing in principle how optimization could occur.

Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase

Pharmacophore hypotheses were developed for a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex (MAC) and human dihydrofolate reductase (hDHFR). Training sets consisting of 20 inhibitors were selected in each case on the basis of the information content of the structures and activity data as required by the HypoGen program in the Catalyst software. In the case of MAC DHFR inhibitors, the best pharmacophore in terms of statistics and predictive value consisted of four features: two hydrogen bond acceptors (HA), one hydrophobic (HY) feature, and one ring aromatic (RA) feature. The selected pharmacophore hypothesis yielded an rms deviation of 0.730 and a correlation coefficient of 0.967 with a cost difference (null cost minus total cost) of approximately 52. The pharmacophore was validated on a large set of test inhibitors. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. This classification scheme is more practical than actual estimated values because these values have no meaning for compounds yet to be tested except that they indicate whether the compounds will be active or inactive in a biological assay. For the training set, the success rate for predicting active and inactive compounds was 100%. For the test set, the success rate in predicting active compounds was greater than 92% while about 7% of the inactive compounds were predicted to be active. This successful prediction was further validated on three structurally diverse compounds active against MAC DHFR. Two compounds mapped well onto three of the four features of the pharmacophore. The third compound was mapped to all four features of the pharmacophore. This validation study provided confidence for the usefulness of the selected pharmacophore model to identify compounds with diverse structures from a database search. Comparison of pharmacophores for inhibitors of human and MAC DHFR is expected to reveal fundamental differences between these two pharmacophores that may be effectively exploited to identify and design compounds with high selectivity for MAC DHFR.