Development of benzodioxine-heteroarylpiperazines as highly potent and selective α2c antagonists.

Here is reported the design and synthesis of a series of highly potent and selective α2C antagonists using benzodioxine methyl piperazine as a central scaffold by pharmacophoric analysis to improve the pharmacokinetics of suboptimal clinical candidate molecules.

2,3-Dihydrobenzo-dioxine piperidine derivatives as potent and selective α2c antagonists.

In this manuscript, we report a series of benzodioxine methyl piperidine derivatives as highly potent and selective α2C antagonists by ligand design to improve the pharmacokinetics of a previous candidate molecule.

Field-based comparison of ligand and coactivator binding sites of nuclear receptors.

A structure-based comparison of the ligand-binding domains of 35 nuclear receptors from five different subfamilies is presented. Their ligand and coactivator binding sites are characterized using knowledge-based contact preference fields for hydrophobic and hydrophilic interactions implemented in the MOE modeling environment. Additionally, for polar knowledge-based field points the preference for negative or positive electrostatic interactions is estimated using the Poisson-Boltzmann equation. These molecular-interaction fields are used to cluster the nuclear receptor family based on similarities of their binding sites. By analyzing the similarities and differences of hydrophobic and polar fields in binding pockets of related receptors it is possible to identify conserved interactions in ligand and coactivator binding pockets, which support e.g. design of specific ligands during lead optimization or virtual screening as docking filter. Examples of remarkable similarities between ligand binding sites of members from phylogenetically different nuclear receptor families (RXR, RAR, HNF4, NR5) and differences between closely related subtypes (LXR, RAR, TR) are discussed in more detail. Significant similarities and differences of coactivator binding sites are shown for NR3Cs, LXRs and PPARs.

Regio- and stereospecific N-glucuronidation of medetomidine: the differences between UDP glucuronosyltransferase (UGT) 1A4 and UGT2B10 account for the complex kinetics of human liver microsomes.

Medetomidine is a chiral imidazole derivate whose dextroenantiomer is pharmacologically active. The major metabolic pathway of dexmedetomidine [(+)-4-(S)-[1-(2,3-dimethylphenyl)ethyl]-1H-imidazole] in humans is N-glucuronidation at the imidazolate nitrogens. We have purified the N3- and N1-glucuronides of dexmedetomidine, termed DG1 and DG2, respectively, according to their elution order in liquid chromatography and determined their structure by 1H nuclear magnetic resonance (NMR). Studying medetomidine glucuronidation by human liver microsomes (HLMs) and recombinant UDP glucuronosyltransferase (UGT) 1A4 indicated that another human UGT plays a major role in these activities. We now demonstrate that this enzyme is UGT2B10. HLMs catalyzed DG1 and DG2 formation, at a ratio of 3:1, with two-enzyme kinetics that contain both a high-affinity component, K(m1) values of 6.6 and 8.7 microM, and a low-affinity component, K(m2) values > 1 mM. The DG1/DG2 ratio in the case of UGT2B10 was lower, 1.4:1, whereas the substrate affinity for both reactions was high, K(m) values of 11 and 16 microM. UGT1A4 produced mainly DG1 (DG1/DG2 ratio of 6.6:1) at low substrate affinities, K(m) values above 0.6 mM, but superior expression-normalized V(max) values. Levomedetomidine [(-)-4-(R)-[1-(2,3-dimethylphenyl)ethyl]-1H-imidazole] glucuronidation by HLMs yielded mostly the N3-glucuronide (LG1, structure determined by NMR), with monophasic kinetics and a K(m) value of 14 microM. The activity of UGT1A4 toward levomedetomide was low and generated both LG1 and LG2, whereas UGT2B10 exhibited relatively high activity and sharp regioselectivity, yielding only LG1, with a K(m) value of 7.4 microM. The results highlight the contribution of UGT2B10 to medetomidine glucuronidation and its potential importance for other N-glucuronidation reactions within the human liver.

A modification of the Hammett equation for predicting ionisation constants of p-vinyl phenols.

Currently there are several compounds used as drugs or studied as new chemical entities, which have an electron withdrawing group connected to a vinylic double bond in a phenolic or catecholic core structure. These compounds share a common feature--current computational methods utilizing the Hammett type equation for the prediction of ionisation constants fail to give accurate prediction of pK(a)'s for compounds containing the vinylic moiety. The hypothesis was that the effect of electron-withdrawing substituents on the pK(a) of p-vinyl phenols is due to the delocalized electronic structure of these compounds. Thus, this effect should be additive for multiple substituents attached to the vinylic double bond and quantifiable by LFER-based methods. The aim of this study was to produce an improved equation with a reduced tendency to underestimate the effect of the double bond on the ionisation of the phenolic hydroxyl. To this end a set of 19 para-substituted vinyl phenols was used. The ionisation constants were measured potentiometrically, and a training set of 10 compounds was selected to build a regression model (r2 = 0.987 and S.E. = 0.09). The average error with an external test set of six compounds was 0.19 for our model and 1.27 for the ACD-labs 7.0. Thus, we have been able to significantly improve the existing model for prediction of the ionisation constants of substituted p-vinyl phenols.

CoMFA modeling of human catechol O-methyltransferase enzyme kinetics.

Three-dimensional QSAR models with different charge calculation methods (MOPAC-AM1-ESP, MOPAC-AM1-Coulson and Gasteiger-Hückel) were developed for predicting all three enzyme kinetic parameters Km, Vmax and Vmax/Km for catecholic substrates of human soluble catechol O-methyltransferase (S-COMT). The empirical parameters of 45 substrates were correlated to the steric and electronic molecular fields of the substrates utilizing Comparative Molecular Field Analysis (CoMFA). Alignment rules for CoMFA were developed based on the catalytic mechanism and crystal structure of S-COMT, and the analysis was optimized using an all-space search technique. Leave-one-out and leave-n-out cross-validation (with 5 and 10 cross-validation groups) was carried out, and all developed models proved to be statistically significant with q2 values up to 0.84. The models based on MOPAC charge calculations predicted the empirical values clearly better than the Gasteiger-Hückel method. The derived CoMFA coefficient contour maps of steric and electrostatic interactions correlated clearly with the S-COMT crystallographic structures.

CoMFA modeling of enzyme kinetics: K(m) values for sulfation of diverse phenolic substrates by human catecholamine sulfotransferase SULT1A3.

Three-dimensional QSAR models were developed for predicting kinetic Michaelis constant (K(m)) values for phenolic substrates of human catecholamine sulfating sulfotransferase (SULT1A3). The K(m) values were correlated to the steric and electronic molecular fields of the substrates utilizing Comparative Molecular Field Analysis (CoMFA). The evaluated SULT1A3 substrate data set consisted of 95 different substituted phenols, catechols, catecholamines, steroids, and related structures for which the K(m) values were available. The data set was divided in three different subgroups in the initial analysis: (1). for the first CoMFA model substrates with only one reacting hydroxyl group were selected (n = 51), (2).the second model was build with structurally rigid substrates (n = 59), and (3). finally all substrates of the data set were included in the analysis (n = 95). Substrate molecules were aligned using the aromatic ring and the reacting hydroxyl group as a template. After the initial analysis different substrate alignment rules based on the existing knowledge of the SULT1A3 active site structure were evaluated. After this optimization a final CoMFA model was built including all 95 substrates of the data set. Cross-validated q(2) values (leave-one-out and leave-n-out) and coefficient contour maps were calculated for all derived CoMFA models. All four CoMFA models were statistically significant with q(2) values up to 0.624. These predictive QSAR models will provide us information about the factors that affect substrate binding at the active site of human catecholamine sulfotransferase SULT1A3.