The synthesis of symmetrical and unsymmetrical P1/P1' cyclic ureas as HIV protease inhibitors

Urea Derivatives in Modern Drug Discovery and Medicinal Chemistry

The urea functionality is inherent to numerous bioactive compounds, including a variety of clinically approved therapies. Urea containing compounds are increasingly used in medicinal chemistry and drug design in order to establish key drug-target interactions and fine-tune crucial drug-like properties. In this perspective, we highlight physicochemical and conformational properties of urea derivatives. We provide outlines of traditional reagents and chemical procedures for the preparation of ureas. Also, we discuss newly developed methodologies mainly aimed at overcoming safety issues associated with traditional synthesis. Finally, we provide a broad overview of urea-based medicinally relevant compounds, ranging from approved drugs to recent medicinal chemistry developments.

Identification of novel HIV 1--protease inhibitors: application of ligand and structure based pharmacophore mapping and virtual screening

A combined ligand and structure-based drug design approach provides a synergistic advantage over either methods performed individually. Present work bestows a good assembly of ligand and structure-based pharmacophore generation concept. Ligand-oriented study was accomplished by employing the HypoGen module of Catalyst in which we have translated the experimental findings into 3-D pharmacophore models by identifying key features (four point pharmacophore) necessary for interaction of the inhibitors with the active site of HIV-1 protease enzyme using a training set of 33 compounds belonging to the cyclic cyanoguanidines and cyclic urea derivatives. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond acceptors and two hydrophobic, showed a correlation (r) of 0.90 and a root mean square of 0.71 and cost difference of 56.59 bits between null cost and fixed cost. The model was validated using CatScramble technique, internal and external test set prediction. In the second phase of our study, a structure-based five feature pharmacophore hypothesis was generated which signifies the importance of hydrogen bond donor, hydrogen bond acceptors and hydrophobic interaction between the HIV-1 protease enzyme and its inhibitors. This work has taken a significant step towards the full integration of ligand and structure-based drug design methodologies as pharmacophoric features retrieved from structure-based strategy complemented the features from ligand-based study hence proving the accuracy of the developed models. The ligand-based pharmacophore model was used in virtual screening of Maybridge and NCI compound database resulting in the identification of four structurally diverse druggable compounds with nM activities.

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis - a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

Computational design of novel cyclic urea as HIV-1 protease inhibitor

A series of novel cyclic urea molecules 5,6-dihydroxy-1,3-diazepane-2,4,7-trione as HIV-1 protease inhibitors were designed using computational techniques. The designed molecules were compared with the known cyclic urea molecules by performing docking studies, calculating their ADME (Absorption, Distribution, Metabolism, and Excretion) properties and protein ligand interaction energy. These novel molecules were designed by substituting the P 1/P′ 1 positions (4th and 7th position of 1, 3-diazepan-2-one) with double bonded oxygens. This reduces the molecular weight and increases the bioavailability, indicating better ADME properties. The docking studies showed good binding affinity towards HIV-1 protease. The biological activity of these inhibitors were predicted by a model equation generated by the regression analysis between biological activity (log 1/K i ) of known inhibitors and their protein ligand interaction energy. The synthetic studies are in progress.

Catalytic enantioselective hetero-Diels-Alder reactions of an azo compound

This communication describes studies in which an azo hetero-Diels-Alder adduct was furnished in high regio- and enantioselectivity using azopyridine as a reagent and silver as a catalyst. The obtained hetero-Diels-Alder adduct was easily converted to the corresponding chiral 1,4-diamino alcohol.

Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks

Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K(i)) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of K(i) overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (Q(LOO)2 = 0.703 vs. 0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; Q(LOO)2 = 0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models.

Combinatorial design of nonsymmetrical cyclic urea inhibitors of aspartic protease of HIV-1

The aspartic protease (PR) of the human immunodeficiency virus type 1 (HIV-1) is an important target for the design of specific antiviral agents dedicated to treatment of HIV-1 infection. We have employed computer-assisted combinatorial chemistry methods to design a small focused virtual library of nonsymmetrically substituted cyclic urea inhibitors of the PR. Nonsymmetrical compounds with decreased peptidic character were namely found to inhibit the PR with comparable inhibition potencies as their C2-pseudosymmetric counterparts and to possess superior pharmacokinetic properties. To generate the virtual library of fully nonsymmetrical cyclic urea analogs, diverse reagents were selected from databases of available chemicals with characteristics similar to those of the building blocks of known potent PR inhibitors. The X-ray structure of the protease-inhibitor complex PR-XV-638 was used as the receptor model in the structure-based focusing and in silico screening of the virtual library. A target-specific LUDI-type scoring function, parameterized for a QSAR training set of known cyclic urea inhibitors and validated on a set of compounds not included into the training set, was used to predict the inhibition constants (Ki) of the generated analogs toward the HIV-1 PR. The fragments most frequently occurring in the analogs with the highest predicted inhibition potencies (Ki*<10 pM) were then selected to constitute a highly focused library subset containing novel nonsymmetrical cyclic ureas with predicted Ki*s 1 order of magnitude lower than the most potent known cyclic urea inhibitors. ADME properties calculated for the most promising analogs suggested that the cyclic ureas are endowed with a wide range of favorable pharmacokinetic properties, which may favor the discovery of a potent orally administrable antiviral drug.

Computational design of new cyclic urea inhibitors for improved binding of HIV-1 aspartic protease

We report in this paper the design, by means of computational techniques, of new cyclic urea inhibitors of the HIV aspartic protease. The relationship between the complexation energies of the enzyme with known inhibitors and the experimentally determined log K(i) have been studied and used to predict inhibition constants for new inhibitors.