Nonsymmetrically substituted cyclic urea 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.

Molecular dynamics investigation on a series of HIV protease inhibitors: assessing the performance of MM-PBSA and MM-GBSA approaches

The binding free energies (ΔG(Bind)) obtained from molecular mechanics with Poisson-Boltzmann surface area (MM-PBSA) or molecular mechanics with Generalized Born surface area (MM-GBSA) calculations using molecular dynamics (MD) trajectories are the most popular procedures to measure the strength of interactions between a ligand and its receptor. Several attempts have been made to correlate the ΔG(Bind) and experimental IC(50) values in order to observe the relationship between binding strength of a ligand (with its receptor) and its inhibitory activity. The duration of MD simulations seems very important for getting acceptable correlation. Here, we are presenting a systematic study to estimate the reasonable MD simulation time for acceptable correlation between ΔG(Bind) and experimental IC(50) values. A comparison between MM-PBSA and MM-GBSA approaches is also presented at various time scales. MD simulations (10 ns) for 14 HIV protease inhibitors have been carried out by using the Amber program. MM-PBSA/GBSA based ΔG(Bind) have been calculated and correlated with experimental IC(50) values at different time scales (0-1 to 0-10 ns). This study clearly demonstrates that the MM-PBSA based ΔG(Bind) (ΔG(Bind)-PB) values provide very good correlation with experimental IC(50) values (quantitative and qualitative) when MD simulation is carried out for a longer time; however, MM-GBSA based ΔG(Bind) (ΔG(Bind)-GB) values show acceptable correlation for shorter time of simulation also. The accuracy of ΔG(Bind)-PB increases and ΔG(Bind)-GB remains almost constant with the increasing time of simulation.

Stochastic versus Stepwise Strategies for Quantitative Structure−Activity Relationship GenerationHow Much Effort May the Mining for Successful QSAR Models Take?

Descriptor selection in QSAR typically relies on a set of upfront working hypotheses in order to boil down the initial descriptor set to a tractable size. Stepwise regression, computationally cheap and therefore widely used in spite of its potential caveats, is most aggressive in reducing the effectively explored problem space by adopting a greedy variable pick strategy. This work explores an antipodal approach, incarnated by an original Genetic Algorithm (GA)-based Stochastic QSAR Sampler (SQS) that favors unbiased model search over computational cost. Independent of a priori descriptor filtering and, most important, not limited to linear models only, it was benchmarked against the ISIDA Stepwise Regression (SR) tool. SQS was run under various premises, varying the training/validation set splitting scheme, the nonlinearity policy, and the used descriptors. With the considered three anti-HIV compound sets, repeated SQS runs generate sometimes poorly overlapping but nevertheless equally well validating model sets. Enabling SQS to apply nonlinear descriptor transformations increases the problem space: nevertheless, nonlinear models tend to be more robust validators. Model validation benchmarking showed SQS to match the performance of SR or outperform it in cases when the upfront simplifications of SR "backfire", even though the robust SR got trapped in local minima only once in six cases. Consensus models from large SQS model sets validate well--but not outstandingly better than SR consensus equations. SQS is thus a robust QSAR building tool according to standard validation tests against external sets of compounds (of same families as used for training), but many of its benefits/drawbacks may yet not be revealed by such tests. SQS results are a challenge to the traditional way to interpret and exploit QSAR: how to deal with thousands of well validating models, nonetheless providing potentially diverging applicability ranges and predicted values for external compounds. SR does not impose such burden on the user, but is "betting" on a single equation or a narrow consensus model to behave properly in virtual screening a sound strategy? By posing these questions, this article will hopefully act as an incentive for the long-haul studies needed to get them answered.

1,3,5-Triazepan-2,6-diones as Structurally Diverse and Conformationally Constrained Dipeptide Mimetics: Identification of Malaria Liver Stage Inhibitors from a Small Pilot Library

The development of the 1,3,5-triazepane-2,6-dione system as a novel, conformationally restricted, and readily accessible class of dipeptidomimetics is reported. The synthesis of the densely functionalized 1,3,5-triazepane-2,6-dione skeleton was achieved in only four steps from a variety of simple linear dipeptide precursors. To extend the practical value of 1,3,5-triazepane-2,6-diones, a general polymer-assisted solution-phase synthesis approach amenable to library production in a multiparallel format was developed. The conformational preferences of the 1,3,5-triazepane-2,6-dione skeleton were investigated in detail by NMR spectroscopy and X-ray diffraction. The ring exhibits a characteristic folded conformation which was compared to that of related dipeptide-derived scaffolds including the more planar 2,5-diketopiperazine (DKP). Molecular and structural diversity was increased further through post-cyclization appending operations at urea nitrogens. Preliminary biological screens of a small collection of 1,3,5-triazepane-2,6-diones revealed inhibitors of the underexplored malaria liver stage and suggest strong potential for this dipeptide-derived scaffold to interfere with and to modulate biological pathways.

Probabilistic Neural Network Model for the In Silico Evaluation of Anti-HIV Activity and Mechanism of Action

A theoretical model has been developed that discriminates between active and nonactive drugs against HIV-1 with four different mechanisms of action for the active drugs. The model was built up using a probabilistic neural network (PNN) algorithm and a database of 2720 compounds. The model showed an overall accuracy of 97.34% in the training series, 85.12% in the selection series, and 84.78% in an external prediction series. The model not only correctly classified a very heterogeneous series of organic compounds but also discriminated between very similar active/nonactive chemicals that belong to the same family of compounds. More specifically, the model recognized 96.02% of nonactive compounds, 94.24% of active compounds that inhibited reverse transcriptase, 97.24% of protease inhibitors, 97.14% of virus uncoating inhibitors, and 90.32% of integrase inhibitors. The results indicate that this approach may represent a powerful tool for modeling large databases in QSAR with applications in medicinal chemistry.

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.

Comparing the conformational behavior of a series of diastereomeric cyclic urea HIV-1 inhibitors using the low mode:monte carlo conformational search method

The conformational flexibility of a series of diastereomeric cyclic urea HIV-1 protease inhibitors has been examined using the Low Mode:Monte Carlo conformational search method. Force fields were validated by a comparison of the energetic ordering of the minimum energy structures on the AMBER/GBSA(water), OPLSAA/GBSA(water) and HF/6-311G/SCRF(water) surfaces. The energetic ordering of the minima on the OPLSAA /GBSA(water) surface was in better agreement with the quantum calculations than the ordering on the AMBER/GBSA(water) surface. An ensemble of low energy structures was generated using OPLSAA/GBSA(water) and used to compare the molecular shape and flexibility of each diastereomer to the experimentally determined binding affinities and crystal structures of closely related systems. The results indicate that diastereomeric solution-phase energetic stability, conformational rigidity and ability to adopt a chair conformation correlate strongly with experimental binding affinities. Rigid body docking suggests that all of the diastereomers adopt solution-phase conformations suitable for alignment with the HIV-1 protease; however, these results indicate that the binding affinities are dependent upon subtle differences in the P1/P1' and P2/P2' substituent orientations.

A mechanistic study of 3-aminoindazole cyclic urea HIV-1 protease inhibitors using comparative QSAR

Comparative QSAR studies on P2/P2' and P1/P1' substituted symmetrical and nonsymmetrical 3-aminoindazole cyclic urea HIV-1 protease inhibitors were performed. The protease inhibitory activity of these compounds was found to decrease with larger and more hydrophobic molecules, whereas the antiviral potency and translation across the cell membrane increases with increase in hydrophobicity and size. These results provide mechanistic insight about the mode of interaction of these compounds with HIV-1 protease receptor and would help in further improving the biological activity.

Anti-HIV activity of HEPT, TIBO, and cyclic urea derivatives: structure-property studies, focused combinatorial library generation, and hits selection using substructural molecular fragments method

Substructural molecular fragments (SMF) method [Solov'ev, V. P.; Varnek, A.; Wipff, G. J. Chem. Inf. Comput. Sci. 2000, 40, 847-858] was applied to assess anti-HIV activity for large data sets for three families of compounds: 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives, tetrahydroimidazobenzodiazepinone (TIBO) derivatives, and cyclic urea (CU) derivatives. The SMF method uses 49 types of topological descriptors (atom/bond sequences and "augmented atoms") which, being coupled with 3 linear and nonlinear fitting equations, allows the user to generate up to 147 structure-property models. For each family of compounds, the modeling was performed on several training sets followed by the validation calculations where three best fit models were applied. Calculated activities well reproduce available experimental data. On the basis of the "optimal" molecular fragments, the focused combinatorial library containing 252 virtual HEPT derivatives has been generated. Its filtering led to several hits potentially possessing anti-HIV activity.

Comparing the Quality and Predictiveness between 3D QSAR Models Obtained from Manual and Automated Alignment

A set of 113 flexible cyclic urea inhibitors of human immunodeficiency virus protease (HIV-1 PR) was used to compare the quality and predictive power of CoMFA and CoMSIA models for manually or automatically aligned inhibitor set. Inhibitors that were aligned automatically with molecular docking were in agreement with information obtained from existing X-ray structures. Both alignment methods produced statistically significant CoMFA and CoMSIA models, with the best q(2) value being 0.649 and the best predictive r(2) being 0.754. The manual alignment gave statistically higher values, whereas the automated alignment gave more robust models for predicting the activities of an external inhibitor set. Both models utilized similar amino acids in the HIV-1 PR active site, supporting the idea that hydrogen bonds form between an inhibitor and the backbone carbonyl oxygens of Gly48 and Gly48' and also the backbone NH group of Asp30, Gly48, Asp29', and Gly48' of the enzyme. These results suggest that an automated inhibitor alignment can yield predictive 3D QSAR models that are well comparable to manual methods. Thus, an automated alignment method in creating 3D QSAR models is encouragable when a well-characterized structure of the target protein is available.

Diversity analysis of 14 156 molecules tested by the National Cancer Institute for anti-HIV activity using the quantitative structure-activity relational expert system MCASE

Using the MCASE program, a procedure to analyze the diversity of the large amount of available HIV-1 antiviral data was proposed. A subset of 1 819 chemicals was logically selected from the original 14 156 chemicals tested by NCI. This subset of chemicals was shown to contain most of the structural and the functional information of the original database. A full analysis of the 1 819 chemicals by the MCASE program produced a correlation between chemical structures and HIV antiviral activity. In our model, 74 fragments were identified as being responsible for all the chemical's HIV antiviral activity. These fragments may be related to different inhibiting mechanisms, some known and some probably still unknown. The expert system resulting from this analysis can be used to predict the activity of new chemicals and to design new agents that can target multiple enzymes. This was shown to be the case by using the model to predict the activity of 10 diverse chemicals whose activities were not known at the time of model development. Of these, 8 were predicted in agreement with experimental observations. As far as we can tell, this is probably the first project ever to attempt to create a quantitative model of activity for such a massive database of diverse chemicals.

Three-dimensional quantitative structure-activity relationship study on cyclic urea derivatives as HIV-1 protease inhibitors: application of comparative molecular field analysis

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been developed using comparative molecular field analysis (CoMFA) on a large data set (118 compounds) of diverse cyclic urea derivatives as protease inhibitors against the human immunodeficiency virus type 1 (HIV-1). X-ray crystal structures of HIV-1 protease bound with this class of inhibitors were used to derive the most probable bioactive conformations of the inhibitors. The enzyme active site was used as a constraint to limit the number of possible conformations that are sterically accessible. The test sets have been created keeping in mind structural diversity as well as the uniform simple statistical criteria (mean, standard deviation, high and low values) of the protease inhibitory activities of the molecules compared to the training sets. Multiple predictive models have been developed with the training sets (93 compounds in each set) and validated with the corresponding test sets (25 compounds in each set). All the models yielded high predictive correlation coefficients (q2 from 0.699 to 0.727), substantially high fitted correlation coefficients (r2 from 0.965 to 0.973), and reasonably low standard errors of estimates (S from 0. 239 to 0.265). The steric and electrostatic effects have approximately equal contributions, 45% and 55% (approximately), respectively, toward explaining protease inhibitory activities. This analysis yielded models with significant information on steric and electrostatic interactions clearly discerned by the respective coefficient contour plots when overlapped on the X-ray structure of the HIV-1 protease. The HINT CoMFA study revealed significant contribution of hydrophobicity toward protease inhibitory activity. The 3D visualization technique utilizing these contour plots as well as the receptor site geometry may significantly improve our understanding of the inhibitor-protease (HIV-1) interactions and help in designing compounds with improved activity.

Synthesis of aryl semicarbazone of 4-aminoacetophenone and their anti-HIV activity

The thioureido derivative of 4-aminoacetophenone aryl semicarbazone have been prepared. These derivatives have been characterised on the basis of different physicochemical evidences. The anti-HIV-1 (HTLV-IIIB) and -HIV-2 (ROD) activity and cytotoxicity of the compounds were tested. The compound VII and VIII showed maximum protection among the series.

Comparative molecular field analysis (CoMFA) of a series of symmetrical bis-benzamide cyclic urea derivatives as HIV-1 protease inhibitors

A 3D-QSAR study using CoMFA methodology was conducted on a series of 29 symmetrical bis-benzamide cyclic urea derivatives having anti-HIV-1-protease activities. Active site minimization of the ligands was used to exclude conformations which are not sterically accessible within the active site. A significant cross validated correlation coefficient q2 (0.724) was obtained indicating the predictive potential of the model for untested compounds of this class. A significant non-cross-validated correlation coefficient (r2) of 0.971 with a low standard error estimate (S) of 0.119 was obtained indicating that the model reliably predicted the ant-protease activities of poorly to highly active compounds. The model was used to predict the anti-protease activities of eight test-set compounds, and the predicted values were in good agreement with the experimental values. The CoMFA coefficient contour plots identified several key features which explain the wide range of activities. The already reported 2D-QSAR along with the CoMFA model presented here may help in designing effective HIV-1 protease inhibitors.