The stereochemistry of N-methyl and aryl substituents determine the biological activities of 3-aryl-8-methyl-8-azabicyclo[3.2.1]oct-2,3-enes.

Aryl substituted tropanes and their 2,3-ene analogs are highly selective inhibitors of monoamine uptake. The solution structures of a series of aryl tropanes were determined using NMR spectroscopy and molecular modeling to identify conformational preferences that may determine the overall activity. The majority of these analogs undergo nitrogen inversion, and the rate of interconversion between the axial and equatorial N-methyl conformers is fast on the NMR timescale at room temperature but slow between 217 and 243 K allowing us to determine the thermodynamic parameters of interconversion using dynamic and magnetization transfer NMR. The biological activities correlate strongly with the nature and the orientation of the aryl group. The relative orientation of the N-methyl further modulates the activity by directly influencing the ligand interaction in the protein binding pocket and/or by forcing a favorable orientation for the aryl substituent to fit in the binding pocket.

Structure-activity relationships of macrolides against Mycobacterium tuberculosis.

Existing 14, 15 and 16-membered macrolide antibiotics, while effective for other bacterial infections, including some mycobacteria, have not demonstrated significant efficacy in tuberculosis. Therefore an attempt was made to optimize this class for activity against Mycobacterium tuberculosis through semisyntheses and bioassay. Approximately 300 macrolides were synthesized and screened for anti-TB activity. Structural modifications on erythromycin were carried out at positions 3, 6, 9, 11, and 12 of the 14-membered lactone ring; as well as at position 4'' of cladinose and position 2' of desosamine. In general, the synthesized macrolides belong to four subclasses: 9-oxime, 11,12-carbamate, 11,12-carbazate, and 6-O-substituted derivatives. Selected compounds were assessed for mammalian cell toxicity and in some cases were further assessed for CYP3A4 inhibition, microsome stability, in vivo tolerance and efficacy. The activity of 11,12-carbamates and carbazates as well as 9-oximes is highly influenced by the nature of the substitution at these positions. For hydrophilic macrolides, lipophilic substitution may result in enhanced potency, presumably by enhanced passive permeation through the cell envelope. This strategy, however, has limitations. Removal of the C-3 cladinose generally reduces the activity. Acetylation at C-2' or 4'' maintains potency of C-9 oximes but dramatically decreases that of 11,12-substituted compounds. Further significant increases in the potency of macrolides for M. tuberculosis may require a strategy for the concurrent reduction of ribosome methylation.

Categorical QSAR Models for skin sensitization based upon local lymph node assay classification measures part 2: 4D-fingerprint three-state and two-2-state logistic regression models.

Three and four state categorical quantitative structure-activity relationship (QSAR) models for skin sensitization have been constructed using data from the murine Local Lymph Node Assay studies. These are the same data we previously used to build two-state (sensitizer, nonsensitizer) QSAR models (Li et al., 2007, Chem. Res. Toxicol. 20, 114-128). 4D-fingerprint descriptors derived from the 4D-molecular similarity paradigm are used to generate these models. A training set of 196 and a test set of 22 structurally diverse compounds were used in this study. Logistic regression, and partial least square coupled logistic regression were used to build the models. The three-state QSAR model gives a classification accuracy of 73.4% for the training set and 63.6% for the test set, while the random average value of classification accuracy for any three-state data set is 33.3%. The two-2-state [four categories in total] QSAR model gives a classification accuracy of 83.2% for the training set and 54.6% for the test set, while the random average value of classification accuracy for any two-2-state data set is 25%. An analysis of the skin-sensitization models developed in this study, as well as the two-state QSAR models developed in our previous analysis, suggests that the "moderate" sensitizers may be the main source of limited model accuracy.

4D-fingerprint categorical QSAR models for skin sensitization based on the classification of local lymph node assay measures.

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the local lymph node assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, for eaxample, quantitative structure-activity relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR) and partial least-square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, X(2)HL, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, whereas that of the PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0% to 86.7%, whereas that of the PLS-logistic regression models ranges from 73.3% to 80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors, and negatively partially charged atoms.

Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses.

Based on 2D-connectivity molecular similarity and cluster analyses, a dataset for HSA binding is divided into the training set and the test set. 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, and SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM), which only takes the most similar compound in the training set into consideration, predicts the binding affinity of a test compound. This scheme has relatively poor predictivity based on 4D-fingerprint similarity analyses. The other three algorithmic schemes (SM, SR, and SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. Further investigation shows that the NP/HA, HS/HA, and HA/HA IPE/IPE type measures predict the test set well. Moreover, these three IPE/IPE type similarity measures are very similar to one another for the particular training and test sets investigated. The 4D-fingerprints have relatively high predictivity for this particular dataset.

Prediction of plasma protein binding of drugs using Kier-Hall valence connectivity indices and 4D-fingerprint molecular similarity analyses.

A 115 compound dataset for HSA binding is divided into the training set and the test set based on molecular similarity and cluster analyses. Both Kier-Hall valence connectivity indices and 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM) predicts the binding affinity of a test compound using only the most similar training set compound's binding affinity. This scheme has relatively poor predictivity based both on Kier-Hall valence connectivity indices similarity measures and 4D-fingerprints similarity analyses. The other three algorithmic schemes (SM SR, SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. This study supports that some types of similarity measures are highly similar to one another for this dataset. Both the Kier-Hall valence connectivity indices similarity measures and the 4D-fingerprints have nearly same predictivity for this particular dataset.

Predicting permeability coefficient in ADMET evaluation by using different membranes-interaction QSAR.

Membrane-interaction quantitative structure activity relationship (MI-QSAR) analysis was applied to a data set with 18 compounds in 18 different membranes. MI-QSAR was used to estimate the ADMET properties including the transport of organic solutes through biological membranes. The most important descriptors are the aqueous solvation free energy, FH2O, and diffusion coefficient for all membranes. The correlation coefficient, r2, and cross-validation correlation coefficient, q2, for DMPG membrane is 0.850 and 0.770, respectively. The relationship between FH2O and permeability is nonlinear. But the detail effect of aqueous solvation free energy and diffusion coefficient to the permeability depends on the type of membrane. The final models also support the solution-diffusion mechanism of transport is important in membrane.

A study of the relationship between cornea permeability and eye irritation using membrane-interaction QSAR analysis.

A methodology termed membrane-interaction QSAR (MI-QSAR) analysis has been used to develop QSAR models to predict drug permeability coefficients across cornea and its component layers (epithelium, stroma, and endothelium). From a training set of 25 structurally diverse drugs, significant QSAR models are constructed and compared for the permeability of the cornea, epithelium, and stroma plus endothelium. Cornea permeability is found to depend on the measured distribution coefficient of the drug, the cohesive energy of the drug, the total potential energy of the drug-membrane "complex," and three other energy refinement descriptor terms. The endothelium may be a more important barrier in cornea permeation than the stroma. Moreover, an investigation of the correlation between cornea permeation and eye irritation is presented as an example of a cross study on different ADMET properties using MI-QSAR analysis. Thirteen structurally diverse drugs, whose molar-adjusted eye irritation scores (MES) have been measured using the Draize rabbit-eye test, were chosen as an eye irritation comparison set. A poor correlation (R(2) = 0.0232) between the MES measures and the predicted cornea permeability coefficients for the drugs in the eye irritation set suggests there is no significant relationship between eye irritation potency and the cornea permeability.

In vitro and in vivo activities of macrolide derivatives against Mycobacterium tuberculosis.

Existing macrolides have never shown definitive clinical efficacy in tuberculosis. Recent reports suggest that ribosome methylation is involved in macrolide resistance in Mycobacterium tuberculosis, a mechanism that newer macrolides have been designed to overcome in gram-positive bacteria. Therefore, selected macrolides and ketolides (descladinose) with substitutions at positions 9, 11,12, and 6 were assessed for activity against M. tuberculosis, and those with MICs of < or = 4 microM were evaluated for cytotoxicity to Vero cells and J774A.1 macrophages. Several compounds with 9-oxime substitutions or aryl substitutions at position 6 or on 11,12 carbamates or carbazates demonstrated submicromolar MICs. For the three macrolide-ketolide pairs, macrolides demonstrated superior activity. Four compounds with low MICs and low cytotoxicity also effected significant reductions in CFU in infected macrophages. Active compounds were assessed for tolerance and the ability to reduce CFU in the lungs of BALB/c mice in an aerosol infection model. A substituted 11,12 carbazate macrolide demonstrated significant dose-dependent inhibition of M. tuberculosis growth in mice, with a 10- to 20-fold reduction of CFU in lung tissue. Structure-activity relationships, some of which are unique to M. tuberculosis, suggest several synthetic directions for further improvement of antituberculosis activity. This class appears promising for yielding a clinically useful agent for tuberculosis.

Constructing optimum blood brain barrier QSAR models using a combination of 4D-molecular similarity measures and cluster analysis.

A new method, using a combination of 4D-molecular similarity measures and cluster analysis to construct optimum QSAR models, is applied to a data set of 150 chemically diverse compounds to build optimum blood-brain barrier (BBB) penetration models. The complete data set is divided into subsets based on 4D-molecular similarity measures using cluster analysis. The compounds in each cluster subset are further divided into a training set and a test set. Predictive QASAR models are constructed for each cluster subset using the corresponding training sets. These QSAR models best predict test set compounds which are assigned to the same cluster subset, based on the 4D-molecular similarity measures, from which the models are derived. The results suggest that the specific properties governing blood-brain barrier permeability may vary across chemically diverse compounds. Partitioning compounds into chemically similar classes is essential to constructing predictive blood-brain barrier penetration models embedding the corresponding key physiochemical properties of a given chemical class.

Characterization of a ligand-receptor binding event using receptor-dependent four-dimensional quantitative structure-activity relationship analysis.

Receptor-dependent four-dimensional quantitative structure-activity relationship (RD-4D-QSAR) analysis is used to map the ligand-receptor binding event characteristic of a set of 47 glucose analogue inhibitors of glycogen phosphorylase (GPb). Specifically, the geometric and energetic binding profiles are constructed, conformational changes are determined, and conformational couplings among structural units are identified for the composite set of ligand-receptor complexes. A pruned ligand-receptor model is used to estimate ligand-receptor thermodynamics. Rather than explicitly handling the large amount of structural data generated from each of the pruned ligand-receptor models, these complexes were divided into three subregions. The subregions consist of a "functional" region, the smallest region providing definitive information about inhibitor binding, and two "allosteric" regions that surround the "functional" region and are based on distances from the center of the catalytic site. Maximum information on inhibitor binding and/or inhibitor-receptor conformational changes is extracted from each of these subregions. The key sites for inhibitor binding and conformational changes in GPb are presented as grid cell occupancy descriptors (GCODs), which can be both numerically and graphically represented. An induced conformational change in both the inhibitor and the binding site of GPb occurs in a distinct manner for each complex. The inter-relationships (correlations) between GCODs from different regions are identified and probed. Such correlations validate the ligand-receptor interactions identified from the "functional" region. A long-range network of conformational associations involving ligands and the receptor is also found by exploring correlations among the GCODs for the set of inhibitors.

4D-QSAR analysis of a series of antifungal p450 inhibitors and 3D-pharmacophore comparisons as a function of alignment.

A training set of 55 antifungal p450 analogue inhibitors was used to construct receptor-independent four-dimensional quantitative structure-activity relationship (RI 4D-QSAR) models. Ten different alignments were used to build the models, and one alignment yields a significantly better model than the other alignments. Two different methodologies were used to measure the similarity of the best 4D-QSAR models of each alignment. One method compares the residual of fit between pairs of models using the cross-correlation coefficient of their residuals of fit as a similarity measure. The other method compares the spatial distributions of the IPE types (3D-pharmacophores) of pairs of 4D-QSAR models from different alignments. Optimum models from several different alignments have nearly the same correlation coefficients, r(2), and cross-validation correlation coefficients, xv-r(2), yet the 3D-pharmacophores of these models are very different from one another. The highest 3D-pharmacophore similarity correlation coefficient between any pair of 4D-QSAR models from the 10 alignments considered is only 0.216. However, the best 4D-QSAR models of each alignment do contain some proximate common pharmacorphore sites. A test set of 10 compounds was used to validate the predictivity of the best 4D-QSAR models of each alignment. The "best" model from the 10 alignments has the highest predictivity. The inferred active sites mapped out by the 4D-QSAR models suggest that hydrogen bond interactions are not prevalent when this class of P450 analogue inhibitors binds to the receptor active site. This feature of the 4D-QSAR models is in agreement with the crystal structure results that indicate no ligand-receptor hydrogen bonds are formed.

Quantitative structure-based design: formalism and application of receptor-dependent RD-4D-QSAR analysis to a set of glucose analogue inhibitors of glycogen phosphorylase.

A method for performing quantitative structure-based design has been developed by extending the current receptor-independent RI-4D-QSAR methodology to include receptor geometry. The resultant receptor-dependent RD-4D-QSAR approach employs a novel receptor-pruning technique to permit effective processing of ligands with the lining of the binding site wrapped about them. Data reduction, QSAR model construction, and identification of possible pharmacophore sites are achieved by a three-step statistical analysis consisting of genetic algorithm optimization followed by backward elimination multidimensional regression and ending with another genetic algorithm optimization. The RD-4D-QSAR method is applied to a series of glucose inhibitors of glycogen phosphorylase b, GPb. The statistical quality of the best RI- and RD-4D-QSAR models are about the same. However, the predictivity of the RD- model is quite superior to that of the RI-4D-QSAR model for a test set. The superior predictive performance of the RD- model is due to its dependence on receptor geometry. There is a unique induced-fit between each inhibitor and the GPb binding site. This induced-fit results in the side chain of Asn-284 serving as both a hydrogen bond acceptor and donor site depending upon inhibitor structure. The RD-4D-QSAR model strongly suggests that quantitative structure-based design cannot be successful unless the receptor is allowed to be completely flexible.