Inhibition of immune complex-mediated neutrophil oxidative metabolism: A pharmacophore model for 3-phenylcoumarin derivatives using GRIND-based 3D-QSAR and 2D-QSAR procedures

In this study, twenty hydroxylated and acetoxylated 3-phenylcoumarin derivatives were evaluated as inhibitors of immune complex-stimulated neutrophil oxidative metabolism and possible modulators of the inflammatory tissue damage found in type III hypersensitivity reactions. By using lucigenin- and luminol-enhanced chemiluminescence assays (CL-luc and CL-lum, respectively), we found that the 6,7-dihydroxylated and 6,7-diacetoxylated 3-phenylcoumarin derivatives were the most effective inhibitors. Different structural features of the other compounds determined CL-luc and/or CL-lum inhibition. The 2D-QSAR analysis suggested the importance of hydrophobic contributions to explain these effects. In addition, a statistically significant 3D-QSAR model built applying GRIND descriptors allowed us to propose a virtual receptor site considering pharmacophoric regions and mutual distances. Furthermore, the 3-phenylcoumarins studied were not toxic to neutrophils under the assessed conditions. (C) 2007 Elsevier Masson SAS. All rights reserved.

2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L

Hologram quantitative structure-activity relationships (HQSAR) were applied to a data set of 41 cruzain inhibitors. The best HQSAR model (Q(2) = 0.77; R-2 = 0.90) employing Surflex-Sim, as training and test sets generator, was obtained using atoms, bonds, and connections as fragment distinctions and 4-7 as fragment size. This model was then used to predict the potencies of 12 test set compounds, giving satisfactory predictive R-2 value of 0,88. The contribution maps obtained from the best HQSAR model are in agreement with the biological activities of the study compounds. The Trypanosoma cruzi cruzain shares high similarity with the mammalian homolog cathepsin L. The selectivity toward cruzam was checked by a database of 123 compounds, which corresponds to the 41 cruzain inhibitors used in the HQSAR model development plus 82 cathepsin L inhibitors. We screened these compounds by ROCS (Rapid Overlay of Chemical Structures), a Gaussian-shape volume overlap filter that can rapidly identify shapes that match the query molecule. Remarkably, ROCS was able to rank the first 37 hits as being only cruzain inhibitors. In addition, the area under the curve (AUC) obtained with ROCS was 0.96, indicating that the method was very efficient to distinguishing between cruzain and cathepsin L inhibitors. (c) 2007 Elsevier Ltd. All rights reserved.

Estudos de QSAR 2D baseados em descritores topológicos e fragmentos moleculares para uma série de derivados azólicos ativos contra Candida albicans

Azole derivatives are the main therapeutical resource against Candida albicans infection in immunocompromised patients. Nevertheless, the widespread use of azoles has led to reduced effectiveness and selection of resistant strains. In order to guide the development of novel antifungal drugs, 2D-QSAR models based on topological descriptors or molecular fragments were developed for a dataset of 74 molecules. The optimal fragment-based model (r² = 0.88, q² = 0.73 and r²pred = 0.62 with 6PCs) and descriptor-based model (r² = 0.82, q² = 0.79 and r²pred = 0.70 with 2 PCs), when analysed synergically, suggested that the triazolone ring and lipophilic properties are both important to antifungal activity.

Estudos de relações estrutura-atividade quantitativas (QSAR) de bis-benzamidinas com atividade antifúngica

This paper describes 2D-QSAR and 3D-QSAR studies against Candida albicans and Cryptococcus neofarmans for a set of 20 bisbenzamidines. In the studies of 2D-QSAR with C. albicans it was obtained a correlation between log MIC-1 and lipolo component-Z (r² = 0.68; Q² = 0.51). In the case of C. neofarmans a correlation between log MIC-1 and lipolo component-Z and of Balaban index (r² = 0.85; Q² = 0.6) was obtained. 3D-QSAR studies using CoMFA showed that the steric fields contributed more to the predicted activities for Candida albicans (94.9%) and Cryptococcus neofarmans (97.9%).

Estudos de relações estrutura-atividade quantitativas (QSAR) de bis-benzamidinas com atividade antifúngica

This paper describes 2D-QSAR and 3D-QSAR studies against Candida albicans and Cryptococcus neofarmans for a set of 20 bisbenzamidines. In the studies of 2D-QSAR with C. albicans it was obtained a correlation between log MIC-1 and lipolo component-Z (r² = 0.68; Q² = 0.51). In the case of C. neofarmans a correlation between log MIC-1 and lipolo component-Z and of Balaban index (r² = 0.85; Q² = 0.6) was obtained. 3D-QSAR studies using CoMFA showed that the steric fields contributed more to the predicted activities for Candida albicans (94.9%) and Cryptococcus neofarmans (97.9%).

Classical and hologram QSAR studies on a series of inhibitors of trypanosomatid Glyceraldehyde-3-Phosphate Dehydrogenase

Leishmaniasis and trypanosomiasis are major causes of morbidity and mortality in both tropical and subtropical regions of the world. The current available drugs are limited, ineffective, and require long treatment regimens. Due to the high dependence of trypanosomatids on glycolysis as a source of energy, some glycolytic enzymes have been identified as attractive targets for drug design. In the present work, classical Two-Dimensional Quantitative Structure -Activity Relationships (2D QSAR) and Hologram QSAR (HQSAR) studies were performed on a series of adenosine derivatives as inhibitors of Leishmania mexicana Glyceraldehyde-3-Phosphate Dehydrogenase (LmGAPDH). Significant correlation coefficients (classical QSAR, r(2)=0.83 and q(2) =0.81; HQSAR, r(2)=0.91 and q(2) =0.86) were obtained for the 56 training set compounds, indicating the potential of the models for untested compounds. The models were then externally validated using a test set of 14 structurally related compounds and the predicted values were in good agreement with the experimental results (classical QSAR, r(pred)(2) = 0.94; HQSAR, r(pred)(2) = 0.92).

Inovação do método MIA-QSAR: aplicação para doenças tropicais negligenciadas

Multivariate image analysis applied to the quantitative structure-activity relationships (MIA-QSAR) is a 2D QSAR technique that has been presenting promising outcomes for the development of new drug candidates, due to its simplicity, rapidity and low cost. In this way, the present study aims at introducing, consolidating and improving the new dimensions named aug-MIA-QSAR and aug-MIA-QSARcolor, as well as applying them to the study of neglected diseases, in order to obtain new drug targets using chemico-biological interpretation of the MIA molecular descriptors. Four compound data sets with experimental bioactivities against Chagas disease, malaria, dengue and schistosomiasis were evaluated using three approaches: MIA-QSARt, aug-MIA-QSAR and aug-MIA-QSARcolor. In general, representations of atoms as spheres with different colors and sizes proportional to the corresponding van der Waals radii (aug-MIA approaches) improved the predictive ability and interpretability in all data sets. The use of colors proportional to the Pauling´s electronegativity showed that MIA descriptors are capable of identifying periodic properties relevant for the studied activity. Finally, solid colors instead of spotlighted atoms allowed a correct identification of atoms by means of pixel values in the studies for malaria, dengue and schistosomiasis, which were, subsequently, useful for the chemical interpretation related to the bioactivity. It can be inferred that semicarbazones and thiosemicarbazones derivative with a tri-substituted ring in R1 group and a trifluoro methyl group in the R 3 position instead of a chlorine antitripanossoma resulted in higher activity. The antimalarial activity of quinolon-4(1H)imines can be improved if: 1) R1 and R2 are electron donor groups, 2) R3 has long aminoalkyl chains, and 3) R4 possesses substituents with big atomic volume. In the study for dengue, it was found that tetrapeptides with unbranched small size amino acids in the A1 and A4 positions can increase the substrate affinity (Km) to the NS3 protein, and when in A1 and A2 positions, the substrate cleavage rate (kcat). On the other hand, acidic amino acids in the A2 and A4 positions were found to be related with low substrate affinity to the NS3 protein and when present in A1, with low substrate cleavage rate. Finally, the presence of metoxy substituents in R1 (or R2) and R5 in the neolignan backbone can favor their antischistosomal activity.

Classical and fragment-based hologram structure-activity relationships for a series of analgesic cyclic imides

Cyclic imides have been widely employed in drug design research due to their multiple pharmacological and biological properties. In the present study, two-dimensional quantitative structure-activity relationship (2D QSAR) studies were conducted on a series of potent analgesic cyclic imides using both classical and hologram QSAR (HQSAR) methods, yielding significant statistical models (classical QSAR, q(2) = 0.80; HQSAR, q(2) = 0.84). The models were then used to evaluate an external data test, and the predicted values were in good agreement with the experimental results, indicating their consistency for untested compounds.

Structure-activity relationships for a class of selective inhibitors of the major cysteine protease from Trypanosoma cruzi

Chagas` disease is a parasitic infection widely distributed throughout Latin America, with devastating consequences in terms of human morbidity and mortality. Cruzain, the major cysteine protease from Trypanosoma cruzi, is an attractive target for antitrypanosomal chemotherapy. In the present work, classical two-dimensional quantitative structure-activity relationships (2D QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 45 thiosemicarbazone and semicarbazone derivatives as inhibitors of T. cruzi cruzain. Significant statistical models (HQSAR, q2=0.75 and r2=0.96; classical QSAR, q2=0.72 and r2=0.83) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 10 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, [image omitted]=0.95; classical QSAR, [image omitted]=0.91), indicating the existence of complementary between the two ligand-based drug design techniques.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

QSAR: a abordagem de Hansch

The Hansch Analysis, also known by QSAR-2D, is an extremely effective tool in the identification and/or improvement of the pharmacological or toxicological profile of xenobiotics. This article presents the theme didactically and with enough detail to clarify the conceptual basis of Hansch Analysis. Besides, it shows the application of the technique in measuring the influence of physicochemical properties on the biological activity of compounds with pharmacological interest.

Two-dimensional QSAR studies on arylpiperazines as high-affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor plays an important role in the delayed onset of antidepressant action of a class of selective serotonin reuptake inhibitors. Moreover, 5-HT(1A) receptor levels have been shown to be altered in patients suffering from major depression. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were performed on a series of arylpiperazine compounds presenting affinity to the 5-HT(1A) receptor. The models were constructed with a training set of 70 compounds. The most significant HQSAR model (q(2) = 0.81, r(2) = 0.96) was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction, with fragment size of 6-9. Predictions for an external test set containing 20 compounds are in good agreement with experimental results showing the robustness of the model. Additionally, useful information can be obtained from the 2D contribution maps.

Advanced QSAR Studies on PPAR delta Ligands Related to Metabolic Diseases

PPAR delta is a nuclear receptor that, when activated, regulates the metabolism of carbohydrates and lipids and is related to metabolic syndrome and type 2 diabetes. To understand the main interactions between ligands and PPAR delta, we have constructed 2D and 3D QSAR models and compared them with HOMO, LUMO and electrostatic potential maps of the compounds studied, as well as docking results. All QSAR models showed good statistical parameters and prediction outcomes. The QSAR models were used to predict the biological activity of an external test set, and the predicted values are in good agreement with the experimental results. Furthermore, we employed all maps to evaluate the possible interactions between the ligands and PPAR delta. These predictive QSAR models, along with the HOMO, LUMO and MEP maps, can provide insights into the structural and chemical properties that are needed in the design of new PPAR delta ligands that have improved biological activity and can be employed to treat metabolic diseases.

CP-MLR Directed QSAR Studies on the Antimycobacterial Activity of Functionalised Alkenols - Topological Descriptors in Modeling the Activity

The antimycobacterial activity of nitro/ acetamido alkenol derivatives and chloro/ amino alkenol derivatives has been analyzed through combinatorial protocol in multiple linear regression (CP-MLR) using different topological descriptors obtained from Dragon software. Among the topological descriptor classes considered in the study, the activity is correlated with simple topological descriptors (TOPO) and more complex 2D autocorrelation descriptors (2DAUTO). In model building the descriptors from other classes, that is, empirical, constitutional, molecular walk counts, modified Burden eigenvalues and Galvez topological charge indices have made secondary contribution in association with TOPO and / or 2DAUTO classes. The structure-activity correlations obtained with the TOPO descriptors suggest that less branched and saturated structural templates would be better for the activity. For both the series of compounds, in 2DAUTO the activity has been correlated to the descriptors having mass, volume and/ or polarizability as weighting component. In these two series of compounds, however, the regression coefficients of the descriptors have opposite arithmetic signs with respect to one another. Outwardly these two series of compounds appear very similar. But in terms of activity they belong to different segments of descriptor-activity profiles. This difference in the activity of these two series of compounds may be mainly due to the spacing difference between the C1 (also C6) substituents and rest of the functional groups in them.

Desenhos de estruturas químicas correlacionam-se com propriedades biológicas: MIA-QSAR

Descriptors in multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) are pixels of bidimensional images of chemical structures (drawings), which were used to model the trichomonicidal activities of a series of benzimidazole derivatives. The MIA-QSAR model showed good predictive ability, with r², q² and r val. ext.² of 0.853, 0.519 and 0.778, respectively, which are comparable to the best values obtained by CoMFA e CoMSIA for the same series. A MIA-based analysis was also performed by using images of alphabetic letters with the corresponding numeric ordering as dependent variables, but no correlation was found, supporting that MIA-QSAR is not arbitrary.