A General QSAR Model for Predicting the Toxicity of Organic Chemicals to Luminescent Bacteria (Microtox® test)

Abstract A large data set of Microtox? toxicity results was used to derive a general QSAR model. Chemicals were described by means of a modified autocorrelation method. The autocorrelation vectors were generated from atomic contributions encoding the hydrophobicity and molar refractivity of the molecules. A three-layer backpropagation neural network was used to design the model. The obtained results were compared with those obtained from a principal components regression analysis.     

Pesticides as estrogen disruptors: QSAR for selective ERα and ERβ binding of pesticides

Evidence suggests that environmental exposure to estrogen-like compounds can cause adverse effects in humans and wildlife. The Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) has advised screening of 87,000 compounds in the interest of human safety. This may best be accomplished by pre-screening using quantitative structure-activity relationship (QSAR) modelling. The present study aimed to develop in silico QSARs based on natural, semi-synthetic, synthetic, and phytoestrogens, to predict the potential estrogenic toxicity of pesticides. A diverse set of 170 compounds including steroidal-, synthetic- and phytoestrogens, as well as pesticides was used to construct the QSAR models using artificial neural networks (ANNs). Mean correlation coefficients between experimentally measured and predicted binding affinities were all greater than 0.7 and models had few false negative results, an important consideration for screening tools. This study demonstrated the utility of ANNs as QSAR models for pre-screening of potential endocrine disruptors.     

Chemometric modeling of PET imaging agents for diagnosis of Parkinson’s disease: a QSAR approach

Structural Chemistry - Recently, adenosine A2A receptor antagonists have been identified as an interesting drug target for the treatment of Parkinson’s disease (PD). Radiolabelled molecular...     

Biological activities of Peristrophe bivalvis extracts: promising potential for anti-snake venoms against Naja kaouthia and Trimeresurus albolabris venoms

This study evaluates the in vitro anti-snake venom potential of Peristrophe bivalvis (PB) extracts against Naja kaouthia (NK) and Trimeresurus albolabris (TA) venoms, including inhibition of cytotoxic effects and enzymatic activities, and the binding-precipitation of extracts and venom proteins analysis. In addition, the antioxidant, cytotoxic and in vivo acute oral toxic activities of PB extracts are also reported. The in vitro cytotoxic and enzymatic analysis reveals that the ethanol extracts of stems and leaves of PB showed good anti-snake venom activity against NK and TA venoms. In addition, the antioxidant result indicated that only the ethanol extract of leaves exhibited weak DPPH radical-scavenging activity. The ethanol whole-plant extract of PB also showed no cytotoxicity against four cell lines. Moreover, the in vivo acute oral toxicity result of the ethanol whole-plant extract showed that all treated rats did not exhibit abnormal toxic signs or deaths.     

Side-chain conformational space analysis (SCSA): A multi conformation-based QSAR approach for modeling and prediction of protein–peptide binding affinities

In this article, the concept of multi conformation-based quantitative structure-activity relationship (MCB-QSAR) is proposed, and based upon that, we describe a new approach called the side-chain conformational space analysis (SCSA) to model and predict protein-peptide binding affinities. In SCSA, multi-conformations (rather than traditional single-conformation) have received much attention, and the statistical average information on multi-conformations of side chains is determined using self-consistent mean field theory based upon side chain rotamer library. Thereby, enthalpy contributions (including electrostatic, steric, hydrophobic interaction and hydrogen bond) and conformational entropy effects to the binding are investigated in terms of occurrence probability of residue rotamers. Then, SCSA was applied into the dataset of 419 HLA-A 0201 binding peptides, and nonbonding contributions of each position in peptide ligands are well determined. For the peptides, the hydrogen bond and electrostatic interactions of the two ends are essential to the binding specificity, van der Waals and hydrophobic interactions of all the positions ensure strong binding affinity, and the loss of conformational entropy at anchor positions partially counteracts other favorable nonbonding effects.     

3D molecular fragment descriptors for structure–property modeling: predicting the free energies for the complexation between antipodal guests and <Emphasis Type=Italic>β</Emphasis>-cyclodextrins

This current work has been conducted mainly to increase solubility and drug release properties for high hydrophobic Dentatin (DEN) by incorporation it into Hydroxypropyl-β-Cyclodextrin (HPβCD) cavity. To confirm that inclusion be succeeded, the produced complex were installed onto different machines. The latter includes: Fourier transform infrared spectroscopy (FT-IR), X-ray diffractometry (XRD), differential scanning calorimetry (DSC), and field emission-scanning electron microscopy (FE-SEM). The hydrodynamic diameter and zeta potential of DEN-HPβCD complex were 2.025?±?0.39 nm and ?33.6 mV, respectively. Ultra-violet spectroscopy was employed to further confirmation of complexation process as well as to determine drug release profile. The result showed an initial burst release (19.9% within first two minutes) and then a continuous release for an extended period of 41 h (100%). The solubility of DEN was enhanced by >300 fold following complexation when a compared to DEN alone. Moreover, MTT finding showed that this complexation did not reduce cytotoxicity of DEN after applying on prostate cancer (LNCaP), human adenocarcinoma breast cancer (MDA-MB-231) and human gastric adenocarcinoma cell line (HDT). However, further investigations are required to validate efficacy of our produced inclusion using molecular analysis and in vivo studies.     

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for α2-adrenoceptors

Two QSAR models have been identified that predict the affinity and selectivity of arylpiperazinyl derivatives for ₁ and ₂ adrenoceptors (ARs). The models have been specified and validated using 108 compounds whose structures and inhibition constants (K _i) are available in the literature [Barbaro etal., J. Med. Chem., 44 (2001) 2118; Betti etal., J. Med. Chem., 45 (2002) 3603; Barbaro etal., Bioorg. Med. Chem., 10 (2002) 361; Betti etal., J. Med. Chem., 46 (2003) 3555]. One hundred and forty-seven predictors have been calculated using the Cerius 2 software available from Accelrys. This set of variables exhibited redundancy and severe multicollinearity, which had to be identified and removed as appropriate in order to obtain robust regression models free of inflated errors for the estimates – so-called bouncing s. Those predictors that contained information relevant to the ₂ response were identified on the basis of their pairwise linear correlations with affinity (–log K _i) for ₂ adrenoceptors; the remaining variables were discarded. Subsequent variable selection made use of Factor Analysis (FA) and Unsupervised Variable Selection (UzFS). The data was divided into test and training sets using cluster analysis. These two sets were characterised by similar and consistent distributions of compounds in a high dimensional, but relevant predictor space. Multiple regression was then used to determine a subset of predictors from which to determine QSAR models for affinity to ₂-ARs. Two multivariate procedures, Continuum Regression (the Portsmouth formulation) and Canonical Correlation Analysis (CCA), have been used to specify models for affinity and selectivity, respectively. Reasonable predictions were obtained using these in silico screening tools.     

Predictive QSAR models for the anti-cancer activity of topoisomerase IIα catalytic inhibitors against breast cancer cell line HCT15: GA-MLR and LS-SVM modeling

Structural Chemistry - Structural analysis of topoisomerase IIα catalytic inhibitors exhibited anti-tumor properties to use them in cancer therapeutic procedures. In this study, a quantitative...     

Organolanthanides with 3-(2-pyridylmethyl) indenyl ligands: synthesis, crystal structures and catalytic activities of divalent complexes for ε-caprolactone polymerization

Reaction of 3-(2-pyridylmethyl)indenyl lithium (1) with LnI₂(THF)₂ (Ln = Sm, Yb) in THF produced the divalent organolanthanides (C₅H₄NCH₂C₉H₆)₂Ln^II(THF) (Ln = Sm (2), Yb (3)) in high yield. 1 reacts with LnCl₃ (Ln = Nd, Sm, Yb) in THF to give bis(3-(2-pyridylmethyl)indenyl) lanthanide chlorides (C₅H₄NCH₂C₉H₆)₂Ln^IIICl (Ln = Nd (4), Sm (5)) and the unexpected divalent lanthanides 3 (Ln = Yb). Complexes 2-5 show more stable in air than the non-functionalized analogues. X-ray structural analyses of 2-4 were performed. 2 and 3 belong to the high symmetrical space group (Cmcm) with the same structures, they are THF-solvated 9-coordinate monomeric in the solid state, while 4 is an unsolvated 9-coordinate monomer with a trans arrangement of both the sidearms and indenyl rings in the solid state. Additionally, 2 and 3 show moderate polymerization activities for ε-caprolactone (CL).     

Imidazole,pyrimidine and diazepine containing heteroaryl-substituted heterocyclic salts as efficient ligand precursors for Mizoroki–Heck coupling reaction: synthesis,structural characterization and catalytic activities

The nine new heteroaryl-substituted imidazolidinium (1a–c), pyrimidinium (2a–c) and diazepinium (3a–c) salts as N-heterocyclic carbene (NHC) precursors were synthesized in good yields and entirely characterized using elemental analyses and conventional spectroscopic methods. In situ formed complexes from heterocyclic salts (1–3), Pd(OAc)₂ and in the presence of KOBu ^t as a base were tested as catalysts for the Mizoroki–Heck coupling reaction in an aqueous media and very high yields were achieved. 1,3-Di(5-methylthiophen-2-ylmethyl)pyrimidinium hexafluorophosphate salt (2b) was structurally characterized by single-crystal X-ray diffraction. In the 2b compound (C₁₆H₂₁N₂S₂)⁺[PF₆]^?, the terminal thiophene rings are twisted with a dihedral angle of 72.8(3)°. In the pyrimidine ring, the three successive C atoms between the N atoms are disordered over two positions [occupancy ratio 0.753(12):0.247(12)]. In the crystal, neighboring molecules are linked by C–H…F hydrogen bonds, running along the b axis.     

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for α<Subscript>2</Subscript>-adrenoceptors

Two QSAR models have been identified that predict the affinity and selectivity of arylpiperazinyl derivatives for alpha1 and alpha2 adrenoceptors (ARs). The models have been specified and validated using 108 compounds whose structures and inhibition constants (Ki) are available in the literature [Barbaro et al., J. Med. Chem., 44 (2001) 2118; Betti et al., J. Med. Chem., 45 (2002) 3603; Barbaro et al., Bioorg. Med. Chem., 10 (2002) 361; Betti et al., J. Med. Chem., 46 (2003) 3555]. One hundred and forty-seven predictors have been calculated using the Cerius 2 software available from Accelrys. This set of variables exhibited redundancy and severe multicollinearity, which had to be identified and removed as appropriate in order to obtain robust regression models free of inflated errors for the beta estimates - so-called bouncing betas. Those predictors that contained information relevant to the alpha2 response were identified on the basis of their pairwise linear correlations with affinity (-log Ki) for alpha2 adrenoceptors; the remaining variables were discarded. Subsequent variable selection made use of Factor Analysis (FA) and Unsupervised Variable Selection (UzFS). The data was divided into test and training sets using cluster analysis. These two sets were characterised by similar and consistent distributions of compounds in a high dimensional, but relevant predictor space. Multiple regression was then used to determine a subset of predictors from which to determine QSAR models for affinity to alpha2-ARs. Two multivariate procedures, Continuum Regression (the Portsmouth formulation) and Canonical Correlation Analysis (CCA), have been used to specify models for affinity and selectivity, respectively. Reasonable predictions were obtained using these in silico screening tools.     

Molecular docking analysis and spectroscopic investigations of zinc(II), nickel(II) N-phthaloyl-β-alanine complexes for DNA binding: Evaluation of antibacterial and antitumor activities

Herein, computational molecular docking, UV/visible and fluorescence spectroscopic techniques have been used to explore the DNA binding interactions of N-phthaloyl-β-alanine (NPA) ligand and its Zn(II) and Ni(II) complexes (NPAZn, NPANi). The compounds were further tested for anti-bacterial and anti-tumor activities. Docking analysis depicted that ligand NPA interacted with DNA via intercalation, while its metal complexes showed mixed mode of interactions. Spectroscopic experiments for DNA binding studies were run under physiological conditions of pH (stomach; 4.7, blood; 7.4) and temperature (37 °C). Based on changes in spectral responses, binding parameters for all the compounds were obtained which showed comparatively greater binding constant values (K_b: UV; 1.16 × 10⁵ M⁻¹, Flu; 1.35 × 10⁵ M⁻¹) and more negative free energy changes (ΔG: UV; −30.00 kJ mol⁻¹, Flu; −30.44 kJ mol⁻¹) for NPAZn at pH 4.7. The overall, binding results were also found more significant at stomach pH. Dynamic “K_D” and bimolecular “K_B” constants were evaluated, and the values affirmed the participation of static process for each compound–DNA binding. The greater binding site size values (n > 1) of metal complexes NPAZn and NPANi indicated other sites availability of intercalative compounds. DNA viscosity variation by increasing compound’s concentration further verified the compound–DNA interaction. Antibacterial and tumor inhibitory activities were observed significant for both metal complexes, while ligand has shown no activity. The greater binding affinity of metal complexes, as evaluated both computationally and spectroscopically, further validated the lower IC50 values of complexes as compared to ligand.