Holographic QSAR of selected esters

The HQSAR (Holographic QSAR) method, which has been recently developed, can offer the ability to rapidly and easily generate QSAR models of high statistical quality and predictive value. HQSAR analysis requires selecting values for parameters that specify the size of the hologram that is to be used, and the size and type of fragment substructures that are to be encoded. The color coding is provided by HQSAR to reflect which molecular fragments may be important contributors to the biological activity. In this work, we studied the quantitative structure activity relationship of selected esters using the HQSAR method. A robust HQSAR model with r² (non-cross-validated regression coefficient) of 0.981 and q² (cross-validated regression coefficient) of 0.912, was developed after optimizing the fragment size and the hologram length. The color coding analysis, which has rarely been reported before, was done here to explain the outlier successfully.     

Progressive study and robustness test of QSAR model based on quantum chemical parameters for predicting BCF of selected polychlorinated organic compounds (PCOCs)

Systematic analyses on the effects of chemical structures of 31 polychlorinated organic compounds (PCOCs) on their bioconcentration behavior in rainbow trout (Oncorhynchus mykiss) were conducted using quantitative structure-activity relationship (QSAR) techniques. The cluster analyses of individual variables as well as the quality control chart of QSAR model implies the existence of outliers, while the simulation model excluding such samples showed an extreme robustness even if it was tested with different methods. Furthermore, the quantum chemical parameters entering into QSAR model were used to describe the bioconcentration pathways, and the results indicated that bioconcentration behaviors of selected compounds were complicated processes involving permeation stages as well as bio-chemical reaction stages.     

酚类化合物对硝化颗粒污泥活性抑制的定量结构-活性相关研究

测定了酚类化合物对硝化颗粒污泥活性抑制的logIC50值,以量子化学参数为自变量,应用偏最小二乘法（PLS）,建立了酚类化合物对硝化颗粒污泥活性抑制的定量结构-活性相关（QSAR）模型。模型所提取的PLS主成分所能解释的因变量总方差的比例Qc2um为0.820,表明模型具有较好的稳定性和预测能力。模型的结果表明,影响酚类化合物对硝化颗粒污泥活性抑制的主要因素是logkow、CCR和Ehomo,酚类化合物对硝化颗粒污泥活性抑制的logIC50随着分子logkow的增大而减小,随着Ehomo和CCR的增大而增大。     

酚类化合物对硝化颗粒污泥活性抑制的定量结构 活性相关研究

测定了酚类化合物对硝化颗粒污泥活性抑制的logIC₅₀值，以量子化学参数为自变量，应用偏最小二乘法（PLS），建立了酚类化合物对硝化颗粒污泥活性抑制的定量结构活性相关（QSAR）模型。模型所提取的PLS主成分所能解释的因变量总方差的比例Q²_cum为0.820，表明模型具有较好的稳定性和预测能力。模型的结果表明，影响酚类化合物对硝化颗粒污泥活性抑制的主要因素是logkow、CCR和E_homo，酚类化合物对硝化颗粒污泥活性抑制的logIC₅₀随着分子logkow的增大而减小，随着E_homo和CCR的增大而增大。     

Mechanistic insights into the specificity of human cytosolic sulfotransferase 2A1 (hSULT2A1) for hydroxylated polychlorinated biphenyls through the use of fluoro-tagged probes

Determining the relationships between the structures of substrates and inhibitors and their interactions with drug-metabolizing enzymes is of prime importance in predicting the toxic potential of new and legacy xenobiotics. Traditionally, quantitative structure activity relationship (QSAR) studies are performed with many distinct compounds. Based on the chemical properties of the tested compounds, complex relationships can be established so that models can be developed to predict toxicity of novel compounds. In this study, the use of fluorinated analogues as supplemental QSAR compounds was investigated. Substituting fluorine induces changes in electronic and steric properties of the substrate without substantially changing the chemical backbone of the substrate. In vitro assays were performed using purified human cytosolic sulfotransferase hSULT2A1 as a model enzyme. A mono-hydroxylated polychlorinated biphenyl (4-OH PCB 14) and its four possible mono-fluoro analogues were used as test compounds. Remarkable similarities were found between this approach and previously published QSAR studies for hSULT2A1. Both studies implicate the importance of dipole moment and dihedral angle as being important to PCB structure in respect to being substrates for hSULT2A1. We conclude that mono-fluorinated analogues of a target substrate can be a useful tool to study the structure activity relationships for enzyme specificity.     

The effect of nitroaromatics' composition on their toxicity in vivo: novel, efficient non-additive 1D QSAR analysis

Novel 1D QSAR approach that allows analysis of non-additive effects of molecular fragments on toxicity has been proposed. Twenty-eight nitroaromatic compounds including some well-known explosives have been chosen for this study. The 50% lethal dose concentration for rats (LD50) was used as the estimation of toxicity in vivo to develop 1D QSAR models on the framework of Simplex representation of molecular structure. The results of 1D QSAR analysis show that even the information about the composition of molecules provides the main trends of toxicity changes. The necessity of consideration of substituents' mutual impacts for the development of adequate QSAR models of nitroaromatics' toxicity was demonstrated. Statistic characteristics for all the developed partial least squares QSAR models, except the additive ones are quite satisfactory (R2=0.81-0.92; Q2=0.64-0.83; R2 test=0.84-0.87). A successful performance of such models is due to their non-additivity i.e. possibility of taking into account the mutual influence of substituents in benzene ring which plays the governing role for toxicity change and could be mediated through the different C-H fragments of the ring. The correspondence between observed and predicted by these models toxicity values is good. This allowing combine advantages of such approaches and develop adequate consensus model that can be used as a toxicity virtual screening tool.     

多元逐步回归对苯胺类化合物结构与毒性模型研究

采用Chemoffice6．0中MOPAC-AMl量子化学法计算了24种苯胺类化合物的6种量子化学结构参数．其中取17个化合物作为样本集对-lgEC50进行多元逐步回归分析．得到最佳方程．经自由度校正的回归系数R=0．985。应用所建立的QSAR模型验证了苯胺类化合物的EC50值．并通过“Jackknife”中的逐一抽取法进行模型检验,得出该模型具有很好的稳定性．平均残差仅为0．05个对数单位．小于文献值。经过7个预测样本对该模型进行验证．结果表明．该模型具有很好的预测能力。同时分析了苯胺类化合物的毒性机理。     

Quantitative structure-activity relationship (QSAR) models for polycyclic aromatic hydrocarbons (PAHs) dissipation in rhizosphere based on molecular structure and effect size

Rhizoremediation is a significant form of bioremediation for polycyclic aromatic hydrocarbons (PAHs). This study examined the role of molecular structure in determining the rhizosphere effect on PAHs dissipation. Effect size in meta-analysis was employed as activity dataset for building quantitative structure-activity relationship (QSAR) models and accumulative effect sizes of 16 PAHs were used for validation of these models. Based on the genetic algorithm combined with partial least square regression, models for comprehensive dataset, Poaceae dataset, and Fabaceae dataset were built. The results showed that information indices, calculated as information content of molecules based on the calculation of equivalence classes from the molecular graph, were the most important molecular structural indices for QSAR models of rhizosphere effect on PAHs dissipation. The QSAR model, based on the molecular structure indices and effect size, has potential to be used in studying and predicting the rhizosphere effect of PAHs dissipation.     

光催化氧化降解垃圾渗滤液中溶解性有机物

研究了UV-TiO2光催化氧化降解垃圾渗滤液过程中溶解性有机物(DOM)的变化特征。结果表明:在适宜条件下,UV-TiO2光催化氧化降解垃圾渗滤液的色度、COD和DOC的去除率分别可达97%、72%和60%;紫外光谱分析说明渗滤液DOM中包括多种含有共轭双键、羰基的大分子有机物及多环芳香类化合物,不同光催化处理液中DOM具有基本一致的结构单元和官能团;红外光谱分析说明渗滤液DOM中含有大量包括羟基、羧基、氨基和苯环的芳香族化合物,在光催化处理液中这几种官能团都能被有效降解;GC/MS分析结果表明,渗滤液DOM中含有72种有机污染物,醇类、羧酸和酮类分别为25、14和12种;在光催化72 h处理液中,有机物减少为44种;酯类和醇类较多,分别为12种和16种;酮类8种,羧酸没有检出。     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure–activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

A comparison of DEMETRA individual QSARs with an index for evaluation of uncertainty

Quantitative structure-activity relationships (QSARs) urgently need to be applied in regulatory programs. Many QSAR models can predict the effect of a wide range of substances to different endpoints, particularly in the case of ecotoxicity, but it is difficult to choose the most appropriate model on the basis of the requirements of the application. During the EC-funded project DEMETRA (www.demetra-tox.net) a huge number of QSAR models have been developed for the prediction of different ecotoxicological endpoints. DEMETRA individual models on rainbow trout LC50 after 96 h, water flea LC50 after 48 h and honey bee LD50 after 48 h have been used as a QSAR database to test the advantages of a new index for evaluating model uncertainty. This index takes into consideration the number of outliers (weighted on the total number of compounds) and their root mean square error. Application on the DEMETRA QSAR database indicated that the index can identify the models with the best performance with regard to outliers, and can be used, together with other classical statistical measures (e.g., the squared correlation coefficient), to support the evaluation of QSAR models.     

On-line real-time measurements at incineration plants: PAHs and a PCDD/F surrogate compound at stationary combustion conditions and during transient emission puffs

Laser mass spectrometry has been applied for on-line monitoring of traces of aromatic compounds from flue gas of incineration plants. The experiments have been carried out at two sampling sites in an industrial hazardous-waste incinerator. With laser mass spectrometry resonance-enhanced multiphoton ionization (REMPI) with time-of-flight mass spectrometry (TOFMS) (REMPI-TOFMS), using the group selective multi-component monitoring approach, aromatic compounds are selectively ionized from the complex flue-gas matrix. In this case, the result of an REMPI-TOFMS on-line measurement is a distinct pattern of aromatic compounds. These patterns are dependent on: (i) the point of measurement, (ii) the incineration plant, (iii) the temperature, and (iv) the fuel. This contribution focuses on the fuel dependence of the pattern. The most transient behavior can be observed when containers filled with hazardous waste are burnt, leading to puffs. Real-time monitoring results of puffs are given. Furthermore, as an approach towards on-line monitoring of the TEQ (PCDD/F toxicity equivalent), REMPI-TOFMS on-line analysis results of chlorobenzene are presented.     

3D QSAR studies of dioxins and dioxin-like compounds using CoMFA and CoMSIA

In the present study we have performed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA and CoMSIA models have given q(2) value of more than 0.5 and r(2) value of more than 0.84. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive r(2) values. Best predictions were obtained with CoMFA model of combined modified training set (q(2) = 0.631, r(2) = 0.900), giving predictive residual value = 0.02 log unit for the test compound. Addition of CoMSIA study has elucidated the role of hydrophobicity and hydrogen bonding along with the effect of steric and electrostatic properties revealed by CoMFA. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands.