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.     

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.     

Acute toxicity of benzene derivatives to the tadpoles (Rana japonica) and QSAR analyses

Acute lethal toxicity (the negative logarithm of molar concentrations of 12 h acute median lethal, expressed as 12 h-log1/LC50) of 46 benzene derivatives to Rana japonica tadpoles was determined. 1-octanol/water partition coefficient (logKow)-dependent models were developed to study the toxicity of different categories chemicals. In an effort to model all chemicals, response surface analyses and stepwise multiple regression analyses were performed and successful models were obtained. A general and robust QSAR model was achieved with the combined application of variables reflecting hydrophobicity, electric property, and molecular size respectively (12h-log1/LC50 = 0.393logKow - 0.428Elumo + 0.0110Vol. + 1.362 n = 51, r2 = 0.834) using stepwise multiple regression analyses. Because of strong dissociation of carboxyl group greatly decreasing their observed toxicity, using logDow in instead of logKow the quality of the models is greatly improved. The conventional r2 and cross-validation r2(CV) were 0.914 and 0.785, respectively, indicating that QSAR was both internally consistent and highly predictive.     

Reliable QSAR for estimating Koc for persistent organic pollutants: correlation with molecular connectivity indices

Several recent studies have shown that n-octanol/water partition coefficients may not be a good predictor for estimating soil sorption coefficients of persistent organic pollutants (POPs), defined here as chemicals with log Kow greater than 5. Thus, an alternative QSAR model was developed that seems to provide reliable estimates for the soil sorption coefficients of persistent organic pollutants. This model is based on a set of calculated molecular connectivity indices and evaluated soil sorption data for 18 POPs. The chemical's size and shape, quantified by 1chi, 3chiC and 4chiC(v) indices, have a dominant effect on the soil sorption process of POPs. The developed QSAR model was rationalized in terms of potential hydrophobic interactions between persistent organic pollutants and soil organic matrix. Its high predictive power has been verified by an extensive internal and external validation procedure.     

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.     

Toxicology of benzyl alcohols: a QSAR analysis

There is an evidence that benzyl alcohols may exhibit toxicity via a radical mechanism. To test this possibility, we studied the toxicity of para substituted benzyl alcohols on rapidly dividing cancer cells (L1210 leukemia). This system has previously found utility in studying the apparent radical toxicity of a variety of phenols. However, no evidence could be found for an electronic effect and the cellular toxicity was associated primarily with hydrophobicity. Comparison of this quantitative structure-activity relationships (QSAR) with others for the reactions of benzyl alcohols in diverse systems provides insight into mechanisms of action. A QSAR for the interaction of benzyl alcohols with protozoa yields an equation that is dependent on both hydrophobicity and acidity of the OH group versus a mixture of bacteria and fungi, the critical dependence on hydrophobicity prevails with a small dependence on a resonance-stabilized, radical mediated electronic effect. The chloramphenicols provide an instructive example, where the radical mediated electronic effect overshadows the hydrophobic contribution to bacterial toxicity. These various QSAR for benzyl alcohols indicate that mechanisms of growth inhibition in vitro vary depending on cell/organism type, the strength of the bond and lability of the hydrogen, and the strength of the initiating radical reagent.     

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 noval method of predicting soil organic carbon content normalized adsorption coefficients (Koc) of polybrominated biphenyls (PBBs)

By a soil column liquid chromatograph (SCLC) method, the soil organic carbon content normalized adsorption coefficients (K(oc)) of six polybrominated biphenyls (PBB15, PBB26, PBB31, PBB49, PBB103 and PBB153) are determined. Based on the similarity between the molecular structures of PBBs and PCBs, a simple linear predictive model has been developed with the correlation coefficient R=0.9812 and standard error SE=0.19. The logK(oc) values of any PBB congeners can be predicted by using the logK(oc) values of the corresponding PCBs according to this model. Using the published data for logK(oc) values of PCB congeners, logK(oc) values of all 209 PBB congeners have been for the first time predicted. Compared with the data obtained from the experiment, the results of prediction are very accurate.     

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.     

Uptake of carbofuran-14C by rice plants after root zone application as a controlled release formulation

Uptake of Carbofuran-¹⁴C by rice plants after root zone application of a EVA based controlled release formulation and of Furadan as a conventional control was investigated under field conditions. Using 3.6–3.9 kg a.i./ha, ¹⁴C-concentrations in grain (1.3–1.8 mg/kg) and straw (15.9–16.1 mg/kg) were in the same range for both formulations, while contamination of paddy water and soil (at μg/kg level) has been 30–40% lower in plots treated with EVA formulation than with Furadan. Besides slowing down the release of a.i., EVA matrix also prevents an early hydrolysis of incorporated Carbofuran to Carbofuranphenol and keeps therefore its insecticidal efficacy.