Showing papers in "Sar and Qsar in Environmental Research in 2005"

Base-line model for identifying the bioaccumulation potential of chemicals

Abstract: The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.

Structure-activity relationships for reactivity of carbonyl-containing compounds with glutathione

Abstract: For toxicological-based structure-activity relationships to advance, will require a better understanding of molecular reactivity. A rapid and inexpensive spectrophotometric assay for determining the reactive to glutathione (GSH) was developed and used to determine GSH reactivity (reactGSH) data for 21 aliphatic derivatives of esters, ketones and aldehydes. From these data, a series of structure-activity relationships were evaluated. The structure feature associated with reactGSH was an acetylenic or olefinic moiety conjugated to a carbonyl group (i.e. polarized alpha,beta-unsaturation). This structure conveys the capacity to undergo a covalent interaction with the thiol group of cysteine (i.e. Michael- addition). Quantitatively reactGSH of the alpha,beta-unsaturated carbonyl compounds is reliant upon the specific molecular structure with several tendencies observed. Specifically, it was noted that for alpha,beta-unsaturated carbonyl compounds: (1) the acetylenic-substituted derivatives were more reactive than the corresponding olefinic-substituted ones; (2) terminal vinyl-substituted derivatives was more reactive than the internal vinylene-substituted ones; (3) methyl substitution on the vinyl carbon atoms diminishes reactivity and methyl-substitution on the carbon atom farthest from the carbonyl group causes a larger reduction; (4) derivatives with carbon-carbon double bond on the end of the molecule (i.e. vinyl ketone) were more reactive than one with the carbon-oxygen bond at the end of the molecule (i.e. aldehyde) and (5) the ester with an additional unsaturated vinyl groups were more reactive than the derivative having an unsaturated ethyl group.

Investigation of anticancer activity of macrocyclic Schiff bases by means of 4D-QSAR based on simplex representation of molecular structure.

Abstract: Influence of the molecular structure of macrocyclic pyridinophanes, their analogues and some other compounds on anticancer activity (Leukemia, central nervous system (CNS) cancer, prostate cancer, breast cancer, melanoma, non-small cell lung cancer, colon cancer, ovarian cancer, renal cancer) was investigated by means of a new 4D-QSAR approach based on the simplex representation of molecular structures (SiRMS). The number of group (N) is a tuning parameter which can be changed. As a rule N = 3 -- 7. For all the investigated molecules, the 3D structural models were first created and the set of conformers (fourth dimension) was used. Each conformer was represented as a system of different simplexes (tetratomic fragments of fixed structure, chirality and symmetry). Statistic characteristics of the QSAR partial least squares (PLS) models were satisfactory (correlation coefficient r = 0.990 - 0.861; cross-validation coefficient {\rm CVR = 0.914 - 0.633} ). The molecular fragments increasing and decreasing anti...

Classification ensembles for unbalanced class sizes in predictive toxicology

Abstract: This paper investigates the effects of the ratio of positive-to-negative samples on the sensitivity, specificity, and concordance. When the class sizes in the training samples are not equal, the classification rule derived will favor the majority class and result in a low sensitivity on the minority class prediction. We propose an ensemble classification approach to adjust for differential class sizes in a binary classifier system. An ensemble classifier consists of a set of base classifiers; its prediction rule is based on a summary measure of individual classifications by the base classifiers. Two re-sampling methods, augmentation and abatement, are proposed to generate different bootstrap samples of equal class size to build the base classifiers. The augmentation method balances the two class sizes by bootstrapping additional samples from the minority class, whereas the abatement method balances the two class sizes by sampling only a subset of samples from the majority class. The proposed procedure is applied to a data set to predict estrogen receptor binding activity and to a data set to predict animal liver carcinogenicity using SAR (structure-activity relationship) models as base classifiers. The abatement method appears to perform well in balancing sensitivity and specificity.

The Impact of variable selection on the modelling of oestrogenicity

Abstract: Many oestrogenic chemicals exert their activity via specific interactions with the oestrogen receptor (ER). The objective of the present study was to identify significant descriptors associated with the ER binding affinities of a large and diverse set of compounds to drive quantitative structure-activity relationships (QSARs). To this end, a variety of statistical methods were employed for variable selection. These included stepwise regression and partial least squares (PLS) analyses, as well as a non-linear recursive partitioning method (Formal Inference-based Recursive Modelling). A total of 157 molecular descriptors including quantum mechanical, graph theoretical, indicator variables and log P were used in the study. Furthermore, cluster analysis of variables was performed to identify groups of descriptors representing similar molecular features. Hierarchical PLS analyses were performed, where the scores of the significant components of either PLS or principle component analysis (PCA), performed separately on each cluster, were used as the variables for the top model. This reduced the number of the variables representing the larger clusters, leading to a similar number of descriptors for each distinct molecular feature. The results showed that the most important molecular properties for stronger ER binding affinity are molecular size and shape, the presence of a phenol moiety as well as other aromatic groups, hydrophobicity and presence of double bonds. The best PLS model obtained, in terms of predictive ability, was a hierarchical PLS model. However, a rigorous validation study showed that the MLR model using descriptors selected by stepwise regression has greater predictive power than the PLS models.

An in silico ensemble method for lead discovery: decision forest.

Abstract: Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, ...

Validity and validation of expert (Q)SAR systems

Abstract: At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure–activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three ‘valid’ classes results in predictivity of ≥ 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well...

Development of an information-intensive structure-activity relationship model and its application to human respiratory chemical sensitizers.

Abstract: Structure-activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.

In silico modelling of ADMET-a minireview of work from 2000 to 2004.

Abstract: This article represents a minireview of work published so far in the 21st century in the in silico ADMET field of research related to investigations in the areas of solubility, hERG and cytochrome P450 3A4. Various approaches including 2D- and 3D-QSARs and pharmacophore modelling are discussed. The pros and cons of the methods used and models derived are examined. More general remarks on model development and validation are also reported.

Structure-based methods for the prediction of the dominant P450 enzyme in human drug biotransformation: consideration of CYP3A4, CYP2C9, CYP2D6.

Abstract: Metabolic drug-drug interactions are receiving more and more attention from the in silico community. Early prediction of such interactions would not only improve drug safety but also contribute to make drug design more predictable and rational. The aim of this study was to build a simple and interpretable model for the determination of the P450 enzyme predominantly responsible for a drug's metabolism. The P450 enzymes taken into consideration were CYP3A4, CYP2D6 and CYP2C9. Physico-chemical descriptors and structural descriptors for 96 currently marketed drugs were submitted to statistical analysis using the formal inference-based recursive modelling (FIRM) method, a form of recursive partitioning. Generally accepted knowledge on metabolism by these enzymes was also used to construct a hierarchical decision tree. Robust methods of variable selection using recursive partitioning were utilised. The descriptive ability of the resulting hierarchical model is very satisfactory, with 94% of the compounds correctly classified.

QSAR Modeling Based on Structure-Information for Properties of Interest in Human Health

Abstract: The development of QSAR models based on topological structure description is presented for problems in human health. These models are based on the structure-information approach to quantitative biological modeling and prediction, in contrast to the mechanism-based approach. The structure-information approach is outlined, starting with basic structure information developed from the chemical graph (connection table). Information explicit in the connection table (element identity and skeletal connections) leads to significant (implicit) structure information that is useful for establishing sound models of a wide range of properties of interest in drug design. Valence state definition leads to relationships for valence state electronegativity and atom/group molar volume. Based on these important aspects of molecules, together with skeletal branching patterns, both the electrotopological state (E-state) and molecular connectivity (chi indices) structure descriptors are developed and described. A summary of four QSAR models indicates the wide range of applicability of these structure descriptors and the predictive quality of QSAR models based on them: aqueous solubility (5535 chemically diverse compounds, 938 in external validation), percent oral absorption (%OA, 417 therapeutic drugs, 195 drugs in external validation testing), AMES mutagenicity (2963 compounds including 290 therapeutic drugs, 400 in external validation), fish toxicity (92 substituted phenols, anilines and substituted aromatics). These models are established independent of explicit three-dimensional (3-D) structure information and are directly interpretable in terms of the implicit structure information useful to the drug design process.

Physico-chemical properties of PCDD/PCDFs and phthalate esters.

Abstract: QSPR models for water solubility (S), n-octanol/water partition coefficient (K OW), and Henry's law constant (H) for polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzo-p-furans (PCDFs) and phthalates have been established based on two different sets of parameters. Those parameters were topology based characteristic root index (CRI) and three semi-empirical molecular descriptors, namely – energies of the highest occupied and the lowest unoccupied molecular orbital (E HOMO and E LUMO), and dipole moment (μ). The best fit equation found by ‘forward multiple linear regression’ showed that the topology based CRI was the most important parameter for the modelling of solubility and n-octanol/water partition coefficient. For n-octanol/water partition coefficient a two-parameter equation including the CRI and E HOMO with a correlation coefficient of r = 0.992 was obtained whereas a three-parameter equation for solubility and Henry's law constant including the CRI, E LUMO and μ with a correlation coefficient of ...

QSAR Modeling of Datasets with Enantioselective Compounds using Chirality Sensitive Molecular Descriptors

Abstract: Shape descriptors used in 3D QSAR studies naturally take into account chirality; however, for flexible and structurally diverse molecules such studies require extensive conformational searching and alignment. QSAR modeling studies of two datasets of fragrance compounds with complex stereochemistry using simple alignment-free chirality sensitive descriptors developed in our laboratories are presented. In the first investigation, 44 alpha-campholenic derivatives with sandalwood odor were represented as derivatives of several common structural templates with substituents numbered according to their relative spatial positions in the molecules. Both molecular and substituent descriptors were used as independent variables in MLR calculations, and the best model was characterized by the training set q2 of 0.79 and external test set r2 of 0.95. In the second study, several types of chirality descriptors were employed in combinatorial QSAR modeling of 98 ambergris fragrance compounds. Among 28 possible combinations of seven types of descriptors and four statistical modeling techniques, k nearest neighbor classification with CoMFA descriptors was initially found to generate the best models with the internal and external accuracies of 76 and 89%, respectively. The same dataset was then studied using novel atom pair chirality descriptors (cAP). The cAP are based on a modified definition of the atomic chirality, in which the seniority of the substituents is defined by their relative partial charge values: higher values correspond to higher seniorities. The resulting models were found to have higher predictive power than those developed with CoMFA descriptors; the best model was characterized by the internal and external accuracies of 82 and 94%, respectively. The success of modeling studies using simple alignment free chirality descriptors discussed in this paper suggests that they should be applied broadly to QSAR studies of many datasets when compound stereochemistry plays an important role in defining their activity.

External validation of EPIWIN biodegradation models.

Abstract: The BIOWIN biodegradation models were evaluated for their suitability for regulatory purposes. BIOWIN includes the linear and non-linear BIODEG and MITI models for estimating the probability of rapid aerobic biodegradation and an expert survey model for primary and ultimate biodegradation estimation. Experimental biodegradation data for 110 newly notified substances were compared with the estimations of the different models. The models were applied separately and in combinations to determine which model(s) showed the best performance. The results of this study were compared with the results of other validation studies and other biodegradation models. The BIOWIN models predict not-readily biodegradable substances with high accuracy in contrast to ready biodegradability. In view of the high environmental concern of persistent chemicals and in view of the large number of not-readily biodegradable chemicals compared to the readily ones, a model is preferred that gives a minimum of false positives without a corresponding high percentage false negatives. A combination of the BIOWIN models (BIOWIN2 or BIOWIN6) showed the highest predictive value for not-readily biodegradability. However, the highest score for overall predictivity with lowest percentage false predictions was achieved by applying BIOWIN3 (pass level 2.75) and BIOWIN6.

Prediction of CL-20 chemical degradation pathways, theoretical and experimental evidence for dependence on competing modes of reaction.

Abstract: Highest occupied and lowest unoccupied molecular orbital energies, formation energies, bond lengths and FTIR spectra all suggest competing CL-20 degradation mechanisms. This second of two studies investigates recalcitrant, toxic, aromatic CL-20 intermediates that absorb from 370 to 430 nm. Our earlier study (Struct. Chem., 15, 2004) revealed that these intermediates were formed at high OH(-) concentrations via the chemically preferred pathway of breaking the C-C bond between the two cyclopentanes, thereby eliminating nitro groups, forming conjugated pi bonds, and resulting in a pyrazine three-ring aromatic intermediate. In attempting to find and make dominant a more benign CL-20 transformation pathway, this current research validates hydroxylation results from both studies and examines CL-20 transformations via photo-induced free radical reactions. This article discusses CL-20 competing modes of degradation revealed through: computational calculation; UV/VIS and SF spectroscopy following alkaline hydrolysis; and photochemical irradiation to degrade CL-20 and its byproducts at their respective wavelengths of maximum absorption.

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor.

Abstract: A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

In silico prediction of receptor-mediated environmental toxic phenomena—application to endocrine disruption

Abstract: It is an objective of our institution to establish a virtual laboratory allowing for a reliable in silico estimation of the harmful effects triggered by drugs, chemicals and their metabolites. In the recent past, we have developed the underlying technology (Multi-dimensional QSAR: Quasar and Raptor) and compiled a pilot system including the 3D models of three receptors known to mediate endocrine-disrupting effects (the aryl hydrocarbon receptor, the estrogen receptor and the androgen receptor, respectively) and validated them against 310 compounds (drugs, chemicals, toxins). Within this set up we could demonstrate that our concepts are able to both recognize toxic compounds substantially different from those used in the training set as well as to classify harmless compounds clearly as being non-toxic. This suggests that our approach can be used for the prediction of adverse effects of drug molecules and chemicals.

A spreadsheet program for modeling quantitative structure-pharmacokinetic relationships for inhaled volatile organics in humans

Abstract: The extent and profile of target tissue exposure to toxicants depend upon the pharmacokinetic processes, namely, absorption, distribution, metabolism and excretion. The present study developed a spreadsheet program to simulate the pharmacokinetics of inhaled volatile organic chemicals (VOCs) in humans based on information from molecular structure. The approach involved the construction of a human physiologically-based pharmacokinetic (PBPK) model, and the estimation of its parameters based on quantitative structure-property relationships (QSPRs) in an Excel spreadsheet. The compartments of the PBPK model consisted of liver, adipose tissue, poorly perfused tissues and richly perfused tissues connected by circulating blood. The parameters required were: human physiological parameters such as cardiac output, breathing rate, tissue volumes and tissue blood flow rates (obtained from the biomedical literature), tissue/air partition coefficients (obtained using QSPRs developed with rat data), blood/air partition coefficients (Pb) and hepatic clearance (CL). Using literature data on human Pb and CL for several VOCs (alkanes, alkenes, haloalkanes and aromatic hydrocarbons), multi-linear additive QSPR models were developed. The numerical contributions to human Pb and CL were obtained for eleven structural fragments (CH3, CH2, CH, C, C [double bond] C, H, Cl, Br, F, benzene ring, and H in the benzene ring structure). Using these data as input, the PBPK model written in an Excel spreadsheet simulated the inhalation pharmacokinetics of ethylbenzene (33 ppm, 7 h) and dichloromethane (100 ppm, 6 h) in humans exposed to these chemicals. The QSPRs developed in this study should be useful for predicting the inhalation pharmacokinetics of VOCs in humans, prior to testing and experimentation.

A new strategy for using supervised artificial neural networks in QSAR.

Abstract: A new type of environmental QSAR model is presented for the common situation in which the biological activity of molecules mainly depends on their 1-octanol/water partition coefficient (log P). In a first step, a classical regression equation with log P is derived. The residuals obtained with this simple linear equation are then modeled from a supervised artificial neural network including different molecular descriptors as input neurons. Finally, results produced by the linear and nonlinear models are both considered for calculating the activity values, which are compared with the initial actual activity values. A heterogeneous database of 569 organic compounds with 96-h LC50s measured to the fathead minnow (Pimephales promelas), randomly divided into a training set of 484 chemicals and a testing set of 85 chemicals, was used as illustrative example to show the potentialities of this new modeling strategy Finally, practical suggestions are given for designing this type of hybrid QSAR model.

Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals

Abstract: A database of chronic lowest observed adverse effect levels (LOAELs) for 234 compounds, previously compiled from different sources (Toxicology Letters79, 131-143 (1995)), was modelled using graph theoretical descriptors. This study reveals that data are not homogeneous. Only those data originating from the U.S. Environmental Protection Agency (EPA) reports could be well modelled by multilinear regression (MLR) and linear discriminant analysis (LDA). In contrast, data available from the specific procedures of the National Toxicology Program (NTP) database introduced noise and did not render good models either alone, or in combination with the EPA data.

POPs: a QSAR system for developing categories for persistent, bioaccumulative and toxic chemicals and their metabolites.

Abstract: This paper presents the framework of a QSAR-based decision support system which provides a rapid screening of potential hazards, classification of chemicals with respect to risk management thresholds, and estimation of missing data for the early stages of risk assessment. At the simplest level, the framework is designed to rank hundreds of chemicals according to their profile of persistence, bioaccumulation potential and toxicity often called the persistent organic pollutant (POP) profile or the PBT (persistent bioaccumulative toxicant) profile. The only input data are the chemical structure. The POPs framework enables decision makers to introduce the risk management thresholds used in the classification of chemicals under various authorities. Finally, the POPs framework advances hazard identification by integrating a metabolic simulator that generates metabolic map for each parent chemical. Both the parent chemicals and plausible metabolites are systematically evaluated for metabolic activation and POPs profile.

Two-step models to predict binding affinity of chemicals to the human estrogen receptor alpha by three-dimensional quantitative structure-activity relationships (3D-QSARs) using receptor-ligand docking simulation.

Abstract: Binding of chemicals to the estrogen receptor (ER) is known to be a key mode of action of endocrine disruption effects. In this study, combined quantitative structure-activity relationship (QSAR) models from discriminant and multilinear regression (MLR) analyses, termed a two-step model, were developed. These were used to predict the binding potency to human ERalpha of four chemical groups, namely alkylphenols, phthalates, diphenylethanes and benzophenones. These groups are considered to be important chemical classes of ER-binders. The descriptors investigated were calculated following the simulation of docking between the receptor and ligand. Discriminant analysis in the first step of a two-step model was applied to distinguish binders from non-binders. It had a concordance, following leave-one-out (LOO), of greater than 87% for all chemical classes. Binders were defined as chemicals whose IC50 was reliably measured in a competitive binding assay. The MLR analysis in the second step was performed for the quantitative prediction of the binding affinity of chemicals that were previously discriminated as binders. The q2 values for alkylphenols and diphenylethanes were 0.75 and 0.74, respectively. However good MLR relationships were not obtained for phthalates and benzophenones as the observed binding affinities of chemicals in these categories were weak and in a too narrow range.

Quantifying hydrogen bonding in QSAR and molecular modeling.

Abstract: Quantitative descriptions of hydrogen bonding for use in QSAR and molecular modeling by means of H-bond descriptors have been analyzed in detail in this paper. Ten new H-bond surface and enthalpy integral descriptors were proposed. The usefulness of these new descriptors, as well as previously developed descriptors was verified using a set of 154 drugs for which data for intestinal absorption in humans were available. The results showed that descriptors such as the number of H-bond acceptor and donor atoms and polar surface area (PSA) did not sufficiently describe the actual H-bonding ability of atoms in molecules. Thus, to enable successful modeling it was necessary to introduce descriptors directly related to the experimental thermodynamics of hydrogen bonding.

External validation of the biodegradability prediction model CATABOL using data sets of existing and new chemicals under the Japanese Chemical Substances Control Law

Abstract: External validation of the biodegradability prediction model CATABOL was conducted using test data of 338 existing chemicals and 1123 new chemicals under the Japanese Chemical Substances Control Law. CATABOL predicts that 1089 chemicals will have a BOD < 60% while 925 (85%) actually have an observed BOD<60%. The percentage of chemicals with an observed BOD value <60% tends to increase as the predicted BOD values decrease. In contrast, 340 chemicals were predicted to have a BOD ≥ 60% and 234 (69%) actually had an observed BOD ≥ 60%. The prediction of poor biodegradability was more accurate than the predictions of high biodegradability. The features of chemical structures affecting CATABOL predictability were also investigated.

Henry's law constant of hydrocarbons in air–water system: The cavity ovality effect on the non-electrostatic contribution term of solvation free energy

Abstract: In this study, a quantitative structure-property relationship (QSPR) model for the prediction of Henry's law constants of aliphatic hydrocarbons in air-water system has been developed, based on a data-set of 189 compounds. The well-known linear thermodynamic relation between the logarithm of Henry's law constant and solvation free energy has been used for developing the model. It is emphasised that the solvent-accessible surface area (SASA) descriptor is not adequate for predicting the solvation free energy of a wide range of aliphatic hydrocarbons; there are many compounds that have the same solvent-accessible surface area with different solvation free energy. Therefore, we have introduced cavity ovality as a good descriptor of molecular cavity shape factor. The root mean square error (RMSE) of the QSPR regression model based on SASA improves from 0.40 to 0.22 by introducing the cavity ovality descriptor. The QSPR linear ovality model has good statistical parameters (r(2) = 0.90). To emphasise the significant effect of the new descriptor, a non-linear neural network model with only two nodes in the hidden layer was developed, and also yielded a RMSE of 0.22.

Comparison of three fish bioaccumulation models for ecological and human risk assessment and validation with field data

Abstract: This article compares two bioconcentration Quantitative Structure Activity Relationships (QSARs) for fish applied in human risk assessments with the mechanistic bioaccumulation model OMEGA and field data. It was found that all models are virtually similar up to a Kow of 106. For substances with a Kow higher than 106, the fish bioconcentration curve in the risk assessment model EUSES decreases parabolically. In contrast, OMEGA bioaccumulation outcomes approximately show a linear increase, based on mechanistic bioconcentration and biomagnification properties of chemicals. The OMEGA-outcomes are close to the fish bioconcentration outcomes of the risk assessment model CalTOX. For very hydrophobic substances, field accumulation data in freshwater and marine fish species are closer to OMEGA- and CalTOX-outcomes compared to EUSES. The results also show that it is important to include biomagnification in fish and lipid content of fish in human exposure models.

Theoretical predictions of chemical degradation reaction mechanisms of RDX and other cyclic nitramines derived from their molecular structures.

Abstract: Analysis of environmental degradation pathways of contaminants is aided by predictions of likely reaction mechanisms and intermediate products derived from computational models of molecular structure. Quantum mechanical methods and force-field molecular mechanics were used to characterize cyclic nitramines. Likely degradation mechanisms for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) include hydroxylation utilizing addition of hydroxide ions to initiate proton abstraction via 2nd order rate elimination (E2) or via nucleophilic substitution of nitro groups, reductive chemical and biochemical degradation, and free radical oxidation. Due to structural similarities, it is predicted that, under homologous circumstances, certain RDX environmental degradation pathways should also be effective for octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) and similar cyclic nitramines. Computational models provided a theoretical framework whereby likely transformation mechanisms and transformation products of cycli...

QSPR analysis of the partitioning of vaporous chemicals in a water–gas phase system and the water solubility of liquid and solid chemicals on the basis of fragment and physicochemical similarity and HYBOT descriptors

Abstract: QSPR analyses of the solubility in water of 558 vapors, 786 liquids and 2045 solid organic neutral chemicals and drugs are presented. Simultaneous consideration of H-bond acceptor and donor factors leads to a good description of the solubility of vapors and liquids. A volume-related term was found to have an essential negative contribution to the solubility of liquids. Consideration of polarizability, H-bond acceptor and donor factors and indicators for a few functional groups, as well as the experimental solubility values of structurally nearest neighbors yielded good correlations for liquids. The application of Yalkowsky's "General Solubility Equation" to 1063 solid chemicals and drugs resulted in a correlation of experimental vs calculated log S values with only modest statistical criteria. Two approaches to derive predictive models for solubility of solid chemicals and drugs were tested. The first approach was based on the QSPR for liquids together with indicator variables for different functional groups. Furthermore, a calculation of enthalpies for intermolecular complexes in crystal lattices, based on new H-bond potentials, was carried out for the better consideration of essential solubility- decreasing effects in the solid state, as compared with the liquid state. The second approach was based on a combination of similarity considerations and traditional QSPR. Both approaches lead to high quality predictions with average absolute errors on the level of experimental log S determination.

3D QSAR investigation of the blood-brain barrier penetration of chemical compounds.

Abstract: In the present study, we investigated structure–permeability relationships for the blood–brain barrier (BBB) of 16 imipramine and phenothiazine derivatives. The compounds belong to structurally related chemical classes of catamphiphiles, representatives of which have previously been investigated for membrane activity and ability to overcome multidrug resistance (MDR) in tumour cells. These studies show that phenothiazines and structurally related drugs (imipramines, thioxanthenes, acridines) interact with membrane phospholipids, and additionally inhibit the MDR transport P-glycoprotein. This study aimed to identify common 3D structural characteristics of these compounds related to their mechanism of transport across the BBB. For this purpose Genetic Algorithm Similarity Programme (GASP), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) were applied. The results demonstrate the importance of the spatial distribution of molecular hydrophobicity for th...

Quantitative structure-activity relationship modeling of insect juvenile hormone activity of 2,4-dienoates using computed molecular descriptors.

Abstract: Juvenile hormone (JH) activity of one hundred and eighty 2,4-dienoates reported for the larvae/pupae of six insect species was modeled using 915 atom pairs and 258 global molecular descriptors (topological and geometrical). Ridge regression, principal component regression and partial least square regression methods were used to model each of the JH activities. The use of all of the available parameters did not yield any good models, and extensive predictor trimming was necessary to improve the models. Ridge regression was found to give the best results among the three statistical tools used. The top ten molecular descriptors selected based on the t-statistic for each of the six models were found to be mostly atom pairs containing heteroatoms and topochemical descriptors. This suggests the importance of the chemical nature of the ligand rather than mere space-filling as the basis of the JH bioactivity. The residual plots indicate the existence of some non-linear relations, and recursive partitioning was us...