Showing papers in "Sar and Qsar in Environmental Research in 2007"
TL;DR: A mathematical model was developed to predict uptake of neutral organic chemicals from soil and air into fruits, which predicts that polar, non-volatile compounds will effectively be transported from soil to fruits, while lipophilic compounds will preferably accumulate from air intoruits.
Abstract: The current European risk assessment for chemicals considers only tap water, while in reality other beverages play an important role. A good part of beverages are made from fruits, for example apple juice and vine. A mathematical model was developed to predict uptake of neutral organic chemicals from soil and air into fruits. The new fruit tree model considers eight compartments, i.e. two soil compartments, fine roots, thick roots, stem, leaves, fruits, and air. Chemical equilibrium, advective transport in xylem and phloem, diffusive exchange to soil and air and growth dilution are the main processes. The parameterization is for a square-meter of an apple orchard. The model predicts that polar, non-volatile compounds will effectively be transported from soil to fruits, while lipophilic compounds will preferably accumulate from air into fruits. Results from various experiments show no disagreement with the model predictions.
150 citations
TL;DR: An overview of ECB activities on computational toxicology is provided, which are intended to promote the development, validation, acceptance and use of (Q)SARs and related estimation methods, both at the European and international levels.
Abstract: Under the proposed REACH (Registration, Evaluation and Authorisation of CHemicals) legislation, (Q)SAR models and grouping methods (chemical categories and read across approaches) are expected to play a significant role in prioritising industrial chemicals for further assessment, and for filling information gaps for the purposes of classification and labelling, risk assessment and the assessment of persistent, bioaccumulative and toxic (PBT) chemicals. The European Chemicals Bureau (ECB), which is part of the European Commission's Joint Research Centre (JRC), has a well-established role in providing independent scientific and technical advice to European policy makers. The ECB also promotes consensus and capacity building on scientific and technical matters among stakeholders in the Member State authorities and industry. To promote the availability and use of (Q)SARs and related estimation methods, the ECB is carrying out a range of activities, including applied research in computational toxicology, the assessment of (Q)SAR models and methods, the development of technical guidance documents and computational tools, and the organisation of training courses. This article provides an overview of ECB activities on computational toxicology, which are intended to promote the development, validation, acceptance and use of (Q)SARs and related estimation methods, both at the European and international levels.
135 citations
TL;DR: Computer program PASS predicts about 2500 kinds of biological activities based on the structural formula of chemical compounds, that providing an estimated profile of compound's action in biological space can be used to recognize the most probable targets and mechanisms of toxicity.
Abstract: Toxicity of chemical compound is a complex phenomenon that may be caused by its interaction with different targets in the organism. Two distinct types of toxicity can be broadly specified: the first one is caused by the strong compound's interaction with a single target (e.g. AChE inhibition); while the second one is caused by the moderate compound's interaction with many various targets. Computer program PASS predicts about 2500 kinds of biological activities based on the structural formula of chemical compounds. Prediction is based on the robust analysis of structure-activity relationships for about 60,000 biologically active compounds. Mean accuracy exceeds 90% in leave-one-out cross-validation. In addition to some kinds of adverse effects and specific toxicity (e.g. carcinogenicity, mutagenicity, etc.), PASS predicts ∼2000 kinds of biological activities at the molecular level, that providing an estimated profile of compound's action in biological space. Such profiles can be used to recognize the most ...
81 citations
TL;DR: Of the models/expert systems evaluated, none performed sufficiently well to act as a standalone tool for hazard identification, and three models are focused on, and their performance against a recently published dataset of 211 chemicals is evaluated.
Abstract: Skin sensitisation potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitisation hazard is normally identified using in vivo test methods, the favoured approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitising potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitisation hazard identification is the use of (Quantitative) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitisation hazard. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chemicals. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a standalone tool for hazard identification.
81 citations
TL;DR: Modelling of the permeability data indicates that (Q)SARs with reasonable statistical fit can be developed for the ability of molecules to cross the placental barrier membrane.
Abstract: The replacement of animal testing for endpoints such as reproductive toxicity is a long-term goal. This study describes the possibilities of using simple (quantitative) structure-activity relationships ((Q)SARs) to predict whether a molecule may cross the placental membrane. The concept is straightforward, if a molecule is not able to cross the placental barrier, then it will not be a reproductive toxicant. Such a model could be placed at the start of any integrated testing strategy. To develop these models the literature was reviewed to obtain data relating to the transfer of molecules across the placenta. A reasonable number of data were obtained and are suitable for the modelling of the ability of a molecule to cross the placenta. Clearance or transfer indices data were sought due to their ability to eliminate inter-placental variation by standardising drug clearance to the reference compound antipyrine. Modelling of the permeability data indicates that (Q)SARs with reasonable statistical fit can be developed for the ability of molecules to cross the placental barrier membrane. Analysis of the models indicates that molecular size, hydrophobicity and hydrogen-bonding ability are molecular properties that may govern the ability of a molecule to cross the placental barrier.
63 citations
TL;DR: It is concluded that for the skin sensitization endpoint, used as a working example, mechanistic understanding expressed as chemical reactivity can be exploited by computational structural similarity methods to augment category formation process.
Abstract: Chemical category is a regulatory concept facilitating filling safety data gaps. Practically, all chemical management programs like the OECD HPV Program, EU REACH, or the Canadian DSL Categorization are planning to use or are already using categorization approaches to reduce resources including animal testing. The aim of the study was to discuss the feasibility to apply computational structural similarity methods to augment formation of a category. The article discusses also how this understanding can be translated into computer readable format, an ultimate need for practical, broad scope applications. We conclude that for the skin sensitization endpoint, used as a working example, mechanistic understanding expressed as chemical reactivity can be exploited by computational structural similarity methods to augment category formation process. We propose a novel method, atom environments ranking (AER), to assess similarity to a reference training set representing a common mechanism of action, as a potential method for grouping chemicals into reactivity domains.
62 citations
TL;DR: Results showed that both the decision tree and logistic regression derived developmental SAR models exhibited modest prediction accuracy, and although prediction accuracy was similar in the two modeling approaches, there was inconsistency in the model descriptors.
Abstract: Structure–activity relationship (SAR) models can be used to predict the biological activity of potential developmental toxicants whose adverse effects include death, structural abnormalities, altered growth and functional deficiencies in the developing organism. Physico-chemical descriptors of spatial, electronic and lipophilic properties were used to derive SAR models by two modeling approaches, logistic regression and Classification and Regression Tree (CART), using a new developmental database of 293 chemicals (FDA/TERIS). Both single models and ensembles of models (termed bagging) were derived to predict toxicity. Assessment of the empirical distributions of the prediction measures was performed by repeated random partitioning of the data set. Results showed that both the decision tree and logistic regression derived developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the prediction accuracy and reduced the variability of prediction measures compared to the single ...
56 citations
TL;DR: This work has explored using hybrid QSPR equations describing individual compound penetration based on the molecular descriptors for the compound modified by a mixture factor which accounts for the physicochemical properties of the vehicle/mixture components.
Abstract: Significant progress has been made on predicting dermal absorption/penetration of topically applied compounds by developing QSPR models based on linear free energy relations (LFER). However, all of these efforts have employed compounds applied to the skin in aqueous or single solvent systems, a dosing scenario that does not mimic occupational, environmental or pharmaceutical exposure. We have explored using hybrid QSPR equations describing individual compound penetration based on the molecular descriptors for the compound modified by a mixture factor (MF) which accounts for the physicochemical properties of the vehicle/mixture components. The MF is calculated based on percentage composition of the vehicle/mixture components and physical chemical properties selected using principal components analysis. This model has been applied to 12 different compounds in 24 mixtures for a total of 288 treatment combinations obtained from flow-through porcine skin diffusion cells and in an additional dataset of 10 of th...
54 citations
TL;DR: A simplified biodegradability kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations, which allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegrading parameters.
Abstract: Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.
51 citations
TL;DR: The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors based on a set of mutagenicity data for nitro-PAHs and were validated for predictivity by both internal and external validation.
Abstract: Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach. †Presented at the 12th International Workshop on Quantit...
44 citations
TL;DR: The analyses indicate that “statistical” (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate.
Abstract: As part of a European Chemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that "statistical" (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary.
TL;DR: A new QSAR approach based on a Quantitative Neighbourhoods of Atoms description of molecular structures and self-consistent regression was developed and provides a good correlation and prediction accuracy, at least as good as the best results obtained with the otherQSAR methods originally used on the same data sets.
Abstract: A new QSAR approach based on a Quantitative Neighbourhoods of Atoms description of molecular structures and self-consistent regression was developed. Its prediction accuracy, advantages and limitations were analysed from three sets of published experimental data on acute toxicity: 56 phenylsulfonyl carboxylates for Vibrio fischeri; 65 aromatic compounds for the alga Chlorella vulgaris and 200 phenols for the ciliated protozoan Tetrahymena pyriformis. According to our findings, the proposed approach provides a good correlation and prediction accuracy (r 2 = 0.908 and Q 2 = 0.866) for the set of 56 phenylsulfonyl carboxylates and the 65 aromatic compounds tested on C. vulgaris (r 2 = 0.885, Q 2 = 0.849). For the 200 phenols tested on T. pyriformis, the prediction accuracy was r 2 = 0.685 and Q 2 = 0.651. This is at least as good as the best results obtained with the other QSAR methods originally used on the same data sets. †Presented at the 12th International Workshop on Quantitative Structure-Activity Rela...
TL;DR: Graph Machines as mentioned in this paper is an alternative approach to traditional machine-learning-based QSAR, which circumvents the problem of designing, computing and selecting molecular descriptors, which is similar in spirit to recursive networks.
Abstract: We describe graph machines, an alternative approach to traditional machine-learning-based QSAR, which circumvents the problem of designing, computing and selecting molecular descriptors. In that approach, which is similar in spirit to recursive networks, molecules are considered as structured data, represented as graphs. For each example of the data set, a mathematical function (graph machine) is built, whose structure reflects the structure of the molecule under consideration; it is the combination of identical parameterised functions, called “node functions” (e.g. a feedforward neural network). The parameters of the node functions, shared both within and across the graph machines, are adjusted during training with the “shared weights” technique. Model selection is then performed by traditional cross-validation. Therefore, the designer's main task consists in finding the optimal complexity for the node function. The efficiency of this new approach has been demonstrated in many QSAR or QSPR tasks, as well...
TL;DR: Using abiotic thiol reactivity and Tetrahymena pyriformis toxicity data for a group of halo-substituted ketones, esters and amides and related compounds a series of structure–activity relationships are illustrated.
Abstract: Using abiotic thiol reactivity (EC50) and Tetrahymena pyriformis toxicity (IGC50) data for a group of halo-substituted ketones, esters and amides (i.e. SN2 electrophiles) and related compounds a series of structure–activity relationships are illustrated. Only the α-halo-carbonyl-containing compounds are observed to be thiol reactive with the order I > Br > Cl > F. Further comparisons disclose α-halo-carbonyl compounds to be more reactive than non-α-halo-carbonyl compounds; in addition, the reactivity is reduced when the number of C atoms between the carbonyl and halogen is greater than one. Comparing reactivity among α-halo-carbonyl-containing compounds with different β-alkyl groups shows the greater the size of the β-alkyl group the lesser the reactivity. A comparison of reactivity data for 2-bromoacetyl-containing compounds of differing dimensions reveals little difference in reactivity. Regression analysis demonstrates a linear relationship between toxicity and thiol reactivity: ; n = 19, s = 0.250, r ...
TL;DR: In this article, a generic predictive model was created using the diverse set of 101 compounds and two submodels were prepared for subsets of 79 cyclic and 22 acyclic chemicals.
Abstract: Quantitative structure-activity relationship (QSAR) models were developed for the prediction of dermal absorption based on experimental log Kp data for a diverse set of 101 chemicals obtained from the literature. Molecular descriptors including topostructural (TS), topochemical (TC), shape or three-dimensional (3D) and quantum chemical (QC) indices were calculated. Based on this information, a generic predictive model was created using the diverse set of 101 compounds. In addition, two submodels were prepared for subsets of 79 cyclic and 22 acyclic chemicals. A modified Gram-Schmidt variable reduction algorithm for descriptor thinning was followed by regression analyses using ridge regression (RR), principal components regression (PCR) and partial least squares regression (PLS). The RR results were found to be superior to PLS and PCR regressions. The cross-validated correlation coefficients for the full set and subsets were 0.67-0.87. Computational methods such as QSAR modelling can be used to augment existing data to prioritise chemicals that need to be studied further for toxicological evaluation and risk assessment.
TL;DR: The goal of the present study is to analyse parameters for all three parts of the QSAR model statistical quality assessment and investigate the flexible weighting approach for the overall statistical quality index development.
Abstract: Assessment of the quality of goodness-of-fit and the confidence in predictivity (prediction power) are the main terms used to define the statistical quality of QSAR models. Three parts of this assessment can be defined as: (1) Measure of goodness-of-fit. (2) Validation of model stability. (3) Predictivity analysis. Currently there are no mandatory requirements for the validation methods to be used and rules for the quantitative confidence estimates. To compare the statistical quality of QSAR models it is necessary to have an overall statistical quality index which will depend on the goodness-of-fit, validation and predictivity results together. To do so it is necessary to define the set of mandatory parameters for all three parts of assessment listed above and develop the approach for overall quality estimates based on these parameters. It is also necessary to include into the overall index the penalty mechanism for parameter absence. The goal of the present study is to analyse parameters for all three parts of the QSAR model statistical quality assessment and investigate the flexible weighting approach for the overall statistical quality index development. Due the different statistical parameters traditionally used for assessment of goodness-of-fit it is necessary to create the mechanism, which allows flexible set of parameters to be used for the overall statistical quality index. Only after approval by scientific community and regulatory boards the final set of mandatory parameters can be selected.
TL;DR: The resulting model showed a reliable dependence of estrogenic activity of the terpenoids on such parameters as molecular shape, number of phenolic groups, surface polarity and the energy of the highest occupied molecular orbital.
Abstract: The relationship between chemical structure and estrogenic activity in a series of terpenoid esters with aromatic and aliphatic acid substituents isolated from Ferula plants, was studied. The fragments of the terpenoid structure that are potentially responsible for estrogenic activity were revealed. A quantitative structure-estrogenic activity study has been carried out using the QSAR approach with use of data derived from quantum-chemical calculations as well as data generated from three-dimensional structures of terpenoids. A number of molecular descriptors was obtained from the density functional theory (DFT) at the B3LYP/6-31G(d, p) level of calculation. Comparative analysis of the quantum-chemical computational data was also performed to confirm hypothesis concerning importance of the distance between the oxygen of alcohol hydroxyl group and the functional group in the para-position of the benzene ring (the hydroxyl or methoxy group). Use of the Genetic Algorithm in the QSAR analysis allowed the structural and physicochemical parameters of the terpenoids responsible for estrogenic activity to be determined. A significant QSAR model was obtained with an r(2) value of 0.892. The resulting model showed a reliable dependence of estrogenic activity of the terpenoids on such parameters as molecular shape, number of phenolic groups, surface polarity and the energy of the highest occupied molecular orbital.
TL;DR: Evaluated the structural inclusion rules implemented in the BfR–DSS for the prediction of skin irritation and corrosion found that the test data set did not match the training set relative to the inclusion of structural alerts associated with skin irritation/corrosion.
Abstract: The proposed REACH regulation within the European Union (EU) aims to minimise the number of laboratory animals used for human hazard and risk assessment while ensuring adequate protection of human health and the environment. One way to achieve this goal is to develop non-testing methods, such as (quantitative) structure-activity relationships ([Q]SARs), suitable for identifying toxicological hazard from chemical structure and physicochemical properties alone. A database containing data submitted within the EU New Chemicals Notification procedure was compiled by the German Bundesinstitut fur Risikobewertung (BfR). On the basis of these data, the BfR built a decision support system (DSS) for the prediction of several toxicological endpoints. For the prediction of eye irritation and corrosion potential, the DSS contains 31 physicochemical exclusion rules evaluated previously by the European Chemicals Bureau (ECB), and 27 inclusion rules that define structural alerts potentially responsible for eye irritation and/or corrosion. This work summarises the results of a study carried out by the ECB to assess the performance of the BfR structural rulebase. The assessment included: (a) evaluation of the structural alerts by using the training set of 1341 substances with experimental data for eye irritation and corrosion; and (b) external validation by using an independent test set of 199 chemicals. Recommendations are made for the further development of the structural rules in order to increase the overall predictivity of the DSS.
TL;DR: The interaction types are becoming less distinct in the lowest activity range for each chemicals of each type; here, the modeling was performed within chemical classes (phenols, phthalates, etc.).
Abstract: A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has been used to investigate chemical binding to the human estrogen receptor (hER). A training set of 645 chemicals included 497 steroid and environmental chemicals (database of the Chemical Evaluation and Research Institute, Japan - CERI) and 148 chemicals to further explore hER-structure interactions (selected J. Katzenellenbogen references). Upgrades of modeling approaches were introduced for multivariate COREPA analysis, optimal conformational generation and description of the local hydrophobicity of chemicals. Analysis of reactivity patterns based on the distance between nucleophilic sites resulted in identification of distinct interaction types: a steroid-like A-B type described by frontier orbital energies and distance between nucleophilic sites with specific charge requirements; an A-C type where local hydrophobic effects are combined with electronic interactions to modulate binding; and mixed A-B-C (AD) type. Chemicals were grouped by type, then COREPA models were developed for within specific relative binding affinity ranges of >10%, 10 > RBA > or = 0.1%, and 0.1 > RBA > 0.0%. The derived models for each interaction type and affinity range combined specific prefiltering requirements (interatomic distances) and a COREPA classification node using no more than 2 discriminating parameters. The interaction types are becoming less distinct in the lowest activity range for each chemicals of each type; here, the modeling was performed within chemical classes (phenols, phthalates, etc.). The ultimate model was organized as a battery of local models associated to interaction type and mechanism.
TL;DR: It is shown that one can also apply the described Quantum Q SPR prediction algorithms to parent problems in the framework of empirical QSPR, based on the molecular space framework.
Abstract: The present theoretical study analyses the Quantum QSPR fundamental linear equation predictive power. Two main alternative algorithms, among several possible choices, are fully described in an add one and add many basis, while the other possibilities are only sketched out. It is shown that one can also apply the described Quantum QSPR prediction algorithms to parent problems in the framework of empirical QSPR, based on the molecular space framework.
TL;DR: A system coefficient approach is proposed for quantitative assessment of the solvent effects on membrane absorption from chemical mixtures using Polydimethylsiloxane membrane-coated fibres and 32 probe compounds to demonstrate the proposed approach.
Abstract: A system coefficient approach is proposed for quantitative assessment of the solvent effects on membrane absorption from chemical mixtures. The complicated molecular interactions are dissected into basic molecular interaction forces via Abraham's linear solvation energy relationship (LSER). The molecular interaction strengths of a chemical are represented by a set of solute descriptors, while those of a membrane/chemical mixture system are represented by a set of system coefficients. The system coefficients can be determined by using a set of probe compounds with known solute descriptors. Polydimethylsiloxane (PDMS) membrane-coated fibres and 32 probe compounds were used to demonstrate the proposed approach. When a solvent was added into the chemical mixture, the system coefficients were altered and detected by the system coefficient approach. The system coefficients of the PDMS/water system were (0.09, 0.49, -1.11, -2.36, -3.78, 3.50). When 25% ethanol was added into the PDMS/water system, the system coefficients were altered significantly (0.38, 0.41, -1.18, -2.07, -3.40, 2.81); and the solvent effect was quantitatively described by the changes in the system coefficients (0.29, -0.08, -0.07, 0.29, 0.38, -0.69). The LSER model adequately described the experimental data with a correlation coefficient (r(2)) of 0.995 and F-value of 1056 with p-value less than 0.0001.
TL;DR: The strength of the theory is in the combination of why metabolic transformation depends both on the BCF and the body size, and the application is illustrated with several data sets from the literature.
Abstract: The LC(50) of compounds with a similar biological effect, at a given exposure period, is frequently plotted log-log against the octanol-water partition coefficient and a straight line is fitted for interpolation purposes. This is also frequently done for physiological properties, such as the weight-specific respiration rate, as function of the body weight of individuals. This paper focuses on the remarkable observation that theoretical explanations for these relationships also have strong similarities. Both can be understood as result of the covariation of the values of parameters of models of a particular type for the underlying processes, while this covariation follows logically from the model structure. The one-compartment model for the uptake and elimination of compounds by organisms is basic to the BioConcentration Factor (BCF), or the partition coefficient; the standard Dynamic Energy Budget model is basic to the (ultimate) body size. The BCF is the ratio of the uptake and the elimination rates; the maximum body length is the ratio of the assimilation (i.e. uptake of resources) and the maintenance (i.e. use of resources) rates. This paper discusses some shortcomings of descriptive approaches and conceptual aspects of theoretical explanations. The strength of the theory is in the combination of why metabolic transformation depends both on the BCF and the body size. We illustrate the application of the theory with several data sets from the literature.
TL;DR: Major improvements are feasible with combined use of three classification schemes: assignments of baseline toxicants are protective, recognition of excess toxicants is acceptable and applicability range increases favourably.
Abstract: Decision support for selecting suitable QSARs for predictive purposes is suggested by a stepwise procedure: The first tier pre-filters the compounds based on substructure indicators for baseline versus excess toxicity. This step, if sufficiently conservative, discriminates chemicals, whose toxicity can be reliably estimated from their log KOW from those, that require further classification by biological and chemical domain. A test set of 115 chemicals from 9 different MOA classes was used to compare the discriminatory power of several classification schemes based on substructure indicators. Performance, evaluated by contingency table statistics, is varied and no single scheme provides sufficient applicability and reliability for pre-filtering chemical inventories. Major improvements are feasible with combined use of three classification schemes: assignments of baseline toxicants are protective, recognition of excess toxicants is acceptable and applicability range increases favourably.
TL;DR: A novel descriptor selection scheme for Support Vector Machine (SVM) classification method has been proposed and its utility demonstrated using a skin sensitisation dataset as an example, where SVM was shown to outperform the LDA.
Abstract: A novel descriptor selection scheme for Support Vector Machine (SVM) classification method has been proposed and its utility demonstrated using a skin sensitisation dataset as an example. A backward elimination procedure, guided by mean accuracy (the average of specificity and sensitivity) of a leave-one-out cross validation, is devised for the SVM. Subsets of descriptors were first selected using a sequential t-test filter or a Random Forest filter, before backward elimination was applied. Different kernels for SVM were compared using this descriptor selection scheme. The Radial Basis Function (RBF) kernel worked best when a sequential t-test filter was adopted. The highest mean accuracy, 84.9%, was obtained using SVM with 23 descriptors. The sensitivity and the specificity were as high as 93.1% and 76.6%, respectively. A linear kernel was found to be optimal when a Random Forest filter was used. The performance using 24 descriptors was comparable with a RBF kernel with a sequential t-test filter. As a c...
TL;DR: The PASS computer program, which is able to simultaneously predict more than one thousand biological and toxicological activities from only the structural formulas of the chemicals, was used to predict the biological activity profile of 681 cyanobacterial secondary metabolites.
Abstract: Over the past decade cyanobacteria have become an interesting source of new classes of pharmacologically active natural products. Some cyanobacterial secondary metabolites (CSMs) are also well known for their toxic effects on living species. The PASS (Prediction of Activity Spectra for Substances) computer program, which is able to simultaneously predict more than one thousand biological and toxicological activities from only the structural formulas of the chemicals, was used to predict the biological activity profile of 681 CSMs. Multivariate methods were employed to structure and analyse this wealth of biological and chemical information. PASS predictions were successfully compared to the available information on the pharmacological and toxicological activity of these compounds.
TL;DR: Polyenes, particularly β-carotene and lycopene, acted as interceptors of growing poly-MMA radicals, and a linear relationship between k inh and the kinetic chain length (KCL) for polyenes was observed; as k inh increased, KCL decreased and also decreased significantly as the number of conjugated double bonds in the polyenes increased.
Abstract: To clarify the non-enzymatic radical-scavenging activity of beta-carotene-related compounds and other polyenes, we used differential scanning calorimetry to study the kinetics of radical polymerization of methyl methacrylate (MMA) by 2,2'-azobisisobutyronitrile (AIBN) or benzoyl peroxide (BPO) in the absence or presence of polyenes under nearly anaerobic conditions at 70 degrees C, and analyzed the results with an SAR approach. The polyenes studied were all-trans retinol, retinol palmitate, calciferol, beta-carotene and lycopene. Polyenes produced a small induction period. The stoichiometric factor (n) (i.e. the number of radicals trapped by each inhibitor molecule) of polyenes was close to 0. Tetraterpenes (beta-carotene, lycopene) suppressed significantly more of the initial rate of polymerization (R(inh)) than did diterpenes (retinol, retinol palmitate). The inhibition rate constants (k(inh)) for the reaction of beta-carotene with AIBN- or BPO-derived radicals were determined to be 1.2-1.6x10(5) l/mol s, similar to published values. A linear relationship between (k(inh)) and the kinetic chain length (KCL) for polyenes was observed; as (k(inh)) increased, KCL decreased. KCL also decreased significantly as the number of conjugated double bonds in the polyenes increased. Polyenes, particularly beta-carotene and lycopene, acted as interceptors of growing poly-MMA radicals.
TL;DR: Topological indices were used in the prediction of the acute toxicity (intraperitoneal and oral LD50) of organophosphorus pesticides on rats and the stability and the prediction performance of the selected topological models were assessed.
Abstract: Topological indices were used in the prediction of the acute toxicity (intraperitoneal and oral LD50) of organophosphorus pesticides on rats. Models with six variables for the prediction of LD50-i.p. (r = 0.849, Q 2 = 0.613) and eight variables for LD50-oral (r = 0.906, Q 2 = 0.701) were selected. External group and cross-validation by use of leave-n-out tests were also performed in order to assess the stability and the prediction performance of the selected topological models.
TL;DR: 3D-QSAR gave a model with high predictive ability and the regression maps indicated the important toxic chemical substituents and the important role of CYP1A2 in allelochemical-like compounds toxicity was confirmed.
Abstract: 3D-QSAR, Docking, Local Binding Energy (LBE) and GRID methods were integrated as a tool for predicting toxicity and studying mechanisms of action. The method was tested on a set of 73 allelochemical-like pesticides, for which acute toxicity (LD50) for the rat was available. 3D-QSAR gave a model with high predictive ability and the regression maps indicated the important toxic chemical substituents. Significant ligand-protein residue interactions and oxidation positions in the binding site were found by docking analysis using CYP1A2 homology modelling. The binding energies of the compounds and the important substituents (Local Binding Energy, LBE) were calculated in order to demonstrate quantitatively the substituent contributions in the metabolism and toxicity. The GRID examination identified the CYP1A2 binding pocket feature. Finally, a 3D-QSAR map was compared to the GRID map, showing good overlaps and confirming the important role of CYP1A2 in allelochemical-like compounds toxicity.
TL;DR: Comparison of the five chemometrical methods of modelling led to the final conclusion, that the use of relatively simple PLS combined with one of the variable pre-selection algorithms (UVE or GA) seems to be the optimal choice in such computational studies for persistent organic pollutants.
Abstract: Molecular descriptors from calculations at the level of Density Functional Theory (B3LYP/6-311++G**) were effectively applied in QSPR estimation of supercooled liquid vapour pressures (P(L)) for individual chloronaphthalene congeners. The estimated values of log P(L) varied from 1.05 Pa to 5.6 x 10(-5) Pa, depending on the number of chlorine substituents present in the molecule and the substitution pattern. Comparison of the five chemometrical methods of modelling (approaches) led to the final conclusion, that the use of relatively simple PLS combined with one of the variable pre-selection algorithms (UVE or GA) seems to be the optimal choice in such computational studies for persistent organic pollutants. The best GA-PLS model was characterized by the value of root mean square error of prediction RMSEP = 0.108 logarithmic Pascal units.
TL;DR: A hierarchical overlay approach was developed that gave valuable information on the quantum chemical level (basis set) at which optimization must be carried out to get the correct order of repellency of the diastereoisomers of Picaridin and 220.
Abstract: 2-(2-Hydroxyethyl)-1-piperidinecarboxylic acid 1-methylpropyl ester (Picaridin), and 1-(cyclohex-3-ene-1-ylcarbonyl)-2-methylpiperidine (AI3-37220; 220) are alternatives to DEET (N,N-diethyl-3-methylbenzamide), the most popular mosquito repellent. Picaridin and AI3-37220 exhibit polychiral diastereoisomerism and each has four diastereoisomers due to the presence of two asymmetric centers in their molecules. The diastereoisomers of these compounds have differing degrees of mosquito-repellent activity according to quantitative behavioral assays conducted at the United States Department of Agriculture. An insight into the stereochemical requirements for repellency is of great importance in the development of better repellents. Molecular overlay of the optimized geometries of the diastereoisomers was considered as a novel tool for Stereochemical Structure-Activity Relationship (SSAR) modeling. An earlier study using molecular mechanics (MM2) optimized geometries showed good promise. In continuation of this effort and to overcome certain defects in using MM2 geometries, a hierarchical overlay approach was developed. In this method geometry of the low energy conformer of each diastereoisomer was optimized using: the following quantum chemical methods in a graduated manner: (a) semiempirical AM1, (b) Hartree Fock (STO3G, 3-21G, 6-31G, and 6-311G), and (c) Density Functional Theory (B3LYP/6-31G, B3LYP/6-311G). The optimized geometries of different diastereoisomers were overlaid in various user defined combinations to calculate the root mean square distances (RMSD) of the overlaid structures. The RMSD with respect to the most active diastereoisomer (220SS) were found to have a strong relationship with biological potency. Common motifs in shapes and molecular surfaces that are probably critical for effective repellent activity were identified. The hierarchical approach gave valuable information on the quantum chemical level (basis set) at which optimization must be carried out to get the correct order of repellency of the diastereoisomers of Picaridin and 220.