Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Model-predicted occurrence of multiple pharmaceuticals in Swedish surface waters and their flushing to the Baltic Sea

An exposure assessment for multiple pharmaceuticals in Swedish surface waters was made using the STREAM-EU model. Results indicate that Metformin (27 ton/y), Paracetamol (6.9 ton/y) and Ibuprofen (2.33 ton/y) were the drugs with higher amounts reaching the Baltic Sea in 2011. 35 of the studied substances had more than 1 kg/y of predicted flush to the sea. Exposure potential given by the ratio amount of the drug exported to the sea/amount emitted to the environment was higher than 50% for 7 drugs (Piperacillin, Lorazepam, Metformin, Hydroxycarbamide, Hydrochlorothiazide, Furosemide and Cetirizine), implying that a high proportion of them will reach the sea, and below 10% for 27 drugs, implying high catchment attenuation. Exposure potentials were found to be dependent of persistency and hydrophobicity of the drugs. Chemicals with Log D > 2 had exposure potentials <10% regardless of their persistence. Chemicals with Log D  <  -2 had exposure potentials >35% with higher ratios typically achieved for longer half-lives. For Stockholm urban area, 17 of the 54 pharmaceuticals studied had calculated concentrations higher than 10 ng/L. Model agreement with monitored values had an r² = 0.62 for predicted concentrations and an r² = 0.95 for predicted disposed amounts to sea.

Evaluation of human pharmaceutical emissions and concentrations in Swedish river basins

An emissions inventory for top consumed human pharmaceuticals in Sweden was done based on national consumption data, human metabolic rates and wastewater treatment removal rates. Concentrations of pharmaceuticals in surface waters in Swedish river basins were predicted using estimated emissions from the inventory and river discharges. Our findings indicate that the top ten emitted pharmaceuticals in our study set of 54 substances are all emitted in amounts above 0.5ton/y to both surface waters and soils. The highest emissions to water were in decreasing order for Metformin, Furosemide, Gabapentin, Atenolol and Tramadol. Predicted emissions to soils calculated with the knowledge that in Sweden sludge is mostly disposed to soil, point to the highest emissions among the studied drugs coming from, in decreasing order, Metformin, Paracetamol, Ibuprofen, Gabapentin and Atenolol. Surface water concentrations in Sweden's largest rivers, all located in low density population zones, were found to be below 10ng/L for all substances studied. In contrast, concentrations in surface waters in Stockholm's metropolitan area, the most populous in Sweden, surpassed 100ng/L for four substances: Atenolol, Metformin, Furosemide and Gabapentin.

A Topological Approach to the Calculation of the n-Electron Energy and Energy Gap of Infinite Conjugated Polymers a

A procedure was developed, on the basis of the distance matrix of the graph, for the calculation of the specific π-electron energy and energy gap in polymerhomologous series and infinite polymers. 25 conjugated polymers were examined by this procedure and a fairly good correlation between these energy characteristics and the sum of all distances in the graph (the Wiener index) was found. A zero energy gap was shown to occur in 12 polymers. For polymers composed of condensed benzenoid and non-benzenoid cycles, a linear correlation was found between the Wiener index normalized to infinite polymer chains and the specific π-electron energy thus revealing possibilities for predictions proceeding from monomer topology only.

TIMES-SS--recent refinements resulting from an industrial skin sensitisation consortium

The TImes MEtabolism Simulator platform for predicting Skin Sensitisation (TIMES-SS) is a hybrid expert system, first developed at Bourgas University using funding and data from a consortium of industry and regulators. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. The model estimates semi-quantitative skin sensitisation potency classes and has been developed with the aim of minimising animal testing, and also to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. In 2007 an external validation exercise was undertaken to fully address these principles. In 2010, a new industry consortium was established to coordinate research efforts in three specific areas: refinement of abiotic reactions in the skin (namely autoxidation) in the skin, refinement of the manner in which chemical reactivity was captured in terms of structure-toxicity rules (inclusion of alert reliability parameters) and defining the domain based on the underlying experimental data (study of discrepancies between local lymph node assay Local Lymph Node Assay (LLNA) and Guinea Pig Maximisation Test (GPMT)). The present paper summarises the progress of these activities and explains how the insights derived have been translated into refinements, resulting in increased confidence and transparency in the robustness of the TIMES-SS predictions.

Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories

Repeated dose toxicity (RDT) is one of the most important hazard endpoints in the risk assessment of chemicals. However, due to the complexity of the endpoints associated with whole body assessment, it is difficult to build up a mechanistically transparent structure-activity model. The category approach, based on mechanism information, is considered to be an effective approach for data gap filling for RDT by read-across. Therefore, a library of toxicological categories was developed using experimental RDT data for 500 chemicals and mechanistic knowledge of the effects of these chemicals on different organs. As a result, 33 categories were defined for 14 types of toxicity, such as hepatotoxicity, hemolytic anemia, etc. This category library was then incorporated in the Hazard Evaluation Support System (HESS) integrated computational platform to provide mechanistically reasonable predictions of RDT values for untested chemicals. This article describes the establishment of a category library and the associated HESS functions used to facilitate the mechanistically reasonable grouping of chemicals and their subsequent read-across.

Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.

Simulation of chemical metabolism for fate and hazard assessment. IV. Computer-based derivation of metabolic simulators from documented metabolism maps

Computer simulation of xenobiotic metabolism and degradation is usually performed proceeding from a set of expert-developed rules modelling the actual enzyme-driven chemical reactions. With the accumulation of extensive metabolic pathway data, the analysis required to derive such chemical reaction patterns has become more objective, but also more convoluted and demanding. Herein we report on our computer-based approach for the analysis of metabolic maps, leading to the construction of reaction rules statistically suitable for simulation purposes. It is based on the set of so-called bare transformations which encompass all unique reaction patterns as obtained by a heuristically enhanced maximum common subgraph algorithm. The bare transformations guarantee that no existing metabolite is missed in simulation at the expense of an enormous amount of false positive predictions. They are rendered more selective by correlating the generated true and false positives to the locations of typical chemical functional groups in the potential reactants. The approach and its results are illustrated for a metabolic map collection of 15 cycloalkanes.

Simulation of chemical metabolism for fate and hazard assessment. III. New developments of the bioconcentration factor base-line model

The new development of the bioconcentration factor (BCF) base-line model of Dimitrov et al. [SAR QSAR Environ. Res. 6 (2005), pp. 531-554] is presented. The model applicability domain was expanded by enlarging the training set of the model up to 705 chemicals. The list of chemical-dependent mitigating factors was expanded by including water solubility of chemicals. The original empirical term for estimating ionization of chemicals was mechanistically analysed using two different approaches. In the first one, the ionization potential of chemicals was estimated based on the acid dissociation constant (pK(a) ). This term was found to be less adequate for inclusion in the ultimate BCF model, due to overestimating ionization of chemicals. The second approach, estimating the ionization as a ratio between distribution and partition coefficients (log P and log D), was found to be more successful. The new ionization term allows modelling of chemicals with both acidic and basic functionalities and chemicals undergoing different degrees of ionization. The significance of the different mitigating factors which can reduce the maximum bioconcentration potential of the chemicals was re-formulated and model parameters re-evaluated.

Simulation of chemical metabolism for fate and hazard assessment. II CATALOGIC simulation of abiotic and microbial degradation

The unprecedented pollution of the environment by xenobiotic compounds has provoked the need to understand the biodegradation potential of chemicals. Mechanistic understanding of microbial degradation is a premise for adequate modelling of the environmental fate of chemicals. The aim of the present paper is to describe abiotic and biotic models implemented in CATALOGIC software. A brief overview of the specificities of abiotic and microbial degradation is provided followed by detailed descriptions of models built in our laboratory during the last decade. These are principally new models based on unique mathematical formalism already described in the first paper of this series, which accounts more adequately than currently available approaches the multipathway metabolic logic in prokaryotes. Based on simulated pathways of degradation, the models are able to predict quantities of transformation products, biological oxygen demand (BOD), carbon dioxide (CO(2)) production, and primary and ultimate half-lives. Interpretation of the applicability domain of models is also discussed.

Simulation of chemical metabolism for fate and hazard assessment. I: approach for simulating metabolism

Information regarding the metabolism of xenobiotic chemicals plays a central role in regulatory risk assessments. In regulatory programmes where metabolism studies are required, the studies of metabolic pathways are often incomplete and the identification of activated metabolites and important degradation products are limited by analytical methods. Because so many more new chemicals are being produced than can be assessed for potential hazards, setting assessment priorities among the thousands of untested chemicals requires methods for predictive hazard identification which can be derived directly from chemical structure and their likely metabolites. In a series of papers we are sharing our experience in the computerized management of metabolic data and the development of simulators of metabolism for predicting the environmental fate and (eco)toxicity of chemicals. The first paper of the series presents a knowledge-based formalism for the computer simulation of non-intermediary metabolism for untested chemicals, with an emphasis on qualitative and quantitative aspects of modelling metabolism.

Androgen receptor binding affinity: a QSAR evaluation

The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used to describe the structural requirements for eliciting rat androgen receptor (AR) binding affinity, accounting for molecular flexibility. Chemical affinity for AR binding was related to the distances between nucleophilic sites and structural features describing electronic and hydrophobic interactions between the receptor and ligands. Categorical models were derived for each binding affinity range in terms of specific distances, local (maximal donor delocalizability associated with the oxygen atom of the A ring), global nucleophilicity (partial positive surface areas and energy of the highest occupied molecular orbital) and hydrophobicity (log Kow) of the molecules. An integral screening tool for predicting binding affinity to AR was constructed as a battery of models, each associated with different activity bins. The quality of the screening battery of models was assessed using a high value (0.9) of the Pearson contingency coefficient. The predictability of the model was assessed by testing the model performance on external validation sets. A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain. Some of the selected chemicals were tested for AR transcriptional activation. The experimental results confirmed the theoretical predictions.

An Introduction to Read-Across for the Prediction of the Effects of Chemicals

The process of the assessment of a property of one chemical based on the same property for one, or a few, different chemicals that are considered to be similar in some context is known as read across or the analogue approach. There is no practical limitation on the type of properties that could be the subject of read across including: physico-chemical properties; environmental fate; toxicological; and ecotoxicological effects. From a formal methodological point of view, read across is not usually recommended for predicting physicochemical properties that may be used as explanatory variables for predicting more complex environmental or (eco)toxicological effects. On the other hand, it should be noted, that grouping of chemicals to estimate the properties of untested chemicals has been routine practice in chemical engineering and physical chemistry for several decades.

Can mutagenicity information be useful in an Integrated Testing Strategy (ITS) for skin sensitization?

Our previous work has investigated the utility of mutagenicity data in the development and application of Integrated Testing Strategies (ITS) for skin sensitization by focusing on the chemical mechanisms at play and substantiating these with experimental data where available. The hybrid expert system TIMES (Tissue Metabolism Simulator) was applied in the identification of the chemical mechanisms since it encodes a comprehensive set of established structure-activity relationships for both skin sensitization and mutagenicity. Based on the evaluation, the experimental determination of mutagenicity was thought to be potentially helpful in the evaluation of skin sensitization potential. This study has evaluated the dataset reported by Wolfreys and Basketter (Cutan. Ocul. Toxicol. 23 (2004), pp. 197-205). Upon an update of the experimental data, the original reported concordance of 68% was found to increase to 88%. There were several compounds that were 'outliers' in the two experimental evaluations which are discussed from a mechanistic basis. The discrepancies were found to be mainly associated with the differences between skin and liver metabolism. Mutagenicity information can play a significant role in evaluating sensitization potential as part of an ITS though careful attention needs to be made to ensure that any information is interpreted in the appropriate context.

Development of a biodegradation model for the prediction of metabolites in soil

The awareness of air, soil and water pollution has driven the search for better methods for the assessment of the environmental fate of industrial chemicals. This paper is focused on the simulation of formation and transformation of metabolites in soil. The key challenges in the development of a simulator for predicting metabolic fate of chemicals in soil are the complexity of the soil compartment and incompleteness of metabolic information. Based on the collected data for metabolic fate of 183 chemicals a set of soil specific transformations were defined and used to develop a simulator for metabolism in soil. The analysis of outliers showed that the low predictability for some chemicals is due to: 1) incomplete documented metabolic pathways with missing intermediates and/or 2) reactions of condensation that are not simulated in the current version of the model. Hence, further improvement of the model requires expanding the metabolism database and further refinement of the logic of metabolic transformations used in the simulator.

Elimination kinetic model for organic chemicals in earthworms

Mechanistic understanding of bioaccumulation in different organisms and environments should take into account the influence of organism and chemical depending factors on the uptake and elimination kinetics of chemicals. Lipophilicity, metabolism, sorption (bioavailability) and biodegradation of chemicals are among the important factors that may significantly affect the bioaccumulation process in soil organisms. This study attempts to model elimination kinetics of organic chemicals in earthworms by accounting for the effects of both chemical and biological properties, including metabolism. The modeling approach that has been developed is based on the concept for simulating metabolism used in the BCF base-line model developed for predicting bioaccumulation in fish. Metabolism was explicitly accounted for by making use of the TIMES engine for simulation of metabolism and a set of principal transformations. Kinetic characteristics of transformations were estimated on the basis of observed kinetics data for the elimination of organic chemicals from earthworms.

Mechanism-based common reactivity pattern (COREPA) modelling of aryl hydrocarbon receptor binding affinity

The aryl hydrocarbon receptor is a ligand-activated transcription factor responsive to both natural and synthetic environmental compounds, with the most potent agonist being 2,3,7,8-tetrachlotrodibenzo-p-dioxin. The aim of this work was to develop a categorical COmmon REactivity PAttern (COREPA)-based structure-activity relationship model for predicting aryl hydrocarbon receptor ligands within different binding ranges. The COREPA analysis suggested two different binding mechanisms called dioxin- and biphenyl-like, respectively. The dioxin-like model predicts a mechanism that requires a favourable interaction with a receptor nucleophilic site in the central part of the ligand and with electrophilic sites at both sides of the principal molecular axis, whereas the biphenyl-like model predicted a stacking-type interaction with the aryl hydrocarbon receptor allowing electron charge transfer from the receptor to the ligand. The current model was also adjusted to predict agonistic/antagonistic properties of chemicals. The mechanism of antagonistic properties was related to the possibility that these chemicals have a localized negative charge at the molecule's axis and ultimately bind with the receptor surface through the electron-donating properties of electron-rich groups. The categorization of chemicals as agonists/antagonists was found to correlate with their gene expression. The highest increase in gene expression was elicited by strong agonists, followed by weak agonists producing lower increases in gene expression, whereas all antagonists (and non-aryl hydrocarbon receptor binders) were found to have no effect on gene expression. However, this relationship was found to be quantitative for the chemicals populating the areas with extreme gene expression values only, leaving a wide fuzzy area where the quantitative relationship was unclear. The total concordance of the derived aryl hydrocarbon receptor binding categorical structure-activity relationship model was 82% whereas the Pearson's coefficient was 0.88.

The use of metabolising systems for in vitro testing of endocrine disruptors

Legislation and prospective legislative proposals in for instance the USA, Europe, and Japan require, or may require that chemicals are tested for their ability to disrupt the hormonal systems of mammals. Chemicals found to test positive are considered to be endocrine active substances (EAS) and may be putative endocrine disruptors (EDs). To date, there is still little or no experience with incorporating metabolic and toxicokinetic aspects into in vitro tests for EAS. This is a situation in sharp contrast to genotoxicity testing, where in vitro tests are routinely conducted with and without metabolic capacity. Originally prepared for the Organisation of Economic Cooperation and Development (OECD), this detailed review paper reviews why in vitro assays for EAS should incorporate mammalian systems of metabolism and metabolic enzyme systems, and indicates how this could be done. The background to ED testing, the available test methods, and the role of mammalian metabolism in the activation and the inactivation of both endogenous and exogenous steroids are described. The available types of systems are compared, and the potential problems in incorporating systems in in vitro tests for EAS, and how these might be overcome, are discussed. Lastly, some recommendations for future activities are made.

Strategic selection of chemicals for testing. Part I. Functionalities and performance of basic selection methods

To develop quantitative structure-activity relationships (QSAR) models capable of predicting adverse effects for large chemical inventories and diverse structures, an interactive approach is presented that includes testing of strategically selected chemicals to expand the scope of a preliminary model to cover a target inventory. The goal of chemical selection in this context is to make the testing more effective in terms of adding maximal new structural information to the predictive model with minimal testing. The aim of this paper is to describe a set of algorithmic solutions and modelling techniques that can be used to efficiently select chemicals for testing to achieve a variety of goals. One purpose of chemical selection is to refine the model thus extending our knowledge about the modelled subject. Alternatively, the selection of chemicals for testing could be targeted at achieving a more adequate structural representation of a specific universe of untested chemicals to extend the model applicability domain on each subsequent step of model development. The chemical selection tools are collectively provided in a software package referred to as ChemPick. The system also allows the visualisation of chemical inventories and training sets in multidimensional (two and three dimensions) descriptor space. The software environment allows one or more datasets to be simultaneously loaded in a three-dimensional (or N-dimensional) chart where each point represents a combination of values for the descriptors for a given conformation of a chemical. The application of the chemical selection tools to select chemicals to expand a preliminary model of human oestrogen receptor (hER) ligand binding to more adequately cover a diverse chemical inventory is presented to demonstrate the application of these tools.

A kinetic model for predicting biodegradation

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.

QSAR and mechanistic interpretation of estrogen receptor binding

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.

Identification of the structural requirements for mutagencitiy, by incorporating molecular flexibility and metabolic activation of chemicals. II. General Ames mutagenicity model

The tissue metabolic simulator (TIMES) modeling approach is a hybrid expert system that couples a metabolic simulator together with structure toxicity rules, underpinned by structural alerts, to predict interaction of chemicals or their metabolites with target macromolecules. Some of the structural alerts representing the reactivity pattern-causing effect could interact directly with the target whereas others necessitated a combination with two- or three-dimensional quantitative structure-activity relationship models describing the firing of the alerts from the rest of the molecules. Recently, TIMES has been used to model bacterial mutagenicity [Mekenyan, O., Dimitrov, S., Serafimova, R., Thompson, E., Kotov, S., Dimitrova, N., and Walker, J. (2004) Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model. Chem. Res. Toxicol. 17 (6), 753-766]. The original model was derived for a single tester strain, Salmonella typhimurium (TA100), using the Ames test by the National Toxicology Program (NTP). The model correctly identified 82% of the primary acting mutagens, 94% of the nonmutagens, and 77% of the metabolically activated chemicals in a training set. The identified high correlation between activities across different strains changed the initial strategic direction to look at the other strains in the next modeling developments. In this respect, the focus of the present work was to build a general mutagenicity model predicting mutagenicity with respect to any of the Ames tester strains. The use of all reactivity alerts in the model was justified by their interaction mechanisms with DNA, found in the literature. The alerts identified for the current model were analyzed by comparison with other established alerts derived from human experts. In the new model, the original NTP training set with 1341 structures was expanded by 1626 proprietary chemicals provided by BASF AG. Eventually, the training set consisted of 435 chemicals, which are mutagenic as parents, 397 chemicals that are mutagenic after S9 metabolic activation, and 2012 nonmutagenic chemicals. The general mutagenicity model was found to have 82% sensitivity, 89% specificity, and 88% concordance for training set chemicals. The model applicability domain was introduced accounting for similarity (structural, mechanistic, etc.) between predicted chemicals and training set chemicals for which the model performs correctly.

Metabolic activation of chemicals: in-silico simulation

The role of metabolism in prioritising chemicals according to their potential adverse health effects is extremely important given the fact that innocuous parents can be transformed into toxic metabolites. Our recent efforts in simulating metabolic activation of chemicals are reviewed in this work. The application of metabolic simulators to predict biodegradation (microbial degradation pathways), bioaccumulation (fish liver metabolism), skin sensitisation (skin metabolism), mutagenicity (rat liver S-9 metabolism) are discussed. The ability of OASIS approach to predict metabolism (toxicokinetics) and toxicity (toxicodynamics) of chemicals resulting from their metabolic activation in a single modelling platform is an important advantage of the method. It allows prioritisation of chemicals due to predicted toxicity of their metabolites.

Base-line model for identifying the bioaccumulation potential of chemicals

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%.

Predicting the biodegradation products of perfluorinated chemicals using CATABOL

Perfluorinated chemicals (PFCs) form a special category of organofluorine compounds with particularly useful and unique properties. Their large use over the past decades increased the interest in the study of their environmental fate. Fluorocarbons may have direct or indirect environmental impact through the products of their decomposition in the environment. It is a common knowledge that biodegradation is restricted within non-perfluorinated part of molecules: however, a number of studies showed that defluorination can readily occur during biotransformation. To evaluate the fate of PFCs in the environment a set of principal transformations was developed and implemented in the simulator of microbial degradation using the catabolite software engine (CATABOL). The simulator was used to generate metabolic pathways for 171 perfluorinated substances on Canada's domestic substances list. It was found that although the extent of biodegradation of parent compounds could reach 60%, persistent metabolites could be formed in significant quantities. During the microbial degradation a trend was observed where PFCs are transformed to more bioaccumulative and more toxic products. Perfluorooctanoic acid and perfluorooctanesulfonate were predicted to be the persistent biodegradation products of 17 and 27% of the perfluorinated sulphonic acid and carboxylic acid containing compounds, respectively.

In silico modelling of hazard endpoints: current problems and perspectives

Major scientific hurdles in the acceptance of quantitative structure-activity relationships (QSAR) for regulatory purposes have been identified. First, when quantifying important features of chemical structure complexities of molecular structure have often been ignored. More mechanistic modelling of chemical structure should proceed on two fronts: by developing a more in-depth understanding and representation of the multiple states possible for a single chemical by achieving greater rigor in understanding of conformational flexibility of chemicals; and, by considering families of activated metabolites that are derived in biological systems from an initial chemical substrate. Second, QSAR research is severely limited by the lack of systematic databases for important risk assessment endpoints, and despite many decades of research, the ability to cluster reactive chemicals by common toxicity pathways is in its infancy. Finally, computational tools are lacking for defining where a specific QSAR is applicable within the domain (universe) of chemical structures that are to be regulated. This paper describes some of the approaches being taken to address these needs. Applications of some of these new approaches are demonstrated for the prediction of chemical mutagenicity, where considerations of both molecular flexibility and metabolic activation improved the QSAR predictability and interpretations. Lastly, the applicability domain for a specific QSAR predicting estrogen receptor binding is presented in the context of a mechanistically-defined chemical structure space for large heterogeneous chemical datasets of regulatory concern. A strategic approach is discussed to selecting chemicals for model improvement and validation until regulatory acceptance criteria for risk assessment applications are met.

Quantitative prediction of biodegradability, metabolite distribution and toxicity of stable metabolites

An evaluation of the capability of organic chemicals to mineralize is an important factor to consider when assessing their fate in the environment. Microbial degradation can convert a toxic chemical into an innocuous one, and vice versa, or alter the toxicity of a chemical. Moreover, primary biodegradation can convert chemicals into stable products that can be difficult to mineralize. In this paper, we present some new results obtained on the basis of a recently developed probabilistic approach to modeling biodegradation based on microbial transformation pathways. The metabolic transformations and their hierarchy were calibrated by making use of the ready biodegradability data from the MITI-I test and expert knowledge for the most probable transformation pathways. A model was developed and integrated into an expert software system named CATABOL that is able to predict the probability of biodegradation of organic chemicals directly from their structure. CATABOL simulates the effects of microbial enzyme systems, generates the most plausible transformation pathways, and quantitatively predicts the persistence and toxicity of the biodegradation products. A subset of 300 organic chemicals were selected from Canada's Domestic Substances List and subjected to CATABOL to compare predicted properties of the parent chemicals with their respective first stable metabolite. The results show that most of the stable metabolites have a lower acute toxicity to fish and a lower bioaccumulation potential compared to the parent chemicals. In contrast, the metabolites appear to be generally more estrogenic than the parent chemicals.

Probabilistic assessment of biodegradability based on metabolic pathways: catabol system

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs (r2 = 0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q2 = 0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.

Androgen receptor binding affinity of pesticide "active" formulation ingredients. QSAR evaluation by COREPA method

The COREPA approach for identifying the COmmon REactivity PAttern of biologically similar chemicals was employed to upgrade the recently derived affinity pattern for high androgen receptor (AR) binding affinity. The training set consisted of 28 steroidal and nonsteroidal ligands whose AR binding affinity was determined in competitive binding assays (in terms of pKi). The interatomic distances between nucleophilic sites and their charges providing distinct and non-overlapping integral patterns for active and inactive chemicals were assumed that it was related with the endpoint, which was under study. These stereoelectronic characteristics were used to predict pKi values of pesticide "active" formulation ingredients in an attempt to identify chemicals with potential AR binding affinity.

A computationally based identification algorithm for estrogen receptor ligands: part 1. Predicting hERalpha binding affinity

The common reactivity pattern (COREPA) approach is a 3-dimensional, quantitative structure activity relationship (3-D QSAR) technique that permits identification and quantification of specific global and local stereoelectronic characteristics associated with a chemical's biological activity. It goes beyond conventional 3-D QSAR approaches by incorporating dynamic chemical conformational flexibility in ligand-receptor interactions. The approach provides flexibility in screening chemical data sets in that it helps establish criteria for identifying false positives and false negatives, and is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. The algorithm was recently used to screen chemical data sets for rat androgen receptor binding affinity. To further explore the potential application of the algorithm in establishing reactivity patterns for human estrogen receptor alpha (hERalpha) binding affinity, the stereoelectronic requirements associated with the binding affinity of 45 steroidal and nonsteroidal ligands to the receptor were defined. Reactivity patterns for relative hERalpha binding affinity (RBA; 17ss-estradiol = 100%) were established based on global nucleophilicity, interatomic distances between electronegative heteroatoms, and electron donor capability of heteroatoms. These reactivity patterns were used to establish descriptor profiles for identifying and ranking compounds with RBA of > 150%, 100-10%, 10-1%, and 1-0.1%. Increasing specificity of reactivity patterns was detected for ligand data sets with RBAs above 10%. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative ER binding affinity potential for large chemical data sets.

Molecular orbital parameters as predictors of skin sensitization potential of halo- and pseudohalobenzenes acting as SNAr electrophiles

The electrophilic reactivity of a training set of 20 halo- and pseudohalobenzenes, 10 of which are reported skin sensitizers and 10 of which are reported nonsensitizers, has been modeled by MO-calculated indices using the AM1 and PM3 Hamiltonians. The electronic structures of parent molecules and the corresponding Meisenheimer intermediates (sigma-complexes) were evaluated. The NH2 group and the H atom were both studied as model nucleophile-derived substituents in the sigma-complexes. The LUMO energy differences between the parent compounds and their Meisenheimer complexes together with the maximum acceptor superdelocalizabilities determined over the aromatic reaction sites were found to discriminate correctly the sensitizing/reactive from nonsensitizing/unreactive compounds of the training set of 20 compounds. The predictive applicability of these MO indices was confirmed with a test set of seven further compounds for which sensitization data are reported in the literature. A statistically based discriminant analysis provides a model which predicts whether or not an SNAr electrophile will be a sensitizer and estimates the degree of confidence in the prediction.

Computer design and syntheses of antiulcer compounds. 2nd Communication: N-substituted N'-[3-[3-(1-piperidinomethyl)phenoxy]propyl]ureas

The in vitro and in vivo antiulcer effect of a series of N-substituted N'3-[3-(1-piperidinomethyl)phenoxy]propyl]ureas was modeled by making use of the OASIS computer system for QSAR analysis. Various research schemes were employed depending on structural representation of chemicals under investigation, such as non-protonated (neutral), protonated at the piperidine and urea fragmental nitrogens, and with intramolecular hydrogen binding. According to the modeling results, it is likely a variety of structural forms of antagonist molecules to take part in the receptor interaction. The QSAR study showed that the larger the electron acceptor properties of the nitrogen and oxygen atoms of the urea fragment, the higher is in vitro and in vivo activity of the antagonists.

Computer design and syntheses of antiulcer compounds. 1st communication: N-[3-[3-(1-piperidinomethyl)phenoxy]propyl]amines and benzamides

Aiming to develop new antiulcer agents, a quantitative structure-activity relationship (QSAR) study on in vitro (pA2) and in vivo histamine H2-receptor antagonistic activity of a series of N-[3-[3-(1-piperidinomethyl)phenoxy]propyl]amines was carried out using the OASIS computer system. The results showed that pA2 increases with the decrease (increase) of electron donor (acceptor) properties of molecules, particularly at the NH-reaction site. The finding is consistent with the assumption for an increase of histamine H2-receptor activity of the antagonists with their ability to form H-bonds with the receptor through NH groups. The correlations with hydrophobicity and related topological indices are consistent with the hypothesis that logP should indirectly reflect receptor interactions. In addition a series of N-[3-[3-(1-piperidinomethyl)phenoxy]propyl]benzamides are synthesized. The theoretically predicted in vitro activities of these compounds were found to be in accordance with in vivo tests (percent of inhibition of gastric juice and acid output [mEq/H+/3 h]).

Application of the dynamic quantitative structure-activity relationship method for modeling antibacterial activity of quinolone derivatives

The dynamic approach to quantitative structure-activity relationship (QSAR) was recently introduced to mimic the multiplicity of 3D-molecular shapes taken from the chemical at the different stages of the processes conditioning the endpoint under investigation. In difference with the conventional QSAR methods, where the structure of each compound is described by a single conformation (usually the one with the lowest calculated energy), the dynamic QSPR is aiming to account for the effects of the different solvent environments at the various reaction steps under which different conformations should be active. The core of the new methodology is the 3DGEN algorithm for an exhaustive 3D molecular design and the related system for an interactive conformation screening, based on the: chemical expertise, stereoelectronic parameter ranges and parameter distributions, depending on hypothesis on interaction mechanism The new methodology is incorporated in the OASIS (optimized approach based on structural indices set) computer system for QSAR/QSPR (quantitative structure activity/property relationship). In the present work it was applied to model in vitro (inhibition of Escherichia coli DNA gyrase) and in vivo (MICs against gram-negative as well as gram positive bacteria) antimicrobial activity (AMA) of quinolone derivatives. It was found that AMA is conditioned by molecular geometry as described by pair of topological indices and electron-acceptor properties, as assessed by the energies of LUMO (Lowest Unoccupied Molecular Orbital) orbitals, charges, bond orders and polarizability of the specific molecular sites. Interaction hypothesis is created, according to which polar-polar intermolecular interactions and bond breaking (cycle "opening", analogous to that of beta-lactam moiety in cephalosporins) condition biological activity. The derived QSAR models are significant according to the conventional statistical criteria as well as to the structure-activity causality requirements stated in literature. The best QSARs are obtained for in vitro AMA (r2 = 0.93 and s2 = 0.003), whereas for in-vivo activity correlations found are with lower statistics (0.54 < r2 < 0.74 and 0.005 < s2 < 0.03). The results are statistically better than those obtained by Computer automated Structure Evaluation (CASE) method.

Bronchospasmolytic activity and toxicity modelling of theophylline derivatives by a microcomputer based method

The OASIS (Optimized Approach based on Structural Index Sets) microcomputer system was applied to model the bronchospasmolytic activity and toxicity of theophylline derivatives. The geometric and electronic factors responsible for biological activity of these compounds were determined. The molecular topology rather than compound metrics is the factor conditioning the theophylline activity. The opposite influence of topology on bronchospasmolytic activity and toxicity was established. Although the acceptor properties (acceptor superdelocalizability indices) determine both the activity and toxicity of the studied compounds the different positions of these effects is of decisive importance in both cases.

Modelling the interaction of small organic molecules with biomacromolecules. II. A generalized concept for biological interactions

In the first part of this series it was shown that, for interactions between substituted pyridines and anti-3-azopyridine antibody, the maximum biological activity is observed for an optimum electronic correspondence between the reactants. This particular result, together with data in the literature which points to the necessity for geometrical and lipophilic correspondence, supports a generalization for the nature of the biological action of chemical compounds. Accordingly in this paper it is proposed that the affinity towards a given biomacromolecule will be maximum only for those chemicals within a series of compounds which are characterized by optimum values of basic factors which condition the biological activity: geometric, electronic, and/or lipophilic. The practical aspects of the hypothesis should be valuable in molecular pharmacology, drug design, and theory of chemical reactivity.

Modelling the interaction of small organic molecules with biomacromolecules. I. Interaction of substituted pyridines with anti-3-azopyridine antibody

An approach is presented for modelling the biological activity of organic molecules. This approach requires a consideration of the influence of all factors (topological, steric, hydrophobic, electronic) which determine the bioactivity. In this work, the interaction between substituted pyridines and antibodies generated by anti-3-azapyridine is studied. The stereoelectronic interactions are responsible for the reaction. Meta-positions to nitrogen are found to be the most probable positions for attack. The most likely reaction products are pi-complexes with charges transfer from the biomolecule to the pyridine derivatives followed by the formation of covalent-type bonds.