Ovanes Mekenyan

A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

The SOLUTIONS project: Challenges and responses for present and future emerging pollutants in land and water resources management

SOLUTIONS (2013 to 2018) is a European Union Seventh Framework Programme Project (EU-FP7). The project aims to deliver a conceptual framework to support the evidence-based development of environmental policies with regard to water quality. SOLUTIONS will develop the tools for the identification, prioritisation and assessment of those water contaminants that may pose a risk to ecosystems and human health. To this end, a new generation of chemical and effect-based monitoring tools is developed and integrated with a full set of exposure, effect and risk assessment models. SOLUTIONS attempts to address legacy, present and future contamination by integrating monitoring and modelling based approaches with scenarios on future developments in society, economy and technology and thus in contamination. The project follows a solutions-oriented approach by addressing major problems of water and chemicals management and by assessing abatement options. SOLUTIONS takes advantage of the access to the infrastructure necessary to investigate the large basins of the Danube and Rhine as well as relevant Mediterranean basins as case studies, and puts major efforts on stakeholder dialogue and support. Particularly, the EU Water Framework Directive (WFD) Common Implementation Strategy (CIS) working groups, International River Commissions, and water works associations are directly supported with consistent guidance for the early detection, identification, prioritisation, and abatement of chemicals in the water cycle. SOLUTIONS will give a specific emphasis on concepts and tools for the impact and risk assessment of complex mixtures of emerging pollutants, their metabolites and transformation products. Analytical and effect-based screening tools will be applied together with ecological assessment tools for the identification of toxicants and their impacts. The SOLUTIONS approach is expected to provide transparent and evidence-based candidates or River Basin Specific Pollutants in the case study basins and to assist future review of priority pollutants under the WFD as well as potential abatement options.

Predicting bioconcentration factors of highly hydrophobic chemicals. Effects of molecular size

The bioconcentration factor (BCF) is a parameter that describes the ability of chemicals to concentrate in aquatic organisms. Traditionally, it is modeled by the log–log quantitative structure -activity relationship (QSAR) between the BCF and the octanol- water partition coefficient (Kow). A significant scatter in the parabolic log(BCF)/log(Kow) curve has been observed for narcotics with log(Kow) greater than 5.5. This study shows that the scatter in the log(BCF)/log(Kow) relationship for highly hydrophobic chemicals can be explained by the molecular size. The significance of the maximal cross-sectional diameter on bioconcentration was compared with the traditionally accepted effective diameter. A threshold value of about 1.5 nm for this parameter has been found to discriminate chemicals with log(BCF) > 3.3 from those with log(BCF) < 3.3. This critical value for the maximum diameter is comparable with the architecture of the cell membrane. This threshold is half thickness of leaflet constituting the lipid bilayer. The existence of a size threshold governing bioconcentration is an indication of a possible switch in the uptake mechanism from passive diffusion to facilitated diffusion or active transport. The value of the transition point can be used as an additional parameter to hydrophobicity for predicting BCF variation. The effect of molecular size on bioconcentration has been studied by accounting for conformational flexibility of molecules.

A systematic approach to simulating metabolism in computational toxicology. I. The TIMES heuristic modelling framework.

Designing biologically active chemicals and managing their risks requires a holistic perspective on the chemical-biological interactions that form the basis of selective toxicity. The balance of therapeutic and adverse outcomes for new drugs and pesticides is managed by shaping the probabilities for transport, metabolism, and molecular initiating events. For chemicals activated as well as detoxified by metabolism, selective toxicity may be considered in terms of relative probabilities, which shift dramatically across species as well as within a population, depending on many factors. The complexity in toxicology that results from metabolism has been troublesome in QSAR research because the parent structure is less relevant to predicting ultimate effects and finding reference species/conditions for metabolic rates seems hopeless. Even the complexity of comparative xenobiotic metabolism itself seems paradoxical in light of the evidence of highly conserved catabolic processes across species. Clearly, predicting the role of metabolism in selective toxicity and adverse health outcomes requires a probabilistic framework for deterministic models as well as the many factors shaping the metabolic probability distributions under specific conditions. This paper presents a tissue metabolism simulator (TIMES), which uses a heuristic algorithm to generate plausible metabolic maps from a comprehensive library of biotransformations and abiotic reactions and estimates for system-specific transformation probabilities. The transformation probabilities can be calibrated to specific reference conditions using transformation rate information from systematic testing. In the absence of rate data, a combinatorial algorithm is used to translate known metabolic maps taken from reference systems into best-fit transformation probabilities. Finally, toxicity test data itself can be used to shape the transformation probabilities for toxicity pathways in which the metabolic activation is the rate-limiting process leading to a toxic effect. The conceptual approach for metabolic simulation will be presented along with practical uses in forecasting plausible activated metabolites.

Workgroup report: review of fish bioaccumulation databases used to identify persistent, bioaccumulative, toxic substances.

Chemical management programs strive to protect human health and the environment by accurately identifying persistent, bioaccumulative, toxic substances and restricting their use in commerce. The advance of these programs is challenged by the reality that few empirical data are available for the tens of thousands of commercial substances that require evaluation. Therefore, most preliminary assessments rely on model predictions and data extrapolation. In November 2005, a workshop was held for experts from governments, industry, and academia to examine the availability and quality of in vivo fish bioconcentration and bioaccumulation data, and to propose steps to improve its prediction. The workshop focused on fish data because regulatory assessments predominantly focus on the bioconcentration of substances from water into fish, as measured using in vivo tests or predicted using computer models. In this article we review of the quantity, features, and public availability of bioconcentration, bioaccumulation, and biota–sediment accumulation data. The workshop revealed that there is significant overlap in the data contained within the various fish bioaccumulation data sources reviewed, and further, that no database contained all of the available fish bioaccumulation data. We believe that a majority of the available bioaccumulation data have been used in the development and testing of quantitative structure–activity relationships and computer models currently in use. Workshop recommendations included the publication of guidance on bioconcentration study quality, the combination of data from various sources to permit better access for modelers and assessors, and the review of chemical domains of existing models to identify areas for expansion.

Skin sensitization: modeling based on skin metabolism simulation and formation of protein conjugates.

A quantitative structure-activity relationship (QSAR) system for estimating skin sensitization potency has been developed that incorporates skin metabolism and considers the potential of parent chemicals and/or their activated metabolites to react with skin proteins. A training set of diverse chemicals was compiled and their skin sensitization potency assigned to one of three classes. These three classes were, significant, weak, or nonsensitizing. Because skin sensitization potential depends upon the ability of chemicals to react with skin proteins either directly or after appropriate metabolism, a metabolic simulator was constructed to mimic the enzyme activation of chemicals in the skin. This simulator contains 203 hierarchically ordered spontaneous and enzyme controlled reactions. Phase I and phase II metabolism were simulated by using 102 and 9 principal transformations, respectively. The covalent interactions of chemicals and their metabolites with skin proteins were described by 83 reactions that fall within 39 alerting groups. The SAR/QSAR system developed was able to correctly classify about 80% of the chemicals with significant sensitizing effect and 72% of nonsensitizing chemicals. For some alerting groups, three-dimensional (3D)-QSARs were developed to describe the multiplicity of physicochemical, steric, and electronic parameters. These 3D-QSARs, so-called pattern recognition-type models, were applied each time a latent alerting group was identified in a parent chemical or its generated metabolite(s). The concept of the mutual influence amongst atoms in a molecule was used to define the structural domain of the skin sensitization model. The utility of the structural model domain and the predictability of the model were evaluated using sensitization potency data for 96 chemicals not used in the model building. The TIssue MEtabolism Simulator (TIMES) software was used to integrate a skin metabolism simulator and 3D-QSARs to evaluate the reactivity of chemicals thus predicting their likely skin sensitization potency.

Towards AOP application – Implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization

Since the OECD published the Adverse Outcome Pathway (AOP) for skin sensitization, many efforts have focused on how to integrate and interpret nonstandard information generated for key events in a manner that can be practically useful for decision making. These types of frameworks are known as Integrated Approaches to Testing and Assessment (IATA). Here we have outlined an IATA for skin sensitization which focuses on existing information including non testing approaches such as QSAR and read-across. The IATA was implemented into a pipeline tool using OASIS technology to provide a means of systematically collating and compiling relevant information which could be used in an assessment of skin sensitization potential. A test set of 100 substances with available skin sensitization information was profiled using the pipeline IATA. In silico and in chemico profiling information alone was able to correctly predict skin sensitization potential, with a preliminary accuracy of 73.85%. Information from other relevant endpoints (e.g., Ames mutagenicity) was found to improve the accuracy (to 87.6%) when coupled with a reaction chemistry mechanistic understanding. This pipeline platform could be useful in the assessment of skin sensitization potential and marks a step change in how non testing approaches can be practically applied.

Potency and characterization of estrogen‐receptor agonists in United Kingdom estuarine sediments

The activity of estrogen-receptor (ER) agonists in sediments collected from the United Kingdom (UK) estuaries was assessed using the in vitro recombinant yeast estrogen screen (YES assay). The YES assay was successfully used to determine the in vitro ER agonist potency of pore waters and solvent extracts of sediments collected from UK estuaries. Estrogen-receptor agonists were detected in 66% of the pore water samples and in 91% of the sediment solvent extracts tested. The pore waters tested had ER agonist potencies from less than 2 to 68 ng 17beta-estradiol (E2) L(-1), whereas sediment extracts had potencies from less than 0.2 to 13 microg E2 kg(-1). A toxicity identification evaluation approach using bioassay-directed fractionation was used in an attempt to identify the ER agonists in extracts of sediments collected from the Tyne and Tees estuaries (UK). Gas chromatography-mass spectrometry was used to provide lists of compounds in the fractions obtained that were evaluated for known ER agonist activity using published data and an ER quantitative structure-activity relationship model. Toxicity identification evaluation characterization failed to identify any ER agonists in pore water extracts; however, three compounds in sediment solvent extracts were identified as ER agonists. Nonylphenol, cinnarizine, and cholesta-4,6-dien-3-one were identified in the sample collected from the Tyne estuary. Important ER agonist substances that contaminate marine sediments remain unidentified. The present study as well as previous work on effluents point toward the involvement of natural products in the estrogenic burdens of marine sediments. Further work is required to establish the relative contribution of natural products and anthropogenic chemicals to current environmental impacts in the context of the Oslo and Paris Commission strategy to eliminate hazardous substances by 2020.

The microcomputer OASIS system for predicting the biological activity of chemical compounds

Abstract A detailed description is presented of a program pack for calculating the biological activity of chemical compounds (drugs, pesticides, toxic agents, etc.). It is based on the OASIS method recently developed within which molecular structure is presented in a systematic manner by means of a large variety of topological, steric and electronic indices. In calculating the latter a number of quantumchemical methods are adopted. The microcomputer version (IBM-PC-compatible) includes a stepwise preliminary screening of the initial set of molecular descriptors thus diminishing strongly the risk for a chance correlation. The procedure produces the best combined mathematical models and their complete statistical evaluation. Numerical estimates for the program pack work are presented along with examples of its application to several series of biologically active compounds.

Graph theoretical approaches to chemical reactivity

1. Introduction to Graph Theory H. Hosoya. 2. The Interplay between Graph Theory and Molecular Orbital Theory N. Trinajistic, Z. Mihalic, A. Graovac. 3. Topological Control of Molecular Orbital Theory: a Comparison of mu2-Scaled Huckel Theory and Restricted Hartree-Fock Theory for Boranes and Carboranes R. Rousseau, S. Lee. 4. Polyhedral Dynamics R.B. King. 5. Reaction Graphs A.T. Balaban. 6. Discrete Representations of Three-Dimensional Molecular Bodies and their Shape Changes in Chemical Reactions P.G. Mezey. 7. The Invariance of Molecular Topology in Chemical Reactions E.V. Babaev. 8. Topological Indices and Chemical Reactivity O. Mekenyan, S.C. Basak. 9. Graph-Theoretical Models of Complex Reaction Mechanisms and their Elementary Steps O.N. Temkin, A.V. Zeigarnik, D. Bonchev. Index.