S Dimitrov

Publications (15)31.13 Total impact

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

  O Mekenyan, S Dimitrov, T Pavlov, G Dimitrova, M Todorov, P Petkov, S Kotov
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  ABSTRACT: 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.
  SAR and QSAR in environmental research 04/2012; 23(5-6):553-606. · 1.68 Impact Factor
• Article: Simulation of chemical metabolism for fate and hazard assessment. IV. Computer-based derivation of metabolic simulators from documented metabolism maps.

  S Karabunarliev, S Dimitrov, T Pavlov, D Nedelcheva, O Mekenyan
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  ABSTRACT: 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.
  SAR and QSAR in environmental research 03/2012; 23(5-6):371-87. · 1.68 Impact Factor
• Article: Simulation of chemical metabolism for fate and hazard assessment. III. New developments of the bioconcentration factor base-line model.

  S Dimitrov, N Dimitrova, D Georgieva, K Vasilev, T Hatfield, J Straka, O Mekenyan
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  ABSTRACT: 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.
  SAR and QSAR in environmental research 01/2012; 23(1-2):17-36. · 1.68 Impact Factor
• Article: Simulation of chemical metabolism for fate and hazard assessment. I: approach for simulating metabolism.

  S Dimitrov, T Pavlov, G Veith, O Mekenyan
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  ABSTRACT: 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.
  SAR and QSAR in environmental research 10/2011; 22(7-8):699-718. · 1.68 Impact Factor
• Article: Simulation of chemical metabolism for fate and hazard assessment. II CATALOGIC simulation of abiotic and microbial degradation.

  S Dimitrov, T Pavlov, N Dimitrova, D Georgieva, D Nedelcheva, A Kesova, R Vasilev, O Mekenyan
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  ABSTRACT: 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.
  SAR and QSAR in environmental research 10/2011; 22(7-8):719-55. · 1.68 Impact Factor
• Article: Development of a biodegradation model for the prediction of metabolites in soil.

  S Dimitrov, D Nedelcheva, N Dimitrova, O Mekenyan
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  ABSTRACT: 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.
  Science of The Total Environment 03/2010; 408(18):3811-6. · 3.29 Impact Factor
• Source

  Available from: Nadezhda Dimitrova

  Article: Elimination kinetic model for organic chemicals in earthworms.

  N Dimitrova, S Dimitrov, D Georgieva, C A M Van Gestel, P Hankard, D Spurgeon, H Li, O Mekenyan
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  ABSTRACT: 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.
  Science of The Total Environment 02/2010; 408(18):3787-93. · 3.29 Impact Factor
• Article: Metabolic activation of chemicals: in-silico simulation.

  O Mekenyan, S Dimitrov, N Dimitrova, G Dimitrova, T Pavlov, G Chankov, S Kotov, K Vasilev, R Vasilev
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  ABSTRACT: 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.
  SAR and QSAR in Environmental Research 03/2006; 17(1):107-20. · 2.09 Impact Factor
• Article: Base-line model for identifying the bioaccumulation potential of chemicals.

  S Dimitrov, N Dimitrova, T Parkerton, M Comber, M Bonnell, O Mekenyan
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  ABSTRACT: The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.
  SAR and QSAR in Environmental Research 01/2006; 16(6):531-54. · 2.09 Impact Factor
• Source

  Available from: pops.int

  Article: Predicting the biodegradation products of perfluorinated chemicals using CATABOL.

  S Dimitrov, V Kamenska, J D Walker, W Windle, R Purdy, M Lewis, O Mekenyan
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  ABSTRACT: 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.
  SAR and QSAR in Environmental Research 03/2004; 15(1):69-82. · 2.09 Impact Factor
• Article: Modeling mode of action of industrial chemicals: Application using chemicals on Canada's Domestic Substances List (DSL)

  S. Dimitrov, Y. Koleva, M. Lewis, R. Breton, G. Veith, O. Mekenyan
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  ABSTRACT: Traditionally quantitative structure activity relationships (QSARs) are derived on the assumption that similar chemicals should behave in a toxicologically similar manner. The development of mechanistic models for acute toxicity requires the chemical classification to be based on similar mode of toxic action. Currently, the classification approaches used to predict the mode of toxic action are predominantly based on chemical fragments and, to a lesser extent, on their electronic properties. The discrete nature of the fragment-based approach, however, yields a classification that does not consider the electronic features of the entire chemical that has “continuous character”. The present study is based on the assumption that chemicals with the same mode of toxic action should possess commonality in their steric and electronic structure. The COmmon REactivity PAtterns (COREPA) approach has been applied to define the global physical properties and quantum-chemical descriptors best clustering chemicals according to their behavioural mode of action (MOA). The derived COREPA models for each mode were translated into decision trees and applied to screen the organic chemicals on Canada's Domestic Substances List (DSL) for their mode of toxic action.
  QSAR & Combinatorial Science 04/2003; 22(1):5 - 17. · 1.55 Impact Factor
• Article: Probabilistic assessment of biodegradability based on metabolic pathways: catabol system.

  J Jaworska, S Dimitrov, N Nikolova, O Mekenyan
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  ABSTRACT: 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.
  SAR and QSAR in Environmental Research 04/2002; 13(2):307-23. · 2.09 Impact Factor
• Article: A kinetic model for predicting biodegradation.

  S Dimitrov, T Pavlov, D Nedelcheva, P Reuschenbach, M Silvani, R Bias, M Comber, L Low, C Lee, T Parkerton, O Mekenyan
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  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.
  SAR and QSAR in Environmental Research 18(5-6):443-57. · 2.09 Impact Factor
• Article: Quantitative prediction of biodegradability, metabolite distribution and toxicity of stable metabolites.

  S Dimitrov, R Breton, D Macdonald, J D Walker, O Mekenyan
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  ABSTRACT: 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.
  SAR and QSAR in Environmental Research 13(3-4):445-55. · 2.09 Impact Factor
• Article: In silico modelling of hazard endpoints: current problems and perspectives.

  O Mekenyan, S Dimitrov, P Schmieder, G Veith
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  ABSTRACT: 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.
  SAR and QSAR in Environmental Research 14(5-6):361-71. · 2.09 Impact Factor