- Bulgaria
Milen Todorov
Assen Zlatarov University Burgas, Chemistry, Faculty Member
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,... more
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.
Research Interests: Chemistry, Computational Biology, Medicine, Mammals, Estrogen Receptor, and 15 moreBiological Sciences, Environmental Sciences, Humans, Computer Simulation, Micronuclei, Animals, Detoxification, Quantitative Structure Activity Relationship, CHEMICAL SCIENCES, Hazard Assessment, Biotransformation, Metabolic pathway, Binding affinity, Micronucleus Test, and Chromosome aberrations
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Research Interests:
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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... more
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.
Research Interests: Chemistry, Risk assessment, Medicine, Multivariate Analysis, Estrogen Receptor, and 13 moreBiological Sciences, Environmental Sciences, Humans, Endocrine disruptors, Quantitative Structure Activity Relationship, Chemical Analysis, CHEMICAL SCIENCES, Risk Assessment, Endocrine Disruptors, Multi Dimensional, Ligands, Molecular Structure, and Binding affinity
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.... more
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.
Research Interests: Chemistry, Medicine, Biological Sciences, Environmental Sciences, QSAR, and 12 moreAnimals, Androgens, Quantitative Structure Activity Relationship, External Validity, CHEMICAL SCIENCES, Rats, Surface Area, Androgen Receptor, Statistical models, Protein Binding, Binding affinity, and Model performance
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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... more
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.
Research Interests: Computer Science, Medicine, Biological Sciences, Environmental Sciences, expert System, and 11 moreQuantitative Structure Activity Relationship, Skin, Sensitization, CHEMICAL SCIENCES, Experimental Evaluation, Structure activity Relationship, Experimental Data, Mutagens, Outlier, Integration Testing, and Mutagenicity tests
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Choosing the best practice and associated components which will guarantee successful selection and further career success of job applicants is a complex process. There are many components that can contribute in order to support the... more
Choosing the best practice and associated components which will guarantee successful selection and further career success of job applicants is a complex process. There are many components that can contribute in order to support the ultimate “right” decision of the recruiter. In the current paper, the role of emotional intelligence, company attractiveness, and the concept of “work-life balance” expressed by the job seeker is investigated. A generalized net model based on mentioned factors in order to support the selection of job applicants by human resources units is proposed. Modelling the evaluation process of job candidates can be used to find out the most appropriate candidates for achieving the goals of the organization.
Rapid and correct identification of endocrine-disrupting chemicals (EDCs) is an important issue in environmental and human health risk assessment. Nowadays it is proved that structural diverse estrogen receptor agonists may induce cancer... more
Rapid and correct identification of endocrine-disrupting chemicals (EDCs) is an important issue in environmental and human health risk assessment. Nowadays it is proved that structural diverse estrogen receptor agonists may induce cancer or endocrine function disruption. Because of the high cost and time-consuming nature of experimental tests, in silico methods are valuable alternative tools for identification of EDCs. However, the larger part of the available models could be applied successfully for prediction the binding potential toward the receptor without supporting information for possible functional activity (agonistic/antagonistic) effect. In this study, a large chemical dataset containing experimental data for estrogen binding and agonistic activity have been evaluated by application of profiling scheme for EDCs incorporated in non-commercial computational software tool.
During the development of pharmaceuticals, the mutagenic potential of a chemical is considered for not only active pharmaceutical ingredients, but also for product-related substances such as metabolites and impurities. As indicated in ICH... more
During the development of pharmaceuticals, the mutagenic potential of a chemical is considered for not only active pharmaceutical ingredients, but also for product-related substances such as metabolites and impurities. As indicated in ICH M7 draft guidance, in silico predictive tools including Quantitative Structure-Activity Relationships (QSARs) and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. In the present study the implemented profiling schemes for mutagenicity prediction in non-commercial computational tool have been used for prediction of a set of so called hard to predict mutagenic compounds. The obtained results suggest that the system allows reliable mutagenic predictions when taking into account structural alerts with transparent reactivity mechanism along with adequate metabolism simulation. INTRODUCTION The genotoxic potential of a chemical is a highly important safety liabili...
The current paper emphasizes on explaining how Virtual Reality (VR) technology works and why it is important to be implemented for education purposes. The main goal of VR in education is to make studying process more involving and... more
The current paper emphasizes on explaining how Virtual Reality (VR) technology works and why it is important to be implemented for education purposes. The main goal of VR in education is to make studying process more involving and effective. The essential distinction of VR from other computer technologies for displaying information is that there is a “feedback” corresponding to the user's actions, i.e., the virtual environment is changing appropriately to his/her reactions. In the paper, a comparison between the different implementations of VR is made and the key elements of a VR system are listed. Also, a case study is presented in the last part of the paper. It consists of a description of the process of choosing which VR technology is best used in different education scenarios and shows a specific example of how VR technology is implemented in the teaching process at “Prof. Dr. Assen Zlatarov” University - Burgas and thus making the education process of students, and especially those from medical and technical science departments, more complete and engaging.
Research Interests: Computer Science and IEEE
The use of digital technologies influences practically almost all aspects of our daily life. Computer technologies have seen dramatic improvements, and laid the foundation for worldwide virtual reality (VR) development. The main goal of... more
The use of digital technologies influences practically almost all aspects of our daily life. Computer technologies have seen dramatic improvements, and laid the foundation for worldwide virtual reality (VR) development. The main goal of virtual reality is simulation of realistic human world. One of the key features of virtual reality is real-time interactivity, which means that the computer or the system has to be able to detect a user's input and modify the virtual world instantaneously. There are lots of educational difficulties related to understanding of complex objects like chemical compounds or parts of human body. A way to overcome them is the use of VR as appropriate technique which can improve the learning process to became more efficient. The aim of the paper is to present an approach for constructing molecules with different structural complexity. In addition, the development of VR models for human anatomy parts is also considered.
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Efforts to develop new predictive methods to assess the likelihood of a xenobiotics being a hepatotoxicants have been challenging due to the increasing concerns of safety chemical assessments. A huge variety of chemicals exist in the... more
Efforts to develop new predictive methods to assess the likelihood of a xenobiotics being a hepatotoxicants have been challenging due to the increasing concerns of safety chemical assessments. A huge variety of chemicals exist in the environment without information for their ability to exert hepatotoxic effect. Based on the assumption that damages on DNA may lead to hepatotoxic outcome it is assumed that available models for identification of mutagens can be used for prediction of the effect. In the current study this assumption is investigated by in silico application of DNA reactivity profilers over dataset of chemicals with known hepatotoxic effect. The results suggest that the approach can be used for identification of hepatotoxins with special concern about the influence of their metabolism. The overall predictions show high statistical performance - 86% correct predictions for hepatotoxins. Regarding non hepatotoxins it was found that the role of metabolic detoxification shoul...
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The primary testing strategy to identify chemical (hepato)carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize... more
The primary testing strategy to identify chemical (hepato)carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. Since the correlation between carcinogenicity and Ames mutagenicty test results was found to be significant enough it is expected that models based on Ames data could be used successfully for identification of chemical carcinogens. In the current study the implemented profiler for DNA binding prediction in non-commercial software tool was used to predict the hepatocarcinogenic effect of 55 representative chemicals. The obtained results show that 73% of the hepatocarcinogens can be successfully identified as genotoxic carcino...
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Risk assessment is a process that characterizes the magnitude of risk that chemicals pose to human and environmental health. Recent findings have suggested that certain chemicals have properties that make them harmful to human health or... more
Risk assessment is a process that characterizes the magnitude of risk that chemicals pose to human and environmental health. Recent findings have suggested that certain chemicals have properties that make them harmful to human health or the environment which may require their restriction or even prohibition. One of the endpoints of major regulatory concern is mutagenicity defined as ability of chemicals to cause damages on DNA. Considering the large number of the registered industrial chemicals and taking in mind the cost and the time needed for testing it is evident that they could not be experimentally evaluated for possible mutagenic effect. However, the task could be accomplished by application of alternative in silico methods such quantitative structure–activity relationships (QSARs). In the present study the mutagenic potential of the high production volume HPV OECD chemicals (4843) have been computationally evaluated by DNA profiling schemes incorporated in the computer platf...
Recent advances in Farnesoid X Receptor (FXR) biology demonstrate that FXR may represent a valuable target for the identification of novel drugs to treat variety of biological disorders. However, for therapeutic purposes the advanced... more
Recent advances in Farnesoid X Receptor (FXR) biology demonstrate that FXR may represent a valuable target for the identification of novel drugs to treat variety of biological disorders. However, for therapeutic purposes the advanced development of selective FXR modulators requires preliminary in silico evaluation of potential ligand candidates. In the current study the capabilities for structure-activity modeling incorporated in the non commercial computational tool have been employed for investigation the activating effect of various non-steroidal FXR ligands. A total of 97 molecules, representing three chemical classes – n-benzylamine, Diarylethene and Cycloalkyl amide have been analyzed. The ultimate models associated to each chemical class provide knowledge about molecular descriptors that may influence the activation of FXR.
Exposures to environmental concentrations of endocrine disrupting compounds are now a known threat to both human and ecological health. In the current study capabilities for structure-activity modeling incorporated in the platform QSAR... more
Exposures to environmental concentrations of endocrine disrupting compounds are now a known threat to both human and ecological health. In the current study capabilities for structure-activity modeling incorporated in the platform QSAR Toolbox were employed for investigation the binding effect of set of chemicals toward glucocorticoid receptor. A total of 39 steroidal ligands were split in categories, representing strong, moderate and weak binders. As a result of comparative analysis a mechanistic reasonable molecular descriptors were found to be useful for prediction of strong and moderate receptor binders. It was found that the important feature related to strong binders is their surface which is assessed by specific range of van der Waals surface area. Regarding moderate binders it was found that the interaction can be assessed by using more specific descriptor van der Waals partial negative surface area. The obtained results suggest that identified descriptors and their specific...
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Progesterone receptor (PR) is a member of the nuclear receptor superfamily of ligand-inducible transcription factors [1] and has roles in multiple physiological processes. The endogenous PR ligand progesterone is involved in regulation of... more
Progesterone receptor (PR) is a member of the nuclear receptor superfamily of ligand-inducible transcription factors [1] and has roles in multiple physiological processes. The endogenous PR ligand progesterone is involved in regulation of uterine cell proliferation/differentiation, implantation, ovulation, and mammary gland growth/differentiation [2]. Various synthetic PR agonists have been developed and used clinically to reduce estrogen-mediated endometrial cancer risk, and treatment of gynecological disorders [3]. Computational tools for early identification of potential ligands toward receptor macromolecules are becoming increasingly useful and accurate, and are now used extensively by medicinal and computational chemists. The quantitative structure-activity relationship (QSAR) method is now becoming an essential part of modern drug design, resulting in cost savings by reducing the laboratory resources needed and the time required to create and investigate new compounds. QSAR is...
Endocrine disrupting chemicals pose a significant threat to human health, society and the environment. Many of these chemicals elicit their toxicological effects through nuclear hormone receptors, like the estrogen receptor. Computational... more
Endocrine disrupting chemicals pose a significant threat to human health, society and the environment. Many of these chemicals elicit their toxicological effects through nuclear hormone receptors, like the estrogen receptor. Computational tools for predicting receptor mediated effects have been envisaged for their potential to be used for prioritization of chemicals for toxicological evaluation to reduce the amount of costly experimental testing and enable early alerts for newly designed compounds. In silico tools like knowledge-based expert systems and (quantitative) structure-activity relationship models have been created or upgraded on the yearly basis and also widely advertized to be used as primary screening technique in studies related to receptor mediated effects. The aim of this study is to provide an overview of the present most popular commercial and non-commercial in silico tools applicable for research studies in the field of receptor mediated effects.
The quantitative structure-activity relationship approach used for modeling and predictions of variety biological/toxic effects is mainly applied for investigation of organic compounds. However, the approach could be also successfully... more
The quantitative structure-activity relationship approach used for modeling and predictions of variety biological/toxic effects is mainly applied for investigation of organic compounds. However, the approach could be also successfully used in cases where the toxic response should be predicted for inorganic chemicals. While molecules of organic compounds reflect their properties as a whole, the inorganic compounds dissociate in various degrees and the properties have to be thus attributed to anions, cations, or undissociated molecules. Depending of each specific case different descriptors could be used for modeling and further screening of chemicals of interest. The aim of this study is to present some examples for QSAR applications used for prediction of cation toxicity.
Phototoxicity is of increasing concern since modern lifestyle is often associated with exposure to sunlight. Therefore characterizing the phototoxic potential of a compound early in its development is of utmost interest, especially for... more
Phototoxicity is of increasing concern since modern lifestyle is often associated with exposure to sunlight. Therefore characterizing the phototoxic potential of a compound early in its development is of utmost interest, especially for compounds likely to undergo sunlight exposure in skin. Traditioanly the phototoxic effect is modeled by using the EHOMO-ELUMO gap (energy difference between the highest occupied and lowest unoccupied molecular orbitals) which was found to be a suitable molecular descriptor that influenced both light absorbance and molecular stability. In the present study the use of this descriptor was evaluated by using a set of drug-like chemicals. The obtained result confirms its suitability for early identification of phototoxic effect.
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The use of digital technologies influences practically almost all aspects of our daily life. In the field of healthcare, in particular, technology plays a very important in activities related to data collection, data storing, and data... more
The use of digital technologies influences practically almost all aspects of our daily life. In the field of healthcare, in particular, technology plays a very important in activities related to data collection, data storing, and data analysis. The aim of technology in healthcare is to provide a range of healthcare professionals with access to information that will help increase the cost-effectiveness of care delivery and improve the efficacy of care. Psychology counseling is an area where specific elements, such as evaluation of emotional health, could be supported by the use of appropriate technologies. Such technology could increase accessibility to this type of assistance by reducing lengthy and costly travel to specialized centers. In addition, technology may enable overburdened professionals to increase the reach of their services, and help people with physical limitations who have restricted ability to travel to receive care. So-called ‘virtual assistants’ (also known as ‘cha...
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We investigated the performance of an integrated approach to testing and assessment (IATA), designed to cover different genotoxic mechanisms causing cancer and to replicate measured carcinogenicity data included in a new consolidated... more
We investigated the performance of an integrated approach to testing and assessment (IATA), designed to cover different genotoxic mechanisms causing cancer and to replicate measured carcinogenicity data included in a new consolidated database. Genotoxic carcinogenicity was predicted based on positive results from at least two genotoxicity tests: one in vitro and one in vivo (which were associated with mutagenicity categories according to the Globally Harmonized System classification). Substances belonging to double positives mutagenicity categories were assigned to be genotoxic carcinogens. In turn, substances that were positive only in a single mutagenicity test were assigned to be mutagens. Chemicals not classified by the selected genotoxicity endpoints were assigned to be negative genotoxic carcinogens and subsequently evaluated for their capability to elicit non-genotoxic carcinogenicity. However, non-genotoxic carcinogenicity mechanisms were not currently included in the develo...
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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... more
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.
Research Interests: Computer Science, Algorithms, Forecasting, Biological Sciences, Environmental Sciences, and 11 moreHumans, Ligand Binding, Quantitative Structure Activity Relationship, Prediction Model, Model development, CHEMICAL SCIENCES, Three Dimensional, Organic Chemicals, Software Package, Adverse effect, and three-dimensions
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Research Interests:
Rapidly and correctly identifying endocrine-disrupting chemicals (EDCs) is an important issue in environmental risk assessment. Because of the high cost and time-consuming nature of experimental tests, in silico methods are valuable... more
Rapidly and correctly identifying endocrine-disrupting chemicals (EDCs) is an important issue in environmental risk assessment. Because of the high cost and time-consuming nature of experimental tests, in silico methods are valuable alternative tools for the identification of EDCs. In silico tools like knowledgebased expert systems and (quantitative) structure-activity relationship models have been created or upgraded on the yearly basis and also widely advertised to be used as primary screening technique in studies related to receptor mediated effects. The aim of the present work is to evaluate the performance of the ER binding profiling schemes implemented within the QSAR Toolbox. The results presented in this article are meant to help a potential user in assessing the uncertainty, which is related to the categorization rules encoded in the profilers.
Background: Computational (in silico) methods, such as quantitative structure-activity relationships (QSARs) are already well recognized and used in many screening programs related to environmental, industrial and medical chemistry. The... more
Background: Computational (in silico) methods, such as quantitative structure-activity relationships (QSARs) are already well recognized and used in many screening programs related to environmental, industrial and medical chemistry. The main idea of the QSAR is that there is a relationship between molecular structure and ultimate biological effect caused by a chemical compound. In this respect the approach could be used successfully for prediction of various biological endpoints caused by chemical compounds including receptor binding affinity. Aim of the study: In the current study the capabilities for structure-activity modelling incorporated in noncommercial software tool have been employed for investigating the binding effect of xenobiotics toward estrogen and human pregnane X receptor. Material and methods: The analysis was performed by making use of the non-commercial software platform QSAR Toolbox. This system allows application of a set of built-in models for different biolog...
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Research Interests:
Carcinogenicity is a complex endpoint of high concern yet the rodent bioassay still used is costly to run in terms of time, money and animals. Therefore carcinogenicity has been the subject of many different efforts to both develop... more
Carcinogenicity is a complex endpoint of high concern yet the rodent bioassay still used is costly to run in terms of time, money and animals. Therefore carcinogenicity has been the subject of many different efforts to both develop short-term tests and non-testing approaches capable of predicting genotoxic carcinogenic potential. In our previous publication (Mekenyan et al., 2012) we presented an in vitro-in vivo extrapolation workflow to help investigate the differences between in vitro and in vivo genotoxicity tests. The outcomes facilitated the development of new (Q)SAR models and for directing testing. Here we have refined this workflow by grouping specific tests together on the basis of their ability to detect DNA and/or protein damage at different levels of biological organization. This revised workflow, akin to an Integrated Approach to Testing and Assessment (IATA) informed by mechanistic understanding was helpful in rationalizing inconsistent study outcomes and categorizing a test set of carcinogens with mutagenicity data on the basis of regulatory mutagenicity classifications. Rodent genotoxic carcinogens were found to be correctly predicted with a high sensitivity (90-100%) and a low rate of false positives (3-10%). The insights derived are useful to consider when developing future (non-)testing approaches to address regulatory purposes.
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Phototoxicity is of increasing concern in dermatology, since modern lifestyle is often associated with exposure to sunlight. The most commonly reported process is via oxidative reactions. Therefore characterizing the... more
Phototoxicity is of increasing concern in dermatology, since modern lifestyle is often associated with exposure to sunlight. The most commonly reported process is via oxidative reactions. Therefore characterizing the "photo-pro-oxidant" potential of a compound early in its industrial development is of utmost interest, especially for compounds likely to undergo sunlight exposure in skin. Today there is a need for filtering compounds to be tested in the 3T3 neutral red uptake in vitro test for phototoxicity since testing requires resources. A computational model aiming at predicting the mechanisms that imply the generation of reactive oxygen species was developed using a diverse set of 56 chemicals having 3T3 NRU data. An historical mechanistic (Q)SAR model developed for polycyclic aromatic hydrocarbons was used to derive the new mechanistic model: descriptors were selected upfront to describe the modeled phenomenon. The historical parabolic relationships between phototoxicity and the energy gap (E(GAP)) between energies of the highest occupied molecular orbital and the lowest unoccupied molecular orbital was confirmed. The model predicts chemicals to be "phototoxic or photodegradable", or "non-phototoxic and non-photodegradable". A four-step testing strategy is proposed to enable the reduction of experimental testing with the in silico model implemented as a first screen.
Research Interests: Artificial Intelligence, Expert Systems, Computational Biology, Software, Humans, and 13 moreComputer Simulation, Virtual screening, Reactive Oxygen Species, Computer Model, Animals, Quantitative Structure Activity Relationship, Polycyclic Aromatic Hydrocarbon, Model development, Experimental Tests, In Vitro Toxicology, Photolysis, Toxicity Tests, and In Silico
Mathematical chemistry has afforded a variety of research areas with important tools to understand and predict the behavior of chemicals without having to consider the complexities of three-dimensional conformations of molecules.... more
Mathematical chemistry has afforded a variety of research areas with important tools to understand and predict the behavior of chemicals without having to consider the complexities of three-dimensional conformations of molecules. Predictive toxicology, an area of increasing importance to toxicity assessments critical to molecular design and risk management, must be based on more explicit descriptions of structure, however. Minimum energy conformations are often used for convenience due, in part, to the difficulty of computing a representative population of conformers in all but rigid structures. Such simplifying assumptions fail to reveal the variance of the stereoelectronic nature of molecules as well as the misclassification of chemicals which initiate receptor-based toxicity pathways. Because these errors impact both the success in discovering new lead and the identification of possible hazards, it is important that mathematical chemistry develop additional tools for conformational analysis. This paper presents a new system for automated 2D-3D migration of chemicals in large databases with conformer multiplication. The main advantages of this system are its straightforward performance, reasonable execution time, simplicity and applicability to building large 3D chemical inventories. The module for conformer multiplication within the 2D-3D migration system is based on a new formulation of the genetic algorithm for computing populations of possible conformers. The performance of the automated 2D-3D migration system in building a centralized 3D database for all chemicals in commerce worldwide is discussed. The applicability of the 3D database in assessing the impact of molecular flexibility on identifying active conformers in QSAR analysis and assessing similarity between chemicals is illustrated.
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Research Interests:
Modeling the potential of chemicals to induce chromosomal damage has been hampered by the diversity of mechanisms which condition this biological effect. The direct binding of a chemical to DNA is one of the underlying mechanisms that is... more
Modeling the potential of chemicals to induce chromosomal damage has been hampered by the diversity of mechanisms which condition this biological effect. The direct binding of a chemical to DNA is one of the underlying mechanisms that is also responsible for bacterial mutagenicity. Disturbance of DNA synthesis due to inhibition of topoisomerases and interaction of chemicals with nuclear proteins associated with DNA (e.g., histone proteins) were identified as additional mechanisms leading to chromosomal aberrations (CA). A comparative analysis of in vitro genotoxic data for a large number of chemicals revealed that more than 80% of chemicals that elicit bacterial mutagenicity (as indicated by the Ames test) also induce CA; alternatively, only 60% of chemicals that induce CA have been found to be active in the Ames test. In agreement with this relationship, a battery of models is developed for modeling CA. It combines the Ames model for bacterial mutagenicity, which has already been derived and integrated into the Optimized Approach Based on Structural Indices Set (OASIS) tissue metabolic simulator (TIMES) platform, and a newly derived model accounting for additional mechanisms leading to CA. Both models are based on the classical concept of reactive alerts. Some of the specified alerts interact directly with DNA or nuclear proteins, whereas others are applied in a combination of two- or three-dimensional quantitative structure-activity relationship models assessing the degree of activation of the alerts from the rest of the molecules. The use of each of the alerts has been justified by a mechanistic interpretation of the interaction. In combination with a rat liver S9 metabolism simulator, the model explained the CA induced by metabolically activated chemicals that do not elicit activity in the parent form. The model can be applied in two ways: with and without metabolic activation of chemicals.
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Skin sensitization is an end point of concern for various legislation in the EU, including the seventh Amendment to the Cosmetics Directive and Registration Evaluation, Authorisation and Restriction of Chemicals (REACH). Since animal... more
Skin sensitization is an end point of concern for various legislation in the EU, including the seventh Amendment to the Cosmetics Directive and Registration Evaluation, Authorisation and Restriction of Chemicals (REACH). Since animal testing is a last resort for REACH or banned (from 2013 onward) for the Cosmetics Directive, the use of intelligent/integrated testing strategies (ITS) as an efficient means of gathering necessary information from alternative sources (e.g., in vitro, (Q)SARs, etc.) is gaining widespread interest. Previous studies have explored correlations between mutagenicity data and skin sensitization data as a means of exploiting information from surrogate end points. The work here compares the underlying chemical mechanisms for mutagenicity and skin sensitization in an effort to evaluate the role mutagenicity information can play as a predictor of skin sensitization potential. The Tissue Metabolism Simulator (TIMES) hybrid expert system was used to compare chemical mechanisms of both end points since it houses a comprehensive set of established structure-activity relationships for both skin sensitization and mutagenicity. The evaluation demonstrated that there is a great deal of overlap between skin sensitization and mutagenicity structural alerts and their underlying chemical mechanisms. The similarities and differences in chemical mechanisms are discussed in light of available experimental data. A number of new alerts for mutagenicity were also postulated for inclusion into TIMES. The results presented show that mutagenicity information can provide useful insights on skin sensitization potential as part of an ITS and should be considered prior to any in vivo skin sensitization testing being initiated.