Browsing by Author "Basak, Subhash C"

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    Computational Techniques to Quantify Chemical Similarity: Tools for Risk Assessment
    (University of Minnesota Duluth, 1995) Basak, Subhash C; Niemi, Gerald J; Host, George E
    The principal goal of the cooperative agreement was to develop new molecular similarity methods and apply them to the risk assessment of environmental chemicals. To this end, our strategy had the following three-fold objectives: 1. Conduct an International workshop on " MOLECULAR SIMILARITY IN RISK ASSESSMENT " where internationally known experts in toxicology, computational chemistry, mathematical chemistry, structure-activity relationships and risk assessment of chemicals were brought together. These experts provided their opinions about how MOLECULAR SIMILARITY methods should be developed and used for risk assessment of chemicals. Ten experts were interviewed by the NRRI team and subsequently their input was summarized in a technical report submitted to USEPA. The workshop was part of QSAR ’92, an international conference held in Duluth, Minnesota jointly by the Natural Resources Research Institute-University of Minnesota and U S Environmental Protection Agency. The experts also submitted written manuscripts as part of the workshop. The workshop report reflected on different aspects of hazard assessment and molecular similarity. 2. Develop molecular similarity methodology by incorporating the inputs of the experts mentioned in item 1 above, along with our expertise in these methods. Special attention was given to the use of NONEMPIRICAL PARAMETERS (e.g., values calculated directly from the chemical structure) as opposed to empirical (or experimental) parameters because most chemicals used in the environment do not have experimental data necessary for detailed hazard assessment. 3. Apply the computational molecular similarity methods developed during the project in the selection of analogs and in estimation of environmentally important properties of these chemicals.
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    Integration of Biodescriptors and Chemodescriptors for Predictive Toxicology: A Mathematical / Computational Approach
    (University of Minnesota Duluth, 2001-08-28) Basak, Subhash C
    i) Attempts have been made to develop quantitative biodescriptors to characterize the effects of chemicals on the proteomics patterns of cells. Applications of biodescriptors to proteomics patterns derived from normal liver cells and cells exposed to peroxisome proliferators showed that quantitative descriptors defined for D/D matrices are capable of rationalizing effects of different types of peroxisome proliferators on the cellular proteome. Such descriptors may find application in predicting the biochemical effects of toxicants from their proteomics patterns. ii) A novel approach to biodescriptor formulation has been taken, using the concept of partial ordering. The embedded graphs derived thereby have been used to develop new types of invariants for the quantification of proteomics maps. Biodescriptors developed and reported in the above two manuscripts show reasonable power of discriminating the proteomics patterns resulting from different toxicants. Biodescriptors already developed in this project and those currently under development will provide a battery of such parameters for predictive toxicology. When a certain number of (say n) of such descriptors are calculated for a proteomics map, the map is characterized by a vector consisting of n entries. Elements of such vectors may be used in: a) predicting toxicity using statistical methods such as PCR, PLS, RR or non-linear methods such as neural networks; b) classifying chemicals into various subsets; or c) quantifying the similarity / dissimilarity of toxicants. In light of our earlier study on the prediction of the mode of action (MOAs) of toxicants from chemodescriptors, it is realistic to speculate that the battery of biodescriptors will be useful in predicting the MOAs of toxicants. Chemodescriptors: i) Chemodescriptors calculated by POLLY and Molconn-Z have been used to predict the cell level toxicity data of halocarbons determined at the Wright Patterson AFB laboratory by Kevin Geiss (unpublished results). The high quality of these models indicate that similar models using calculated chemodescriptors may find application in the prediction of cellular toxicity arising from exposure to pollutants and xenobiotics. ii) In collaboration with Dr. Hawkins, PCR, RR and PLS analyses have been applied-to the prediction of the property / activity / toxicity of various groups of chemicals from their structural descriptors, vis., topostructural indices, topochemical indices, 3-D or shape parameters, and semi-empirical quantum chemical descriptors. This research has resulted in the formulation of robust and useful QSTR methods. iii) Successful quantitative structure-toxicity relationship (QSTR) models have been developed for the exposure assessment of volatile organic chemicals (VOCs), such as halocarbons, using chemodescriptors with the ridge regression statistical technique. These models will be useful in the physiologically-based pharmacokinetic modeling of VOCs. iv) Novel molecular shape descriptors have been developed and applied to predicting properties dependant on molecular shape. These new molecular shape parameters will be useful in QSAR/QSTR studies where molecular shape is a critical determinant of ligand-biotarget interaction in the cellular milieu. v) New methods have been developed to characterize DNA sequences and their modifications as a result of exposure to toxicants using matrix invariants. Such invariants were defined from newly formulated matrices used to represent macromolecular sequences in a condensed manner. These invariants will be useful tools for research in genomics and bioinformatics.
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    Predicting Toxicity and Degradability of Quadricyclane, Fluorocarbon Ethers and their Analogs (1994-1995)
    (University of Minnesota Duluth, 1995) Basak, Subhash C; Lodge, Keith B; Schubauer-Berigan, Joseph
    In a large number of cases, we have to assess the risk of chemicals and predict the toxic potential of molecules in the face of limited experimental data. Structural criteria and functional criteria (if available) are routinely used to estimate the possible hazard posed by a chemical to the environment and ecosystem. Frequently, no biological or relevant physicochemical properties of the chemical species of interest are available to the risk assessor. In the proposed project, we will develop and implement a number of methods of quantifying molecular similarity of chemicals using techniques of computational and mathematical chemistry. Some of the methods are new and will be based on our own research on the theoretical development and implementation of molecular similarity methods. These techniques will be implemented in a user friendly computer environment of the Silicon Graphics workstation. The similarity methods will be used to select analogs of chemicals of interest to the Air Force, viz., QUADRICYCLANE, FLUOROCARBON ETHERS AND THEIR ANALOGS, from databases containing high quality physicochemical data and toxicity endpoints for large number of chemicals. The databases used in the project will come from three sources: a) public domain databases, b) our own in-house databases, and c) databases acquired from commercial vendors. The set of selected analogs, called probe-induced subsets, will be used to: a) develop structure-activity relationships (SAR), and b) carry out ranking of chemicals. Both of these methods will be used to estimate the hazard of the chemicals of interest. A set of chemicals (five to ten) will be chosen for experimental work with the purpose of evaluating and refining computer models. The set will include quadricyclane and fluorocarbon ethers of interest to the Air Force. It will also include a selection of analogs (probe-induced subset) that are readily available, suitable for experimentation, and for which data are lacking. Experiments will be performed to assess the biodegradability and photochemical degradability of the members of the set. Their toxicity will be tested by MicroTox and MutaTox. In cases where significant degradation is observed, the toxicity of the degradation products will also be tested. Direct measurement of the hydrophobicity (octanol-water partition coefficient) will be performed on the members of the set.
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    Predicting Toxicity and Degradability of Quadricyclane, Fluorocarbon Ethers and their Analogs (1996-1997)
    (University of Minnesota Duluth, 1997) Basak, Subhash C; Lodge, Keith B; Schubauer-Berigan, Joseph
    In a large number of cases, we have to assess the risk of chemicals and predict the toxic potential of molecules in the face of limited experimental data. Structural criteria and functional criteria (if available) are routinely used to estimate the possible hazard posed by a chemical to the environment and ecosystem. Frequently, no biological or relevant physicochemical properties of the chemical species of interest are available to the risk assessor. In the proposed project, we will develop and implement a number of methods of quantifying molecular similarity of chemicals using techniques of computational and mathematical chemistry. Some of the methods are new and will be based on our own research on the theoretical development and implementation of molecular similarity methods. These techniques will be implemented in a user friendly computer environment of the Silicon Graphics workstation. The similarity methods will be used to select analogs of chemicals of interest to the Air Force, viz., QUADRICYCLANE, FLUOROCARBON ETHERS AND THEIR ANALOGS, from databases containing high quality physicochemical data and toxicity endpoints for large number of chemicals. The databases used in the project will come from three sources: a) public domain databases, b) our own in-house databases, and c) databases acquired from commercial vendors. The set of selected analogs, called probe-induced subsets, will be used to: a) develop structure-activity relationships (SAR), and b) carry out ranking of chemicals. Both of these methods will be used to estimate the hazard of the chemicals of interest. A set of chemicals (five to ten) will be chosen for experimental work with the purpose of evaluating and refining computer models. The set will include quadricyclane and fluorocarbon ethers of interest to the Air Force. It will also include a selection of analogs (probe-induced subset) that are readily available, suitable for experimentation, and for which data are lacking. Experiments will be performed to assess the biodegradability and photochemical degradability of the members of the set. Their toxicity will be tested by MicroTox and MutaTox. In cases where significant degradation is observed, the toxicity of the degradation products will also be tested. Direct measurement of the hydrophobicity (octanol-water partition coefficient) will be performed on the members of the set.
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    Prediction of Health and Environmental Hazards of Chemical: A Hierarchical Approach Using QMSA and QSAR (1997-1998)
    (University of Minnesota Duluth, 1998) Basak, Subhash C
    During the past few years we have been involved in the development of new computational methods forquantifying similarity/dissimilarity of chemicals and applications of quantitative molecular similarity analysis (QMSA) techniques in analog selection and property estimation for use in the hazard assessment of chemicals. We have also explored the mathematical nature of the molecular similarity space in order to better understand the basis of analog selection by QMSA methods. The parameter spaces used for QMSA and analog selection were constructed from nonempirical parameters derived from computational chemical graph theory. Occasionally, graph invariants were supplemented with geometrical parameters and quantum chemical indices to study the relative effectiveness of graph invariants vis-a-vis geometrical and quantum chemical parameters in analog selection and property estimation. We carried out comparative studies of nonempirical descriptor spaces and physicochemical property spaces in selecting analogs. Molecular similarity methods were applied in predicting modes of toxic action (MOA) of chemicals. Our similarity/dissimilarity methods have also found successful applications in the discovery of new drag leads by US drag companies. In this project, we will have four primary goals: 1) development of a hierarchical approach to molecular similarity, 2) formulation of quantitative structure-activity relationship (QSAR) models for predictive toxicology using a hierarchical approach, 3) applications of hierarchical QSAR and QMSA approaches in computational toxicology related to human health and ecological hazard assessment, and 4) the application of hierarchical QMSA and QSAR approaches in estimating potential toxicity of deicing agents. The first goal of the project is the use of parameters of gradually increasing complexity, viz., topological, topochemical, geometrical, and quantum chemical indices, in the quantification of molecular similarity/dissimilarity of chemicals. We will take a two-tier approach in this area. First, similarity methods will be used in ordering sets of molecules and in selecting structural analogs of toxic chemicals which pose human health and ecological hazards. Secondly, we will use the properties of selected analogs in estimating toxicologically important properties for chemicals. Although different classes of parameters have been used in the characterization of molecular similarity, no systematic study has been carried out in the use of all four classes of parameters, mentioned above, in analog selection and property estimation. We will apply a hierarchical approach to the use of these four types of theoretical molecular descriptors in the quantification of molecular similarity/dissimilarity. The second goal consists of the development of hierarchical QSAR models for predicting the toxic potential of chemicals using topological and quantum chemical indices. Initially, we will use parameters calculated by semi-empirical methods such as MOP AC and AMP AC. Parameters calculated by ab initio quantum chemical methods will be used in limited cases of QSAR model development, if they are considered necessary. The third goal of the project will be the prediction of human health hazard and ecotoxicological effects of chemicals using QSAR and QMSA methods developed in the project. Attempts will be made to estimate endpoints, such as, carcinogenicity, mutagenicity, xenoestrogenicity, acute toxicity, transport of chemicals through the blood-brain barrier, biodegradation, and bioconcentration factor. The fourth goal will involve the utilization of QMSA and QSAR methods developed as part of this project in predicting the potential toxicity of deicing agents.
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    Prediction of Health and Environmental Hazards of Chemicals: A Hierarchical Approach using QMSA and QSAR (1997-2000)
    (University of Minnesota Duluth, 2000) Basak, Subhash C
    During the past few years we have been involved in the development of new computational methods for quantifying similarity/dissimilarity of chemicals and applications of quantitative molecular similarity analysis (QMSA) techniques in analog selection and property estimation for use in the hazard assessment of chemicals. We have also explored the mathematical nature of the molecular similarity space in order to better understand the basis of analog selection by QMSA methods. The parameter spaces used for QMSA and analog selection were constructed from nonempirical parameters derived from computational chemical graph theory. Occasionally, graph invariants were supplemented with geometrical parameters and quantum chemical indices to study the relative effectiveness of graph invariants vis-a-vis geometrical and quantum chemical parameters in analog selection and property estimation. We carried out comparative studies of nonempirical descriptor spaces and physicochemical property spaces (n selecting analogs. Molecular similarity methods were applied in predicting modes of toxic action (MOA) of chemicals. Our similarity/dissimilarity methods have also found successful applications in the discovery of new drug leads by US drug companies.
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    Prediction of Health and Environmental Hazards of Chemicals: A Hierarchical Approach using QMSA and QSAR (1998-1999)
    (University of Minnesota Duluth, 1999) Basak, Subhash C
    During the past few years we have been involved in the development of new computational methods for quantifying similarity/dissimilarity of chemicals and applications of quantitative molecular similarity analysis (QMSA) techniques in analog selection and property estimation for use in the hazard assessment of chemicals. We have also explored the mathematical nature of the molecular similarity space in order to better understand the basis of analog selection by QMSA methods. The parameter spaces used for QMSA and analog selection were constructed from nonempirical parameters derived from computational chemical graph theory. Occasionally, graph invariants were supplemented with geometrical parameters and quantum chemical indices to study the relative effectiveness of graph invariants vis-a-vis geometrical and quantum chemical parameters in analog selection and property estimation. We carried out comparative studies of nonempirical descriptor spaces and physicochemical property spaces in selecting analogs. Molecular similarity methods were applied in predicting modes of toxic action (MOA) of chemicals. Our similarity/dissimilarity methods have also found successful applications in the discovery of new drug leads by US drug companies. In this project, we will have four primary goals: 1) development of a hierarchical approach to molecular similarity, 2) formulation of quantitative structure-activity relationship (QSAR) models for predictive toxicology using a hierarchical approach, 3) applications of hierarchical QSAR and QMSA approaches in computational toxicology related to human health and ecological hazard assessment, and 4) the application of hierarchical QMSA and QSAR approaches in estimating potential toxicity of deicing agents. The first goal of the project is the use of parameters of gradually increasing complexity, viz., topological, topochemical, geometrical, and quantum chemical indices, in the quantification of molecular similarity/dissimilarity of chemicals. We will take a two-tier approach in this area. First, similarity methods will be used in ordering sets of molecules and in selecting structural analogs of toxic chemicals which pose human health and ecological hazards. Secondly, we will use the properties of selected analogs in estimating toxicologically important properties for chemicals. Although different classes of parameters have been used in the characterization of molecular similarity, no systematic study has been carried out in the use of all four classes of parameters, mentioned above, in analog selection and property estimation. We will apply a hierarchical approach to the use of these four types of theoretical molecular descriptors in the quantification of molecular similarity/dissimilarity. The second goal consists of the development of hierarchical QSAR models for predicting the toxic potential of chemicals using topological and quantum chemical indices. Initially, we will use parameters calculated by semi-empirical methods such as MOP AC and AMP AC. Parameters calculated by ab initio quantum chemical methods will be used in limited cases of QSAR model development, if they are considered necessary. The third goal of the project will be the prediction of human health hazard and ecotoxicological effects of chemicals using QSAR and QMSA methods developed in the project. Attempts will be made to estimate endpoints, such as, carcinogenicity, mutagenicity, xenoestrogenicity, acute toxicity, transport of chemicals through the blood-brain barrier, biodegradation, and bioconcentration factor. The fourth goal will involve the utilization of QMSA and QSAR methods developed as part of this project in predicting the potential toxicity of deicing agents.
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    Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology (1996-1997)
    (University of Minnesota Duluth, 1997) Basak, Subhash C; Gute, Brian D
    Four classes of theoretical structural parameters, viz., topostructural, topochemical,geometrical and quantum chemical descriptors, have been used in the development of quantitative structure-activity relationship (QSAR) models for a set of sixty-nine benzene derivatives. None of the individual classes of parameters was very effective in predicting toxicity. A hierarchical approach was followed in using a combination of the four classes of indices in QSAR model development. The results show that the hierarchical QSAR approach using the algorithmically derived molecular descriptors can estimate the LC50 values of the benzene derivatives reasonably well.
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    Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology (1996-1999)
    (University of Minnesota Duluth, 2000) Basak, Subhash C
    The major aims of the proposed project were: a) development of quantitative methods for the characterization of structure spaces, b) application of these newly developed methods in selecting analogs, c) development of estimation methods for predicting toxicologically relevant properties of chemicals from their analogs, and d) development of neural network methods for property estimation and analog selection.
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    Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology (1997-1998)
    (University of Minnesota Duluth, 1998) Basak, Subhash C
    During the last year, our work has focused on the first three tasks of the project, viz., a) characterization of molecular similarity spaces, b) selection of analogs, and c) similarity based estimation of properties. In the area of Task 1, the effectiveness of theoretical molecular descriptors vis-avis experimental physicochemical properties in quantifying intermolecular similarity has explored for several sets of compounds with varying physicochemical and biological properties. In Task 2, the various structure spaces developed in Task 1 have been used in the selection of analogs for specific probe compounds. In Task 3, we have used the /c-nearest neighbor (KNN) method to estimate properties of chemicals from various databases. For these experiments, k has been varied from 1-40. The results showed that, for different physicochemical, toxicological and biochemical properties, optimal property estimation is generally obtained in the range of k = 5-10.
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    Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology (1998-1999)
    (University of Minnesota Duluth, 1999) Basak, Subhash C
    During the third year of the project, our work on the first three tasks of the project; viz., a) characterization of molecular similarity spaces, b) selection of analogs, and c) similarity-based estimation of properties; has continued. However, the focus of our work has shifted to the fourth and final task of the project, viz., the application of neural networks in property estimation. In the area of Task 1, the effectiveness of theoretical molecular descriptors vis-a- vis experimental physicochemical properties in quantifying intermolecular similarity has been explored for several sets of compounds with varying physicochemical and biological properties. In Task 2, the various structure spaces developed in Task 1 have been used in the selection of analogs for specific probe compounds. In Task 3, we have used the /(-nearest neighbor (KNN) method to estimate properties of chemicals from various databases. For these experiments, k has been varied from 1-40. The results showed that, for different physicochemical, toxicological and biochemical properties, optimal property estimation is generally obtained in the range of k= 5-10. Finally, in Task 4, we have used neural networks for the prediction of toxicological endpoints. In addition, we examined several methods for feature (independent variable) selection using a machine learning techniques, GEFS (genetic ensemble feature selection), based on genetic algorithms. The results show that neural networks, in general, show slight improvement in modeling power over statistical methods, but the use of GEFS to select relevant features for modeling greatly improves the performance of the neural networks.
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    Report of the Internal Workshop on Molecular Similarity in Risk Assessment
    (University of Minnesota Duluth, 1993) Basak, Subhash C; Hunter, Bob; Niemi, Gerald J; Host, George E
    In an attempt to adequately capture the different aspects of molecular similarity, our group thought it would be appropriate to solicit, a variety of opinions regarding chemical similarity and its uses in different situations. While we have some experience and expertise in this field, we felt it important to consider a variety of opinions of internationally known experts about the concept of chemical similarity and its uses. Along those lines, we were fortunate enough to be able to access many researchers and regulators who had intended to participate in the QSAR 92 Conference held in Duluth, MN during July 19-23, 1992. In fact, we felt it essential that we take advantage of the collective body of expertise. To that end, we, in conjunction with United States Environmental Protection Agency (USEPA), sponsored nine key speakers and presenters who, we felt, had broad background in their area of expertise and could share with us their perspectives of what it means for two chemicals to be similar. After selecting our key speakers, we. arranged for many of them to be present at QSAR 92. During the course of the conference, we made arrangements to meet and have open discussions regrading chemical similarity with these speakers. The participants were questioned about what they thought were the critical elements or processes relevant to their subject area and the relevancy or uses of chemical similarity in their field of expertise. Many of these participants provided papers, which were reviewed for content relevant to chemical similarity and are provided in Appendix A. The goal of this exercise was to distill the common elements critical to operationalizing a method or system of components to formulate, implement, test, and validate chemical similarity models. This would lead to the development of a computer system design that incorporates many of the essential elements together under a common interface. We felt that it was essential that regulatory, toxicological, and computational perspectives of chemical similarity be taken into account during the course of this project. The remainder of this report will detail these different perspectives, and then discuss and review the common features to be used, with the hope that this will facilitate a computer software system design to accomplish the objectives of this project.
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    Stereo-electronic Factors in Molecular Similarity and Risk Assessment
    (University of Minnesota Duluth, 1994) Basak, Subhash C; Hunter, Bob; Niemi, Gerald J; Host, George E
    Three strategic tasks for the risk assessment of the chemicals can be defined nowadays. The first one is related to the critical evaluation of the existing test data. For example, in the area of industrial chemicals, after exploration the availability of toxicity endpoints, the National Research Council concluded that for many of these chemicals minimum of tests or, in many cases, no tests at all are performed [1]. On the other hand, the available test data mostly consist of acute toxicity and eye/skin irritation tests. Recently, an analysis of an environmental database of more than 30,000 chemicals showed [2] that the total number of chemicals possessing measured values of either boiling/melting points or vapor pressure is only 3,692. The second task should handle the identification problem of potential analogues of chemicals. An effective solution of this problem based on similarity methods can allow the selection of analogues of a query chemical possessing similar (hazardous) properties. The third task is closely related with the second one and is directed to estimating the properties of chemicals by using quantitative structure-activity relationships (QSAR) models.
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    Use of Biodescriptors and Chemodescriptors in Predictive Toxicology: A Mathematical/Computational Approach
    (University of Minnesota Duluth, 2002) Basak, Subhash C
    Development and applications of biodescriptors for predictive toxicology by our NRRI team has been expanded to incorporate three major types of biodescriptors: a) global descriptors from invariants of matrices associated with proteomics maps, b) a set of local invariants describing various aspects of each map (instead of one global biodescriptor), and c) spectrum-like descriptors for the characterization of proteomics patterns.A successful HiQSAR model has been developed for a set of 55 halocarbons for which cellular level toxicity data is available. It may be noted that this is the "superset" of compounds from which the "subset" of twenty halocarbons, currently being tested by WPAFB and Dr. Witzmann using the DNA microarray and proteomics analysis, was selected. Selection of parameters for the HiQSAR was based on the mechanistic hypothesis that dissociate electron attachment and subsequent formation of free radical$, leading to lipid peroxidation, is a major factor in halocarbon toxicity. This conclusion was derived from Dr. Balasubramanian's previous research, based on high-level quantum chemical calculations. If the hypothesis is correct, calculated parameters such as vertical electron aftinity(VEA) should be strongly related to a chemical's toxicity.