Browsing by Subject "Health hazards"
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Item Integration of Biodescriptors and Chemodescriptors for Predictive Toxicology: A Mathematical / Computational Approach(University of Minnesota Duluth, 2001-08-28) Basak, Subhash Ci) 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predict the cell level toxicity data of halocarbons determined at the Wright Patterson AFB laboratory by Kevin Geiss (unpublished results).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.Item Prediction of Health and Environmental Hazards of Chemical: A Hierarchical Approach Using QMSA and QSAR (1997-1998)(University of Minnesota Duluth, 1998) Basak, Subhash CDuring 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.Item Prediction of Health and Environmental Hazards of Chemicals: A Hierarchical Approach using QMSA and QSAR (1997-2000)(University of Minnesota Duluth, 2000) Basak, Subhash CDuring 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.Item Prediction of Health and Environmental Hazards of Chemicals: A Hierarchical Approach using QMSA and QSAR (1998-1999)(University of Minnesota Duluth, 1999) Basak, Subhash CDuring 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.Item Use of Biodescriptors and Chemodescriptors in Predictive Toxicology: A Mathematical/Computational Approach(University of Minnesota Duluth, 2002) Basak, Subhash CDevelopment 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.