Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT237
P. Georgopoulos, S. Isukapalli, I. Androulakis, M. Ierapetritou, W. Welsh
This chapter presents a systematic summary overview of coordinated efforts taking place at the environmental bioinformatics and Computational Toxicology Center (ebCTC.org) towards developing a mechanistic modeling framework that integrates multiple scales of coupled toxicokinetic and toxicodynamic processes. This framework employs highly modular “whole body” computational descriptions of the human and of selected model organisms that incorporate a hierarchy of alternative formulations representing biological events in “virtual” tissues and organs. This approach allows the mechanistic consideration of multiple scales of interlinked phenomena that include molecular interactions; dynamics of intracellular biomolecular networks; spatial and stochastic aspects of cell biochemistry; integrative coupling of cellular-level processes related to common endpoints; extracellular signaling and cell-cell communication and interaction; aspects of functional heterogeneity in multicellular structures; dynamics of histomorphological and histopathological processes at the tissue level; and integrative coupling of processes across scales, resulting in different physiosystem phenotypes for health and disease states. The ebCTC framework offers a range of combined approaches for coupling processes and “transferring and integrating” information across multiple strata of biological organization, ranging from formal methods of multiscale analysis, including statistical descriptions of multicellular kinetics and dynamics, to simplified “top-down” approaches, incorporating both mechanistic and phenomenological formulations that rely on point and distributional parameterizations of the finer scales. The computational implementation of the framework employs a combination of agent-, network-, and field-based modeling methods. Example applications of various components of this framework are presented, spanning the range from molecular interactions to bionetwork and cellular dynamics to multiscale problems at the tissue and organ levels.Keywords:exposure biology;multiscale physiologically-based toxicokinetic and toxicodynamic modeling;field-;network- and agent-based modeling;virtual tissues and virtual organs;mechanistic dose-response analysis;polymorphisms and susceptibility;dioxin;arsenic;endotoxin;ethanol;acetaminophen;nanoparticles;oxidative stress;inflammation
{"title":"Multiscale Integration of Toxicokinetic and Toxicodynamic Processes: From Cell and Tissue to Organ and “Whole Body” Models","authors":"P. Georgopoulos, S. Isukapalli, I. Androulakis, M. Ierapetritou, W. Welsh","doi":"10.1002/9780470744307.GAT237","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT237","url":null,"abstract":"This chapter presents a systematic summary overview of coordinated efforts taking place at the environmental bioinformatics and Computational Toxicology Center (ebCTC.org) towards developing a mechanistic modeling framework that integrates multiple scales of coupled toxicokinetic and toxicodynamic processes. This framework employs highly modular “whole body” computational descriptions of the human and of selected model organisms that incorporate a hierarchy of alternative formulations representing biological events in “virtual” tissues and organs. This approach allows the mechanistic consideration of multiple scales of interlinked phenomena that include molecular interactions; dynamics of intracellular biomolecular networks; spatial and stochastic aspects of cell biochemistry; integrative coupling of cellular-level processes related to common endpoints; extracellular signaling and cell-cell communication and interaction; aspects of functional heterogeneity in multicellular structures; dynamics of histomorphological and histopathological processes at the tissue level; and integrative coupling of processes across scales, resulting in different physiosystem phenotypes for health and disease states. The ebCTC framework offers a range of combined approaches for coupling processes and “transferring and integrating” information across multiple strata of biological organization, ranging from formal methods of multiscale analysis, including statistical descriptions of multicellular kinetics and dynamics, to simplified “top-down” approaches, incorporating both mechanistic and phenomenological formulations that rely on point and distributional parameterizations of the finer scales. The computational implementation of the framework employs a combination of agent-, network-, and field-based modeling methods. Example applications of various components of this framework are presented, spanning the range from molecular interactions to bionetwork and cellular dynamics to multiscale problems at the tissue and organ levels.Keywords:exposure biology;multiscale physiologically-based toxicokinetic and toxicodynamic modeling;field-;network- and agent-based modeling;virtual tissues and virtual organs;mechanistic dose-response analysis;polymorphisms and susceptibility;dioxin;arsenic;endotoxin;ethanol;acetaminophen;nanoparticles;oxidative stress;inflammation","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130608694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT205
A. Williams, C. Wise, D. Lovera, B. McCabe-Sellers, M. Bogle, J. Kaput
Personal and public health information is usually derived from studies of large population groups. Although reported as population attributable risk (PAR), these estimates are often applied to individuals. PARs for intake of nutrients, exposure to toxins, responses to drug, having certain genetic variants, and, more recently, nutrient{--}gene interactions are statistical estimates of the percentage reduction in disease in the population if the risk were to be avoided or the gene variant were not present. Individuals differ in genetic makeup, life-style, and dietary patterns and may not be represented by individuals in the study population. Although these risk factors are valuable guidelines, they may not apply to individuals. Intervention studies are likewise limited by small sample sizes, short time frames to assess physiological changes, and variable experimental designs that often preclude comparative or consensus analyses. A fundamental challenge for personalizing nutritional recommendations to optimize health and medicine for getting the right drug to the right person at the right time will be to develop a means to sort individuals into groups and, eventually, develop risk factors for individuals. The classic case{--}control prospective design may need to be revised in order to develop individual risk factors. A promising approach for more complete analyses of the interaction of genetic makeups and environment relies on translational research strategies where the study participant is physiologically monitored over time. Community-based participatory research (CBPR) methodology is a form of translational research whose central focus is developing a partnership among researchers and individuals in a community that allows for more in-depth life-style analyses but simultaneously helps improve the health of individuals and communities through application of research outcomes. Keywords: community-based participatory research; healthcare; nutrigenomics; personalized nutrition; population attributable risk; translational research
{"title":"A Community-Based Participatory Research/Translational Biomedical Research Strategy for Personalizing Nutrition, Medicine, and Healthcare","authors":"A. Williams, C. Wise, D. Lovera, B. McCabe-Sellers, M. Bogle, J. Kaput","doi":"10.1002/9780470744307.GAT205","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT205","url":null,"abstract":"Personal and public health information is usually derived from studies of large population groups. Although reported as population attributable risk (PAR), these estimates are often applied to individuals. PARs for intake of nutrients, exposure to toxins, responses to drug, having certain genetic variants, and, more recently, nutrient{--}gene interactions are statistical estimates of the percentage reduction in disease in the population if the risk were to be avoided or the gene variant were not present. Individuals differ in genetic makeup, life-style, and dietary patterns and may not be represented by individuals in the study population. Although these risk factors are valuable guidelines, they may not apply to individuals. Intervention studies are likewise limited by small sample sizes, short time frames to assess physiological changes, and variable experimental designs that often preclude comparative or consensus analyses. A fundamental challenge for personalizing nutritional recommendations to optimize health and medicine for getting the right drug to the right person at the right time will be to develop a means to sort individuals into groups and, eventually, develop risk factors for individuals. The classic case{--}control prospective design may need to be revised in order to develop individual risk factors. A promising approach for more complete analyses of the interaction of genetic makeups and environment relies on translational research strategies where the study participant is physiologically monitored over time. Community-based participatory research (CBPR) methodology is a form of translational research whose central focus is developing a partnership among researchers and individuals in a community that allows for more in-depth life-style analyses but simultaneously helps improve the health of individuals and communities through application of research outcomes. \u0000 \u0000 \u0000Keywords: \u0000 \u0000community-based participatory research; \u0000healthcare; \u0000nutrigenomics; \u0000personalized nutrition; \u0000population attributable risk; \u0000translational research","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114325993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT235
Y. Okuno, Yohsuke Minowa, H. Yamada, Y. Ohno, T. Urushidani
Toxicogenomics holds the promise of unprecedented advances in two broad, overlapping fields: mechanistic or investigative toxicology, and predictive toxicology. The progress of toxicogenomics has been supported by DNA microarray technology, a powerful tool for directly monitoring patterns of cellular perturbations through the identification and quantification of global shifts in gene expression resulting from pathological alterations within cells and tissues. Microarrays provide a large amount of transcriptional expression data for thousands of individual genes under various experimental conditions. Bioinformatics technologies can determine which genes are meaningful, facilitating the analysis of huge pools of toxicogenomics data in mechanistic and predictive toxicology. This chapter is devoted to computational approaches for the data mining of biomarker genes from toxicogenomics data, leading to toxicity prediction. Many algorithms have been developed for feature gene selection. Most studies on feature selection have found that wrapper methods are superior to filter methods, but many of these studies have over-emphasized prediction accuracy and over-looked the robustness of the selected genes. In fact, this study illustrates that intensity-based moderated t-statistics–support vector machine (SVM) produces more stable gene lists than recursive feature elimination–SVM. Therefore, we have to carefully gauge not only prediction performance but also the robustness of gene sets in feature gene selection. Keywords: biomarker; feature selection; gene selection; machine learning; microarray; support vector machine; toxicogenomics
{"title":"In Silico Toxicology Prediction Using Toxicogenomics Data","authors":"Y. Okuno, Yohsuke Minowa, H. Yamada, Y. Ohno, T. Urushidani","doi":"10.1002/9780470744307.GAT235","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT235","url":null,"abstract":"Toxicogenomics holds the promise of unprecedented advances in two broad, overlapping fields: mechanistic or investigative toxicology, and predictive toxicology. The progress of toxicogenomics has been supported by DNA microarray technology, a powerful tool for directly monitoring patterns of cellular perturbations through the identification and quantification of global shifts in gene expression resulting from pathological alterations within cells and tissues. Microarrays provide a large amount of transcriptional expression data for thousands of individual genes under various experimental conditions. Bioinformatics technologies can determine which genes are meaningful, facilitating the analysis of huge pools of toxicogenomics data in mechanistic and predictive toxicology. This chapter is devoted to computational approaches for the data mining of biomarker genes from toxicogenomics data, leading to toxicity prediction. Many algorithms have been developed for feature gene selection. Most studies on feature selection have found that wrapper methods are superior to filter methods, but many of these studies have over-emphasized prediction accuracy and over-looked the robustness of the selected genes. In fact, this study illustrates that intensity-based moderated t-statistics–support vector machine (SVM) produces more stable gene lists than recursive feature elimination–SVM. Therefore, we have to carefully gauge not only prediction performance but also the robustness of gene sets in feature gene selection. \u0000 \u0000 \u0000Keywords: \u0000 \u0000biomarker; \u0000feature selection; \u0000gene selection; \u0000machine learning; \u0000microarray; \u0000support vector machine; \u0000toxicogenomics","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128446204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT208
Cheng Wang, Lei Guo, T. Patterson, W. Slikker
Systems biology has been defined as the iterative and integrative study of biological systems as they respond to perturbations. This chapter highlights the application of the systems biology approach to enhance the understanding of complex biological processes such as neurodegeneration in the developing brain. Although not yet fully delineated, the working model for anesthetic (e.g., ketamine)-induced neurodegeneration during development involves the modulation of normally occurring brain-sculpting mechanisms that control CNS development. Exposure of the developing mammal to anesthetics such as ketamine perturbs the endogenous N-methyl-d-aspartate (NMDA) receptor system and results in enhanced neuronal cell death. The working model is that prolonged ketamine exposure produces up-regulation of NMDA receptors and subsequent over-stimulation of the glutamatergic system by endogenous glutamate, triggering enhanced apoptosis of developing neurons. When the nervous system was perturbed with ketamine-induced anesthesia and gene expression changes were monitored, NMDA receptor genes were significantly up-regulated and this finding was confirmed by in situ hybridization studies. Systems biology, as applied to toxicology, provides a framework in which information can be arranged in the form of a biological model and various global datasets can be collected and integrated to determine whether they support the model. Discrepancies can be identified and hypotheses-driven studies conducted in order to address them. Thus, data generated via iteration of this process can be used to reformulate the model in light of the new data. Keywords: systems biology; toxicology; development; genomics; proteomics; metabolomics
{"title":"Application of Systems Biology in Neurotoxicological Studies During Development","authors":"Cheng Wang, Lei Guo, T. Patterson, W. Slikker","doi":"10.1002/9780470744307.GAT208","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT208","url":null,"abstract":"Systems biology has been defined as the iterative and integrative study of biological systems as they respond to perturbations. This chapter highlights the application of the systems biology approach to enhance the understanding of complex biological processes such as neurodegeneration in the developing brain. Although not yet fully delineated, the working model for anesthetic (e.g., ketamine)-induced neurodegeneration during development involves the modulation of normally occurring brain-sculpting mechanisms that control CNS development. Exposure of the developing mammal to anesthetics such as ketamine perturbs the endogenous N-methyl-d-aspartate (NMDA) receptor system and results in enhanced neuronal cell death. The working model is that prolonged ketamine exposure produces up-regulation of NMDA receptors and subsequent over-stimulation of the glutamatergic system by endogenous glutamate, triggering enhanced apoptosis of developing neurons. When the nervous system was perturbed with ketamine-induced anesthesia and gene expression changes were monitored, NMDA receptor genes were significantly up-regulated and this finding was confirmed by in situ hybridization studies. Systems biology, as applied to toxicology, provides a framework in which information can be arranged in the form of a biological model and various global datasets can be collected and integrated to determine whether they support the model. Discrepancies can be identified and hypotheses-driven studies conducted in order to address them. Thus, data generated via iteration of this process can be used to reformulate the model in light of the new data. \u0000 \u0000 \u0000Keywords: \u0000 \u0000systems biology; \u0000toxicology; \u0000development; \u0000genomics; \u0000proteomics; \u0000metabolomics","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132258389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT219
W. Pathmasiri, R. Snyder, J. Burgess, J. Popp, T. Fennell, S. Sumner
This chapter summarizes a selection of literature regarding the development of biomarkers of acetaminophen (APAP)–induced liver injury (AILI), and provides new data related to the use of metabolomics for the development of a noninvasive marker profile for AILI. Our research investigation used male rats dosed daily with APAP at 0, 10, or 1500 per kg per day for up to 9 days. Urine, blood, and liver were obtained following a day and nine. Metabolomics of urine and extracts of liver showed separation of groups based on dose and duration of dose. Serum transaminases were evaluated. Histopathological analysis revealed complex pathological incidences for three lobes of the liver (left, right, and median), where centrilobular hypertrophy, centrilobular necrosis, and centrilobular inflammation were prominent in all lobes. Subsets of metabolites were identified that correlated with presence of AILI, even in cases were clinically relevant serum enzymes were not consistently correlated with pathology findings. Metabolite changes consistent with the presence of liver damage correlated with interruptions in amino acid, histidine, purine and pyrimidine, and glutamate metabolism. This study reveals markers that could find pre-clinical and clinical use, and provides insights into mechanisms involved in AILI. Keywords: acetaminophen; APAP; liver injury; NMR; metabolomics
{"title":"Biomarkers for the Assessment of Acetaminophen Induced Liver Injury","authors":"W. Pathmasiri, R. Snyder, J. Burgess, J. Popp, T. Fennell, S. Sumner","doi":"10.1002/9780470744307.GAT219","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT219","url":null,"abstract":"This chapter summarizes a selection of literature regarding the development of biomarkers of acetaminophen (APAP)–induced liver injury (AILI), and provides new data related to the use of metabolomics for the development of a noninvasive marker profile for AILI. Our research investigation used male rats dosed daily with APAP at 0, 10, or 1500 per kg per day for up to 9 days. Urine, blood, and liver were obtained following a day and nine. Metabolomics of urine and extracts of liver showed separation of groups based on dose and duration of dose. Serum transaminases were evaluated. Histopathological analysis revealed complex pathological incidences for three lobes of the liver (left, right, and median), where centrilobular hypertrophy, centrilobular necrosis, and centrilobular inflammation were prominent in all lobes. Subsets of metabolites were identified that correlated with presence of AILI, even in cases were clinically relevant serum enzymes were not consistently correlated with pathology findings. Metabolite changes consistent with the presence of liver damage correlated with interruptions in amino acid, histidine, purine and pyrimidine, and glutamate metabolism. This study reveals markers that could find pre-clinical and clinical use, and provides insights into mechanisms involved in AILI. \u0000 \u0000 \u0000Keywords: \u0000 \u0000acetaminophen; \u0000APAP; \u0000liver injury; \u0000NMR; \u0000metabolomics","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133241310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT217
L. Schnackenberg, Jinchun Sun, R. Beger
Metabolomics along with systems biology technologies such as transcriptomics and proteomics has the capability of providing translational diagnostic, prognostic and mechanistic biomarkers of drug-induced injury. Metabolomics has several advantages over the other omics platforms, such as ease of sample preparation, data acquisition and use of biofluids collected through minimally invasive procedures in pre-clinical and clinical studies. The role of metabolomics in systems toxicology is reviewed and examples of metabolomics in pre-clinical and clinical studies are provided. The role of metabolomics in personalized medicine is provided with special sections focusing on drug–drug and drug–nutrient interactions, metabolic fluxes and challenge tests. Keywords: metabolomics; metabonomics; metabolic fluxes; biomarkers; personalized medicine; systems biology
{"title":"Metabolomics in Systems Toxicology: Towards Personalized Medicine","authors":"L. Schnackenberg, Jinchun Sun, R. Beger","doi":"10.1002/9780470744307.GAT217","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT217","url":null,"abstract":"Metabolomics along with systems biology technologies such as transcriptomics and proteomics has the capability of providing translational diagnostic, prognostic and mechanistic biomarkers of drug-induced injury. Metabolomics has several advantages over the other omics platforms, such as ease of sample preparation, data acquisition and use of biofluids collected through minimally invasive procedures in pre-clinical and clinical studies. The role of metabolomics in systems toxicology is reviewed and examples of metabolomics in pre-clinical and clinical studies are provided. The role of metabolomics in personalized medicine is provided with special sections focusing on drug–drug and drug–nutrient interactions, metabolic fluxes and challenge tests. \u0000 \u0000 \u0000Keywords: \u0000 \u0000metabolomics; \u0000metabonomics; \u0000metabolic fluxes; \u0000biomarkers; \u0000personalized medicine; \u0000systems biology","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124729161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT202
H. Ellinger-Ziegelbauer, J. Aubrecht
The recent progress in sequencing, genomic technologies and system biological tools has enabled interrogating cellular responses of the whole genome to toxic stimuli via monitoring gene expression profiles and evaluating toxic effects in context of molecular pathways. The feasibility of using “fingerprints” to delineate molecular networks associated with toxicity has also been demonstrated by applying functional genomic approaches that utilized collection of yeast mutants or cancer cell lines. The potential of toxicogenomic analysis for analysis of genotoxic mechanisms to facilitate risk assessment of genotoxicity findings in in vitro assay systems has been extensively discussed. In this review, we focus on discussing recent progress in investigating genotoxic and carcinogenic mechanisms via gene expression profile analysis in in vitro and in vivo assay systems. Furthermore, we provide a perspective on potential application of toxicogenomic analysis as a tool for hazard identification and risk assessment of genotoxic and carcinogenic properties of chemicals. Keywords: genotoxic carcinogens; genotoxicity in vitro; mechanistic toxicogenomics; non-genotoxic carcinogens
{"title":"System Toxicology Approaches for Evaluating Chemical Carcinogenicity","authors":"H. Ellinger-Ziegelbauer, J. Aubrecht","doi":"10.1002/9780470744307.GAT202","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT202","url":null,"abstract":"The recent progress in sequencing, genomic technologies and system biological tools has enabled interrogating cellular responses of the whole genome to toxic stimuli via monitoring gene expression profiles and evaluating toxic effects in context of molecular pathways. The feasibility of using “fingerprints” to delineate molecular networks associated with toxicity has also been demonstrated by applying functional genomic approaches that utilized collection of yeast mutants or cancer cell lines. The potential of toxicogenomic analysis for analysis of genotoxic mechanisms to facilitate risk assessment of genotoxicity findings in in vitro assay systems has been extensively discussed. In this review, we focus on discussing recent progress in investigating genotoxic and carcinogenic mechanisms via gene expression profile analysis in in vitro and in vivo assay systems. Furthermore, we provide a perspective on potential application of toxicogenomic analysis as a tool for hazard identification and risk assessment of genotoxic and carcinogenic properties of chemicals. \u0000 \u0000 \u0000Keywords: \u0000 \u0000genotoxic carcinogens; \u0000genotoxicity in vitro; \u0000mechanistic toxicogenomics; \u0000non-genotoxic carcinogens","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131855195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT234
V. Umashankar, S. Gurunathan
Molecular modeling is the atomic-level description of molecular systems. The development of molecular modeling programs and their application in pharmaceutical research has been formalized as a field of study known as computer-assisted drug design (CADD) or computer-assisted molecular design (CAMD). Molecular modeling methods are now routinely used to investigate the structure, dynamics and thermodynamics of biological, inorganic and polymeric systems. Although initially models were physically drawn or made using appropriate materials, the development of computer programs introduced CADD or CAMD. Stages of molecular modeling are covered in this chapter and a few in silico tools available are discussed. Molecular modeling has been of great use to many scientists by decreasing time taken for the otherwise tedious task of modeling and associated analysis such as potential drug candidate identification, docking, protein–protein interactions, etc. Keywords: computer-assisted drug design (CADD); computer-assisted molecular design (CAMD); in silico tools; homology modeling; molecular drawing; molecular modeling; molecular visualization
{"title":"In Silico Tools for Molecular Modeling","authors":"V. Umashankar, S. Gurunathan","doi":"10.1002/9780470744307.GAT234","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT234","url":null,"abstract":"Molecular modeling is the atomic-level description of molecular systems. The development of molecular modeling programs and their application in pharmaceutical research has been formalized as a field of study known as computer-assisted drug design (CADD) or computer-assisted molecular design (CAMD). Molecular modeling methods are now routinely used to investigate the structure, dynamics and thermodynamics of biological, inorganic and polymeric systems. Although initially models were physically drawn or made using appropriate materials, the development of computer programs introduced CADD or CAMD. Stages of molecular modeling are covered in this chapter and a few in silico tools available are discussed. Molecular modeling has been of great use to many scientists by decreasing time taken for the otherwise tedious task of modeling and associated analysis such as potential drug candidate identification, docking, protein–protein interactions, etc. \u0000 \u0000 \u0000Keywords: \u0000 \u0000computer-assisted drug design (CADD); \u0000computer-assisted molecular design (CAMD); \u0000in silico tools; \u0000homology modeling; \u0000molecular drawing; \u0000molecular modeling; \u0000molecular visualization","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133888327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT242
M. Clift, F. Blank, P. Gehr, B. Rothen‐Rutishauser
Nanotechnology is an ever-growing industry that is expected to reach a net worth of over 15 billion by 2015 (Service, 2004). Although there are many proposed advantages related to this new industrial revolution, there are increased concerns regarding the potential adverse health effects that exposure to the production of nanotechnology-related products might pose (Maynard et al., 2006). Although not fully understood or confirmed, these concerns are well founded due to the plethora of research and knowledge related to the effects of environmental air pollution on human health relative to the nanoparticle (NP) component contained within it. Due to the inevitable exposure of humans to NPs, owing to their use in a wide and diverse range of applications, it is imperative to understand how NPs interact with the human body. In vivo research, however, has many disadvantages, and so increased research into the possible adverse effects of NPs has been performed using in vitro models. Therefore, the aim of this chapter is to provide an in-depth description of the many different toxicity tests used in vitro, how they are beneficial in filling the knowledge gap related to the potential toxic effects of NPs and the many pitfalls that are associated with them, in addition to providing an overview of the field of nanotoxicology as well as suggesting how in vitro testing strategies may be used to demonstrate the effects of in vivo exposure to NPs. Keywords: nanotechnology; nanoparticle; in vitro; in vivo; human health effects; nanotoxicology; environmental air pollution; in vitro toxicology testing
{"title":"Nanotoxicology: A Brief Overview and Discussion of the Current Toxicological Testing In Vitro and Suggestions for Future Research","authors":"M. Clift, F. Blank, P. Gehr, B. Rothen‐Rutishauser","doi":"10.1002/9780470744307.GAT242","DOIUrl":"https://doi.org/10.1002/9780470744307.GAT242","url":null,"abstract":"Nanotechnology is an ever-growing industry that is expected to reach a net worth of over 15 billion by 2015 (Service, 2004). Although there are many proposed advantages related to this new industrial revolution, there are increased concerns regarding the potential adverse health effects that exposure to the production of nanotechnology-related products might pose (Maynard et al., 2006). Although not fully understood or confirmed, these concerns are well founded due to the plethora of research and knowledge related to the effects of environmental air pollution on human health relative to the nanoparticle (NP) component contained within it. Due to the inevitable exposure of humans to NPs, owing to their use in a wide and diverse range of applications, it is imperative to understand how NPs interact with the human body. In vivo research, however, has many disadvantages, and so increased research into the possible adverse effects of NPs has been performed using in vitro models. Therefore, the aim of this chapter is to provide an in-depth description of the many different toxicity tests used in vitro, how they are beneficial in filling the knowledge gap related to the potential toxic effects of NPs and the many pitfalls that are associated with them, in addition to providing an overview of the field of nanotoxicology as well as suggesting how in vitro testing strategies may be used to demonstrate the effects of in vivo exposure to NPs. \u0000 \u0000 \u0000Keywords: \u0000 \u0000nanotechnology; \u0000nanoparticle; \u0000in vitro; \u0000in vivo; \u0000human health effects; \u0000nanotoxicology; \u0000environmental air pollution; \u0000in vitro toxicology testing","PeriodicalId":325382,"journal":{"name":"General, Applied and Systems Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128421067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-15DOI: 10.1002/9780470744307.GAT246
A. Baeza-Squiban, S. Boland, Salik Hussain, F. Marano
Increasing utilizations of nanomaterials in the industrial as well as consumer products augment the possibilities of environmental and occupational human exposures. Because of this fact, nanoparticles (NPs) have become potential candidates for the risk assessment. Among the possible exposure routes, inhalation represents the most important route of non-intentional exposure to NPs. There are increasing evidences that NPs exhibit ability to cross biological barriers getting access to the bloodstream and secondary target organs where they could accumulate and induce pathological consequences. The surface area and reactivity of particles increase many fold relative to particle mass as particle size is reduced. Together with chemical composition, they constitute important determinants of NPs toxicity. They contribute in the production of reactive oxygen species (ROS) leading to the toxicological outcomes induced by NPs. In this study, we present a systemic overview of the current knowledge on the exposure, secondary organ translocation and potential health effects of the NPs. Moreover, potential mechanisms of NP-induced cellular effects and role of physico-chemical characteristics are elaborated. The potentially deleterious effects of NPs require further studies in order to build on our mechanistic understanding of the toxicological events in which they can be implicated. Keywords: exposure; inflammation; internalization; oxidative stress; surface area; surface reactivity; toxicity; translocation
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