An introduction to systems toxicology

The science of toxicology is the science of the system. Toxicologists aim to understand and predict the adverse effects of chemicals on biological systems. As biological systems are extremely complex, the challenge of predicting human toxicity early in the drug discovery process is immense. In the past decades, a huge effort has been undertaken to characterise the impact of chemicals on biological systems using in vitro, pre-clinical and clinical approaches. This has led to a vast amount of knowledge on the biology of systems, especially as a result of the data deluge from -omic level investigations. However, a lack of robust and comprehensive integration has meant that this wealth of data has still not led to accurate prediction of toxicity in a single system, or the ability to extrapolate robustly between systems. The new discipline of systems toxicology aims to take the computational approaches developed in systems biology and apply them to toxicology-related questions. This review will examine approaches ranging from relational databases that are both repositories for curated information and screening tools in their own right, to the potential of digital organisms in systems toxicology. Both the basic methodologies and how best they may be applied to safety assessment of chemicals will be covered. This integrated examination of toxicological data is predicted to herald a step-change in our ability to both understand and predict adverse effects of chemicals.

[1]  M. Ierapetritou,et al.  A Rational Design Approach for Amino Acid Supplementation in Hepatocyte Culture , 2009 .

[2]  Elaine Holmes,et al.  Systems toxicology: integrated genomic, proteomic and metabonomic analysis of methapyrilene induced hepatotoxicity in the rat. , 2006, Journal of proteome research.

[3]  Alison J. Foster,et al.  Glutathione metabolism modeling: a mechanism for liver drug-robustness and a new biomarker strategy. , 2013, Biochimica et biophysica acta.

[4]  Rolf Apweiler,et al.  IntEnz, the integrated relational enzyme database , 2004, Nucleic Acids Res..

[5]  Neal F. Cariello,et al.  Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity. Deductive Estimate of Risk from Existing Knowledge. Toxicity Prediction by Komputer Assisted Technology. , 2002, Mutagenesis.

[6]  A. Kierzek,et al.  Selection of objective function in genome scale flux balance analysis for process feed development in antibiotic production. , 2008, Metabolic engineering.

[7]  Lei Shi,et al.  SABIO-RK—database for biochemical reaction kinetics , 2011, Nucleic Acids Res..

[8]  J. Lehár,et al.  Synergistic drug combinations improve therapeutic selectivity , 2009, Nature Biotechnology.

[9]  N. Kikuchi,et al.  CellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks , 2008, Proceedings of the IEEE.

[10]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[11]  C. Ouzounis,et al.  Expansion of the BioCyc collection of pathway/genome databases to 160 genomes , 2005, Nucleic acids research.

[12]  Hiroaki Kitano,et al.  CellDesigner: a process diagram editor for gene-regulatory and biochemical networks , 2003 .

[13]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[14]  Monika Heiner,et al.  JAK/STAT signalling--an executable model assembled from molecule-centred modules demonstrating a module-oriented database concept for systems and synthetic biology. , 2012, Molecular bioSystems.

[15]  Mario Niepel,et al.  Adaptive informatics for multi-factorial and high content biological data , 2011, Nature Methods.

[16]  Tanya Coleman,et al.  In Silico and in Vitro Modeling of Hepatocyte Drug Transport Processes: Importance of ABCC2 Expression Levels in the Disposition of Carboxydichlorofluroscein , 2009, Drug Metabolism and Disposition.

[17]  Elisabeth Coudert,et al.  HAMAP in 2013, new developments in the protein family classification and annotation system , 2012, Nucleic Acids Res..

[18]  C. von Mering,et al.  PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life , 2012, Molecular & Cellular Proteomics.

[19]  B. Kholodenko,et al.  Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades , 2004, The Journal of cell biology.

[20]  G. Patlewicz,et al.  An evaluation of the implementation of the Cramer classification scheme in the Toxtree software , 2008, SAR and QSAR in environmental research.

[21]  N. Plant,et al.  A PXR-Mediated Negative Feedback Loop Attenuates the Expression of CYP3A in Response to the PXR Agonist Pregnenalone-16α-Carbonitrile , 2011, PloS one.

[22]  Frank J Bruggeman,et al.  Design principles of nuclear receptor signaling: how complex networking improves signal transduction , 2010, Molecular systems biology.

[23]  Thomas Bernard,et al.  MetaNetX.org: a website and repository for accessing, analysing and manipulating metabolic networks , 2013, Bioinform..

[24]  A. Goldbeter,et al.  Toward a detailed computational model for the mammalian circadian clock , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[25]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

[26]  Rafael C. Jimenez,et al.  The IntAct molecular interaction database in 2012 , 2011, Nucleic Acids Res..

[27]  P J Goss,et al.  Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Hans V. Westerhoff,et al.  Optimization of stress response through the nuclear receptor-mediated cortisol signalling network , 2013, Nature Communications.

[29]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[30]  Tudor I. Oprea,et al.  ChemProt-2.0: visual navigation in a disease chemical biology database , 2012, Nucleic Acids Res..

[31]  Paul Harten,et al.  A Hierarchical Clustering Methodology for the Estimation of Toxicity , 2008, Toxicology mechanisms and methods.

[32]  Mudita Singhal,et al.  COPASI - a COmplex PAthway SImulator , 2006, Bioinform..

[33]  Emilio Benfenati,et al.  Comparison of In Silico Models for Prediction of Mutagenicity , 2013, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

[34]  S. Rees,et al.  Principles of early drug discovery , 2011, British journal of pharmacology.

[35]  François Bouzom,et al.  Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs? , 2012, Biopharmaceutics & drug disposition.

[36]  T. Singer,et al.  Comparative evaluation of in silico systems for ames test mutagenicity prediction: scope and limitations. , 2011, Chemical research in toxicology.

[37]  Milton H. Saier,et al.  The Transporter Classification Database , 2013, Nucleic Acids Res..

[38]  Nan Xiao,et al.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli , 2008, Bioinform..

[39]  Carlos Prieto,et al.  APID: Agile Protein Interaction DataAnalyzer , 2006, Nucleic Acids Res..

[40]  J. Larosa,et al.  Effect of statins on risk of coronary disease: a meta-analysis of randomized controlled trials. , 1999, JAMA.

[41]  R G Ulrich,et al.  Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. , 2001, Toxicology and applied pharmacology.

[42]  Peter D. Karp,et al.  The Pathway Tools software , 2002, ISMB.

[43]  P. Achermann,et al.  Mathematical models of sleep regulation. , 2003, Frontiers in bioscience : a journal and virtual library.

[44]  D. Dix,et al.  The ToxCast program for prioritizing toxicity testing of environmental chemicals. , 2007, Toxicological sciences : an official journal of the Society of Toxicology.

[45]  R. Tennant,et al.  Systems toxicology and the Chemical Effects in Biological Systems (CEBS) knowledge base. , 2002 .

[46]  Jinfeng Zhang,et al.  IMID: integrated molecular interaction database , 2012, Bioinform..

[47]  Rick L. Stevens,et al.  High-throughput generation, optimization and analysis of genome-scale metabolic models , 2010, Nature Biotechnology.

[48]  C. Gille,et al.  HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology , 2010, Molecular systems biology.

[49]  Ben van Ommen,et al.  Systems toxicology: applications of toxicogenomics, transcriptomics, proteomics and metabolomics in toxicology , 2005, Expert review of proteomics.

[50]  J. Lehár,et al.  Systematic discovery of multicomponent therapeutics , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[51]  A. Hopkins.  Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[52]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.

[53]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[54]  Andreas Krämer,et al.  Causal analysis approaches in Ingenuity Pathway Analysis , 2013, Bioinform..

[55]  Natalia Ivanova,et al.  The ERGOTM genome analysis and discovery system , 2003, Nucleic Acids Res..

[56]  Ting-Chao Chou,et al.  Theoretical Basis, Experimental Design, and Computerized Simulation of Synergism and Antagonism in Drug Combination Studies , 2006, Pharmacological Reviews.

[57]  N. Plant.  Strategies for using in vitro screens in drug metabolism. , 2004, Drug discovery today.

[58]  Hans V Westerhoff,et al.  Multiscale modelling approach combining a kinetic model of glutathione metabolism with PBPK models of paracetamol and the potential glutathione-depletion biomarkers ophthalmic acid and 5-oxoproline in humans and rats. , 2013, Integrative biology : quantitative biosciences from nano to macro.

[59]  H. Westerhoff,et al.  Modern theories of metabolic control and their applications , 1984, Bioscience reports.

[60]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[61]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[62]  Sergio Contrino,et al.  ArrayExpress—a public repository for microarray gene expression data at the EBI , 2004, Nucleic Acids Res..

[63]  Andrzej M. Kierzek,et al.  QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells , 2013, Bioinform..

[64]  Barbara M. Bakker,et al.  Emergence of the silicon human and network targeting drugs. , 2012, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[65]  Andrew G McDonald,et al.  Fifty‐five years of enzyme classification: advances and difficulties , 2014, The FEBS journal.

[66]  Bruce Tidor,et al.  Optimal Drug Cocktail Design: Methods for Targeting Molecular Ensembles and Insights from Theoretical Model Systems , 2008, J. Chem. Inf. Model..

[67]  A simple kinetic model with explicit predictions for nuclear transport. , 2013, Biophysical journal.

[68]  Tamotsu Noguchi,et al.  Systematic comparison of catalytic mechanisms of hydrolysis and transfer reactions classified in the EzCatDB database , 2006, Proteins.

[69]  J. Meulenbelt,et al.  Review of acute chemical incidents as a first step in evaluating the usefulness of physiologically based pharmacokinetic models in such incidents , 2014, Clinical toxicology.

[70]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[71]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[72]  Mathieu Vinken,et al.  The adverse outcome pathway concept: a pragmatic tool in toxicology. , 2013, Toxicology.

[73]  Michael J. Keiser,et al.  Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets , 2012, Nature.

[74]  H. Coley.  Mechanisms and strategies to overcome chemotherapy resistance in metastatic breast cancer. , 2008, Cancer treatment reviews.

[75]  G. Edelman,et al.  Degeneracy and complexity in biological systems , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[76]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[77]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[78]  H. Westerhoff,et al.  Systems biology tools for toxicology , 2012, Archives of Toxicology.

[79]  J. L. Snoep,et al.  Java Web Simulation (JWS); A Web Based Database of Kinetic Models , 2004, Molecular Biology Reports.

[80]  B. Palsson,et al.  Regulation of gene expression in flux balance models of metabolism. , 2001, Journal of theoretical biology.

[81]  J. Kazius,et al.  Derivation and validation of toxicophores for mutagenicity prediction. , 2005, Journal of medicinal chemistry.

[82]  H. Kitano,et al.  Computational systems biology , 2002, Nature.

[83]  Michael D. Waters,et al.  Toxicogenomics and systems toxicology: aims and prospects , 2004, Nature Reviews Genetics.

[84]  Philip N. Judson,et al.  Assessing confidence in predictions made by knowledge-based systems , 2013 .

[85]  Alexander Bauer,et al.  Quantification of statin effects on hepatic cholesterol synthesis by transient (13)C-flux analysis. , 2009, Metabolic engineering.

[86]  Tanmoy Das,et al.  Engineering three-dimensional topological insulators in Rashba-type spin-orbit coupled heterostructures , 2013, Nature Communications.

[87]  B. Kholodenko,et al.  Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. , 2000, European journal of biochemistry.

[88]  Damian Szklarczyk,et al.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..

[89]  Antje Chang,et al.  BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA , 2012, Nucleic Acids Res..

[90]  Ina Koch,et al.  Model validation of biological pathways using Petri nets , 2004 .

[91]  D. Sanderson,et al.  Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System , 1991, Human & experimental toxicology.

[92]  Sui Huang,et al.  Back to the biology in systems biology: what can we learn from biomolecular networks? , 2004, Briefings in functional genomics & proteomics.