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1 ère Journée de Biologie Systémique Université Paris 5 La Biologie des Systèmes en Toxicologie Robert Barouki UMR-S 74

1 ère Journée de Biologie Systémique Université Paris 5 La Biologie des Systèmes en Toxicologie Robert Barouki UMR-S 747 INSERM Université Paris 5 Pharmacologie Toxicologie et Signalisation Cellulaire Centre des Saints Pères 22 Mai 2006. A variety of Systems in Toxicology.

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1 ère Journée de Biologie Systémique Université Paris 5 La Biologie des Systèmes en Toxicologie Robert Barouki UMR-S 74

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  1. 1ère Journée de Biologie Systémique Université Paris 5 La Biologie des Systèmes en Toxicologie Robert Barouki UMR-S 747 INSERM Université Paris 5 Pharmacologie Toxicologie et Signalisation Cellulaire Centre des Saints Pères 22 Mai 2006

  2. A variety of Systems in Toxicology • Drug and polluants toxicity: differences and similarities • Global systems • The Organism as a system • Cellular and molecular systems

  3. Environmental Toxicology: a global system

  4. Environmental Toxicology: a global system exposure External contact Internal dose Preclinical response Clinical response sources contaminants Internal contamination biomarkers New technologies Can we predict toxicity?

  5. Drug Toxicity: the organism as a system Target tissues Toxicity

  6. Drug Toxicity: a health and economical issue Impact de la toxicité des médicaments Can we predict toxicity?

  7. Paradise on earthlow cost, high efficiency Predictive and Mechanistic Toxicology Can New Technologies help? • High throughput technologies: the « omics » • Lessons from molecular and cellular biology • Analytical Methods • Systems biology • In silico prediction

  8. physiome metabolome proteome transcriptome genome Invasion of Toxicology by the OMICS Metabonomics Metabolomics Functional genomics Proteomics Just add Toxico- Structural genomics

  9. Is it all in the gene structure?? • Large scale detection of polymorphisms, • in particular SNPs • A fraction of toxicity can be explained by gene structure • Individual susceptibility • Pharmaco- and Toxico-genetics

  10. The number of genes (1) 20 000 genes The Worm C elegans 25 000 genes The most powerful man in the world Not Surprised??

  11. The number of genes (2) 25 000 genes René Descartes 20 000 genes The Worm C elegans Complexity is not only related to the number of genes

  12. Where does complexity come from? • gene regulation (toxicogenomics) • mRNA splicing (toxicogenomics) • mRNA degradation (toxicogenomics) • Protein stability (toxicoproteomics) • Post translational regulation (toxicoproteomics) • Protein-protein interaction (interactomes) • connection of metabolic parthways (metabolomics) • Systems biology: a comprehensive description

  13. The Xenobiotics Stress System • Xenobiotics are low molecular weight foreign • Substances: • Drugs • Pollutants • Nutrients Similar responses at the cellular level Exposure to xenobiotics is accompanied by a stress

  14. What is a stress?? • Stress: the word • Physics: response of a metal • Physiology: a defined set of responses to extreme situations (Selye) • Cell biology: response of a cell to aggression • Psychology-social sciences: response of an individual or of a group Stress is an adaptive response to a significant shift in cellular conditions This response has a cost

  15. Xenobiotics stress Xenobiotics Receptor: Detection and induction O-Conj Enzymes (XMEs) and transporteurs: Metabolism and exits elimination Adaptation: 1- detection of xenobiotics and gene induction 2- transformation and elimination

  16. Xenobiotic Receptor MDR MRP CYP GST UGT O-Conj OH O-Conj PhaseI PhaseII PhaseIII Metabolism of Xenobiotics the Detoxication System

  17. AhR PXR - CAR Xenobiotics receptors Legitimate and Illegitimate Receptors for Xenobiotics Multiple Pathways and Dangerous Liaisons Xenobiotics steroid hormones lipids ER PPAR Endocrine disruption Adaptation and stress possible toxicity Metabolic disruption Both legitimate and illegitimate liaisons can be dangerous

  18. Cl Cl O Cl O Cl Dioxin TetraChloroDibenzoDioxin: TCDD • Lessons from the chemistry • Receptor: AhR, shared with other pollutants, xenobiotics and endogenous compounds • Induction of XMEs (CYP1A1): adaptation and stress response • Regulation of dozens of other genes: What for??

  19. The Dioxin Receptor System: lessons from genomics Hundreds maybe Thousands of ligands: xeno or endo Lipid metabolism Cell cycle Xenobiotics metabolism Cell migration Large number of toxicogenomics studies; Marchand et al, Mol Pharmacol, 2005

  20. TCDD Cell Morphology and Motility Diry et al, Oncogene, 2006,

  21. The Dioxin Receptor System: lessons from protein interaction Rb Src NFkB inflammation proliferation ARNT HIF hypoxia Few large scale studies. Use of Protein interaction network in yeast Yao et al, PLOS Biology, 2004

  22. The Dioxin Receptor System: lessons from metabolism CYP OH DNA adduct genotoxicity BP BP H2O2 p53 Oxidative stress The p53 system apoptosis Large scale studies: predictive pharmaco-metabonomic phenotyping using urinary samples (Clayton et al, Nature, 2006)

  23. Consortia and databases in Toxicogenomics • ILSI Health and Environmental Service Institute (collab European Bioinformatics Institute) • Toxicogenomics Research Consortium (National Center for Toxicogenomics) • COMET: Consortium for Metabonomics Technology • EDGE: Environment, Drugs and Gene Expression • PharmGKB: PharmacoGenomics Knowledge Base • CEBS: Chemical Effect in Biological Systems Knowledge Base • Protein Interaction Network

  24. Structural biology Major breakthroughs in drug metabolism (CYP3A4) and drug inductioin (PXR)

  25. Structural biology The promiscuity of the PXR revealed by its structure: 3 possible positions for a single molecule

  26. In silico prediction Mosly developped for ADMET: Absorption, Distribution, Metabolism, excretion, Toxicity Data modelling: QSAR (Quantitative Structure Activity Relationship). Correlate a set of molecular or structural descriptors of a drug with a defined property (such a particular toxicity) Highly dependent on the quality of the data and the mathematical approach Molecular modelling: mostly based on structural information and modelling to predict ligand protein interaction

  27. Iterative modelling for drug development integrating ADMET

  28. A Systems Biology Approach Goal: build a model integrating all data: genomics, protein interaction, metabolic pathway, toxicity… Be as quantitative as possible Predict the consequences of perturbation in the system Can be more focused: gene regulation networks protein interaction networks Metabolic pathways….

  29. A Systems Biology Approach: the case of 4-OH-tamoxifen Metadrug (http:/www.genego.com)

  30. Toxicology Systems Biology: a global approach

  31. Systems Toxicology • Molecular and global aspects: integrates systems biology as well as more traditional toxicological data • Describes new mechanisms • High Predictive power: development of safer drugs and safer chemicals (Reach protocol of the EU)

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