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The McKim Conferences for the Strategic Use of Testing Gitchee Gumee Conference Center Duluth, Minnesota June 27-29, 2006.

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Presentation on theme: "The McKim Conferences for the Strategic Use of Testing Gitchee Gumee Conference Center Duluth, Minnesota June 27-29, 2006."— Presentation transcript:

1 The McKim Conferences for the Strategic Use of Testing Gitchee Gumee Conference Center Duluth, Minnesota June 27-29, 2006

2 2 Test Methods for Every Hazard The matrix of species, hazards and exposure routes resulted in hundreds of test guidelines The search for “alternative” in vitro methods has added significantly to the batteries of tests to interpret The number of chemicals reviewed is a small percentage of chemicals being produced and used Our assessment infrastructure limits the number of chemicals which can be assessed for safety

3 3 Mortality Systemic Toxicity Disease Cancer Reproductive Fitness Viable Offspring Fertility Developmental Impairment Terato Prenatal Deficits Battery-Driven Paradigm “We toxicologists are the interpreters of data!” Current Perspective -- “What test data involving all possible adverse effects would I need to see before I would be willing to consider this chemical to be safe?” Systematic BATTERY of Animal Testing EPA risk assessor

4 4 Hypothesis-Driven Paradigm The purpose of models is to improve our ability to generate hypotheses and prescribe tests Screening of untested chemicals requires a new generation of extrapolation tools The core of extrapolation is the development of classes, categories, and mechanistic analogues

5 5 The CHALLENGE 30 Years of developing alternative methods has not reduced the use of animals or costs of testing One reason is a serious mismatch of the domains of alternative methods involving in vivo tests For untested chemicals, the primary priority-setting approach to date is production volume and not risk We need a new paradigm where testing sequences are driven by hypotheses from models/existing data

6 6 The BARRIERS One major obstacle is that test data are scattered throughout the literature or in private databases Even in comprehensive and critiqued databases like AQUIRE, existing data may be for different chemicals, species or endpoints The bridge is a systematic approach to modeling chemical interactions within virtual animal models

7 7 Across Chemicals (QSAR) Across Species Biological Effects Endpoints Extrapolation of Test Data

8 8 Major Hurdles Chemical Speciation –Reactivity –Hydrogen Bonding Comparative Metabolism –Species –Populations Selective Toxicity –Species Biological Models –Biochemical Vulnerability

9 9 Delineation of Toxicity Pathways Linkages Across Levels of Biological Organization Chemical Reactivity Profiles Receptor binding DNA alteration Proteins adducts Membrane effects Gene Activation Protein Syn/deg Cell Signaling GSH balance Respiration Osmoregulation Liver Function Gonad Devel Lethality Growth Development Reproduction MolecularCellularOrganIndividual In Silico Methods In vitro MethodsIn vivo Methods Steric & Electronic

10 Delineation of Toxicity Pathways Linkages Across Levels of Biological Organization Chemcal Reactivity Profiles Reversible Nonspecific Reversible Specific Covalent Membrane Effects Receptor Binding Protein Adducts DNA alteration Respiration Osmoregulation Liver Function Gonad Devel Lethality Growth Development Reproduction MolecularCellular OrganIndividual In Silico Methods In vitro MethodsIn vivo Methods Electronic

11 Delineation of Toxicity Pathways Linkages Across Levels of Biological Organization Chemical Reactivity Profiles Reversible Nonspecific Reversible Specific Covalent Lethality Growth Development Reproduction MolecularCellular OrganIndividual In Silico Methods In vitro MethodsIn vivo Methods Electronic Molecular Initiating Events Membranes Energy Charge Nuclear Receptors Protein Synthesis DNA Integrity 2-D Fragments Genome System Biology Regulatory Endpoints Exposure/ Metabolism Penetration Detoxification Activation Non-Mechanistic Models of Large Training Sets (low-level descriptors, no metabolism, multi-pathway)

12 12 Molecular Initiating Events Speciation and Metabolism Measurable System Effects Adverse Outcomes Parent Chemical Knoxville Workshop Framework for Predicting Reactive Toxicity Rather than developing statistical models of complex endpoints, molecular initiating events are identified as well-defined QSAR endpoints…..and used to estimate the probabilities for important downstream biological effects based on transparent assumptions

13 13 Molecular Initiating Events Speciation and Metabolism Measurable System Effects Adverse Outcomes Parent Chemical Delineating the Models for Toxicity Pathways 1.Establish Plausible Molecular Initiating Events 2. Design Database for Abiotic Binding Affinity/Rates 3. Explore Correlations/ Pathways to Downstream Effects QSAR Systems Biology

14 14 Major Pathways for Reactive Toxicity from Soft Electrophiles Systemic Responses Skin Liver Lung Michael Addition Schiff base Formation S N 2 Acylation Irreversible Protein Modification (Rates) Immunogenic In Chemico Mechanisms Molecular Initiating Events Exposed Surface Irritation Systemic Immune Responses Necrosis Skin Lung/Gills GI Tract In vivo Endpoints Yes No

15 15 Molecular Initiating Events Speciation and Metabolism Measurable System Effects Adverse Outcomes Parent Chemical IQF Framework for Reactive Toxicity Using Glutathione Thiol Reactivity as a Model Soft Nucleophile: 1.Correlate R(GSH) with Excess Toxicity in EcoTox Data 2.Correlate R(GSH) with Mammalian Skin Sensitization 3.Correlate R(GSH) with Mammalian Inhalation Toxicity 4.Correlate R(GSH) with Chemical Induced Liver Failure

16 16 -6.00-5.50-5.00-4.50-4.00-3.50-3.00-2.50 Log Molar Concentration 0 25 50 75 100 125 Growth-Wet Weight (mg) 0 20 40 60 80 100 % Survival Octanol Excitability Lethargy/ CNS depression Coma Death

17 17 -6.00-5.50-5.00-4.50-4.00-3.50-3.00-2.50 Log Molar Concentration 0 25 50 75 100 125 Growth-Wet Weight (mg) 0 20 40 60 80 100 % Survival Octanol

18 18 -6.00-5.50-5.00-4.50-4.00-3.50-3.00-2.50 Log Molar Concentration 0 25 50 75 100 125 Growth-Wet Weight (mg) 0 20 40 60 80 100 Aniline % Survival

19 19 Appropriate Exposure Metric Response Endpoint Subcellular Cell Tissue Chronic Whole Organism Acute Whole Organism Is this really the Order for Industrial Chemicals?

20 20 Appropriate Exposure Metric Response Endpoint Subcellular Cell Tissue Chronic Whole Organism Acute Whole Organism Can We Predict the Order of Effects Along Major Toxicity Pathways? Factor of 10 for many chemicals

21 21 Increasing Concentration Response Endpoint 30d EC 50 (growth) 4d LC 50 repeated Dose EC 50 (repro) repeated dose EC 50 (development?) Can We Use Knowledge About Mechanisms To Predict the Most Protective Endpoint?

22 22 Develop QSAR and Biological Models for screening and initial testing hypotheses Integrate In-vitro and HTP capabilities to test hypotheses and generate new hypotheses downstream Reduce time and cost by focusing resources Goals for Predictive Toxicology


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