Toxicity Testing in the 21st Century Adverse Outcome Pathways and Development of Alternative Environmental Testing Methods

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1 1//1 Toxicity Testing in the 1st Century Adverse Outcome Pathways and Development of Alternative Environmental Testing Methods Daniel L. Villeneuve, US EPA, ORD, NHEERL Mid-Continent Ecology Division, Duluth, MN, USA October The, contents 1 of this presentation neither constitute nor, necessarily, reflect US EPA views or policies. 1 The aim of REACH is to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances. The highest priority for personal care products companies is the safety and health of consumers of all ages who use and enjoy our products. 1

2 1//1 Historically, toxicity testing paradigm has favored generation of in vivo animal toxicity test data for all possible outcomes to determine which of all possible effects are relevant. (Jones and Bradbury, US EPA, 5). S. Sanchez/The Image Bank/Getty Problems with the historical approach: 1. Costs of whole animal testing Example, pesticide registration total costs around $5 M Estimated cost tier 1 screening $-4K Estimated cost tier testing $1-.5M Animal use (approx. 6-1 animals per chemical a ). Tens of thousands of chemicals to evaluate 3. Species diversity (5,-3, species of fish alone) 4. Mixtures a Center For Alternatives To Animal Testing. The Johns Hopkins University

3 1//1 Demands for data to support human health and ecological risk assessments are increasing, not decreasing - Broader scope of adverse outcomes being considered. Data needs $ animals time Four competing objectives Depth providing the most accurate, detailed, characterization possible. Breadth providing data on the broadest universe of chemicals, endpoints, species, life-stages, etc. Animal welfare using the fewest animals possible and minimizing suffering. Conservation minimizing expenditure of money and time on testing and review. 3

4 1//1 Transform toxicity testing from a system based on whole-animal testing to one founded primarily on in vitro methods that evaluate changes in biologic processes using cells, cell lines, or cellular components, preferably of human origin The committee envisions a new toxicity-testing system that evaluates biologically significant perturbations in key toxicity pathways by using new methods in computational biology and a comprehensive array of in vitro tests based on human biology Toxicity pathway: Cellular response pathways that, when sufficiently perturbed, are expected to result in adverse health effects. Examples: antioxidant response, heat shock response, nuclear receptor response, DNA response, endogenous hormone Needs: 1. Scientifically sound alternatives to whole animal testing In vitro; Shorter term, smaller sample size, - biomarkers, high through-put, low cost. Scientifically-sound extrapolation across chemical classes/structures and prioritization tools QSAR, Read-across, Predictive models 3. Scientifically sound extrapolation among taxa Phylogenetics, comparative physiology, comparative genomics, predictive models 4. Scientifically sound predictions of the effects of mixtures Predictive models 4

5 fmol/h/mg fmol/h/mg 1//1 Research, develop, validate and translate innovative chemical testing methods that characterize toxicity pathways. ToxCast 7 6 Ovary A B 5 C 4 3 IC5 = /-.8 μm D E fadrozole (um) 5 A 45 B C 5 IC5=8.8 +/ μm 15 D 1 5 Brain E F fadrozole (um) Villeneuve et al. 6. Aquat. Toxicol. 76:

6 1//1 Sanderson et al. 1, EHP, 19: A bit of fish reproductive biology ER Agonist Estradiol Estrogen Receptor Agonism Hepatocyte Vtg production Ovary Oocyte development Female Ovulation & spawning Population Stable or increasing trajectory OH OH HO HO ERE-Vtg Cellular response pathway Biologic inputs Normal Biological Function 1 6

7 Cumulative Number of Eggs (Thousands) Vtg (mg/ml) E (ng/ml) Aromatase Activity (fmol/mg -1 hr -1 ) 1//1 Effects of Aromatase Inhibition on Reproduction in the Fathead Minnow (1 d) N Population N Biochemical 15 a b Male Female Fadrozole (Functional Linkage) 75 CN c c 1 8 Fadrozole (ug/l) Control 8 5 Fadrozole (µg / L) Exposure (d) Ankley et al.. Toxicol. Sci. 67: Miller et al. 7. Environ. Toxicol. Chem. 6: Control 1 5 Fadrozole (µg/l) An Adverse Outcome Pathway (AOP) is a conceptual framework that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome, at a level of biological organization relevant to risk assessment (Ankley et. al. 1, Environ. Sci. Techol. 9:73-741). Toxicant Chemical Properties Macro- Molecular Interactions Receptor/Ligand Interaction DNA Binding Protein Oxidation Cellular Responses Gene activation Protein production Altered signaling Organ Responses Altered physiology Disrupted homeostasis Altered tissue development/ function Organism Responses Lethality Impaired Development Impaired Reproduction Population Responses Structure Recruitment Extinction Toxicity pathway: Cellular response pathways that when sufficiently perturbed are expected to result in adverse health effects Toxicity Testing in the 1 st Century, NRC 7 7

8 1//1 What is an Adverse Outcome Pathway? Anchor 1: a Molecular Initiating Event in which a chemical interacts with a specific biomolecule... Toxicant Chemical Properties Macro-Molecular Interactions Receptor/Ligand Interaction DNA Binding Protein Oxidation Anchor 1 Molecular Initiating Event 15 What is an Adverse Outcome Pathway?... Anchor : an Adverse Outcome at the organismor population-level that is relevant to risk assessment Organism Responses Lethality Impaired Development Impaired Reproduction Population Responses Structure Recruitment Extinction Anchor Adverse Outcome 16 8

9 1//1 What is an Adverse Outcome Pathway? The cellular and organ responses through which the molecular initiating event produces an adverse outcome completes the Adverse Outcome Pathway Toxicant Chemical Properties Anchor 1 Molecular Initiating Event Macro- Molecular Interactions Receptor/Ligand Interaction DNA Binding Protein Oxidation Cellular Responses Gene Activation Protein Production Altered Signaling Protein Depletion Organ Responses Altered Physiology Disrupted Homeostasis Altered Tissue Development or Function Organism Responses Lethality Impaired Development Impaired Reproduction Cancer Population Responses Structure Recruitment Extinction Anchor Adverse Outcome Linkages between levels of organization may be causal, mechanistic, inferential, or correlation based Establishes scientifically defensible connection between initiating event and adverse outcome. 17 AOP: Narcosis Baseline Toxicity Not all linkages are known with absolute certainty in this AOP Non-polar Narcotics Numerous Chemicals Cellular Membranes Changes in fluidity / transport Neurons? (Multiple)?? CNS/ Multiple Organ types Respiration, Metabolic rate Organism Equilibrium loss, Mortality Population Declining trajectory but the relationship between chemical property and adverse outcome is well established 18 9

10 96-h Log LC5 (mg/l) 1//1 AOP : Narcosis Baseline Toxicity 6 Fathead Minnow 96-h Toxicity Log Kow Data from Russom et al ET&C, 16, AOP : Narcosis Baseline Toxicity Non-polar Narcotics Numerous Chemicals Cellular Membranes Changes in fluidity / transport Neurons (Multiple)??? CNS/ Multiple Organ types Respiration, Metabolic rate Organism Equilibrium loss, Mortality Population Declining trajectory Log Kow 96 h LC5 Predictive QSAR based upon log Kow 1

11 1//1 Why Adverse Outcome Pathways? Provides a conceptual framework in which data and knowledge collected at multiple levels of biological organization can be synthesized in a way that is useful to risk assessors and ecotoxicologists. Key uncertainties and research priorities identified Promotes effective communication between research scientists, risk assessors, risk managers, others Helps establish scientifically credible linkages between suborganismal responses/properties and whole organism outcomes foundation for acceptance of alternative methods 1 Toxicity-pathway identification what are the key pathways whose perturbation results in toxicity? Adversity What adverse effects are linked to specific TP perturbations? 11

12 1//1 Compartment Brain? GABA? GnRH D1 R Neuronal System Y R GnR H GAB A A R D R NPY? Dopamine Chemical Probes 1 Fipronil (-) Muscimol (+) Pituitary PACAP GAB Y R A B R Follistatin GnRH Activi PAC 1 R n R Gonadotroph Activin R GPa Y 1 R D R 3 4 Apomorphine (+) Haloperidol (-) FSHb LHb Blood Circulating LDL, Circulating LH, FSH HDL LDL R LH R FSH R HDL R Cholestero Outer mitochondrial l membrane StAR 5 6 Trilostane (-) Ketoconazole (-) Gonad (Generalized, gonadal, steroidogenic cell) Activi n Inhibi n Inner mitochondrial P45scc membrane pregnenolone 3bHSD 17 α -hydroxyprogesterone progesterone P45c1 7 βhs androstenedion D e 17βHSD 17α,β-P (MIS) testosterone P45arom P4511β. 11βHSD Fadrozole (-) Prochloraz (-,-) Vinclozolin (-) Blood Androgen / Estrogen Responsive Tissues (e.g. liver, fatpad, gonads) 11-ketotestosterone Circulating Sex Steroids / Steroid Hormone Binding Globlulin ER AR estradio l Flutamide (-) β-trebolone (+) Ethynyl estradiol (+) 1

13 1//1 THREE-PHASED TESTING STRATEGY Fathead minnow 1 d, reproduction Ground to ecologically-relevant response. Supervised (hypothesis-based) analyses across biological levels of organization Zebrafish 4-96 h, microarray, proteomics Unsupervised analysis to identify impacted pathways/functions. Hypothesis formulation Biomarkers Fathead minnow 16 d, time-course microarray, metabolomics + Short-term time-course Temporal dynamics, including recovery Hypothesis testing Biomarker validation Species comparisons Transcriptional networks AOPs Converging at Impaired Vitellogenesis: Alternative Assays AR Agonist Androgen Receptor Hepatocyte Ovary Female 3) 17ß-Trenbolone Agonism Reduced Vtg production Oocyte development Ovulation & spawning Toxicity pathways require functioning multi-organ system ) Aromatase Inhibitor Fadrozole Aromatase Enzyme Inhibition Hepatocyte Reduced Vtg production Ovary Oocyte development Female Ovulation & spawning Toxicity pathway spanning multiple cell types/tissues 1) ER Antagonist Fenarimol Estrogen Receptor Antagonism Hepatocyte Reduced Vtg production Ovary Oocyte development Female Ovulation & spawning Toxicity pathway within a single cell type & tissue 13

14 CYP19A mrna CYP11A mrna Ex vivo E (fold-change relative to control; log ) FSHR mrna StAR mrna 1//1 N N 5 o C, 1 h RIA CN 3 1 CON FAD-3 FAD direct effect compensation A Day CON FAD-4 FAD B for proteins rate limiting for steroid 3 Up-regulation of transcripts CON coding FAD-4 FAD-33 biosysnthesis in response to fadrozole exposure. Rapid, concentration-dep, less persistent Day C CON FAD-4 FAD D CON FAD-4 FAD Day Day Villeneuve et al. 9, Environ. Health Perspect. 117:

15 Relative change (% Control) GSI Estradiol (ng/ml) Vitellogenin (mg/ml) Testosterone (ng/ml) Vitellogenin (mg/ml) Estradiol (ng/ml) Testosterone (ng/ml) GSI T (ng/ml)/g T (ng/ml)/g 1//1 Effect of KTC on Steroid (E and T) production In vitro Ex vivo A AB ovary a 14 E 1 T Ketoconazole (um) A AB AB B B B testes AB B Ketoconazole (µg/l) b a a A 1 A A B B b c Female.3 b.4.18 Histopathology, male gonad c Male 1 15 d d Proliferation of Ketoconazole Interstitial (ug/l) Cells Involved Ketoconazole in Steroid (ug/l) Synthesis A, B = Controls; C= 6 g/l; D= 4 g/l 15

16 1//1 Molecular initiating event Perturbed cellular response pathway Adverse outcome relevant to risk assessment Exposure Uptake-Delivery to Target Tissues Cellular response pathway Biologic inputs Perturbation Normal Biological Function Adaptive Responses Early cellular changes Cell inury, Inability to regulate Adverse Outcomes (e.g., mortality, Reproductive Impairment) Understanding and modeling adaptive responses to stressors Critical for: Concentration-duration-response extrapolation (hh & eco) In vitro to in vivo extrapolation (hh & eco) Aspects of population genetics and fitness (eco) Nichols et al. (1) 16

17 1//1 Effects of exposure duration Low-dose response concentration-response in general Biologically-based modeling Overshoot Compensation during exposure 17

18 1//1 Reverse Engineering Adverse Outcome Pathways Adverse effects mediated through unknown, or poorly characterized, mechanisms of action. Global analysis tools provide new capabilities for probing biological systems and identifying those features that respond to a given stressor. Chemical exposure Gene expression response Reverse engineering of interaction network Refinement of network with binding interactions Computational model of network architecture Defining interaction network Relate network to known pathways and outcomes Expression effects Exposed animal Expression effects Expression across many effects Expression experimental across many effects Expression conditions experimental across many effects Expression conditions experimental across many effec Expression conditions experimental across many eff Expression conditions experimental across man e Expression conditions experimenta across Expressio ma conditions experimen across Express m condition experime across Expre conditio experim acros Expr condi expe acr con exp Ex a E co ex Mutual information a measure of how much one random variables tells us about another -can be thought of as the reduction in uncertainty about one random variable given knowledge of another 18

19 1//1 Mutual information network Physiology measurements Receptor hubs Targets Mapping Functional Modules onto Network none none Metabolic processes Inflammatory response Regulation of biol. processes none mrna processing Cell cycle Protein secretion DNA damage response system development organ development developmental process anatomical structure development intrinsic to plasma membrane 19

20 Ex vivo E (fold-change relative to control; log ) 1//1 Mapping Specific Chemical Effects onto Network Testosterone Mapping the transcriptional response to Flutamide exposure (an anti-androgen) Flutamide genes modulated by flutamide exposure cluster in proximity of module 4 enriched in the GO terms anatomical structure development, cell motility and inflammatory response (FDR<1%). AR Testosterone Flutamide effects Plasma testosterone Connected to testosterone Down-regulated Flutamide Up-regulated Flutamide Perkins et al. 1 (ET&C; in press) 3 1 CON FAD-3 FAD direct effect compensation d. 1 d. 4 d. 8 d. 1 post-exposure d. post-exposure Habib et al. (in preparation)

21 1//1 Multiple Pathways What alteration in response can be expected from simultaneous perturbations of multiple pathways. 1

22 1//1 The Systems Biology Perspective Systems biology is the study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life. Systems are comprised of parts which interact. Interaction of these parts gives rise to "emergent properties". Emergent properties cannot be attributed to any single parts of the system. Irreducible. To understand systems, and to be able to fully understand a system's emergent properties, systems need be studied as a whole.

23 1//1 Long Term Goal Predictive (Eco)Toxicology A science of capable of making viable predictions of toxicity outcomes based on previously untested relationships (Ramos et al. 7) Relies on an understanding of organizing principles that underlie biological response to chemicals Uses that knowledge in a systematic fashion to predict, based on physical/chemical properties, a priori knowledge, and/or simplified bioassays, the likelihood that a given chemical will elicit an adverse effect (prospective) or that an observed response might be associated with a given chemical (diagnostic) Grounded in established and verifiable theory Transparent Reasonable and quantifiable uncertainty Optimal use of available resources and data Many challenges Ready or not decisions will be made. What are the next incremental steps toward the goal? Make effective use of existing knowledge and technology to make more informed decisions (value added) 3

24 1//1 USEPA, NERL Cincinnati, OH D. Bencic, M. Kostich, A. Biales, D. Lattier, J. Lazorchak, G. Toth, R. Wang, USEPA, NHEERL Duluth, MN, and Grosse Isle, MI G. Ankley, E Durhan, M Kahl, K Jensen, E Makynen, D. Miller, D. Villeneuve, USEPA, NERL Athens, GA T. Collette, D. Ekman, M. Henderson, K. Ralston-Hooper, Q. Teng USEPA, NHEERL, -RTP, NC M. Breen, R. Conolly, S. Edwards, L. Burgoon USEPA, NCER, STAR Program N. Denslow (Univ. of Florida), E. Orlando, (Florida Atlantic University), K. Watanabe (Oregon Health Sciences Univ.), M. Sepulveda (Purdue Univ.) USACE, ERDC Vicksburg, MS E. Perkins, T. Habib, M. Mayo Other partners Small Fish Comp Tox Research Team Jackson St. University, N. Garcia-Reyero, University of St. Thomas, D. Martinovic Birmingham University, Birmingham, UK, F. Falciani UC Berkeley, C. Vulpe UC Santa Barbara, J. Shoemaker, F. Doyle III Joint Genome Institute, DOE (Walnut Creek, CA) Sandia, DOE (Albuquerque, NM) Pacific Northwest National Laboratory (Richland, WA) Acknowledgements Acknowledgements 4

25 1//1 Acknowledgements SETAC Pellston Workshop Participants 5