Opportunities for Strategic Planning using Systems Models or A Biologist’s View of using Computers in Risk Assessment Gary Ankley Second Annual McKim Conference September 25-27, 2007

Source Environmental Concentration ExposureDose Biological Event Effect/Outcome Ultimate goal should be linked, predictive models for each aspect of the continuum Fate Exposure Effects Continuum

Chemical Reactivity Profiles Receptor/Ligand Interaction DNA Binding Protein Oxidation Gene Activation Protein Production Altered Signaling Protein Depletion Altered Physiology Disrupted Homeostasis Altered Tissue Development or Function Lethality Impaired Development Impaired Reproduction Cancer Toxicant Macro-Molecular Interactions Cellular Responses Organ Responses Individual Responses Structure Recruitment Extinction Population Responses Source Environmental Concentration ExposureDose Biological Event Effect/OutcomeBiological Event Effect/Outcome QSAR Models

QSARs for Regulatory Decision-Making: Critical Attributes Transparent, transferable Reflects toxicity mechanism of concern Relatable to possible adverse outcome(s) relevant to risk assessment Wide acceptance by all sectors in regulatory community

Fathead Minnow Narcosis Toxicity/Kow Plot

QSARs for Predicting Narcosis Toxicity Transparent, transferable model based on well-defined biological response Reflects basis of toxicity (membrane penetration/disruption) Relatable to adverse outcome highly relevant to risk assessment Widely used as a basis for regulatory decision-making/research

Chemical Binding to Estrogen Receptor (ER) LogKow LogRBA

QSAR for Predicting ER Binding Transparent, transferable model potentially indicative of chronic response(s) Mechanistic reflection of an important point of control within the vertebrate HPG axis ER perturbation could produce adverse effects relevant to risk assessment Translation to regulatory decision-making remains challenging

Historic Challenges to Implementation of QSARs for Chronic Effects Many attempts not based on mechanistic understanding of biology/initiating events (e.g., derived from regression relationships) Reflect only limited (usually one) point of control within biological axis/response of concern Apical outcomes uncertain due to complexity of the toxicity pathways under consideration (e.g., multiple biological outcomes, feedback controls, compensation)

Chemical Reactivity Profiles Receptor/Ligand Interaction DNA Binding Protein Oxidation Gene Activation Protein Production Altered Signaling Protein Depletion Altered Physiology Disrupted Homeostasis Altered Tissue Development or Function Lethality Impaired Development Impaired Reproduction Cancer Toxicant Macro-Molecular Interactions Cellular Responses Organ Responses Individual Responses Structure Recruitment Extinction Population Responses Biological Event Effect/Outcome QSAR Models Systems Models

Brain Liver Pituitary Ovary transcriptional activation translational activation transcription inhibition dissociation Genes mRNA protein Simple molecule receptor Pheno- type state transition Catalysis (including activation) activated protein association Figure Key Blood a b c d e f g h i j k l m n o p q r Graphical Systems Model for Small Fish HPG Axis

Systems Model Overview Developed for small fish, but due to conserved nature of vertebrate HPG axis, has broad applicability Reflects interaction of >105 proteins and 40 simple molecules, regulation of about 25 genes and >300 reactions within six tissues (with multiple cell types) Multiple intended uses  Organizing/understanding genomic data  Identifying key points of control within the HPG axis  Relating molecular initiating events to adverse outcomes

11βHSD P45011β. GnRH Neuronal System GABADopamine Brain Gonadotroph Pituitary D1 R D2 R ? ? Y 2 R NPY Y 1 R GnRH R ? Activin Inhibin GABA A R GABA B R Y 2 R D2 R GnRH Circulating Sex Steroids / Steroid Hormone Binding Globlulin PACAP Follistatin ActivinPAC 1 R Activin R FSH  LH  GP  Circulating LH, FSH FSH RLH R Circulating LDL, HDL LDL R HDL R Outer mitochondrial membrane Inner mitochondrial membrane StAR P450scc pregnenolone 3  HSD P450c17 androstenedione testosterone Cholesterol estradiol Gonad (Generalized, gonadal, steroidogenic cell) Androgen / Estrogen Responsive Tissues (e.g. liver, fatpad, gonads) ARER Blood Compartment 17βHSD P450arom 11-ketotestosterone progesterone17 α -hydroxyprogesterone 17α,20β-P (MIS) 20βHSD Fipronil (-) Muscimol (+) Apomorphine (+) Haloperidol (-) Trilostane (-) Ketoconazole (-) Fadrozole (-) Prochloraz (-,-) Vinclozolin (-) Flutamide (-) β-Trenbolone (+) Ethynyl estradiol (+) Chemical Probes

Types of Genomic Data Transcriptomics Proteomics Metabolomics Fathead Minnow Microarray Fathead Minnow Liver NMR Scan Data from EPA/ EcoArray© CRADA Peptide Mass Fingerprinting Representative protein expression profile in testes of control zebrafish Data from EPA-Cincinnati Fathead Minnow (male) Data from EPA-Athens

Some General Observations to Date Despite the large number of genomic endpoints examined in fathead minnow and zebrafish studies with probe chemicals to date, only a relative handful related to HPG axis function are affected (although many changes are observed in other “non-HPG” related parameters) Chemical probes with different MOA within the HPG axis often affect the same genes, suggesting common nodes of perturbation and/or control (e.g., 20  HSD, FSH , CYP19A)

Common Responsive Genes in Fish HPG Axis

General Observations cont’. The further “up” the axis in terms of perturbation, the less profound the apical effects (e.g., agonists/antagonists of the GABA and dopamine receptors seem to produce less pronounced effects than inhibitors of terminal steroidogenic enzymes and ER, AR agonists/antagonists)  Differences in innate chemical potency?  Differences in specificity of interaction with HPG vs. non-HPG function?  Opportunity for biological adaptation/compensation within the HPG axis?

Ketoconazole Model conazole fungicide Reversible, competive inhibitor of cytochrome P450 (CYP) activities Reduces testosterone production in mammals

Effect of Ketoconazole on Fathead Minnow Reproduction * *

Effect of Ketoconazole on Ex vivo Steroid Production in Fathead Minnows ♀ ♂

Effects of Ketoconazole on Fathead Minnow In vivo Steroid Levels Female Male

Proliferation of Interstitial Cells Involved in Steroid Synthesis A, B = Controls; C= 6  g/L; D= 400  g/L Ketoconazole Effects on Male Gonad GSI a b a a b Ketoconazole (ug/L)

Male Gonad Module from Systems Model

Pregnenolone Progesterone 17  -OH-Pregnenolone 17  -OH-Progesterone CYP17 (hydroxylase) CYP11A 3  -HSD DHEA Androstenedione Testosterone CYP17 (lyase) 3  -HSD 17  -HSD CYP19 17  -estradiol Cholesterol 20  -HSD 17α20β-dihydroxy-4- pregnen-3-one CYP17 (hydroxylase) CYP17 (lyase) estrone CYP19 17  -HSD 11β-OH- Testosterone CYP11B1 11-Ketotestosterone 11βHSD 11-deoxycorticosterone CYP21 corticosterone CYP11B1 aldosterone CYP11B2 11-deoxycortisol CYP21 cortisol CYP11B2 Steroidogenic Compensation to Ketoconazole

Relating Molecular Alterations to Adverse Outcomes Critical both to use of genomic data and mechanistic (QSAR) predictions Toxicity pathway concept essential to establishing linkage across biological levels of organization, but this can only be successful if pathway is considered as network/web rather than linear chain of events  Feedback/homeostatic processes can modulate biological responses  Single initiating event can elicit multiple responses  Multiple initiating events (mechanisms) may trigger toxicity via same mode of action Systems models facilitate consideration of pathway complexity

Key Nodes in Toxicity Pathways: Initiation versus Response Molecular initiating event (e.g., receptor activation, enzyme inhibition) is logical focus of mechanistic QSAR models But, this is not necessarily the key “choke point” modulating adverse apical responses Need understanding/depiction of toxicity pathway to discern between the two different types of nodes and relate them to one another

Key Nodes in Toxicity Pathways: Illustration from the HPG Axis in Fish Vitellogenein (vtg), egg yolk protein, is produced normally by oviparous female vertebrates in response to stimulation of the ER by 17β-estradiol Commonly used exposure biomarker in males for exposure to exogenous estrogens Effective production of vtg in females critical to successful egg production Vtg production in females can hypothetically be decreased via several discreet mechanisms within the HPG axis

Chemical Inhibitors of VTG Synthesis in the Fathead Minnow Fenarimol: conazole fungicide with multiple hypothesized MOA, including ER antagonism Prochloraz: conazole fungicide which inhibits several CYPs involved in steroid production (CYP17, CYP19) Fadrozole: specific pharmaceutical inhibitor of CYP19 17β-trenbolone: anabolic androgen that causes feedback inhibition of steroid production 17α-trenbolone: anabolic androgen metabolite that causes feedback inhibition of steroid production

11βHSD P45011β GnRH Neuronal System GABADopamine Brain Gonadotroph Pituitary D1 R D2 R ? ? Y 2 R NPY Y 1 R GnRH R ? Activin Inhibin GAB A A R GAB A B R Y 2 R D2 R GnR H Circulating Sex Steroids / Steroid Hormone Binding Globlulin PACAP Follistatin Activi n PAC 1 R Activin R FSH  LH  GP  Circulating LH, FSH FSH RLH R Circulating LDL, HDL LDL R HDL R Outer mitochondrial membrane Inner mitochondrial membrane StAR P450scc pregnenolone 3  HSD P450c17 androstenedion e testosterone Cholestero l estradiol Gonad (Generalized, gonad, steroidogenic cells and oocytes) Androgen / Estrogen Responsive Tissues (e.g. liver, fatpad, gonads) AR ER Fenarimol Blood Compartment 17βHSD P450arom 11-ketotestosterone progesterone17 α -hydroxyprogesterone 17α,20β-P (MIS) 20βHSD Fadrozole Prochloraz β trenbolone α trenbolone Estradiol + Vtg (steroidogenic cells) (oocytes) Molecular Mechanisms of Inhibition of VTG Production

Effects of Aromatase Inhibition on Reproduction in the Fathead Minnow Fadrozole

Linking Molecular Responses to Apical Effects: VTG and Fecundity ChemicalExposure Concentrations 17β-trenbolone0.005µg/l, 0.05µg/l, 0.5µg/l, 5µg/l, and 50µg/l 17α-trenbolone0.003µg/l, 0.01µg/l, 0.03µg/l, and 0.1µg/l Prochloraz0.03mg/l, 0.1mg/l, and 0.3mg/l Fenarimol0.1mg/l and 1mg/l Fadrozole2µg/l, 10µg/l, and 50µg/l Fecundity = * Vtg (R 2 = 0.88)

Chemical Reactivity Profiles Receptor/Ligand Interaction DNA Binding Protein Oxidation Gene Activation Protein Production Altered Signaling Protein Depletion Altered Physiology Disrupted Homeostasis Altered Tissue Development or Function Lethality Impaired Development Impaired Reproduction Cancer Toxicant Macro-Molecular Interactions Cellular Responses Organ Responses Individual Responses Structure Recruitment Extinction Population Responses Biological Event Effect/Outcome QSAR Models Systems Models Population Models

prochloraz fenarimol fadrozole Vtg 17β-trenbolone 17α-trenbolone prochloraz fenarimol fadrozole Measurement of vtg concentrations and fecundity for female fathead minnows Life table with age specific vital rates of survival and fecundity for the fathead minnow population Carrying capacity for the fathead minnow population Projection of density dependent logistic population trajectories for the fathead minnow population based upon change in vtg Fecundity Population projection for populations at carrying exposed to stressors that depress vitellogenin production Population Forecasts Based on Molecular Responses

Summary: Conceptual Systems Models in Research and Regulatory Ecotoxicology Provide a framework whereby data from multiple biological levels of organization (including “omics”) can be integrated and understood in the context of toxicity pathways Guide hypothesis-driven testing of chemicals/pathway components Help identify key molecular initiating events within an axis/pathway that subsequently can be represented by in vitro assay systems and/or QSAR models Serve as a basis for defining linkages between molecular/biochemical changes and adverse outcomes, in part, through identification of key response nodes

Future Steps Proof-of-concept studies focused on well- defined axes such as HPG/HPT Cataloging other pathways and building first-generation conceptual systems models Linkage of systems frameworks with other models (e.g., QSAR, PB-PK, population) as a basis for making predictions/guiding testing