Use of Quantitative Structure-Activity Relationships (QSAR) to Predict Chemical Toxicity in Aquatic Organisms P. Schmieder US EPA Mid-Continent Ecology.

Use of Quantitative Structure-Activity Relationships (QSAR) to Predict Chemical Toxicity in Aquatic Organisms P. Schmieder US EPA Mid-Continent Ecology Division-Duluth, MN H. Aladjov NRC Post-doctoral Fellow

Predicting Chemical Toxic Potential
Ecological and Human Health Risk Assessments for chemicals seek to anticipate and limit adverse outcomes from exposure to chemicals Challenges: Limited data; limited authority to ask for test data US EPA Toxic Substances Control Act (TSCA) Pre-Manufacture Notifications ~ 2500 new chemicals/yr Assessments based on chemical structure Large number of chemicals to assess quickly TSCA, IUCLID; tens of thousands of chemicals to assess for any potential to cause toxicity- FQPA; SDWA -Eco and HH RA seek to “anticipate and limit adverse outcomes” – thus methods are needed to “anticipate” Chemical Toxic Potential -With both limited data and/or need to screen a large number of chemicals, there are limitations in time and resources available to evaluate risks. there are also pressures to reduce the numbers of animals tested while also trying to achieve low/no risk . The Agency needs to develop ranking, prioritization, screening methods to: To determine what to focus on -- which types of chemicals are most likely to produce what adverse outcomes; This can be used to inform which screening tests would best evaluate risks for a group of chemicals. Some specific challenges are listed here relating to certain EPA Program Office needs: PMN – etc.

QSARs are based upon the following principles:
Chemicals that produce the same type of toxicity, through the same mechanism (pathway), are “similar” (chemical similarity is defined in the context of biological similarity) A chemical’s structure governs it’s properties and thus it’s toxicological potential There is a history of QSAR development at ORD/NHEERL/Mid-Continent Ecology Division (Duluth) since the late 1970s and early ’80s when the first QSAR models were delivered to OPPTS for use in PMN reviews. From many years of study, involving in vivo testing, in vitro measures, and computational approaches, several QSAR principles were arrived at, including the ones listed here.

Toxicological QSAR Potential Chemical Structure/ Property D Chemical
D Dose Metric (kinetics/ metabolism) D Endpoint Potency Toxic potency is correlated to chemical concentration at the site of action -C. Hansch

Steps in QSAR Development
Starts with a well-defined biological system Reproducible assay; chemical kinetics; A number of chemicals are tested that produce an “effect” of interest, by the same mechanism, Chemicals are grouped by their toxicological similarity A QSAR equation is developed, i.e., features of chemical structure are quantified that are associated with the observed “activity”, QSARs are developed from a known or hypothesized “mode/mechanism” of action, and the hypothesis is examined through additional testing and model refined if necessary There is a history of QSAR development at ORD/NHEERL/Mid-Continent Ecology Division (Duluth) since the late 1970s and early ’80s when the first QSAR models were delivered to OPPTS for use in PMN reviews. From many years of study, involving in vivo testing, in vitro measures, and computational approaches, several QSAR principles were arrived at, including the ones listed here.

Computational Toxicology: Prioritizing Assessment Questions within Large Chemical Inventories
Define Toxicity Pathway Test Multiple Chemicals For Ability to Initiate Pathway High Quality Data Sets Strategic Testing Structural Requirements Evaluate Inventory Coverage QSAR Models Chemical Inventories An approach for strategically expanding a knowledge-base – 1st need to understand the toxicology (elucidate tox pathway that links in vitro measure with in vivo response impt to risk assessment). If you determine initiating point into the pathway, and if you have an in vitro model that measures the point of chemical-biological interaction (and you know that the response of assay is due to parent chemical (not metabolite) and is not confounded by pharmacokinetic considerations) you can use that assay to develop a high-quality database for QSAR. You keep testing, modeling, evaluating agst chemical universe of EPA concern, until you get model that’s good enough (depends on what the use is – e.g., you would likely accept more uncertainty in a model used to prioritize for further testing than in one used for more quantitative RA). It’s important to do this with the users (the Program Office) to know when it’s good enough. Regulatory Acceptance Criteria Estimation of Missing Data QSAR Libraries Modeling Engine Analogue Identification Prioritization/Ranking

Measure chemical form and concentration in your system
Well-Defined Biological System (Know what you know and what you don’t know) Metabolism Is the system used for collection of empirical data capable of xenobiotic metabolism? Is what you’re measuring due to parent chemical or a metabolite? Kinetics What do you understand about the chemical kinetics within the system? Is the chemical in solution Bound and unavailable Loss to hydrolysis There is a history of QSAR development at ORD/NHEERL/Mid-Continent Ecology Division (Duluth) since the late 1970s and early ’80s when the first QSAR models were delivered to OPPTS for use in PMN reviews. From many years of study, involving in vivo testing, in vitro measures, and computational approaches, several QSAR principles were arrived at, including the ones listed here. Measure chemical form and concentration in your system

QSARs for Large Lists of Diverse Chemicals
TSCA ~ 40,000 discrete chemicals Fish Acute (LC50) Fish Chronic (LC50/EC50) Not talking about small homologous series of compounds – although the same principles of how you approach it should apply – The difference is whether you want to use your predictions beyond that homologous series. What do you want to use your QSAR for, will dictate somewhat how you approach it.

Predicting Acute Toxicological Potential and Potency
from Chemical Structure: Non-receptor-based Pathways ASSAYS DATA MODELS FHM Bioassay FHM Database (1978) Developed (1977) Methods to Evaluate Toxic Potential -Joint Action -Behavior -Physiological Assessments (1984) Toxic Potential (TP) Narcosis I, II, III and Uncoupler QSARs Developed (1987) Knowledge Base (1984) The PROCESS that was used in development of first QSARs at the Division is shown FHM assay Collect Database – 600 Acutes, >100 chronics Could see differences in toxic potential – needed methods to sort pathways, to group chemicals by “potential” In fact, we were able to do this for 80% of chemicals in the large industrial inventory, and code in an expert system for prediction of untested chemicals [Chris Russom’s POSTER yesterday]. (1990) (1991) (1991) Toxicodynamic-based Expert System to Predict Toxic Pathways: ASTER (1993)

Example – acute aquatic tox (fathead minnow (FHM) LC50) of > 600 chemicals of diverse structure
- One QSAR did not explain all the toxicities measured – The was a realization that several toxicity pathways and modes of toxic action were represented, and that you must sort and classify chemicals by the type of toxicity they produce (by MOA) to develop QSARs – Sorting was done using 1) observations of fish behavior during toxicity tests; 2) Fish Acute Toxicity Syndrome (FATS) studies measuring respiratory cardiovascular responses to at least two “known” chemicals from each MOA and discriminant function analysis to id parameters key to distinguishing each MOA; 3) mixtures test – testing binary mixtures of test chemicals with chemical representative of that MOA;

Nonpolar Narcotic Toxicants
When MOA is same between Acute (LC50) and Chronic 30 day MATC (growth, lethality of early life stages) about a factor of 10 difference in potency is noted, i.e., Acute to Chronic ratio is 10. Yellow line QSAR for FHM Acute for Non-polar narcotic MOA; Red line is QSAR for FHM 30d MATC for Non-polar narcosis.

Polar Narcotic Toxicants
When MOA same between Acute and Chronic (growth) about factor of 10 difference in potency Acute to Chronic (therefore – we have QSAR for Chronic Tox for non-polar narcotics).

Oxidative Phosphorylation Uncouplers
Water Solubility LC50-96hr MATC-30 day LC50-96hr Somewhat the same for Uncouplers – more difference at low LogP end of curve But, if MOA is not the same Acute to Chronic you will not get this same relationship, I.e., reactives MATC-30 day

Fathead Minnow Acute Toxicity Database
Narcosis I -2 Narcosis III -4 Narcosis II Log Fathead Molar Toxicity (1/LC50) Uncoupler -6 Shows QSARs (linear relationships with Log P) for additional MOAs. -8 -10 -2 2 4 6 8 Log P

MOA assigned based upon:
Behavioral Observations - 96 h LC50 Analysis of Conc-Rsp Curves - 96 h LC50 24h LC50/96h LC50 = 1 (Narcosis I) Fish Acute Toxicity Syndromes (FATS) Joint Toxic Action Studies Literature Not talking about small homologous series of compounds – although the same principles of how you approach it should apply – The difference is whether you want to use your predictions beyond that homologous series. What do you want to use your QSAR for, will dictate somewhat how you approach it.

Chemical Risk Assessments
Defining Toxicity Pathways: Linking Observations/Effects Across Levels of Biological Organization QSAR Biology/Toxicology (toxicokinetics/toxicodynamics) Chemistry Receptor/Ligand Interactions DNA Binding Protein Oxidation Gene Activation Protein Production Cell Signaling GSH depletion Altered Respiration Faulty Osmoregulation Liver Necrosis Altered Gonad Development Lethality Altered Growth Delayed Development Altered Reproduction Molecular Interactions Cellular Responses Organ/Tissue Effects Individual Chemical Toxicant Generalized Toxicity pathway - . Risk Assessment Relevance Toxicological Understanding

Defining Toxicity Pathways Across Levels of Biological Organization:
Acute Nonpolar Narcosis Assigning Chem Toxicol. Similarity for QSAR In vivo Assays Xenobiotic MOLECULAR TARGETS/RESPONSES TISSUE/ORGAN SYSTEM PHYSIOLOGY -Decreased Respiration -Decreased Circulation -Faulty Osmoregulation INDIVIDUAL Membrane Partitioning Ion Gradient Interruption Failed ATP Production Lethality Illustrates toxicity pathway for reactive chemical toxicities (but redox and arylation are thrown together). Should pull them out. In vitro assays are used to gain better toxicological understanding (at lower levels of biological organization) put must be plausibly linked to higher level of biological organization for risk assessent relevance. Eventual purpose is to develop predictive models to prioritize chemicals of highest concern for producing the adverse outcome of RA concern. Toxicological Understanding Risk Assessment Relevance

Acute Uncoupling of Oxidative Phosphorylation Assigning Chem Toxicol. Similarity for QSAR In vivo Assays Xenobiotic MOLECULAR TARGETS TISSUE/ORGAN SYSTEM PHYSIOLOGY -Increased Respiration -Increased O2 Consumption -Decreased O2 Utilization INDIVIDUAL Lethality Chemical Partitioning Membrane Proteins/ Ion Channels Illustrates toxicity pathway for reactive chemical toxicities (but redox and arylation are thrown together). Should pull them out. In vitro assays are used to gain better toxicological understanding (at lower levels of biological organization) put must be plausibly linked to higher level of biological organization for risk assessent relevance. Eventual purpose is to develop predictive models to prioritize chemicals of highest concern for producing the adverse outcome of RA concern. Toxicological Understanding Risk Assessment Relevance

Reactive Toxicants Some chemicals did not exhibit a MOA that could be predicted using a linear relationship with Log P. In fact these “reactive” chemicals (electrophiles and pro-electrophiles) represent several MOAs, thus, until they are distinguished by MOA to group like chemicals by the same reactive MOA (e.g., arylation, redox cycling, etc), we can identify them as “reactive” but cannot develop a QSAR to predict potency within a MOA.

“reactive” chemicals (electrophiles – hard and soft) and pro-electrophiles were all more acutely toxic than other more non-specific mechanisms (non-polar narcosis – Narco I) (polar narcosis – Narco II) Uncouplers of oxid phosphory (not shown), etc. Reactives (red dots) are a bunch of different mechanisms – current work was to develop in vitro methods to discriminate type of reactive toxicity so we could group chemicals by toxicological similarity (I.e., first step toward developing a quantitative structure-activity model (QSAR) to predict toxic potential of untested chemicals).

Redox cycling_Arylation In vivo Assays Assigning Chem Toxicol. Similarity for QSAR In vitro Assays Xenobiotic CELLULAR GSH Oxidation PrSH Oxidation ROS Production Decr. Energy Chg Disrupt Cytoskel. (MT;IF); Blebbing Altered Cell Signaling Cell Death TISSUE/ORGAN INDIVIDUAL Liver Toxicity Multiple Organ System Toxicities/Disease MOLECULAR Lethality Impaired Growth Binding to cytoskeletal components -Redox cycling - SH Arylation Illustrates toxicity pathway for reactive chemical toxicities (but redox and arylation are thrown together). Should pull them out. In vitro assays are used to gain better toxicological understanding (at lower levels of biological organization) put must be plausibly linked to higher level of biological organization for risk assessent relevance. Eventual purpose is to develop predictive models to prioritize chemicals of highest concern for producing the adverse outcome of RA concern. Toxicological Understanding Risk Assessment Relevance

Important QSAR concept that emerged from these studies is that MOA was not the same as chemical class. Another way of saying this is that within a chemical class multiple MOAs may be represented, thus QSAR should be developed for a group of chemicals only after they have been grouped by a common toxicity pathway they initiate, and not by classic chemical classes.

Examples of structural alerts used in ASTER expert system to identify MOA and determine if there is a QSAR available for prediction of potency. For the reactives, the alert identifies them in the “electrophile, pro-electrophile” group and warns that they are more potent than the other MOAs that have QSARs available, but exact toxicity cannot be predicted. This could be used for prioritizing chemicals for further testing.

EDC Challenge Large diversity in types of chemicals potentially causing ED Large lists of chemicals to be evaluated -pesticide active ingredients/high production volume inerts -current chemical selection is exposure-based -effects-based prioritization allows hypothesis generation to focus testing efforts Hypothesis-Driven Strategic Testing -minimize the generation of unused test data -optimize the selection of dose-response tests Current focus of QSAR research is to develop models to prioritize for EDC toxicity pathways, as proof of principle for receptor-based toxicity pathways.

How do you develop QSAR(s) applicable to a large chemical inventory?
Systematic approach 1. Delineate toxicity pathway (well-defined endpoint) 2. Obtain initial data on a number of chemicals that can/can’t initiate the pathway 3. Hypothesis structural determinants of activity (chemical intuition; early iteration QSAR); Test chemicals to confirm hypotheses 4. Systematically expand knowledge-base to provide accurate predictions where most needed (strategic chemical selection within defined domain)

Delineate Toxicity Pathway Direct Chemical Binding to ER
QSAR Focus Area In vivo Assays In vitro Assays Xenobiotic TISSUE/ORGAN Response CELLULAR Response INDIVIDUAL POPULATION MOLECULAR Interactions Chg 2ndry Sex Char, Altered Repro. Skewed Sex Ratios, Altered Repro. Chg Hormone Levels, Liver VTG, Ova-testis Altered Protein Expression ER Binding Toxicological Understanding Risk Assessment Relevance

Chemical Binding to rtER

rat ER RBA TrSet – TSCA HPVC

QSAR Generated Hypotheses
If nucleophilicity is important for binding, chemicals with regions of atomic charge similar to known active chemicals should be investigated for activity -aryl nitrogen and oxygen can have similar nucleophilicity, (atomic charge); -alkylphenols bind ER; -will alkylanilines bind and produce resulting toxicity?

QSAR Focus Area In vivo Assays In vitro Assays Xenobiotic TISSUE/ORGAN Response CELLULAR Response INDIVIDUAL POPULATION MOLECULAR Interactions Chg 2ndry Sex Char, Altered Repro. Skewed Sex Ratios, Altered Repro. Chg Hormone Levels, Liver VTG, Ova-testis Altered Protein Expression ER Binding Toxicological Understanding Risk Assessment Relevance

Rainbow trout ER Binding Assay
Chemical Log Kow RBA(%) 17-β-Estradiol 4-nonylphenol 4-n-octylphenol 4-n-butylphenol 4-n-octylaniline 4-n-butylaniline

QSAR Focus Area In vivo Assays In vitro Assays Xenobiotic TISSUE/ORGAN Response CELLULAR Response INDIVIDUAL POPULATION MOLECULAR Interactions Chg 2ndry Sex Char, Altered Repro. Skewed Sex Ratios, Altered Repro. Chg Hormone Levels, Liver VTG, Ova-testis Altered mRNA/ Protein Expression ER Binding Toxicological Understanding Risk Assessment Relevance

Rainbow Trout Liver Slice Vitellogenesis Assay
Chemical Estrogen Hepatocyte Ligand-ER Metabolite ERE—Vitellogenin (Vtg) Vtg mRNA (RT-PCR) Vtg Protein

 Estradiol  p-nonylphenol  p-n-butylphenol

p-n-butylphenol RBA 0.0035% LogP 3.71 48h, 96h
PNBP- p-n-butylphenol (solid 48 h); dashed lines (96h)

p-n-butylaniline RBA 0.0004% LogP 3.00 48h, 96h
P-n-butylaniline (BA) solid = 48h; dashed = 96h

p-n-octylaniline RBA 0.0009% LogP 5.12 48h, 96h
OA (solid = 48h); dashed line = 96h

Two Approaches for Strategic Chemical Selection TSCA-HPV ratERTrSet(red), ChemPick Even Coverage (blue), ChemPick 30 Furthest (yellow) Blue triangle - TSCA chemicals from even coverage of extrapolation area Red square – NCTR Yellow circle – TSCA furthest dispread TSCA – muliti-colored

OPP Inerts (green/blue)– Chemical Prioritization (High Affinity Mechanism)
This shows 12 chemicals in orange (conformationally multiplied) that satisfy a criteria we are exploring as being related to the highest affinity ER chemicals. This would be one strategy of picking chemicals to test to give us a lot of structural information quickly.

EDC Challenges Addressed using Tox Pathway-Specific QSAR approaches
Create a virtual escape from the EDC screening dilemma for EPA chemical lists and inventories in silico identification of chemicals for further laboratory testing balance exposure and effects-based prioritization in screening large inventories Provide a scientific foundation for a hypothesis-driven testing paradigm for EPA risk assessment processes Introduce risk management thresholds along toxicity pathways reduce animal testing by minimizing “negative” laboratory tests

Xenopus Metamorphosis Model for Thyroid System Disruption
Molecular Cellular Tissue Individual Gene/Protein Expression Circulating TH Status Thyroid Histology Altered Morphology An example of one area, the thyroid axis where we’re trying to elucidate the pathways associated with disruption in development controlled through the thyroid axis. We’re using gene arrays, proteomics, biochemical, histological and whole organism measures to establish linkages in the pathway. We’re soliciting additional funds to focus in on the thryoid synthesis part of the axis as initiation of the toxicity by xenobiotic chemicals. Thyroid Gland Thyroid Hormone Synthesis Peripheral Tissues Deiodination Morphology Pituitary Gland TSH Release Hypothalamus TRH (CRH) Release

Overview of Thyroid Hormone Pathway
Hypothalamus TRH (CRH) (-) Pituitary Thyrotropes Inhibition of T4 Synthesis TSH Thyroid Gland Competitive Displacement from Carrier Proteins Thyroglobulin TPO MIT DIT Altered Local Deiodination Iodine This slide depicts a general scheme of the thyroid pathway. I won’t go into details here, but there are four major toxicological pathways through which thyroid disruption occurs. Inhibition of T4 synthesis Increased elimination Displacement from carrier proteins Altered local deiodination Our approach is to expose Xenopus to chemicals with known toxicological action representing these major pathways and to examine the endpoints that I described previously. The details of the specific chemicals, exposure design, and results will be presented in the associated poster. DIT T4 Increased TH Elimination Transthyretin Inactive TH Deiodination T3 Deiodination Inactive TH T3+TR/RXR DNA mRNA Conjugation Deiodination Liver Peripheral Tissues

Thank You ! Well-defined biological system means that the system is at close to ss (that kinetics is not the factor controlling the effect endpoint that you are trying to model – I’ll be getting to some examples of what I mean by this

Ligand Mechanism A-B Receptor

Receptor/Ligand Complex
A-B Binding Mechanism electronic interaction electronic interaction

Ligand Mechanism A-C Receptor

A-B-C Binding Mechanism electronic interaction hydrophobic interaction electronic interaction

Ligand Mechanism A-C Receptor

A-C Binding Mechanism hydrophobic interaction electronic interaction

OPP Lists Pesticide Active Ingredients
Total = (873 discrete & 237 mixtures) 873 discrete chemicals include: 751 organics 102 inorganics 20 organometallics Pesticide Inert Ingredients Total = 653 – 3 repeats = 650 (441 discrete, 209 mixtures) 441 discretes chemicals include: 412 organics 25 inorganics 4 organometallics What we’ve found out about the lists provided to us so far. We are verifying CAS for all discrete chemicals OPP provided.

Current MED Activities
Systematic expansion of models to apply to OPP Active Ingredients & OPP Inerts (includes antimicrobials) strategic chemical selection in a hypothesis-driven testing process with important endpoints Development and application of bioassays at multiple levels of biological organization provide the scientific basis for risk management threshholds along toxicity pathways Use metabolism simulations in toxicity pathway and QSAR research In collaboration with ORD NERL-Athens lab Continuing delineation of EDC toxicity pathways (AR, thyroid) and expansion of predictive models to other endpoints last pt. Some of the assays ( in vitro) under EDC MYP were being funded thru CT redirect - progress made so ready to develop High-quality databases (funding uncertain)

How do you develop a model to predict for all chemicals within a large chemical inventory?
Quantify features of chemical structure associated with biological activity (QSAR) Systematic approach -delineate toxicity pathway (well-defined endpoint) -obtain initial data on a number of chemicals that can/can’t initiate the pathway -systematically expand knowledge to provide accurate extrapolation where most needed

Direct Chemical Binding to ER In vitro Assays QSAR In vivo Assays Xenobiotic INDIVIDUAL POPULATION TISSUE/ORGAN CELLULAR Chg 2ndry Sex Char, Altered Repro. Skewed Sex Ratios, Altered Repro. MOLECULAR Altered Hormone Levels, Ova-testis Altered Protein Expression ER Binding Toxicological Understanding Risk Assessment Relevance

Predicted Relative hERa Binding Affinity for 39,469 TSCA Inventory Chemicals
Here is an example of a first proof of principle exercise done several years ago. We were able to predict potential for chemical interaction with of ER for 39,000 TSCA chemicals – to show the approach can be applied (3-D structures and energetically-reasonable conformations of chemicals are able to be handled by this method for this large number of chemicals). The pie indicates that the vast majority of the industrial universe 58% + 34% may have no, or low, affinity for the receptor and are unlikely to initiate toxicity thru this pathway. Thus, first priority for testing could be given to the 8% of the chemicals that are much more likely to cause an effect through this pathway. This was only a first attempt model because we know that the training set of chemicals used to build the models was very limited – the majority of chemicals are structures not included in the training set, so reliability of predictions are low for these. However, using a strategic testing approach to identify what types of structures are underrepresented, the model can be vastly improved relatively quickly with minimal new testing. The next few slides describe this approach.

rat ER TrSet (red & blue) – TSCA HPV Chemicals (green)
Shows where we were when the first exercise was done – Blue (non-binders) and red (ER binders) were the chemicals in the rat ER data used to develop the model. All other dots are the TSCA chemicals. It can be readily seen that there are many chemicals that have electronic parameters, charge, etc (structural attributes) that have not been evaluated in the current training set. Until a similar structure is evaluated there will be large uncertainty in the predictions for those types of chemicals.

TSCA-HPV Inventory, ratER Training Set (red), and Two Strategies for Chemical Selection (explained in next slide). TSCA chemicals are the light green and dark blue (variation in color reflective of the degree of charge (Q), Ehomo value, etc) Two different approaches for chemical selection to strategically expand the knowledge-base (Blue and Yellow) are shown. Blue triangle - TSCA chemicals from even coverage of extrapolation area Red square – rat ER binding Training Set Yellow circle – TSCA furthest dispread TSCA – muliti-colored Red square – NCTR

ASsessment Tools for the Evaluation of Risk
ASTER ASsessment Tools for the Evaluation of Risk ECOTOX - Aquatic Toxicity empirical data QSAR Expert System Toxicity pathway based QSARs Measured/predicted chemical properties Physical chemical properties; Environmental Partitioning; Biodegradation; Bioaccumulation Components of ASTER system.

TSCA-HPV Inventory, ratER Training Set (red), and Two Strategies for Chemical Selection (explained in next slide). TSCA chemicals are the light green and dark blue (variation in color reflective of the degree of charge (Q), Ehomo value, etc) Two different approaches for chemical selection to strategically expand the knowledge-base (Blue and Yellow) are shown. Blue triangle - TSCA chemicals from even coverage of extrapolation area Red square – rat ER binding Training Set Yellow circle – TSCA furthest dispread TSCA – muliti-colored Red square – NCTR

ratER TrSet(red) with results of Strategic Chemical Selection Approaches a) even coverage of structure space (blue); b)most disparate structures (yellow) HPV chemicals are now removed to see: Red squares = ratER training set Blue triangles = Chemical selected from HPVs to evenly coverage the structure space where no testing has yet been done (i.e, extrapolation area) Yellow circles = another chemical selection strategy which selects the 30 chemical structures that are the furthest from anything yet tested, I.e., those that , most disparate

An example of binding curves – chemical binding to rainbow trout ER (measured at the Duluth lab). We are focusing on the low affinity chemicals, those binding with 0.05% and lower relative binding affinities (RBA), as well as apparent negative binders. We are testing these low or no affinity binders in a trout liver slice assay where vitellogenin (egg-yolk protein) is produced in response to chemicals that bind the trout ER. We have determined that BBC and BAM are negative binders - (these showed start of binding, but could not get above 50% at their solubility). Interestingly, RES with really low affinity but a complete binding curve did induce vitellogenin, and we would recommend to be tested in vivo. The purpose of the work is to develop a QSAR to prioritize the OPP actives and inerts that should be tested first.

Rainbow Trout Liver Slice Vitellogenesis Assay
Chemical Estrogen Hepatocyte Ligand-ER Metabolite ERE—Vitellogenin (Vtg) Vtg mRNA (RT-PCR) Vtg Protein

Direct Chemical Binding to ER In vitro Assays QSAR In vivo Assays Xenobiotic INDIVIDUAL POPULATION TISSUE/ORGAN CELLULAR Chg 2ndry Sex Char, Altered Repro. Skewed Sex Ratios, Altered Repro. MOLECULAR Altered Hormone Levels, Ova-testis Altered Protein Expression ER Binding Toxicological Understanding Risk Assessment Relevance

OPP-Inerts (blue) rtER TrSet (red)
This is what the OPP inerts (Blue = the 350 of 441 verified so far) and 88 of the trout chemicals (RED) tested so far look like, to start to see where we need to test. Each chemical has multiple conformers plotted, so it looks like more than

Mapping Toxicity Paths to Adverse Outcomes Estrogen Signaling Pathway
Structure Molecular Cellular Organ Individual Chemical 2-D Structure ER Binding ER Transctivation VTG mRNA Altered Reproduction/ Development Vitellogenin Induction Sex Steroids Initiating Events Impaired Reproduction/Development Chemical 3-D Structure/ Properties While I’ve been focusing on one EDC pathway, that iniated by ER binding, ORD is working on elucidation many more pathways. For several there is enough known (or we’re almost there) to start developing high quality datasets and preliminary models. Libraries of Toxicological Pathways

Libraries of Toxicological Pathways
Mapping Toxicological Paths to Adverse Outcomes Initiating Events Impaired Reproduction/Development So, we have QSARs for many pathway, we know how to develop them for many more – as we continue to elucidate a new pathways we can add new QSARs and be able to prioritize for additional endpoints. All that is needed is a focus on collection of high-quality data for the endpoints in the pathway that were shown linked to the RA endpoint of relevance. An in vitro assay that measures a response close to the point of initial chemical-biological interaction would be ideal, but not always necessary. Using strategic chemical selection, the number of additional chemicals that need to be tested can be very small. The testing and modeling is done in such a way that you always check back with the client to see if the model is acceptable for their needs or if, and how much, it needs to be improved. The whole approach is shown in next slide. Libraries of Toxicological Pathways

Molecular Flexibility
Nicotine 2D-3D Migration Molecular Flexibility Example of going from a 2-D chemical representation to an equivalent 3-D structure, and further to conformations of that structure that are energetically (kinetically and thermodynamically) reaonable. Consideration of molecular flexibility was not possible for earlier QSAR work, nor was it essential. However, it is essential to consider when modeling chemical interaction with biological receptors of the nuclear hormone receptor superfamily (e.g., ER, AR, etc).

Nicotine 2D-3D Migration Rat Liver Metabolism Molecular Flexibility Predicting metabolites – we also have strategies to consider them. M e t a b o l i s m

Nicotine 2D-3D Migration Molecular Flexibility M e t a b o l i s m

Linkages Across Levels of Biological Organization Reactivity – Redox Cycling Chemical 2-D Structure/ Properties Individual Cell Organ Lethality Impaired Growth Disease GSH Oxid PrSH Oxidation ROS Production Decr. Energy Charge Altered Cell Signaling Liver Toxicity Multiple Organ System Toxicities/Diseases Chemical 3-D Structure/ Properties Reactive MOA -- Redox cycling Molecular/biochemical (redox cycling – electron transfer/ROS generation - LPO/GSH depletion/PrSH depletion/cellular energy charge decreased (ATP/ADP/AMP); Cell death; Organ/Tissue (?); Acute Level – 2-D structural alerts (SAR) identifies toxicity greater than baseline (non-polar narcosis) but need to better sort the variety of reactive modes (cellular level- not whole organism) to develop potency predictions (QSAR) Understanding Relevance

Linkages Across Levels of Biological Organization Chemical 2-D Structure/ Properties Chemical 3-D Structure/ Molecular Cellular Organ Individual Respiration Osmoregulation Liver Function Gonad Development ADME Receptor/Ligand Interaction DNA Binding Protein Oxidation Gene Activation Protein Production Signaling GSH depletion ADME Lethality Growth Development Reproduction ADME Have to establish likely chain of events “Toxic Chemical” now shown as 2-D structure or 3-D structure which has to be considered in some cases Understanding Relevance

Link Across Levels of Biological Organization Reactivity – Electrophilicity Chemical 2-D Structure/ Properties Molecular Cellular Organ Individual DNA/Protein Alkylation Altered Protein Function Cell Death Multiple Organ System Toxicities Death Altered Growth/ Development Chemical 3-D Structure/ Properties REACTive (electrophile/pro-electro)?? Molecular (DNA alkylation) ; Cell (misreading leading to uncontrolled cell growth; Tissue/organ - preneoplastic foci; Indiv (tumor) Understanding Relevance

Links Across Levels of Biological Organization Receptor-Mediated Pathways Organ Physiology/ Pathology Chemical 2-D Structure/ Properties Molecular Targets Cellular Response Adverse Effect On Individual -Gonad Development (Ova-Testis) -Altered Hormone Levels -Impaired Kidney Function Receptor/Ligand Interaction Gene Activation Protein Production Impaired Reproduction Chemical 3-D Structure/ Properties Receptor-Based MOA – ER Molecular (ER binding); Cell (gene activation); Tissue (ex-male fish-VTG induction - accumulation in plasma - kidney toxicity; Ova-testis – reduced testicular size (??APs). 3-D structure needed for chemical descriptors reflective of the chem-bio interaction So, how do you translate a 2-D or 3-D chemical structure into toxic potential (Next Slide) Understanding Relevance

Structural Domain of Found Data1
Yellow = “found” data (literature, etc)., Green = the universe of chemicals for risk assessments; How do you systematically expand model (I.e., sytematically select chemicals for testing) to cover chemical universe of concern

Quantitative Structure-Activity Relationships
Quantifying features of chemical structure associated with biological activity

Risk Assessment Roles for QSAR
Developing Chemical Categories clustering chemical behavior for interpolation Classification and Prioritization - broad hazard identification profiles (PBT) Hypothesis-Driven Strategic Testing sequential testing to minimize unused test data Estimated Values for Untested Chemicals - extend the universe of initial risk assessments

Linkages Across Levels of Biological Organization Reactivity – Redox Cycling Chemical 2-D Structure/ Properties Individual Cell Organ GSH Oxidation PrSH Oxidation ROS Production Decreased Energy Charge Altered Cell Signaling Lethality Impaired Growth Liver Toxicity Multiple Organ System Toxicities/Diseases Chemical 3-D Structure/ Properties Reactives Understanding Relevance

Use of Quantitative Structure-Activity Relationships (QSAR) to Predict Chemical Toxicity in Aquatic Organisms P. Schmieder US EPA Mid-Continent Ecology.

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Use of Quantitative Structure-Activity Relationships (QSAR) to Predict Chemical Toxicity in Aquatic Organisms P. Schmieder US EPA Mid-Continent Ecology.

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Presentation on theme: "Use of Quantitative Structure-Activity Relationships (QSAR) to Predict Chemical Toxicity in Aquatic Organisms P. Schmieder US EPA Mid-Continent Ecology."— Presentation transcript:

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