High Throughput Exposure Prediction for the ExpoCast Project · Project . Office of Research and...

John Wambaugh U.S. EPA, National Center for Computational Toxicology

High Throughput Exposure Prediction for the ExpoCast Project

Office of Research and Development

August 23, 2012

The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA

Office of Research and Development 1

Introduction

There are thousands of environmental chemicals, many without enough data for evaluation Risk is the product of hazard and exposure High throughput in vitro methods beginning to bear fruit on hazard for many of these chemicals Methods exist for approximately converting these in vitro results to daily doses needed to produce similar levels in a human (Wetmore et al. (2011)) Without a similar capacity for exposure cannot place risk early into prioritizations

Judson et al., (2011) “Estimating Toxicity-Related Biological Pathway Altering Doses for High-throughput Chemical Risk Assessment” Chemical Research in Toxicology 24 451-462

Strategic Guidance: A Framework for a Computational Toxicology Research Program (November 2003)

Source-to-Outcome Continuum

2 Office of Research and Development

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High Throughput Exposure Prioritization

Proof of Concept: Using off-the-shelf models capable of quantitatively predicting exposure determinants in a high throughput (1000s of chemicals) manner To date have found only fate and transport models to have sufficient throughput These models predict the contribution from manufacture and industrial use to overall exposure rapidly and efficiently

Applying and developing new high throughput models of consumer use and indoor exposure

Goal: A high-throughput exposure approach to use with the ToxCast chemical hazard identification.


Environmental Fate and Transport

Consumer Use and Indoor Exposure

Framework for High Throughput Exposure Screening


Estimate Uncertainty

Space of Chemicals (e.g. ToxCast, EDSP21)

(Bio) Monitoring

Infe

rred

Exp

osur

e

Model 1

Model 2 …

Calibrate models

Apply calibration and uncertainty to other chemicals

Evaluate Model Performance

Joint Regression on Models

Exposure Inference

Dataset 1

Dataset 2 …

USEtox RAIDAR

Off the Shelf Models

Treat different models like related high-throughput assays


United Nations Environment Program and Society for Environmental

Toxicology and Chemistry toxicity model Version 1.01

Rosenbaum et al. 2008

Risk Assessment IDentification And Ranking

model Version 2.0 Arnot et al. 2006

Model parameters mostly predicted from structure (SMILES)

Parameterizing the Models Variable Description Unit Source Default QSAR USEtox RAIDAR

Chemical Name ToxCast Yes Yes CAS ToxCast Yes Yes MW Molecular Weight g/mol ToxCast Yes Yes Data

Temperature Degrees C 25 Yes

KOW Octanol:Water Partition

Coefficient 1 Episuite Yes Yes Yes

Koc Organic Carbon:Water

Partition Coefficient L/kg USEtox QSAR Yes Yes

KH25C Henry's Law Coefficient

(25 degrees C) Pa*M^3/mol Episuite Yes Yes Yes

Pvap25 Vapor Pressure (25

degrees C) Pa Episuite Yes Yes Yes

Sol25 Solubility (25 degrees C) mg/L Episuite Yes Yes Yes

KDOC

Dissolved Organic Carbon:Water Partition

Coefficient L/kg USEtox QSAR

Yes Yes

kdegA Degredation Rate in Air 1/s Episuite Yes

Yes Yes

kdegW Degredation Rate in

Water 1/s Episuite Yes

Yes Yes

kdegSd Degredation Rate in

Sediment 1/s Episuite Yes

Yes Yes

kdegSl Degredation Rate in Soil 1/s Episuite Yes

Yes Yes

kdegbiota Degredation Rate in

Biota 1/s 2.40E+12 Yes

kdeghuman Degredation Rate in

Humans 1/s 2.40E+12 Yes

pKa Acid Dissociation

Constant 1 QikProp Yes

Yes

BAF Bioaccumulation Factor L/kg EpiSuite Yes

Yes

LC50 Average Log Aquatic 50%

Lethal Concentration Log(mg/L) EcoSAR Yes Yes

Cl/C(Cl)=C/C3C(C(=O)OCc2cccc(Oc1ccccc1)c2)C3(C)C


Chemical Landscape Range of physico-chemical properties for the 1600 chemicals evaluated Principal component one: half-life in soil, water, and sediment Principal component two: octonol-water partition coefficient (logP) Principal component three: half-life in air Larger spheres are those for which NHANES data was available


Urban Air Rural Air

Freshwater

Sea water Natural soil

Agricultural soil

Soil Water

Air

Models predict fate depending upon release profile (Level III Fugacity Model) Release profile can be chemical-specific, class-specific, or default depending on data Estimated behavior/consumption can in turn yield human and ecological prediction

USEtox RAIDAR

Partitioning Release into the Environment


If we have the data then we would use it, but we don’t Assuming an “average” release profile

Urban Air Rural Air

Freshwater


Agricultural soil

TSCA / Industrial

Urban Air Rural Air

Freshwater


Agricultural soil

Food-use Pesticide

Partitioning Release into the Environment

Soil Water

Air

Soil Water

Air

USEtox

RAIDAR


Production Volume is an Overall Multiplier

Using EPA Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) Rule (Formerly Inventory Update Reporting – IUR) data for production volumes Crop Protection Research Institute has data on many pesticides (which are heavily favored in ToxCast Phase I) although the data is old (2002)

Urban Air Rural Air

Freshwater


Agricultural soil

Urban Air Rural Air

Freshwater


Agricultural soil

Production Volume (kg/day)

100,000,000

Production (kg/day)

Release Partitioning (fractional)


Clustering 1678 chemicals by the media into which they partition most Could infer behavior of understudied chemicals from similar, well-known counterparts – “fate read-across” Fate predictions not terribly consistent

High Throughput Fate Predictions


Population Exposure from Environmental Media

Likely Bioaccumulator Log Kow < -1


Both models assume exposure scenarios that relate environmental media to food and inhalation exposure Can calculate intake fraction (population exposure in kg per kg emitted) General agreement for most chemicals – putative bioaccumulators predicted to be highest Issue with accumulation in plants causes larger predictions for RAIDAR in some cases

McKone et al. (2007)

Cowan-Ellsbury et al. (2009) PBDE99

Chlorpyrifos

Diazinon

Dimethoate, Malathion, Oxydemeton-methyl

o Measured x Model Predicted

Literature Ground-truthing Efforts


Data Availability for Model Predictions and Ground-truthing

Chemicals Current Models can Handle (1678)

Production / Release Data IUR (6759 compounds with production of >25,000 lbs a year) CPRI (242 pesticides with total lbs applied) NHANES

“Ground-truthing” Chemicals

Chemicals of Interest (2127)

51 33

volatile, insoluble

Ground-truth with CDC NHANES urine data Focusing on U.S. median initially Capable of adding population variability, but will need consumer use models


Linking NHANES Urine Data and Exposure

( ) ( )∑=i i

i

MWmg/kg/dayMWmg/kg/day 00

Parent

Products

1

2

3

4

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g*gmg

kg 701mg/kg/day creatine

creatine

ii =

Lakind and Naiman (2008)

Steady-state assumption

Office of Research and Development 16

17

Mapping of NHANES Parents and Urine Products


Parent Compound Product Monitored In Urine

Degeneracy of an Exposure Biomarker

Unknowns: 2 (mass, bioavailability)

Knowns: 1 (mass)

Unknowns: 2

Knowns: 3 (mass)

Unknowns: 3 (fractions), Knowns: 1 (mass balance)

Unknowns: 6

Knowns: 1 (mass)

Unknowns: 3 (fractions), Knowns: 3 (mass balance)

parent

metabolite



Bayesian Model for fij

The real situation may be even more complicated Further complicated by limit of detection of NHANES chemicals – many chemicals that are checked for are below the LoD

However, we still can predict that N parent exposure are related to P=f*N urine products, and many fij are zero, Use MCMC to explore range of possible parent predictions Also incorporate uncertainty in production volume and use all quantiles of NHANES data

( ) ( )∑=i i

iijjj f

MWmg/kg/dayMWmg/kg/day

Unknown fraction fij for each urine product j

due to parent i:

Framework for High Throughput Exposure Screening


Estimate Uncertainty

Space of Chemicals (e.g. ToxCast, EDSP21)

(Bio) Monitoring

Infe

rred

Exp

osur

e

Model 1

Model 2 …

Calibrate models

Apply calibration and uncertainty to other chemicals

Evaluate Model Performance

Joint Regression on Models

Exposure Inference

Dataset 1

Dataset 2 …

21

Calibrate ExpoCast Predictions to CDC NHANES Data

Comparison between model predictions and

biomonitoring data indicates correlation

Indoor/consumer use is

critical: Compounds with near-field applications more

than 100x greater


)log()log(*~ 3221 vrmvumNbbY +++

22

Use Categories from ACToR

The sources for various chemical data were assigned to various use categories. Chemicals from multiple sources were assigned to multiple categories. Four categories – personal care products, consumer use, fragrance, and food additive – were aggregated into a single “near field” indicator variable.


)log()log(*~ 3221 vrmvumNbbY +++

Highest Priority Uncertainty of prediction indicated by

the horizontal confidence interval from the empirical calibration

to the NHANES data

Horizontal dotted line indicates the fiftieth

percentile rank and the vertical dotted line indicates the cutoff

between overlapping top-half and lower half

confidence intervals


Exposure Prioritization from ExpoCast

24

Wetmore et al. (2011) ToxCast Oral Equivalents

Lines indicate ExpoCast prediction 95% CI indoor/consumer use in red, blue otherwise


25

Model Appropriateness

Statistical Near Field Model

Further investigating near field use determinants using expanded information


26

Conclusions

• ExpoCast can use environmental fate and transport models to make high-throughput exposure predictions

• These prioritizations have been compared with CDC NHANES ground truth

• This biomonitoring data gives empirical calibration and estimate of uncertainty

• Indoor/consumer use is a primary determinant

• Next steps:

• HT models for exposure from consumer use and indoor environment

• Use and evaluate these models as additional HT exposure assays

• ORISE Postdoc position for high throughput modeling of nearfield indirect exposure (e.g. flooring, furniture)


NERL Peter Eghehy Kathie Dionisio Dan Vallero

University of Toronto at Scarborough Jon Arnot University of Michigan Olivier Jolliet USDA FSIS Jade Mitchell-Blackwood

NCCT Elaine Cohen-Hubal David Dix Alicia Frame Sumit Gangwal Richard Judson Robert Kavlock Thomas Knudsen Stephen Little Shad Mosher James Rabinowitz David Reif Woody Setzer Amber Wang

U.S. E.P.A. Office of Research and Development ExpoCast Team

The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA

High Throughput Exposure Prediction for the ExpoCast Project · Project . Office of Research and...

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