Slide 1 

# FIFRA Scientific Advisory Panel Meeting Integrated Bioactivity and Exposure Ranking: A Computational Approach for the Screening of Chemicals in the Endocrine Disruptor Screening



# Program Arlington, VA December 2 – December 5, 2014Slide 2 

# High Throughput Exposure (HTE)

and High Throughput Toxicokinetics

(HTTK) John Wambaugh U.S. E.P.A., Office of Research and Development

# Presentation Roadmap

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Introduction Estrogen Receptor Bioactivity Androgen Receptor Bioactivity Exposure

Toxicokinetics/Dosimetry

Integrated Bioactivity Exposure Ranking

Future Directions

# Introduction
 ExpoCast is an initiative to develop the necessary approaches and tools for rapidly predicting exposure for thousands of chemicals (Cohen - Hubal, et al. , 2010) Environmental Fate and Transport

Consumer Use and Indoor Exposure 

##  July, 2014 FIFRA SAP “New High throughput methods to estimate chemical exposure”:  High Throughput Exposure (HTE) 

## predictions for 7968 chemicals, White paper Section 4  High throughput toxicokinetics (HTTK), White paper Section 5 “All cases are unique, and very similar to others.”

― T.S. Eliot FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014

# In vitro Bioactivity, HTTK, and in Vivo Toxic



# Doses Comparison of HTTK predicted oral equivalent doses (box and whisker plots in mg/kg/day) with doses for no effect and low effect groups in animal studies Lowest Observed Effect Level No Observed Effect Level (NEL) NEL/100 Estimated chronic exposure levels from food residues are indicated by vertical red lines. All values are in mg/kg/day. Judson et al. (2011)
FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014

# In Vitro Bioactivity, In Vivo Toxicokinetics, and

Exposure for Humans

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

###  Studies like Wetmore et al. (2012),addressed the need for toxicokinetic data Section 5, Fig. 5.1, p. 87

# In Vitro Bioactivity, In Vitro Toxicokinetics, and

Exposure for Humans

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

###  Studies like Wetmore et al. (2012),addressed the need for toxicokinetic data  As in Egeghy et al. (2012), there is a paucity of data for providing context to HTS 

### data Section 5, Fig. 5.1, p. 87

# Exposure Science in the 21 st 

# Century

 2012 NRC report:

 New tools needed for screening and prioritization of chemicals for targeted toxicity testing
 New, focused exposure assessments or monitoring studies needed
 Better quantification of population vulnerability needed Figure from Egeghy et al. (2012), “The exposure data landscape for manufactured chemicals” FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, p. 78

# Exposure Forecasting (ExpoCast)

 Develop the tools and data necessary to rapidly quantify human and ecological exposure potential of chemicals
 Focus is distinct from many existing exposure tools that support either screening level assessments on a 

### per chemical basis or full regulatory risk assessment
 a prediction is a specific statement
 a forecast is a probabilistic statement FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 e.g. Judson et al., (2011) No Priority No

Priority Lower

Priority Medium

Priority Higher

Priority 

### Section 4.2, p. 78

# Goals for High Throughput Exposure

• Incorporate multiple models into consensus predictions for 1000s of chemicals • Evaluate/calibrate predictions with available measurement data across many chemical classes • Empirically estimate uncertainty in predictions

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.1, p. 77

# Applying ExpoCast

 There are 1000s of chemicals to which we might be 

## exposed  How can we use ExpoCast to pick 

## chemicals with more likely exposure?  What about 

## uncertainty?

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014



### Chemicals That Are Potentially In the Environment

Images from Thinkstock 

### Section 4.1, p. 77

# Applying ExpoCast

 The CDC targets some chemicals for exposure 

### biomonitoring

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### NHANES

Images from Thinkstock 

### Section 4.1, p. 77

# Applying ExpoCast

 They find evidence of high exposures for some 

### chemicals  Moderate exposures for others 

###  And many chemicals are below the limit of detection

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Images from Thinkstock 

### Section 4.1, p. 77

# Applying ExpoCast

 We use the chemical descriptors and high level use 

### information (ACToR UseDB) that is available for thousands of EDSP chemicals to organize the NHANES chemicals

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### High Exposure Moderate Exposure 

### < Limit of Detection

Images from Thinkstock 

### Section 4.1, p. 77

# Applying ExpoCast

 We can then predict which chemicals without 

### monitoring data are most like high, moderate, and low exposure NHANES chemicals

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### There will still be other chemicals without characteristics that are predictive of NHANES chemicals High Exposure Moderate Exposure 

### < Limit of Detection

Images from Thinkstock 

### Section 4.1, p. 77

# Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.2, p. 81

Chemical Manufacture Consumer Products, Articles, Building Materials Environmental Release Food Air, Soil,

Water Air, Dust, Surfaces

Human Direct Use (e.g ., lotion) Residential Use ( e.g . ,flooring)

MONITORING DATA RECEPTORS MEDIA

Biomarkers of Exposure Media Samples

Waste

# Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.2, p. 81

Chemical Manufacture Consumer Products, Articles, Building Materials Environmental Release Food Air, Soil,

Water Air, Dust, Surfaces Near- Field Direct Near- Field

Indirect Human Dietary Far- Field Direct Use (e.g ., lotion) Residential Use ( e.g . ,flooring)

MONITORING DATA RECEPTORS MEDIA EXPOSURE PATHWAY (MEDIA + RECEPTOR)

Biomarkers of Exposure Media Samples

Waste

# Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.2, p. 81

Chemical Manufacture Consumer Products, Articles, Building Materials Environmental Release Food Air, Soil,

Water Air, Dust, Surfaces Near- Field Direct Near- Field

Indirect Human Dietary Far- Field Direct Use (e.g ., lotion) Residential Use ( e.g . ,flooring)

MONITORING DATA RECEPTORS MEDIA EXPOSURE PATHWAY (MEDIA + RECEPTOR)

Biomarkers of Exposure Media Samples

Waste Data and Models

# Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.2, p. 81

Chemical Manufacture Consumer Products, Articles, Building Materials Environmental Release Food Air, Soil,

Water Air, Dust, Surfaces Near- Field Direct Near- Field

Indirect Human Dietary Far- Field Direct Use (e.g ., lotion) Residential Use ( e.g . ,flooring)

MONITORING DATA RECEPTORS MEDIA EXPOSURE PATHWAY (MEDIA + RECEPTOR)

Biomarkers of Exposure Media Samples

Waste

Data and Models 

### • Biomarkers reflect all pathways, not just those in forward model –

missed pathways contribute to estimated uncertainty

# Systematic Empirical Evaluation



# of Models (SEEM)
 There are four basic steps in the SEEM framework 1. Forward prediction of exposures, which involves model curation and parameterization 

## 2. Inference of exposures from monitoring data 3. Systematic evaluation and calibration of the predictions against the inferred exposures 4. Extrapolation of the calibrated model predictions and estimated uncertainty to chemicals with no monitoring data. 

#  To achieve these aims the SEEM framework used Bayesian formalism and multivariate, linear regression for 

# demonstrating and evaluating predictive ability

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, p. 78- 79

# Illustration of the SEEM Framework

Estimate Uncertainty EDSP Chemicals QSARs and HTE Data Biomonitoring Data Inferred (Reverse) Exposure

Model 1 Model 2 

# ... Calibrate models 

### Apply calibration and uncertainty to

other chemicals

Evaluate Model Performance

and Refine Models Forward Predictions Exposure Inference Dataset 1 Dataset 2 

# ... FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.1, p. 79

# SEEM Evolution – Human Exposure Model and Predictors
• Existing complex fate and transport models have low correlation to measured exposures • Near field factor most important

• Simple, readily available data • Better correlation to measured exposures • Similar predictions across demographics 1 st Gen 

### Calibration/Evaluation Data SEEM Conclusion

2 nd Gen USEtox RAIDAR Near Field / Far Field NHANES Urine Data Production Volume Use Categories Production Volume Phys- Chem Properties NHANES Urine Data

3 rd Gen • Analysis in progress SHEDS- HT Literature Models CPcat Database NHANES Urine and Blood Data Pesticide REDs FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, p. 79 -82

### Parent 

### Products 1 2 3 4 

## Steady-state assumption 

# Linking NHANES Urine Data and Exposure

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Generalization of LaKind and Naiman (2008) 

### Section 4.2, p. 79

### Parent 

### Products 1 2 3 4 

## Steady-state assumption 

# Linking NHANES Urine Data and Exposure

Observations (CDC NHANES urine samples)

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Generalization of LaKind and Naiman (2008) 

### Section 4.2, p. 79

### Parent 

### Products 1 2 3 4 Generalization of LaKind and Naiman (2008) 

## Steady-state assumption 

# Linking NHANES Urine Data and Exposure

Observations (CDC NHANES urine samples) Unknowns (we choose to use Bayesian analysis via Markov Chain Monte Carlo or MCMC) FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, p. 79

# Mapping of NHANES Parents to Analytes

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Parent chemical Chemical measured i n urine Lots of unknowns, but MCMC can try many possibilities (JAGS and STAN samplers used) 

### Section 4.2, p. 79

# Limit of Detection (LOD)  If observations < analytic detection limits: model as left censored observations from lognormal population distribution

Fraction < LOD 

#  Parameters for distribution: log geometric mean (ln(GM)) and standard deviation • Also estimate these with MCMC 

#  Generally, these estimates have greater uncertainty
FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, p. 79

## Log(Parent Exposure) = a + m * log(Model Prediction) + b * Use Indicator + ε 

# Multiple regression models : 

# SEEM is a Linear Regression

ε ~ N(0, 

2 

### ) Residual error, unexplained by the 

### regression model Any pathways missed by forward models increase this error Inferred Exposure



### Weighted HTE Model Predictions FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, p. 82 -83

# Exposure Predictions for 7968 Chemicals

 These are the current calibrated and evaluated predictions from the SEEM framework Upper 95% Prediction Median Prediction

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.4, p. 83

# Exposure Predictions for 7968 Chemicals

 We focus on the median and upper 95% predictions because the lower 95% is below the NHANES limits of detection ( LoD )  Dotted lines indicate 25%, median, and 75% of the LoD distribution Upper 95% Prediction Median Prediction NHANES LoD

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.4, p. 83

# Exposure Predictions for 7968 Chemicals

 Chemicals currently monitored by NHANES are distributed throughput the predictions  Chemicals with the first and ninth highest 95% limit are monitored by NHANES NHANES LoD

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.4, p. 83

# Exposure Predictions for 7968 Chemicals

 The grey stripes indicate the 4182 chemicals with no use indicated by ACToR UseDB for any of the four use category heuristics NHANES LoD

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 4.2, Figure 4.4, p. 83

# High Throughput Exposure

Summary
 We used the SEEM framework to compare HTE factors to the NHANES data for a subset of chemicals in order to determine an empirical calibration and estimate of uncertainty
 The second generation calibrated meta - model can estimate relative levels of chemical exposures for 7968 chemicals • These include thousands of chemicals with no other data on human exposure • Same factors are predictive across multiple demographics (including age and gender) characterized by NHANES


##  New data and models should help decrease uncertainties and increase 

## confidence in extrapolation

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014

# High Throughput Exposure

Future Directions
 Developing new methods to draw inferences from CDC blood/serum samples


##  Developing new data sources for consumer products
 Developing and evaluating new mechanistic HT models for exposure from consumer use and indoor environment ( e.g. , SHEDS- HT)
 Coordinating with biomonitoring studies to obtain additional data on both: • Chemicals expected to be similar to those currently measured at high levels • Chemicals unlike any that are currently being monitored
FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014

# Presentation Roadmap

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Introduction Estrogen Receptor Bioactivity Androgen Receptor Bioactivity Exposure

Toxicokinetics/Dosimetry

Integrated Bioactivity Exposure Ranking

Future Directions

# High Throughput Toxicokinetics (HTTK)

 Toxicokinetics (TK) provides a bridge between HTS and HTE by predicting tissue concentrations due to exposure
 HTTK methods have been used by the pharmaceutical industry to 

## determine range of efficacious doses and to prospectively evaluate success of planned clinical trials
 HTTK data can be applied to perform in vitro- in vivo extrapolation 

## (IVIVE) or “reverse dosimetry” in order to provide a human dose context for in vitro concentrations from HTS • Reverse dosimetry can approximately convert in vitro HTS results to daily doses needed to produce similar levels in a human 

## • This allows direct comparisons with exposure

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 5

# ToxCast in vitro AC50s

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

#  One point for each chemical - in vitro assay combination with a systematic (Hill function) concentration response curve Wetmore et al. (2012) 

### Section 1, Figure 1.2, p. 11Oral dose in (mg/kg/day) Sum of hepatic and renal clearance (mg/kg/day) 

# Steady-State Plasma Concentration

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

##  In vitro plasma protein binding and metabolic clearance assays allow approximate hepatic 

## and renal clearances to be calculated
 At steady state this allows conversion from concentration to administered dose
 No oral absorption/ bioavailability included Section 5, p. 89Add plasma (6 donor pool for human) to one well Add chemical Determine concentration in both wells (analytical chemistry) Double-wells connected by semi permeable membrane on a Rapid Equilibrium Dialysis (RED) Plate Incubate plates to allow wells with and without protein to come to equilibrium 

### . . . . 

### . .

. 

### . . . . 1 2



### RED Method: Waters et al. (2008)  Data on ToxCast chemicals initially collected at Hamner Institutes  Published:  Rotroff et al. (2010) - Pilot study using 38 Phase I ToxCast Chemicals  Wetmore et al. (2012) - Remainder of easily analyzed Phase I chemicals  Wetmore et al. (2013) - Rat TK for 50 ToxCast/ToxRefDB compounds  Wetmore et al. ( 2014) – Assessed variability in metabolism for a dozen ToxCast compounds 

# Plasma Protein Binding (Fraction Unbound in Plasma)

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 5, p. 89Cryopreserved Hepatocytes (10 donor pool for human) Add Chemical

(1 and 10 μM ) Remove Aliquots

at 15, 30, 60, 120 min Analytical

Chemistry The rate of disappearance of parent compound (slope of line) is the hepatic clearance (μL/min/10 6 hepatocytes ) We perform the assay at 1 and 10 μM to check for saturation of metabolizing enzymes. 10 μM 1 μM 0 50 100 150

Time (min) Log Conc (μM)


### Cryopreserved hepatocyte Method: Shibata et al. (2002) 

# Intrinsic Hepatic Clearance

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014  Data on ToxCast chemicals initially collected at Hamner Institutes 

### Section 5, p. 89

# Steady-State Model is Linear

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Steady - state Concentration ( m M )



### Daily Dose (mg/kg/day) 0 Prediction

 Can calculate predicted steady-state concentration ( C ss ) for a 1 mg/kg/day dose and multiply to get concentrations for other doses 

### Slope = C ss 

### for 1 mg/kg/day Section 5, Figure 5.3, p. 91

# Steady-State In Vitro- In Vivo

Extrapolation (IVIVE)

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Oral Equivalent Daily Dose



### Steady-state Concentration ( m M ) = in vitro AC50 0 Prediction

 Swap the axes  Can divide bioactive concentration by C ss for for a 1 mg/kg/day dose to get oral equivalent dose 

### Slope = mg/kg/day per C ss 1 mg/kg/day

Wetmore et al. (2012) 

### Section 5, Figure 5.3, p. 91

#  It appears harder to prioritize on bioactive in vitro concentration without in vivo context 

# ToxCast in vitro AC50s

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Wetmore et al. (2012) 

### Section 1, Figure 1.2, p. 11

#  Translation from in vitro to steady-state oral equivalent doses allow greater discrimination between effective chemical potencies 

# HTTK Oral Equivalents

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Wetmore et al. (2012) 

### Section 1, Figure 1.2, p. 11

# Reverse Dosimetry with HTTK

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### High Throughput 

### In Vitro Bioactive Concentration Simulated Human In Vivo Doses Populations that are More Sensitive HTTK in vitro data Monte Carlo

Simulation of Biological Variability Combination of higher exposure 

### and sensitivities

Images from Thinkstock 

### Section 5, p. 89

# Variability in Steady-State TK Model

 In vitro clearance (μL/min/10 6 

### hepatocytes) is scaled to a whole organ clearance using the density of hepatocytes per gram of liver and the volume of the liver (which varies between individuals)  Glomerular filtration rate (GFR) and blood flow to the liver ( Ql 

### ) both vary from 

### individual to individual  Further assume that measured HTTK parameters have 20% coefficient of variation FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### (Passive) Renal Clearance Hepatic Clearance

(Metabolism) Section 5, p. 89

# Monte Carlo (MC) Approach to Variability

SimCYP ( Pharma ) Approach

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Probability
log Liver Flow ( Ql ) Probability
log Glomerular Filtration Rate (GFR) Probability
log Liver Volume Probability
log Cl intin vitro Probability
log fub Probability
Css

Wetmore et al. (2012) 

### Section 5, Figure 5.4, p. 92FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Oral Equivalent Daily Dose



### Steady-state Concentration ( m M ) = in vitro AC50 0 Median 

### Predicted Css

 The higher the predicted C ss, the lower the oral equivalent dose, so the upper 95% predicted C ss from the MC has a lower oral equivalent dose 

### Lower 95% Predicted C ss 

### Upper 95%

Predicted C ss 

### Section 5, Figure 5.3, p. 91 

# Steady-State In Vitro- In Vivo 

# Extrapolation (IVIVE )

# Evaluation of HTTK Performance and Domain

of Applicability

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

###  Through comparison to in vivo data, a cross -validated (random forest) predictor of success or failure of HTTK has been constructed  The largest single class of 

### chemicals fall into the category of “On the order” (within approximately a factor of three) 

###  More likely to overestimate (conservative error) than underestimate plasma concentrations from an exposure

# HTTK Summary

 HTTK methods developed for pharmaceuticals have been adapted to environmental testing  We can infer daily doses that produce plasma concentrations equivalent to bioactive concentrations • Can identify environmentally- relevant in vitro concentrations for high throughput screening 

###  All methods and data publicly available: R package “ httk” and two manuscripts submitted to peer- reviewed journals (one guide to using R package and one statistical evaluation of performances)  Chemical specific analytical chemistry is a rate limiting step: throughput is higher 

### than in vivo testing but not as fast as high throughput bioactivity screening • 100s of chemicals per year

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 5

# HTTK Future Directions

 Collecting new HTTK data on chemicals using improved (serial dilution of plasma) methodology for measuring plasma binding  Expanding populations variability analysis to include sensitive population groups to go beyond NHANES (current MC simulations focus on adults)  Current in vivo data for evaluation of models is from heterogeneous studies – 

### ongoing efforts to collect data from limited in vivo studies and to organize data from larger, systematic studies (e.g., National Toxicology Program) into computable format  Improved evaluation data will allow better assessment of predictive ability and 

### determination of domain of applicability

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 5