High throughput Toxicokinetics

(HTTK) and Reverse Toxicokineti

(RTK) for EDSP

High throughput Toxicokinetics

(HTTK) and Reverse Toxicokineti

(RTK) for EDSP

Presented by John Wambaugh

Office of Research and Development

National Center for Computational Toxicology

Presented by John Wambaugh

Office of Research and Development

National Center for Computational Toxicology
Presentation Roadma

Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

• RTK for in vitro HTS data

• Evaluation of Confidence

and Domain of Applicabil

Future Appli
Reverse Toxicokinetics (RTK) for Screen

Prioritization

RTK

ToxCast

HTE

Methods

This SAP

 Toxicokinetics provides a bridge

between in vitro, concentration‐

based ToxCast data to in vivo,

dose‐based exposure estimates

from ExpoCast
Introduction

 HTS methods exist for identifying putative chemical‐induc

changes to human biology in vitro

 HTE methods exist for predicting human exposure

 Toxicokinetics (TK) provides a bridge between HTS and HT

predicting tissue concentrations due to exposure

 Reverse TK (RTK) can approximately convert in vitro HTS r

daily doses needed to produce similar levels in a human

 These RTK methods have been used by the pharmaceutic

to determine range of efficacious doses and to prospectiv

evaluate success of planned clinical trials
Goals for High Throughp

Toxicokinetics

 The goal of RTK is to provide a human dose context for in vitro

concentrations from HTS

• This allows direct comparisons with exposure

1. To address the large EDSP universe we collect in vitro, high t

toxicokinetic (HTTK) data to make RTK predictions

 We evaluate in vitro RTK predictions two ways:

2. We compare in vitro predictions and in vivo measurements

3. We perform simulation studies to examine key assumptions
Goals for High Throughp

Toxicokinetics

 The goal of RTK is to provide a human dose context for in vitro

concentrations from HTS

• This allows direct comparisons with exposure

1. To address the large EDSP universe we collect in vitro, high t

toxicokinetic (HTTK) data to make RTK predictions

 We evaluate in vitro RTK predictions two ways:

2. We compare in vitro predictions and in vivo measurements

3. We perform simulation studies to examine key assumptions
Human Toxicokinetics and Ex

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environmen

Release

Air, Dust, Surfaces Food 

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
Concentrat

Response

In vitro Assay AC

Concentration 

Assay Awith Un

High-Throughput Bioac

 Tox21: Examining >10,000 chemicals using

~50 assays intended to identify interactions

with biological pathways (Schmidt, 2009)

 ToxCast: For a subset (>1000) of Tox21

chemicals ran >500 additional assays

(Judson et al., 2010)

 Most assays conducted in dose‐response

format (identify 50% activity concentration

– AC50 – and efficacy if data described by a

Hill function)

 All data is public: http://actor.epa.gov/
In vitro Bioactivity, RTK and i

Toxic Doses

Comparison ora

doses (box plots in mgpredicted wand NEL valhypertroph

studies

Lowest ObsNo Observe

(NEL)

NEL/100

Estimated chron

levels from are indicate

red lines. A

mg/kg/day.

Judson et al. (2011)
In Vitro Bioactivity, In Vivo To

and Exposure

 As in Egeghy et al. (2012), there is a paucity of data for providin

to HTS data

0

50

100150200250300

ToxCast Phase I (Wetmore et al.

2012)

ToxCast Phase II (Wetmore et

al. in preparation)

ChemicaChemica

Exposur

Chemica
ToxCast in vitro AC50

 One point for each chemical‐in vitro assay combinatio

systematic (Hill function) concentration response cur

Wetmore et al. (2012)
Oral dose in

(mg/kg/day)

  

    l ub in

int ub l ub Q F * GFR*F Q *F *

oral dose rate

Cl

Cl

Css

Steady‐State Plasma Concent

 In vitro plasma protein

binding and metabolicclearance 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
Add plasma (6

donor pool for

human) to one

well

Add chemical 

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

.

. .

.

. .

.

. .

.

.

2

1

,

well

well

ub p C

C F 

1 2

RED Method:

Waters et al. (2008)

 Data on ToxCast chemicals initially collected at 

Institutes

 Published:

 Rotroff et al. (2010) ‐ Pilot study using 38 Phase I ToxC Wetmore et al. (2012) ‐ Remainder of easily analyzed 

 Wetmore et al. (2013) Rat PK for 50 ToxCast/ToxRefDB 

Plasma Protein Binding

(Fraction Unbound in Plas
Cryopreserved

Hepatocytes

(10 donor pool for

human)

Add Chemical

(1 and 10 μM)

Remove Aliquots

at 15, 30, 60, 120

min

Analy

Chem

The rate of disappearance of

parent compound (slope of

line) is the hepatic clearance

(μL/min/106 hepatocytes)

We perform 

and 10 μM 

saturation of 

enzy

10 μM

1 μM

0 50 100 150

Time (min)

Log Conc (μM)

Cryopreserved hepatocyteMethod: Shibata et al. (2002)

Intrinsic Hepatic Clearan

 Data on Tox

initially colle

Institutes
Steady

‐State Model is Lin

Steady-state Concentration (M)

Daily Dose (mg/kg/day)

0

Pre

  











l ub int

int

ub l ub

Q

F *

GFR*F

Q *F *

oral dose rate

Cl

Cl

Css

 Can calculate predicted steady‐state c

for

a

1 mg/kg/day dose and multiply t

for other doses

Slope

=

Css for

1 m

Wetmore et al. (2012)
Steady‐State In Vitro‐In V

Extrapolation (IVIVE)

Oral Equivalent Daily Dose

Steady-state Concentration (M) = in vitro AC5

0

Pre

 Swap the axes

 Can divide bioactive concentration by Css for for a 1 mg/kg/day dose to get oral 

Slope = mg/kg/da

Wetmore et al. (2012)
 It appears harder to prioritize on bioactive in vitro

concentration without in vivo context

ToxCast in vitro AC50

Wetmore et al. (2012)
 Translation from in vitro to steady‐state oral equivale

allow greater discrimination between effective chempotencies

RTK Oral Equivalents

Wetmore et al. (2012)
Reverse Toxicokinetics (R

High

Throughput

In Vitro

Bioactive

Concentration

Simulated

Human

In Vivo

Doses P

th

HTPK

in vitro

data

Monte Carlo

Simulation of Biological

Variability

Images from Thinkstock
Variability in Steady‐State TK 

 In vitro clearance (μL/min/106 hepatocytes) is scaled to a 

organ clearance using the density of hepatocytes per gram 

and the volume of the liver (which varies between individ

 Glomerular filtration rate (GFR) and blood flow to the live

vary from individual to individual

 Further assume that measured HTTK parameters have 20

coefficient of variation

  

 

  

ub l ub 

GFR*F Q *F *

oral dose r

Css

(Passive) Renal

Clearance

He

Wetmore et al. (2012)
Monte Carlo (MC) Approach to Va

SimCYP (Pharma) Approac

       l ub int int ub l ub Q F * GFR*F Q *F *

oral dose rate

Cl

Cl

Css

Probability

log Liver Flow (Ql)

Probability

log Glomerular Filtration Rate (GFR)

Probability

log 

Probability

log Clintin vitro

Probability

log fub

Probability

Css

Wetmore et al. (2012)
Steady‐State In Vitro‐In Vivo Extra

(IVIVE)

Oral Equivalent Daily Dose

Steady-state Concentration (M) = in vitro AC5

0

MediaPredic

 The higher the predicted Css, the lower the oral equivalent dose, so the upper 9

from the MC has a lower oral equivalent dose

Lower 95%Predicted Css

Upper 9Predicte
,

ub p 

F 

Add plasma

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

 In some cases 

concentration without plasm

limit of detect

 Pharmaceutic

might have re

address this – 

examining 10

 All we know is 

fraction is sm

 In Wetmore e

free was assu

Difference Between High Throu

Evaluation and Pharmaceuti

Application
Modified fub Distribution fo

       l ub int int ub l ub Q F * GFR*F Q *F *

oral dose rate

Cl

Cl

Css

Probability

log Liver Flow (Ql)

Probability

log Glomerular Filtration Rate (GFR)

Probability

log 

Probability

log Clintin vitro

Probability

Css

Probability

Log fub

Fub Limit 
Steady‐State In Vitro‐In Vivo Extra

(IVIVE)

Oral Equivalent Daily Dose

Steady-state Concentration (M) = in vitro AC5

0

MediaPredic

 Taking into account the limit of detection issues does not change the median (o

does change the upper Css, causing lower oral equivalent dose predictions (gre

Upper 9Predicte

Upper PredictWith mdistrib
Chemicals with HTTK Da

 In vitro assays limited by time needed to develop chemical‐specific an

chemistry method

0 100 200 300 400

Existing Human data

Existing Rat data

Chemicals with HTTK Data

Rotroff et al. 2010Wetmore et al. 20Tonnelier et al. 20Wetmore et al. 20Hamner InstitutesToxCastPharmaceutical Lit
RTK Summary

 HTTK methods developed for pharmaceuticals have been adapted to

environmental testing

 RTK allows the EDSP to infer daily doses that produce plasma concent

equivalent to the bioactive concentrations, but:

• Chemical‐specific analytical chemistry methods make this slower 

bioactivity HTS or HTE

• HTTK methods developed for pharmaceuticals must be adapted t

environmental testing

 Although we used MC simulation to characterize some aspects of hum

variability (e.g., body weight of adults), any key determinants of varia

not included in our simulation have not been be assessed

 We must carefully characterize the uncertainty in our approach
Goals for High Throughp

Toxicokinetics

 The goal of RTK is to provide a human dose context for in vitro

concentrations from HTS

• This allows direct comparisons with exposure

1. To address the large EDSP universe we collect in vitro, high t

toxicokinetic (HTTK) data to make RTK predictions

 We evaluate in vitro RTK predictions two ways:

2. We compare in vitro predictions and in vivo measurements

3. We perform simulation studies to examine key assumptions
In vivo Predictive Ability and D

of Applicability

 In drug development, HTTK methods estimate therapeutic dos

clinical studies – predicted concentrations are typically on the o

values measured in clinical trials (Wang, 2010)

 For environmental compounds, there will be no clinical trials th

uncertainty must be well characterized ideally with rigorous sta

methodology

 We will use direct comparison to in vivo data in order to ge

empirical estimate of our uncertainty

 Any approximations, omissions, or mistakes should work to 

the estimated uncertainty when evaluated systematically a

chemicals
Using in vivo Data to Evalua

 When we compa

predicted from in with in vivo Css va

determined from 

literature we find 

correlation (R2 ~0

 The dashed line i

identity (perfect 

line:

 overestimat

 underestima

 The white lines in

discrepancy betw

measured and pr

values (the residu
Predicting When RTK Will 

 We can use computer algorithms to analyze chemical descrip

to predict when the residual will be small

 Factors included are:

• Physico‐chemical properties

– Log(Kow), molecular weight, acid/base association constants (pKa), 

pharmaceutical or perfluorinated compound classification

• In vitro HTTK data

– Plasma protein binding (Fub) and hepatic clearance

• Active chemical transport

– Use quantitative structure activity relationships (QSA

predict likelihood each compound is a substrate for 17 

transporters (From Alexander Sedykh and Alex Tropsh

and Sieto Bosgra (TNO))
Recursive Partitioning Tree 

Residuals

 The higher the Css, the lower the oral equivalent dose

 Ideally the residuals (difference between the literature value a

predicƟon) are small or R ≡ Csslit./Csspred. ≈ 1

 If a residual is large, we would prefer to over‐predict Css to be

conservative, i.e. R < 1

R = 5

Fub= 0.5

R = 1.02

Fub= 0.06

R = 10

Fub= 0.08

R = 0.5

Fub= 0.04

Fub < 0.07

R = 5

Fub= 0.

R = 1.02

Fub= 0.06

R = 0.5

Fub= 0.04

R = 0.9

Fub= 0.02

Yes No
Recursive Partitioning Tree 

(Log) Residuals

 If the (log) ratio of literature to prediction is greater than 0, then t

Css is higher than the predicted Css

Predicted from in vitro

Literature in vivo
Predict RTK Will Overestimat

 When 

estimadose i

 Regres

predicresiduphysic

propetransp

and in 

data

 We ca

predic

chemi

estimaaccura

predic

RTK PredictionUnderestimatesLiteratureRTK PredictionOverestimatesLiterature
Goals for High Throughp

Toxicokinetics

 The goal of RTK is to provide a human dose context for in vitro

concentrations from HTS

• This allows direct comparisons with exposure

1. To address the large EDSP universe we collect in vitro, high t

toxicokinetic (HTTK) data to make RTK predictions

 We evaluate in vitro RTK predictions two ways:

2. We compare in vitro predictions and in vivo measurements

3. We perform simulation studies to examine key assumptions
RTK Assumptions We Wil

 To date, the TK models used for environmental chemicals have 

relatively simple, making three key assumptions:

1) Whole body is at the same concentration (i.e., plasma)

2) Environmental exposure is constant and uniform (i.e., co

infusion)

3) Enough time has passed that the plasma concentration i

state with respect to the environment

 QSARs for tissue‐specific properties address the first

 We can test the second two assumptions using dynamic simula

more realistic, sporadic dosing) of physiologically‐based toxico

(PBTK) models
High Throughput Physiologi

based Toxicokinetic Models (H

 PBTK models are more complicated, but can build generic, high

throughput PBTK (HTPBTK) models parameterized with

• the same in vitro HTTK data used for RTK, plus

• QSARs for tissue‐specific properties

• Assumptions about unknown dynamic processes, such as a

 We use these HTPBTK models perform both simulation experim

compare model predictions from in vitro data with human and 

measurements
Inhaled Gas

Qliver

Qgut

Qgut

Kidney Blood

Gut Blood

Gut Lumen

QGFR Kidney Tissue

Liver Blood

Liver Tissue

Qrest

Lung Blood

Lung Tissue Qcardiac

Qmetab

Body Blood

Rest of Body

Qkidney

Arterial  Blood

Venous  Blood

Physiologically‐based Toxicok

(PBTK) Model

 Some tissues (e.g., arterial blood) are s

compartments, while others (e.g., kidn

compound compartments consisting of 

blood and tissue sections.

 Some specific tissues (lung, kidney, gut, 

modeled explicitly, others (e.g., fat, bra

lumped into the “Rest of Body” compar

 Chemical enters the body primarily thro

absorption, but we don’t know absorpt

bioavailability (assume “fast”, i.e. 1/h a

 The only ways chemicals “leaves” the b

through metabolism (change into a me

liver or excretion by glomerular filtratio

proximal tubules of the kidney (which f

lumen of the kidney).
Physiological Data

Units Mouse Rat Dog 

Total Body Water ml/kg 725.00 668.00 603.60 Plasma Volume ml/kg 50.00 31.20 51.50 Cardiac Output ml/min/kg 400.00 296.00 120.00 Average BW kg 0.02 0.25 10.00 

Total Plasma Protein g/ml 0.06 0.07 0.09 Plasma albumin g/ml 0.03 0.03 0.03 Plasma a‐1‐AGP g/ml 0.01 0.02 0.00 Hematocrit fraction 0.45 0.46 0.42 Urine ml/min/kg 0.035 0.139 0.021 Bile ml/min/kg 0.069 0.063 0.008 GFR ml/min/kg 14.0 5.2 6.1 

Volume (L/kg) Blood Flow (ml/min/kg)

Tissue Mouse Rat Dog Human Rabbit Mouse Rat Dog Human RabbitAdipose 0.07 0.07 0.05 0.21 0.05 10.80 1.60 3.50 3.71 12.80

Bone 0.05 0.04 0.04 0.07 0.04 23.31 36.11 1.30 3.36 36.11

Brain 0.02 0.01 0.01 0.02 0.01 13.20 5.20 4.50 10.00 5.20Gut 0.04 0.03 0.04 0.02 0.05 72.50 39.20 23.00 16.43 44.40

Heart 0.00 0.00 0.01 0.00 0.00 14.00 15.60 5.40 3.43 6.40Kidneys 0.02 0.01 0.01 0.00 0.01 65.00 36.80 21.60 17.71 32.00

Liver 0.05 0.03 0.03 0.02 0.04 90.00 47.20 30.90 20.71 70.80

Lung 0.01 0.00 0.01 0.01 0.01 2.00 6.22 10.56 2.00 6.22

Muscle 0.37 0.39 0.44 0.38 0.54 45.50 30.00 25.00 10.71 62.00

Skin 0.15 0.17 0.17 0.03 0.04 20.50 23.20 10.00 4.29 23.20

Spleen 0.00 0.00 0.00 0.00 0.00 5.50 4.07 1.65 1.10 3.60

Rest 0.03 0.05 0.00 0.05 0.03 110.19 90.00 5.59 2.97 90.00

Volume

from ScRabbit 

Other parameters from

Davies and Morris

(1993) + Rabbit (Sipes)
 Tissue‐specific

partitioningestimated(Schmitt, 2008)

using:

 Physicochemic

al properties

(logP, pKa)predictedfrom structure

(EPI Suite)

 Measured

fractionunbound in

plasma (fub)

Predicted Partition Coeffic

Partitioning figure is fr
Predicted TK Metrics

 Can use

HTPBTK to

predicttraditional TK

metrics, such as

peak

concentration

(Cmax) and time

integrated area

under the curve

(AUC) for

various tissues
Evaluation Data for HTT

 For most non‐therapeutic compounds, it is unlikely that there w

controlled human TK data

 Though some in vivo TK data exists to allow statistical assessme

predictions, these data are predominantly for pharmaceutical c

 Rat HTTK data for 53

 Collect in vivo TK data for some or all of the rat HTTK compoun

• Evaluation data for properties that can predicted in vitro and with 

clearance and volume of distribution)

• Measurement of other key processes (e.g., absorption rates, extra

metabolism).

• For example, if in vivo data could be collected for both oral and in

dosing, the relative concentrations could give an approximate ass

bioavailability that might then be extrapolated to humans.
Evaluating HTPBTK Predict

from In Vitro Data

 HTPBTK pr

for the AUintegrated 

concentratUnder the 

 in vivo me

from the livarious tre(dose and 

rat.

 Prediction

enerally co– i.e., predhigher tha

 Oral dose 

higher thaintravenou(p‐Value 0
Evaluating HTPBTK Predict

from In Vitro Data

 Cmax pred

and in vivare corre(R2 ~ 0.69
Evaluating RTK Assumpt

 Most in vivo data is for pharmaceuticals Can use HTPBTK simulations to evaluate appropriateness of RTK assumptions

 For example, below we show approach to “steady‐state” due to three simulate
Evaluation of Steady‐State Pre

 Using HTPBTK and 

three daily doses 

hours)

 This allows us to e

plausibility of the dosing assumptio

 We find that the m

chemicals reach sin a few weeks

 A second populat

chemicals never rstate.
Peak Concentration vs. 

 Peak serum

concentrations 

HTPBTK modecompared agaisteady‐stateconcentration 

by the threecompartment 

a constant infuexposure (as in 

et al. 2012)

 The dashed, id

line indicates t

most compoun

peak concentra

very similar to 
Summary of RTK Evaluat

 We have compared in vitro predicted Css with in viv

measured Css for some chemicals and observed po

~0.19), but usually conservative correlation

 However, we can model the difference between in 

measurements and HTTK predictions (i.e., the resierrors), achieving a much greater ability to predict 

(R2 ~0.69) than to make overall predictions

 We used simulation studies via HTPBTK models to 

assumptions of RTK, finding that for most chemica

peak plasma concentrations are either very similar 

than the Css calculated using more simple models
0 100 200 300 400 500 600

Existing TK Data

Anticipated Data

Chemicals with HTTK Data and In Vivo Evalution Data

Pharmaceuticals et al. 2008)Environmental C(Wetmore et al. National ToxicolProgram Legacy New Experiment

New Data for Evaluating H

Predictions

 Goal of HTTK modeling of pharmaceuticals is to determin

efficacious doses for follow‐on human clinical trials, the d

confidence needed is different

 For environmental compounds, their uncertainty must be 

characterized ideally with rigorous statistical methodolog
239

Chemicals

88Collected Summer 2014

Wetmore et al., (2012)

Intrinsic hepatic clearance

and plasma protein

binding data

To be published in 2014

181

New Data for HTTK

 ToxCast HTTK testing:

 Measuring metabolism by hu

hepatocytes

 Improved assays for measurin

chemicals to human plasma p

 Obtain data on ToxCast chem

investigated by the Hamner Instudies

 Reinvestigate chemicals that 

difficult in previous efforts

 This data will eventually allow dete

human oral equivalent doses (mg/

for most ToxCast chemicals.
NERLRocky GoldsmithMike Tornero

*Trainees

*

*

*

Elaine HubalCSS Deputy National Program DirectorTina BahadoriCSS National Program Director

Acknowledgements

EPA Office of Research 

and Development

Hamner Institutes

NHEERLHisham El-MasriJane Ellen SimmonsMike Hughes

University of NortChapel HillIndiana UniversityNetherlands Orgafor Applied ScientResearch (TNO)

NCCTRichard JudsonChantel NicolasRobert PearceJames RabinowitzWoody SetzerImran ShahNisha SipesCory StropeRusty Thomas

Barbara WetmoreAlexander SedykhAlex TropshaJames SlukaSieto Bosgra

*

*