Overview: Value of ADME/PK Studies in Safety Assessment Harvey J. Clewell, PhD, DABT, FATS Director, Center for Human Health Assessment The Hamner Institutes for Health Sciences Research Triangle Park, NC

Overview

Background on Biokinetics (ADME/PK) Applications of Biokinetics

– The Past: Safety Assessments based on in vivo data Methylene Chloride Methylmercury Perchlorate

– The Future: Safety Assessments based on in vitro data Toxicity Testing in the 21st Century In vitro to in vivo extrapolation (IVIVE) In vitro based risk assessment approaches

Biokinetics

Also referred to as pharmacokinetics or toxicokinetics

Describes the change in chemical distribution over time in the body

Explores the quantitative relationship between Absorption, Distribution, Metabolism, and Excretion of a given chemical

Classical compartmental/non-compartmental modeling – ‘Data-based’, empirical descriptions – Describes chemical time-course with fitted parameters

Physiologically-based modeling: – Compartments are based on real tissue volumes, physiological structure – Mechanistically based description of chemical movement using tissue

blood flow and simulation of in vivo transport processes.

The Traditional Role of Biokinetics: Relating Animal Doses to Equivalent Human Exposures

Physiologically Based Pharmacokinetic (PBPK) Modeling

The purpose of a PBPK model is to define the relationship between an external measure of (administered) exposure/dose and an internal measure of (biologically effective) exposure/dose in both the experimental animal and the human

Methylene Chloride: Using a PBPK Model in Risk Assessment

First use of a PBPK model in an agency risk assessment

Used by EPA, OSHA, and Health Canada, but not FDA, to estimate lung cancer risk from a 2-yr mouse bioassay

Predicted substantially lower risk in humans compared to default approaches

Alternative Risk Assessment Approaches for Methylene Chloride

5.64e-5

1.45e-6

1.45e-6

3.48e-6

4.46e-6

1.15e-7

1.15e-7

2.75e-7

1.06e-5

5.55e-7

6.38e-6

n/a

8.4e-7

4.38e-8

5.04e-7

n/a

Weighted averageof unit risk = 2.1x10-7

MFO0.2

GST0.7

DCM0.1

PB-PK0.7

Applied0.3

Body Surface0.3

MFO0.2

GST0.7

DCM0.1

PB-PK0.7

Applied0.3

Body Weight0.7

Applied0.2

MFO0.2

GST0.7

DCM0.1

PB-PK1.0

Applied0.0

Body Surface0.2

MFO0.2

GST0.7

DCM0.1

PB-PK1.0

Applied0.0

Body Weight0.8

PB-PK0.8

UnitRiskDose MetricHuman

Biokinetics

Species to HumanBiodynamics

SpeciesBiokinetics

5.64e-5

1.45e-6

1.45e-6

3.48e-6

4.46e-6

1.15e-7

1.15e-7

2.75e-7

1.06e-5

5.55e-7

6.38e-6

n/a

8.4e-7

4.38e-8

5.04e-7

n/a

Weighted averageof unit risk = 2.1x10-7

MFO0.2

GST0.7

DCM0.1

PB-PK0.7

Applied0.3

Body Surface0.3

MFO0.2

GST0.7

DCM0.1

PB-PK0.7

Applied0.3

Body Weight0.7

Applied0.2

MFO0.2

GST0.7

DCM0.1

PB-PK1.0

Applied0.0

Body Surface0.2

MFO0.2

GST0.7

DCM0.1

PB-PK1.0

Applied0.0

Body Weight0.8

PB-PK0.8

UnitRiskDose MetricHuman

Biokinetics

Species to HumanBiodynamics

SpeciesBiokinetics

FDA

Old EPA

HC

New EPA

Methylmercury: Using a PBPK Model to Reconstruct Accidental Exposure in Iraq

(Shipp et al. 2000)

Using the PBPK Model to Estimate the Impact of Population Variability on Safe Exposure

Distribution of daily methylmercury ingestion rates for women of childbearing age in the U.S. corresponding to the NOAEL hair concentration derived by USEPA from the study of the Iraqi poisoning incident.

(Clewell et al. 1999) RfD = 0.1

Perchlorate: Using a PBPK model to link urinary concentrations to dietary exposure

Predicted (in blue) vs. measured (in red) creatinine-adjusted urinary perchlorate concentrations (in lg perchlorate g1 creatinine) for 340 non-pregnant women, age 15–45, in NHANES 2001–2002.

(Yang et al. 2012)

in vitro assays in human cells designed for purpose

mode-of-action based analysis

evaluate perturbations of specific biological signaling pathways – “toxicity pathways”

define adversity at the cellular level

Assess dose response and estimate equivalent in vivo exposures

The NAS Vision: Toxicity Testing in the 21st Century

Eight Years Later: How’s that working for you?

Goal: Profiling and Prioritization

Predict results of animal studies

Prioritize for in vivo testing

Assist in risk assessment

Toxcast Phase 1 predictive power: No better than QSAR (Thomas et al. 2012)

Tox21™

TT21C: How 21st Century Toxicity Testing May Look Some Day …

in vitro-in vivo extrapolation

(IVIVE)

in vivo human exposure ‘standard’

mg/kg/day

Accepted Toxicity Pathway assays

Computational Systems Biology Pathway (CSBP) Modeling

Assessing adversity in vitro

Point of Departure

in vitro (mg/l))

Acceptable concentration in vitro (mg/l)

In Vitro to In Vivo Extrapolation (IVIVE)

QIVIVE Approach

Potential Target Tissue

Biokinetic Model

In Vivo Human Toxicty Estimate

In vitro Dynamics

In Vitro Kinetics

QSAR QSPR

Metabolite ID

Metabolite ID

Metabolite ID

Nature of Toxicity

Hepatic clearance Intestinal uptake / metabolism

Renal clearance Partitioning

QIVIVE

(Yoon et al. 2012)

A Simple IVIVE Approach for Interpreting High Throughput In Vitro Assay Results

-5-4-3-2-10123

0 50 100 150

Ln Co

nc (u

M)

Time (min)

Hepatocellular clearance

Plasma protein binding

Estimated renal clearance

Prediction of in vivo clearance

Reverse Dosimetry

Equivalent in vivo

exposure

Effective concentration

from in vitro assay

Comparison of IVIVE with estimates from in vivo PK

(Wetmore et al. 2012)

Defining Dosimetry and Exposure in HTS (Joint Hamner / EPA NCCT Effort)

The Same In Vitro EC50 Does Not Imply to the Same In Vivo Dose!

This Simple Use of IVIVE for HTS Demonstrated That PK is Crucial

(Rotroff et al. 2010)

Key Limitation in Current In Vitro Testing: Failure to adequately consider metabolism

Current in vitro assay development is focused almost exclusively on detecting parent chemical toxicity

Experience indicates that in vivo toxicity from repeated exposures is often due to production of metabolites

Identification of metabolite toxicity may not be compatible with high throughput testing

Examples of Compounds for Which Metabolism Is Responsible for Their Critical Toxicity:

– chloroform – coumarin – ethanol – methylene chloride – phthalates

In Vitro Based Risk Evaluation Approach

Estimating an in vivo dose-response curve from an in vitro concentration-response

Response to Arsenic Treatment of Human Uroepithelial Cells from Different Subjects

(Yager et al. 2013)

Tiered Approach for Risk Evaluation – Tier 1

(Thomas et al. 2013)

In Vitro Studies Can Predict Safe In Vivo Doses

Tiered Approach for Risk Evaluation – Tier 2

(Thomas et al. 2013)

Short-Term Genomic Studies Can Predict Safe Chronic Doses

Acknowledgements

The Hamner Mel Andersen Miyoung Yoon Barbara Wetmore Jerry Campbell Russell Thomas (now at EPA)

Toxicity Pathway and IVIVE Research Funding: American Chemistry Council CEFIC Dow Chemical Dow-Corning ExxonMobil Foundation Unilever

Others Mike Bolger (FDA/CFSAN) Ben Blount (CDC/NCEH) Bas Blaauboer (Utrecht U.)

References Blaauboer BJ, Boekelheide K, Clewell HJ, Daneshian M, Dingemans MM, Goldberg AM, Heneweer

M, Jaworska J, Kramer NI, Leist M, Seibert H, Testai E, Vandebriel RJ, Yager JD, Zurlo J. 2012. The use of biomarkers of toxicity for integrating in vitro hazard estimates into risk assessment for humans. ALTEX 29(4):411-425

Rotroff DM, Wetmore BA, Dix DJ, Ferguson SS, Clewell HJ, Houck KA, Lecluyse EL, Andersen ME, Judson RS, Smith CM, Sochaski MA, Kavlock RJ, Boellmann F, Martin MT, Reif DM, Wambaugh JF, Thomas RS. 2010. Incorporating Human Dosimetry and Exposure into High-Throughput In Vitro Toxicity Screening. Toxicol Sci, 117(2):348-358.

Shipp AM, Gentry PR, Lawrence G, VanLandingham C, Covington T, Clewell HJ, Gribben K, and Crump K. 2000. Determination of a site-specific reference dose for methylmercury for fish-eating populations. Toxicol Indust Health 16(9-10):335-438.

Thomas RS, Black MB, Li L, Healy E, Chu T-M, Bao W, Andersen ME, Wolfinger RD. 2012. A comprehensive statistical analysis of predicting in vivo hazard using high-throughput in vitro screening. Toxicol Sci 128(2):398-417.

Thomas RS, Philbert MA, Auerbach SS, Wetmore BA, Devito MJ, Cote I, Rowlands JC, Whelan MP, Hays SM, Andersen ME, Meek ME, Reiter LW, Lambert JC, Clewell HJ 3rd, Stephens ML, Zhao QJ, Wesselkamper SC, Flowers L, Carney EW, Pastoor TP, Petersen DD, Yauk CL, Nong A. 2013. Incorporating New Technologies into Toxicity Testing and Risk Assessment: Moving from 21st Century Vision to a Data-Driven Framework. Toxicol Sci. 136(1):4-18.

References Yager, JW, Thomas, RS, Gentry, PR, Pluta, L, Efremenko, A, Black, M, Arnold, LL, McKim, JM,

Wilga, P, Gill, G, Choe, K-Y, and HJ Clewell. 2013. Evaluation of gene expression changes in human primary uroepithelial cells following 24 hour exposures to inorganic arsenic and its methylated metabolites. Environmental and Molecular Mutagenesis. 54:82-98.

Yang Y, Tan Y-M, Blount B, Murray C, Egan S, Bolger M, and Clewell H. 2012. Using a physiologically based pharmacokinetic model to link urinary biomarker concentrations to dietary exposure of perchlorate, Chemosphere 88(8):1019-1027.

Yoon M, Campbell JL, Andersen ME, and Clewell HJ. 2012. Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results. Crit Rev Toxicol. 42(8):633-652.

Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, Clewell HJ 3rd, Dix DJ, Andersen ME, Houck KA, Allen B, Judson RS, Singh R, Kavlock RJ, Richard AM, Thomas RS. 2012. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicol Sci. 125(1):157-174.

Wetmore BA, Allen B, Clewell HJ 3rd, Parker T, Wambaugh JF, Almond LM, Sochaski MA, Thomas RS. 2014. Incorporating Population Variability and Susceptible Subpopulations into Dosimetry for High-Throughput Toxicity Testing. Toxicol Sci. 142(1):210-224.