HESIHESI ILSI Health and Environmental Sciences InstituteILSI Health and Environmental Sciences Institute

Workshop Organizing Committee

Melvin Andersen (CIIT)

Matthew Bogdanffy (DuPont)

James Bus (Dow)

Rory Conolly (CIIT)

Raymond David (Kodak)

Christopher DeRosa (ATSDR)

Nancy Doerrer (HESI)

John Doull (University of Kansas)

William Farland (EPA)

Penelope Fenner-Crisp (ILSI RSI)

David Gaylor (Gaylor and Associates)

Dale Hattis (Clark University)

Gary Kimmel (EPA)

Christopher Portier (NIEHS)

Bernard Schwetz (FDA)

R. Woodrow Setzer, Jr. (EPA)

William Slikker, Jr. (FDA)

Bob Sonawane (EPA)

James Swenberg (University of NC)

Kendall Wallace (University of MN)

Mildred Williams-Johnson (ATSDR)

HESIHESI ILSI Health and Environmental Sciences InstituteILSI Health and Environmental Sciences Institute

Publications

Slikker, W., Jr., Andersen, M.E., Bogdanffy, M.S., Bus, J.S., Cohen, S.D., Conolly, R.B., David, R.M., Doerrer, N.G., Dorman, D.C., Gaylor, D.W., Hattis, D., Rogers, J.M., Setzer, R.W., Swenberg, J.A., Wallace, K., 2004a. Dose-dependent transitions in mechanisms of toxicity. Toxicol. Appl. Pharmacol. 201, 203-225.

Slikker, W., Jr., Andersen, M.E., Bogdanffy, M.S., Bus, J.S., Cohen, S.D., Conolly, R.B., David, R.M., Doerrer, N.G., Dorman, D.C., Gaylor, D.W., Hattis, D., Rogers, J.M., Setzer, R.W., Swenberg, J.A., Wallace, K., 2004b. Dose-dependent transitions in mechanisms of toxicity: case studies. Toxicol. Appl. Pharmacol. 201, 226-294

HESIHESI ILSI Health and Environmental Sciences InstituteILSI Health and Environmental Sciences Institute

Examples of Dose-Dependent Transitions in Kinetic Disposition and Dynamic Expression

• Absorption Active or passive via GI or respiratory tract

• Distribution Protein binding, active transport

• Elimination Renal organic anion transport

• Chemical transformation– Activation Butadiene

– Detoxification Vinyl chloride, Methylene chloride• Enzyme saturation Vinylidine chloride, Ethylene glycol• Co-substrate depletion Acetaminophen

• Receptor interaction PPAR, progesterone/hydroxyflutamide

• Repair or reversal Vinyl chloride

• Altered homeostasis Propylene oxide, Formaldehyde

– Induction Vinyl acetate, Manganese, Zinc

– Metabolic switch– Cell proliferation

Mode of action

CYP 2E1 catalyzed:

CH2Cl2 CHOHCl2 HCOCl CO + CO2

formyl chlorideCOHb

GST catalyzed:

CH2Cl2 GSCH2Cl GSCH2OH HCHO

chloromethylglutathione GSCHO HCOOH CO2

Using Bradford Hill criteria (Framework analysis) for MOA

Criterion Data to support

Identify key events Yes (most) – value of genomics

Biological plausibility Yes based on dose-response and association with GST activity (reactive intermediate not isolated, but

DNA-metabolite interaction demonstrated )

Strength, consistency, and specificity of association with tumor data

Consistency demonstrated by dose-response (use of PK models); tissue localization/ of enzyme activity

consistent with tumor response

Dose-Response and Temporal Association

Dose-response and temporal association consistent with genetic reactivity in bacteria with GST activity

Alternate MOA No plausible alternative proposed

Confidence High confidence that MOA reflects cellular events in animals

0

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1 2 3 4 5 6 7 8 9 10

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13 A B C D E

Cle

well

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Exp

ert

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itati

on

OS

HA

Pri

or

OS

HA

Post

eri

or

Individual Values (Sweeney et al., 2004)Individual Values (Jonsson et al. (2001)

Population Values

Vm

axc/K

m (

/hr)

Human data

Key components of the formaldehyde risk assessment (I)

• Regional dosimetry and effects data in the respiratory tract– DPX– Labeling index

• Time-course and dose-response data– Labeling index– DPX– tumors

• Sophisticated extrapolation tools– CFD modeling– Rat and rhesus

Key components of the formaldehyde risk assessment (II)

• Sophisticated extrapolation tools– CFD modeling– Effects data from rat and rhesus monkey– Human physiology

DPX submodel – simulation of rhesus monkey data

1 2 3 4 5 6 710

-4

10-3

10-2

10-1

PPM

DP

X (p

mol

/mm

3 )DPX dose-response for Rhesus monkey

Vmax: 91.02. pmol/mm3/min

Km: 6.69 pmol/mm3 kf: 1.0878 1/min Tissue thickness ALWS: 0.5401 mm MT: 0.3120 mm NP: 0.2719 mm

A1A2A3A4

A5

B1B2B3B4B5

C1C2C3C4C5

D1D2D3D4D5

E1E2E3E4E5

F1F2F3F4F5

0 . 1 4

0 . 5 7

1 . 2 9

6 . 0 0

1 3 . 0 0

2 6 . 0 0

5 2 . 0 0

7 8 . 0 0

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

3 0 0

0 . 1 40 . 5 71 . 2 96 .1 3 .2 6 .5 2 .7 8 .12

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La b e li n g i n d e x

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C1C2C3C4C5

D1D2D3D4D5

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0 . 1 4

0 . 5 7

1 . 2 9

6 . 0 0

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2 6 . 0 0

5 2 . 0 0

7 8 . 0 0

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Uptake PatternsF344 Rat

Rhesus Monkey

Human

High

Low

Q1--Improvements to Exposure and Dose Monitoring--Beyond “Dose-Response”

• Need to think in terms of dose-time-response relationships to inform collection or modeling of external exposure and dose information in relevant time periods.

• Both exposure duration and age-at-exposure and are relevant, especially for developmental effects.

• Sensitivity is not necessarily constant within a “window of vulnerability” (e.g. per modeling by Luecke)

Q3--Does modeling of adaptive responses require any changes in current regulatory

testing strategies? In assessment approaches?

• In general it is not sufficient for a good assessment to establish the presence of “adaptive responses” at particular dose levels to assure safety. Such responses are not necessarily biologically costless or perfectly effective in preventing adverse effects in all people.

Q4--What type of dose-response models or approaches might be “better” used to integrate the diverse data? For

characterizing variability and uncertainty?

• There is need to replace all the “uncertainty factor’s with distributions based on empirical data for analogous cases. See, as a preliminary

effort, http://www2.clarku.edu/faculty/dhattis. This is, among other things, the only way to produce estimates of finite exposure control benefits to juxtapose with exposure control costs.

• In general, the more non-linear the model is at relevant exposure levels, the more important it is to make quantitative assessments of uncertainty and variability—both for judging risks to relatively sensitive segments of the population and for producing “expected value” estimates of risk and cost.

Relationships of Exposure and Dose to Risk

Individual versus Population Risks

Risk Descriptors~Central Estimates~High End~Reasonable Worst Case~Theoretical Upper Bound Estimate (TUBE)

0.01 1.0E-060.02 8.0E-060.03 0.0000270.04 0.0000640.05 0.0001250.06 0.0002160.07 0.0003430.08 0.0005120.09 0.0007290.1 0.001

0.11 0.0013310.12 0.0017280.13 0.0021970.14 0.0027440.15 0.0033750.16 0.0040960.17 0.0049130.18 0.0058320.19 0.0068590.2 0.008

0.21 0.0092610.22 0.0106480.23 0.0121670.24 0.0138240.25 0.0156250.26 0.0175760.27 0.0196830.28 0.0219520.29 0.0243890.3 0.027

0.31 0.029791

0 0.2 0.4 0.6 0.8 1 1.2

0

0.2

0.4

0.6

0.8

1

1.2

X-Axis

Y-A

xis

Data B

Title

Typical non-linear, “threshold”, dose-response relationship (R=Ad3)*

* Adapted from Heitzmann and Wilson (1997)

R(R

esp

on

se)

d(Dose)

0.01 1.0E-060.02 8.0E-060.03 0.0000270.04 0.0000640.05 0.0001250.06 0.0002160.07 0.0003430.08 0.0005120.09 0.000729

0.1 0.0010.11 0.0013310.12 0.0017280.13 0.0021970.14 0.0027440.15 0.0033750.16 0.0040960.17 0.0049130.18 0.0058320.19 0.006859

0.2 0.0080.21 0.0092610.22 0.0106480.23 0.0121670.24 0.0138240.25 0.0156250.26 0.0175760.27 0.0196830.28 0.0219520.29 0.024389

0.3 0.0270.31 0.029791

0 0.2 0.4 0.6 0.8 1 1.2

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0.2

0.4

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X-Axis

Y-A

xis

Data B

Title

* Adapted from Heitzmann and Wilson (1997)

R(R

esp

on

se)

d(Dose)

Additivity to Background *

0.01 1.0E-060.02 8.0E-060.03 0.0000270.04 0.0000640.05 0.0001250.06 0.0002160.07 0.0003430.08 0.0005120.09 0.000729

0.1 0.0010.11 0.0013310.12 0.0017280.13 0.0021970.14 0.0027440.15 0.0033750.16 0.0040960.17 0.0049130.18 0.0058320.19 0.006859

0.2 0.0080.21 0.0092610.22 0.0106480.23 0.0121670.24 0.0138240.25 0.0156250.26 0.0175760.27 0.0196830.28 0.0219520.29 0.024389

0.3 0.0270.31 0.029791

0 0.2 0.4 0.6 0.8 1 1.2

0

0.2

0.4

0.6

0.8

1

1.2

X-Axis

Y-A

xis

Data B

Title

* Adapted from Heitzmann and Wilson (1997)

R(R

esp

on

se)

d(Dose)

Comparison of Slopes *

RO

dOdh

Linear r

esponse

(high dose

)

ßinc(high dose)

ßinc(low dose)

Tumor Incidence in Heterogeneous Population

Monogenic Determination of Sensitivity

Carcinogen Dose

Spont.

Max.

Population A

Population B

Lutz, 1990

Tumor Incidence in Heterogeneous Population

Polygenic Determination of Sensitivity

Carcinogen Dose

Spont.

Max.

Lutz, 1990

Tumor Incidence in Heterogeneous Population

Sensitivity Governed by Multiple Genes + Modulation by Lifestyle

Carcinogen Dose

Spont.

Max.

Lutz, 1990

Tumor Incidence in Heterogeneous Population

Carcinogen Dose

Spont.

Max.

Population A

Population B

Lutz, 1990

Sensitivity Governedby Multiple Genes +

Modulation by Lifestyle

PolygenicDeterminationof Sensitivity

MonogenicDeterminationof Sensitivity

Carcinogen Dose

Spont.

Max.

Carcinogen Dose

Spont.

Max.

Food and Drug Administration

Dose-Dependent Transitions in Mechanisms of Toxicity:

Impact of Testing Strategies and Risk Assessment Approaches

Society of Toxicology, March 7, 2005

David Jacobson-Kram, Ph.D., DABT

Center for Drug Evaluation and Research

Office of New Drugs

Food and Drug Administration

Center for Drug Evaluation and Research, FDA

CDER generally does not perform quantitative risk assessment except for drug impurities and degradation products

CDER generally has rigorous exposure and metabolism data in humans and animals, often at comparable doses

Safety studies for specific human populations can be modeled in parallel animal studies, eg. Juvenile animal tox studies, geriatric possible but not practical

Food and Drug Administration

Challenges for CDER

Detection of rare adverse events (eg Vioxx)

Development of animal models capable of predicting rare AEsAnimals engineered with rare genetic

polymorphismsAnimal models compromised because of

other exposures, pharmaceutical, life style or environment