hesi ilsi health and environmental sciences institute workshop organizing committee melvin andersen...
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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
(1995
)Jo
nss
on a
nd
Jo
hanso
n (
20
01)
Exp
ert
Elic
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
34
5
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45
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1
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45
1
2345
1
234
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1
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
3 0 0
La b e li n g i n d e x
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
34
5
23
45
1
23
45
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45
1
2345
1
234
5
1
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
3 0 0
La b e li n g i n d e x
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
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)
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