Transcript RASS Reception HESI Panel

HESI
ILSI 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)
HESI
ILSI 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
Examples
of Dose-Dependent Transitions in
HESI
ILSI Health
and Environmental Sciences
Kinetic
Disposition
andInstitute
Dynamic Expression
•
•
•
•
Absorption
Distribution
Elimination
Chemical transformation
– Activation
– Detoxification
• Enzyme saturation
• Co-substrate depletion
• Receptor interaction
• Repair or reversal
• Altered homeostasis
– Induction
– Metabolic switch
– Cell proliferation
Active or passive via GI or respiratory tract
Protein binding, active transport
Renal organic anion transport
Butadiene
Vinyl chloride, Methylene chloride
Vinylidine chloride, Ethylene glycol
Acetaminophen
PPAR, progesterone/hydroxyflutamide
Vinyl chloride
Propylene oxide, Formaldehyde
Vinyl acetate, Manganese, Zinc
Mode of action
CYP 2E1 catalyzed:
CH2Cl2  CHOHCl2  HCOCl  CO + CO2
formyl chloride
COHb
GST catalyzed:
CH2Cl2  GSCH2Cl
GSCH2OH  HCHO
chloromethylglutathione  GSCHO  HCOOH  CO2
Using Bradford Hill criteria
(Framework analysis) for MOA
Criterion
Identify key events
Data to support
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
Confidence
No plausible alternative proposed
High confidence that MOA reflects cellular events in
animals
Human data
35
30
25
20
15
10
OSHA Posterior
OSHA Prior
E
D
C
B
A
Individual
Values
(Jonsson et al.
(2001)
Clewell (1995)
Jonsson and
Johanson (2001)
Expert Elicitation
Individual Values (Sweeney et al., 2004)
13
12
11
10
9
8
7
6
5
4
3
0
2
5
1
Vmaxc/Km (/hr)
40
Population Values
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
DPX dose-response for Rhesus monkey
3
DPX (pmol/mm)
10
10
-1
-2
Vmax: 91.02. pmol/mm3/min
Km: 6.69 pmol/mm3
kf: 1.0878 1/min
10
10
-3
Tissue thickness
ALWS: 0.5401 mm
MT: 0.3120 mm
NP: 0.2719 mm
-4
1
2
3
4
PPM
5
6
7
300
250
150
150
100
100
4
4
1
5
4
50
50
3
2p 2
pm
1
5
4
0.7 3 2
pp
m
1
5
4
A5
A4
A3
A2
A1
2
B5
B4
B3
B2
B1
3
6p
pm
C5
C4
C3
C2
C1
54
D5
D4
D3
D2
D1
F5
F4
F3
F2
F1
5
10 3 2 1
pp
m
E5
E4
E3
E2
E1
5
15 3 2
pp 1
m
con 3
tro
l
2
0.57 0.14
6. 1.29
0. 1 4
re
exposu
f
o
n
o
i
t
Dura
)
(weeks
0. 5 7
1 78.
7 8 . 00
13.
52. 26.
1 3 . 00
2 6 . 00
5 2 . 00
1 .2 9
6 . 00
0
Labeling index
200
200
Uptake Patterns
F344 Rat
Rhesus Monkey
High
Human
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)
Typical non-linear, “threshold”,
dose-response relationship (R=Ad3)*
1.0E-06
8.0E-06
0.000027
0.000064
0.000125
0.000216
0.000343
0.000512
0.000729
0.001
0.001331
0.001728
0.002197
0.002744
0.003375
0.004096
0.004913
0.005832
0.006859
0.008
0.009261
0.010648
0.012167
0.013824
0.015625
0.017576
0.019683
0.021952
0.024389
0.027
0.029791
Title
1.2
1
0.8
Y-Axis
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
0.31
0.6
Data B
0.4
0.2
0
0
0.2
0.4
0.6
X-Axis
d(Dose)
0.8
1
1.2
* Adapted from Heitzmann
and Wilson (1997)
Additivity to Background *
1.0E-06
8.0E-06
0.000027
0.000064
0.000125
0.000216
0.000343
0.000512
0.000729
0.001
0.001331
0.001728
0.002197
0.002744
0.003375
0.004096
0.004913
0.005832
0.006859
0.008
0.009261
0.010648
0.012167
0.013824
0.015625
0.017576
0.019683
0.021952
0.024389
0.027
0.029791
Title
1.2
1
0.8
Y-Axis
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
0.31
0.6
Data B
0.4
0.2
0
0
0.2
0.4
0.6
X-Axis
d(Dose)
0.8
1
1.2
* Adapted from Heitzmann
and Wilson (1997)
Comparison of Slopes *
1.0E-06
8.0E-06
0.000027
0.000064
0.000125
0.000216
0.000343
0.000512
0.000729
0.001
0.001331
0.001728
0.002197
0.002744
0.003375
0.004096
0.004913
0.005832
0.006859
0.008
0.009261
0.010648
0.012167
0.013824
0.015625
0.017576
0.019683
0.021952
0.024389
0.027
0.029791
Title
1.2
1
0.8
Y-Axis
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
0.31
0.6
Data B
ßinc(high dose)
0.4
ßinc(low dose)
0.2
R
O
0
0
0.2
0.4
dO
0.6
X-Axis
d(Dose)
0.8
1
dh
1.2
* Adapted from Heitzmann
and Wilson (1997)
Tumor Incidence in Heterogeneous Population
Monogenic Determination of Sensitivity
Lutz, 1990
Max.
Population B
Population A
Spont.
Carcinogen Dose
Tumor Incidence in Heterogeneous Population
Polygenic Determination of Sensitivity
Max.
Spont.
Carcinogen Dose
Lutz, 1990
Tumor Incidence in Heterogeneous Population
Sensitivity Governed by Multiple
Genes + Modulation by Lifestyle
Max.
Spont.
Carcinogen Dose
Lutz, 1990
Tumor Incidence in Heterogeneous Population
Lutz, 1990
Max.
Population B
Monogenic
Determination
of Sensitivity
Population A
Spont.
Polygenic
Determination
of Sensitivity
Max.
Carcinogen Dose
Spont.
Carcinogen Dose
Max.
Sensitivity Governed
by Multiple Genes +
Modulation by Lifestyle
Spont.
Carcinogen Dose
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 AEs

 Animals
engineered with rare genetic
polymorphisms
 Animal models compromised because of
other exposures, pharmaceutical, life style or
environment
Food and Drug Administration