studying biology to understand risk: dosimetry models and ... · early intermediate late skin lung...
TRANSCRIPT
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Studying Biology to Understand Risk: Studying Biology to Understand Risk: Dosimetry Models and Quantitative
Adverse Outcome PathwaysAdverse Outcome Pathways
Rory Conolly
RASS
April 13, 2016
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Disclaimer
This is a presentation of the opinions of Rory Conolly, not of official policies of ry n y, n f ff a p f the US EPA.
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How do we get from here to there?How do we get from here to there?
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The short answerThe short answer
Understand the relevant biologyDevelop a computational model of the Develop a computational model of the biologySi l t d k di ti f d Simulate and make predictions of dose-response and time course
Coordinate with decision-makers
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The long answer: This presentationThe long answer: This presentation
Biology as the underpinning to dose-response and riskresponse and r sk
Mechanistic studies and computational modeling to bridge from hazard to riskmodeling to bridge from hazard to risk
Quantitative AOP exampleChallenges in developing these models amd Challenges in developing these models amdusing the predictions
4
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What do we do?What do we do?
Environmental fate &Environmental fate &transport
Exposures
Hazards
Dose-responses & Risks
5
time-courses
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What do we do?What do we do?
Environmental fate &Environmental fate &transport
Exposures
Hazards
Dose-responses & Risks
6
time-courses
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Historically, the data haven’t d th tianswered the questionns
eR
espo
n
Dose
Interspecies
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Historically, the data haven’t d th tianswered the questionns
eR
espo
n
Dose
IVIVE
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It’s the biologyIt s the biology…
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OrganismEarly Intermediate Late
Skin
Lung
SkinSkin
Lung
Tissue
Liver
Kidney
Bladder
Residual
K
Vmax, Km
Kur
Ven
ous Arte
rial
Kb
Liver
Kidney
BladderBladder
ResidualResidual
K
Vmax, Km
Kur
Ven
ous Arte
rial
Kb
GI Tract
Ka Kb
GI Tract
Ka Kb
Cellular
Molecular
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Parsing the problem:g pPharmacokinetics & pharmacodynamics
Tissue Tissue
Pharmacokinetics
Exposure Tissuedose
Tissueinteraction
Tissuei t ti
Early tissue Irreversibleth linteraction response pathology
Pharmacodynamics
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Modern version:Adverse Outcome Pathway
TissueExposure Tissuedose MIE
MIE AOKeyevents AO
Pharmacodynamics
events
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Modern version:Adverse Outcome Pathway
TissueExposure Tissuedose MIE
MIE AOKeyevents AO
Pharmacodynamics
events
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Modern version:Adverse Outcome Pathway
MIE AOKeyevents AO
Pharmacodynamics
events
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Modern version:Adverse Outcome Pathway
TiExposure Tissuedose MIE
Exposure Tissuedose MIE
MIE AOKeyeventsevents
TissueExposure Tissuedose MIE
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Historically, the data haven’t d th tianswered the questionns
eR
espo
n
Dose
IVIVE
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The challengeThe challenge
How do we integrate laboratory data on g yADME and AOPs to obtain quantitative understanding of dose-response and g ptime-course behaviors?
17
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The long answer: This presentationThe long answer: This presentation
Biology as the underpinning to dose-response and riskresponse and r sk
Mechanistic studies and computational modeling to bridge from hazard to riskmodeling to bridge from hazard to risk
Quantitative AOP exampleChallenges in developing these models amd Challenges in developing these models amdusing the predictions
18
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Computers are a big help!Computers are a big help!
19
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The model reflects current d diunderstanding
20http://www.mdpi.com/2218-1989/4/4/1034/htm
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Computational BiologyComputational Biology
“If I were a senior or first-year graduate student interested in biology, I would migrate as fast as I could into the field of computationalcould into the field of computational biology.”
- Francis Collins, Director, NIHFrancis Collins, Director, NIH
21
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Exposure Tissuedose
Tissueinteraction
Tissueinteraction
Early tissueresponse
Irreversiblepathology
Pharmacodynamics
22
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PBPK model Exposure Tissuedose
Tissueinteraction
Tissueinteraction
Early tissueresponse
Irreversiblepathology
Pharmacodynamics
23
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PBPK model Exposure Tissuedose
Tissueinteraction
Tissueinteraction
Early tissueresponse
Irreversiblepathology
Pharmacodynamics
Important principles:
Relevant biology-Relevant biology-parsimony
24
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102[Styrene] in venous blood - 6 hr inhalation, 80 & 600 ppm
101
100
1
10
mg/
L
10-1
10-2
25
0 5 10 15 2010-3
Hours
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102[Styrene] in venous blood - 6 hr inhalation, 80 & 600 ppm
101
30 fold difference in blood concentration but
100
30-fold difference in blood concentration but7.5-fold difference in inhaled concentration
1
10
mg/
L
10-1
10-2
26
0 5 10 15 2010-3
Hours
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Formaldehyde dosimetry in the h human nose
27
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2082 pmol/(mm2-hr-ppm)37 L/min
0
nasopharynx
Anteriornose
Wholenose
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29toxicological sciences 128(2), 500–516 (2012)
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Power of PBPK - extrapolationPower of PBPK extrapolation
30
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Power of PBPK - extrapolationPower of PBPK extrapolation
31
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Power of biologically based d li t l timodeling - extrapolation
32
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Number of papers with "PBPK" or
140
u be o pape s t o"Physiologically‐Based Pharmacokinetic" in
their title. (PubMed, Feb 2016)
80
100
120
140
0
20
40
60
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The long answer: This presentationThe long answer: This presentation
Biology as the underpinning to dose-response and riskresponse and r sk
Mechanistic studies and computational modeling to bridge from hazard to riskmodeling to bridge from hazard to risk
Quantitative AOP exampleChallenges in developing these models amd Challenges in developing these models amdusing the predictions
34
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Modern version:Adverse Outcome Pathway
Tissue
In vivo
Exposure Tissuedose MIE
MIE AOKeyevents
In vitro
AO
Pharmacodynamics
events
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Aromatase inhibition leading to reproductive dysfunction in fish
Aromatase inhibition AOP
36
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Small Fish Computational Toxicology GroupSmall Fish Computational Toxicology Group Academia
b l h dK. Watanabe, Oregan Health and Science University
USACE – Vicksburg, MSM. Mayo, E. Perkins, N. Garcia‐Reyero
USEPA (NHEERL)– Duluth, MN, and Grosse Ile, MIG. Ankley, E. Durhan, M. Kahl, K. Jensen, E. Makynen, D. Martinovic, D. Miller, A. Schroeder, D. Villeneuve
USEPA‐RTP, NCR. Conolly, W. Cheng (ISTD) 37
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Molecular initiating event:Aromatase inhibition
AromataseInhibition
XAromatase (CYP 19A)
X
Testosterone 17β-estradiol (E2)
38
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Modern version:Adverse Outcome Pathway
Tissue
In vivo
Fadrozole Tissuedose MIE
MIE AOKeyevents
In vitro
AO
Pharmacodynamics
events
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Structure of the AOP
Aromatase Granulosa Circulation Hepatocyte Ovary Female PopulationCirculation
Structure of the AOP
AromataseInhibition
GranulosaReduced E2 synthesis
CirculationReduced E2 concentration
HepatocyteReduced VTG
production
OvaryImpaired
Oocyte Dev.
FemaleDecreased ovulation/spawning
PopulationDeclining Trajectory
CirculationReduced VTG
concentration
40
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Data supporting the AOPData supporting the AOP
Aromatase Granulosa Circulation Hepatocyte Ovary Female PopulationCirculationAromataseInhibition
GranulosaReduced E2 synthesis
CirculationReduced E2 concentration
HepatocyteReduced VTG
production
OvaryImpaired
Oocyte Dev.
FemaleDecreased ovulation/spawning
PopulationDeclining Trajectory
CirculationReduced VTG
concentration
Aromatase inhibition
88
10
Fadrozole (ug/L)
10
Fadrozole (ug/L)
Reduced E2, Vtg synthesis
Impaired vitellogenesis/ oocyte dev. Reduced fecundity
CN
NN
inhibition
0
2
4
6
E2 (n
g/m
l)
*
*
10
20
Vtg
(mg/
ml)
*
0
2
4
6
E2 (n
g/m
l)
*
*
10
20
Vtg
(mg/
ml)
*
2
4
6
8
(Tho
usan
ds)
Cum
ulat
ive
Num
ber o
f Egg
s
Control21050
***
2
4
6
8
(Tho
usan
ds)
Cum
ulat
ive
Num
ber o
f Egg
s
Control21050
***
0
10 *
* *Control 2 10 50
Fadrozole (µg/l)
0
10 *
* *Control 2 10 50
Fadrozole (µg/l)
-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20Exposure (d)
0-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20
Exposure (d)
0
Fadrozole, fathead minnow: Toxicol. Sci. 2002. 67:121-130
41
,Prochloraz, fathead minnow: Toxicol. Sci. 2005. 86: 300-308Propiconazole, fathead minnow: Toxicol. Sci. 2013. 132: 284-297.Letrozole, Japanes medaka: Compar. Biochem. Physiol. Pt. C, 2007, 145: 533-541
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Key events:Reduced VTG in circulationReduced VTG in circulation
CirculationReduced VTG
concentration
42Environ Health Perspect 117:624–631 (2009).
Control Fad 3 ug/L Fad 30 ug/L
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Key events:Impaired oocyte development & spawningImpaired oocyte development & spawning
OvaryImpaired
Oocyte Dev.
FemaleDecreased ovulation/spawning
A B
8
10
ggs
Control2
Fadrozole (ug/L)
8
10
ggs
Control2
Fadrozole (ug/L)
A B
4
6
(Tho
usan
ds)
ulat
ive
Num
ber o
f Eg 2
1050
***
4
6
(Tho
usan
ds)
ulat
ive
Num
ber o
f Eg 2
1050
***
20 18 16 14 12 10 8 6 4 2 0 2 4 6 8 10 12 14 16 18 200
2
Cum
u
20 18 16 14 12 10 8 6 4 2 0 2 4 6 8 10 12 14 16 18 200
2
Cum
u
43
-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20Exposure (d)
-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20Exposure (d)
Toxi Sci 2002 67:121-130
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Temporal concordance of key eventsAromataseInhibition
GranulosaReduced E2 synthesis
CirculationReduced E2 concentration
HepatocyteReduced VTG
production
OvaryImpaired
Oocyte Dev.
FemaleDecreased ovulation/spawning
PopulationDeclining Trajectory
CirculationReduced VTG
concentration
3
4
2 (n
g/m
l) <6h 12 h 24 h weeks yearsweeks12-24 hA.
0
1
2
**
Ex v
ivo
E2
**
6
8
2 (n
g/m
l)
ml)
B.
6 Hour 12 Hour 24 Hour
0
2
4
***
Plas
ma
E2
6 Hour 12 Hour 24 Hour 30
40
50
*
(c)
VTG
(mg/
m
C.
P 6 Hour 12 Hour 24 Hour
0
10
20
Plas
ma
V
6 Hour 12 Hour 24 Hour
2011 Aquatic Toxicol. 103:170-178 44
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Development of the QAOP
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AOP QAOP
AOPAOP
Qualitative: Defines association between a molecular initiating event and an adverse outcome.
Quantitative: Dose-response and time-course predictions
QAOP
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The QAOP:A combination of linked quantitative models
HPG axis modelHPG axis modelAromataseInhibition
GranulosaReduced E2 synthesis
CirculationReduced E2 concentration
HepatocyteReduced VTG
production
CirculationReduced VTG
concentration
Oocyte growth dynamics modelO FemaleOvary
Impaired Oocyte Dev.
FemaleDecreased ovulation/spawning
Population dynamics modelPopulationDeclining
47
Trajectory
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Fathead minnow HPG axis model
FAD, E2,
Brain
LH/FSH
E2LH/FSHFAD, E2
Ovary LH/FSHVenous blood
VTGE2, VTG
GillOvary
FAD, E2, VTG
FAD, E2, VTG TE2
FAD CYP19A CYP19AmRNAOocyte
VTG VTG
LH/FSH
LH/FSHFAD, E2, VTG
Venous blood
LH/FSH
LH/FSH
FAD, E2, VTG
LH/FSHFAD, E2, VTG
Gill
LH/FSHFAD, E2, VTG
FADE2
Liver
VTG E2FADE2, E2,
VTGVTG E2 , E2,
VTG
FAD, E2, VTG
FAD, E2, VTG
Rest of body
48Tox Sci 133(2), 234–247 2013
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Homeostasis:d /CAdaptation/Compensation
Exposure
Uptake‐Delivery to Target Tissues
Perturbation
Biologicinputs
“Normal” BiologicalFunction
Perturbation
Cellular response pathway
inputs
AdaptiveEarly cellularchanges
AdverseOutcomes
(e.g., mortality, Reproductive
Cell injury, Inability to regulate
Responses
49
pImpairment)
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Adaptation: Plasma estradiol in fathead i s s d t f d lminnows exposed to fadrazole
2 CON
1
FAD-3FAD-30*
rol;
log
2)
-1
0
*ma
E2iv
e to
con
tr
-2
#
*Plas
mha
nge
rela
ti
0 2 4 6 8-4
-3
10 12 14 16
# ##(f
old-
ch
50
0 2 4 6 8 10 12 14 16
direct effect
compensation recoveryVilleneuve et al. (2010) EHP
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HPG axis model:Effect of aromatase inhibition on venous estradiol
0 10 Simulated Control 0 10 Simulate FAD 0 5 ug/LA B
0.04
0.06
0.08
0.10
ous E
2 (µM)
Simulated Control
Measured Control
0.04
0.06
0.08
0.10
ous E
2 (µM)
Simulate FAD 0.5 ug/L
Measured FAD 0.5 ug/LExposure ExposureA. B.
0.00
0.02
‐5 0 5 10 15 20 25 30 35
Veno
Time (day)
0.00
0.02
‐5 0 5 10 15 20 25 30 35
Veno
Time (day)
0.08
0.10
µM)
Simulate FAD 3 ug/L
Measured FAD 3 ug/L0.08
0.10
µM)
Simulate FAD 30 ug/L
Measured FAD 30 ug/LExposure ExposureC. D.
0.02
0.04
0.06
Veno
us E2 (µ
0.02
0.04
0.06
Veno
us E2 (µ
0.00‐5 0 5 10 15 20 25 30 35
Time (day)
0.00‐5 0 5 10 15 20 25 30 35
Time (day)
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HPG axis model:Effect of aromatase inhibition on venous estradiol
0.10 Simulate FAD 30 ug/LMeasured FAD 30 ug/L
0.06
0.08
E2 (µ
M)
0.02
0.04
Veno
us
0.00‐5 0 5 10 15 20 25 30 35
Time (day)
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HPG axis model:ff f h b
300 Simulated controlLab control
Effect of aromatase inhibition on venous VTG
A. B.300 Simulated FAD 0.5 ug/LLab FAD 0.5 ug/L
100
150
200
250
asma VT
G (µ
M) Exposure Exposure
100
150
200
250
asma VT
G (µ
M)
0
50
100
‐5 0 5 10 15 20 25 30
Pla
Time (day)
0
50
100
‐5 0 5 10 15 20 25 30
Pla
Time (day)
200
250
300
(µM)
Simulated FAD 30 ug/LLab FAD 30 ug/L
200
250
300
(µM)
Simulated FAD 3 ug/LLab FAD 3 ug/L
Exposure Exposure
C. D.
50
100
150
Plasma VT
G
50
100
150
Plasma VT
G
0‐5 0 5 10 15 20 25 30
Time (day)
0‐5 0 5 10 15 20 25 30
Time (day)
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Oocyte growth dynamics model(Egg development in the fathead minnow ovary)
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Oocyte growth dynamics model:Predicts fecundity based on VTG levels
A. B.
Model prediction Laboratory data
Prediction of normal fecundity vs Lab
Effects of fadrozole on predicted fecundity vs
55
fecundity vs Lab (mean) results at 21-
days
predicted fecundity vs lab results
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Population dynamics model:Prediction of population dynamics
10 ug/l
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The aromatase inhibition QAOPThe aromatase inhibition QAOPHPG axis modelA t Granulosa Circulation Hepatocyte CirculationAromataseInhibition
GranulosaReduced E2 synthesis
CirculationReduced E2 concentration
HepatocyteReduced VTG
production
CirculationReduced VTG
concentration
Oocyte growth dynamics modelOvary
Impaired Female
Decreased l ti /
P l i d i d l
Oocyte Dev. ovulation/spawning
Population dynamics modelPopulationDeclining Trajectory
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Does the new AOP terminology help?Does the new AOP terminology help?
• AOP specifies information needed to support regulatory decision makingpp g y g– Molecular initiating event– Key eventsKey events– AO for individuals
AO for the population– AO for the population• Richer language facilitates communication
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The long answer: This presentationThe long answer: This presentation
Biology as the underpinning to dose-response and riskdose response and r sk
Mechanistic studies and computational modeling to bridge from hazard to riskmodeling to bridge from hazard to risk
Quantitative AOP exampleChallenges in developing these models amd Challenges in developing these models amdusing the predictions
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Experimental designExperimental design
PBPK, BBDR, and qAOP models can simulate behavior of PK and the AOP simulate behavior of PK and the AOP over timeS b d b i l So best supported by experimental designs that include both time-course
d dand dose-response
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Adaptation: Plasma estradiol in fathead i s s d t f d lminnows exposed to fadrazole
2 CON
1
FAD-3FAD-30*
rol;
log
2)
-1
0
*ma
E2iv
e to
con
tr
-2
#
*Plas
mha
nge
rela
ti
0 2 4 6 8-4
-3
10 12 14 16
# ##(f
old-
ch
61
0 2 4 6 8 10 12 14 16
direct effect
compensation recoveryVilleneuve et al. (2010) EHP
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Formaldehyde
33003300
y
Dose-time response 2
2
200
250
L2
2
200
250
Lresponse surface for regenerative
ll l 1150
Label ing in d
1150
Label ing in d
cellular proliferation in nasal epithelium
4D5E
1E2E3E4E5F1F2F3F4F5
10
2345
1
2345
1
2345
1
6
10 ppm
15 ppm100
ex
4D5E
1E2E3E4E5F1F2F3F4F5
10
2345
1
2345
1
2345
1
6
10 ppm
15 ppm100
ex
pof the F344 rat.
4A5
B1B2B3B4B5
C1C2C3C4C5D
1D2D3D4D
0. 1 4
0. 5 7
0
50
0.140.571.2961323
45
234
5
1
2345
1
1
c
0.7 ppm
2 ppm
6 ppm
0
50
4A5
B1B2B3B4B5
C1C2C3C4C5D
1D2D3D4D
0. 1 4
0. 5 7
0
50
0.140.571.2961323
45
234
5
1
2345
1
1
c
0.7 ppm
2 ppm
6 ppm
0
50
1A2A3A4A 1 . 2 9
6 . 00
1 3 . 002 6 . 00
5 2 . 00
7 8 . 00
Duration of exposure
(weeks)
6.13.26.52.78.12control 1A
2A3A4A 1 . 2 9
6 . 00
1 3 . 002 6 . 00
5 2 . 00
7 8 . 00
Duration of exposure
(weeks)
6.13.26.52.78.12control
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Confidence (uncertainty-1)Confidence (uncertainty )
Concern: The model increases uncertainty relative to not having the uncertainty relative to not having the model. Complicated structure relative to defaults Complicated structure relative to defaults Errors in the model Uncertainty about mechanism depicted in Uncertainty about mechanism depicted in
the model
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Complicated structureComplicated structure…D li ti f f t i t Delineation of sources of uncertainty does not mean uncertainty is increased.
As long as good modeling practice is observed then model development coordinated with laboratory experimetns is informative about roles of PK and key events.
Uncovers hidden assumptionsp
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Errors in model
C di d fi it l ibl Coding errors definitely possible.
But observation of good modeling practice, including rigorous code p , g gchecking, addresses this concern.
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Plenty of published guidance on Plenty of published guidance on good modeling practice
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Uncertainty about mechanismUncertainty about mechanism
This can be a valid concern but it applies to any work involving mechanisms not to any work involving mechanisms, not just development of computational modelsmodels.
Addressed by peer review, scientific rigorrigor
Bradford Hill criteria
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Return on the investmentReturn on the investment
• A fully developed QAOP is a powerfulA fully developed QAOP is a powerful predictive tool.– Input exposure scenario of interest– Input exposure scenario of interest– Output prediction of change in adverse outcome
• But data needs are large– Expensive and time consuming
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Return on the investmentMature QAOP could serve as an “in silico” description of in vivo biology to aid in design of in vitro tests and interpretation of in vitro dataHTS assays for MIE activation
in vitro
interpretation of in vitro data
in vitroin vivo
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TEQ application to predict the population effects of mixture exposure
Multi component mixtureMulti-component mixture
25 uM Fad and 25 uM Ima
26.5 uM TEQ fadrozole
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PerfectionPerfection
Wanting perfection is a trap Wanting perfection is a trap. The model should reduce uncertainty
relative to where you stand without the relative to where you stand without the model.
M d l i l i d t b f l Model is only required to be useful. Sophisticated evaluation requires
ffi i i i l bi l sufficient expertise in relevant biology, modeling technology, and an ability to “ b k” d i li h bi i“step back” and visualize the big picture.
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http://sbw.kgi.edu/
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If you don't know where know where you're going,
i ht t you might not get there.
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AcknowledgementsAcknowledgements
Mel Andersen and the PBPK modeling group at Wright-Patterson AFB during the 1980’s
Many colleagues CIIT / The Hamner (rip) 1989 - 2005 CIIT / The Hamner (rip), 1989 2005 EPA, 2005 - ?
Small fish group at MED Small fish group at MED WanYun Cheng
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End
75
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Uncertainty(unknown) upper bound on possible risk
Range of uncertainty where actualrisk may exceed predicted risk
Best estimate of riskBest estimate of risk
Range of uncertainty where actualrisk may be lower than predicted risk
77
(unknown) lower bound on possible risk
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How does uncertainty change as more data y gare incorporated?
Range of uncertainty
Risk
uncertainty
Information
78
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Alternatively…y
Range of uncertainty
Risk
uncertainty
Information
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Virtual tissues: Dose at the cellular levelcellular level
80http://www.ploscompbiol.org/article/slideshow.action?uri=info:doi/10.1371/journal.pcbi.1000756&imageURI=info:doi/10.1371/journal.pcbi.1000756.g007
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Dose‐response predictions for the mixture of 8 h i l f 21 d fchemicals after 21 days of exposure
160Control
120
140 XControl
80
100
G con
(uM)
40
60VTG
0
20
0 10 20 30 40 50 60 70
5 ug/L 10 ug/L25 ug/LX X X
81
0 10 20 30 40 50 60 70Equivalent fadrozole dose (ug/L)