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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

0

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.

How do we get from here to there?How do we get from here to there?

2

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

3

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

What do we do?What do we do?

Environmental fate &Environmental fate &transport

Exposures

Hazards

Dose-responses & Risks

5

time-courses

What do we do?What do we do?

Environmental fate &Environmental fate &transport

Exposures

Hazards

Dose-responses & Risks

6

time-courses

Historically, the data haven’t d th tianswered the questionns

eR

espo

n

Dose

Interspecies

Historically, the data haven’t d th tianswered the questionns

eR

espo

n

Dose

IVIVE

It’s the biologyIt s the biology…

9

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

Parsing the problem:g pPharmacokinetics & pharmacodynamics

Tissue Tissue

Pharmacokinetics

Exposure Tissuedose

Tissueinteraction

Tissuei t ti

Early tissue Irreversibleth linteraction response pathology

Pharmacodynamics

Modern version:Adverse Outcome Pathway

TissueExposure Tissuedose MIE

MIE AOKeyevents AO

Pharmacodynamics

events

Modern version:Adverse Outcome Pathway

TissueExposure Tissuedose MIE

MIE AOKeyevents AO

Pharmacodynamics

events

Modern version:Adverse Outcome Pathway

MIE AOKeyevents AO

Pharmacodynamics

events

Modern version:Adverse Outcome Pathway

TiExposure Tissuedose MIE

Exposure Tissuedose MIE

MIE AOKeyeventsevents

TissueExposure Tissuedose MIE

Historically, the data haven’t d th tianswered the questionns

eR

espo

n

Dose

IVIVE

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

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

Computers are a big help!Computers are a big help!

19

The model reflects current d diunderstanding

20http://www.mdpi.com/2218-1989/4/4/1034/htm

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

Exposure Tissuedose

Tissueinteraction

Tissueinteraction

Early tissueresponse

Irreversiblepathology

Pharmacodynamics

22

PBPK model Exposure Tissuedose

Tissueinteraction

Tissueinteraction

Early tissueresponse

Irreversiblepathology

Pharmacodynamics

23

PBPK model Exposure Tissuedose

Tissueinteraction

Tissueinteraction

Early tissueresponse

Irreversiblepathology

Pharmacodynamics

Important principles:

Relevant biology-Relevant biology-parsimony

24

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

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

Formaldehyde dosimetry in the h human nose

27

2082 pmol/(mm2-hr-ppm)37 L/min

0

nasopharynx

Anteriornose

Wholenose

29toxicological sciences 128(2), 500–516 (2012)

Power of PBPK - extrapolationPower of PBPK extrapolation

30

Power of PBPK - extrapolationPower of PBPK extrapolation

31

Power of biologically based d li t l timodeling - extrapolation

32

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

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

Modern version:Adverse Outcome Pathway

Tissue

In vivo

Exposure Tissuedose MIE

MIE AOKeyevents

In vitro

AO

Pharmacodynamics

events

Aromatase inhibition leading to reproductive dysfunction in fish

Aromatase inhibition AOP

36

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

Molecular initiating event:Aromatase inhibition

AromataseInhibition

XAromatase (CYP 19A)

X

Testosterone 17β-estradiol (E2)

38

Modern version:Adverse Outcome Pathway

Tissue

In vivo

Fadrozole Tissuedose MIE

MIE AOKeyevents

In vitro

AO

Pharmacodynamics

events

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

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

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

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

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

Development of the QAOP

45

AOP  QAOP

AOPAOP

Qualitative: Defines association between a molecular initiating event and an adverse outcome.

Quantitative: Dose-response and time-course predictions

QAOP

46

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

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

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)

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

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)

51

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)

52

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)

53

Oocyte growth dynamics model(Egg development in the fathead minnow ovary)

54

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

Population dynamics model:Prediction of population dynamics

10 ug/l

56

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

57

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

58

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

59

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

60

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

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

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

63

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

64

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.

65

Plenty of published guidance on Plenty of published guidance on good modeling practice

66

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

67

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

68

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

69

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

70

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.

71

http://sbw.kgi.edu/

If you don't know where know where you're going,

i ht t you might not get there.

73

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

74

End

75

76

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

How does uncertainty change as more data y gare incorporated?

Range of uncertainty

Risk

uncertainty

Information

78

Alternatively…y

Range of uncertainty

Risk

uncertainty

Information

79

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

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)