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Biomarkers of Susceptibility

Environmental Disease

Biomarkers of Exposure Biomarkers of Effect

Biologically-BasedPharmacokinetic

Modeling

Biochemical Modeling

Stochastic ModelingOf Health Effects

InternalDose

ExposureBiologically

EffectiveDose

EarlyBiological

Effect

AlteredStructure &

Function

ClinicalDisease

Prognosis

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Stochastic Modeling and Risk Assessment Group - LCBRA

In each focused research area– mathematical and statistical methods– novel modeling structures

• strong biological linkage

– analysis tools for DIR scientists– translation from the laboratory to health

policy• qualitative• quantitative

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PBPK Model for DioxinArterial + Venous Blood

Metabolite to Urine

Metabolite to Feces

80%

20%

TopicalDose

Capillary Blood

Remote Skin

Capillary Blood

Muscle

Capillary Blood

Kidney

Capillary Blood

Fat

Capillary Blood

Rapidly Perfused

Capillary Blood

Local Skin

Gut Lumen

GI Tract

Capillary Blood Capillary Blood

LiverTCDD

metabolite

biochemistry

OralDose

QA

QG

QL

QKi

QF

QM

QR

QSr

QSl

QV VB

VG

VKi

VM

VSrVSl

VR

VF

VL

PL,FLPG,FG

PF,FF

PKi,FKi

PR,FR

PM,FM

PSr,FSr

PSl,FSl

Vm,km

Vu,ku

Vi,ki

Vt,kt

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Other PBPK Models

1,3-butadiene Methyleugenol Anthraquinone Sodium Nitrite Dichloromethane PCB’s Primidone

AZT Melatonin Estrogen,

progesterone, etc.

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Uses of PBPK Models

Test hypotheses– Shape of exposure/internal dose – Equivalence of metabolism, etc. across

species, sexes Prediction

– Equivalent exposures for a given risk– Design experiments– Tissue concentrations

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TCDD Liver Biochemistry

Liver

Capillary BloodTCDD

TCDD+Ah Ah.TCDD

+

ER

-Bound TCDD(CyP1A2)

CYP1A1

+CYP1A2CYP1B1

+

Liver Interstitium

Growth

Growth

Peptide

+

+

EGFR

Signal

Damage

2

2

2

+ E OH

E

ER.E

DNA

MetabolismCell Lysis

Metabolite

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TCDD Transcriptional Activation

Message includes poly(A) tail– Poly(A) nuclease

degrades tail• 24 nt/hr

– Message removed after tail

• 1.2 hours T1/2

– Ribosome binding protects message

TCDDLiganded

Ah Receptor

Low-affinity Binding Site

High-affinity Binding Site

Bind to DioxinResponsive Elements

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Mathematically…..

d

d

total

totald

totalfree

free

KtmRNA

tmRNAVv

KtmRNA

RR

RtmRNAkv

mRNAK

mRNAmRNA

mRNAkv

mRNApAN

kv

total

synthesissynthesis

total

synthesissynthesis

ndegradationdegradatio

Ade

iondeadenylationdeadenylat

1/

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10-6

10-5

10-4

10-3

10-2

10-1

100

101

102

0 20 40 60 80 100 120

mR

NA

. p

mo

le/g

Dose, ng/kg/day

CYP1B1

CYP1A1

CYP1A2

CYP mRNA Induction

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Other Biochemical Models

Privileged Access Model– Localized metabolism

Cell-Cycle Kinetics– G0-G1 in hepatocytes

Receptor binding/gene expression Sodium channel kinetics

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Uses of Biochemical Models

Hypothesis testing– Proposed mechanisms

• Alpha-2U• TCDD activates• Epoxide is the penultimate carcinogen• Thresholds

– Equivalence across species, sex Prediction

– Mechanism-based extrapolation– Use of in-vitro data– Impact of genetic polymorphisms– Impact of competition for binding sites– Identify data gaps

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Malignant CellsInitiated CellsNormal Cells

Simple Two-Stage Cancer Model

(t,d)

(t,d)

(t,d)

(t,d)

(t,d)

(t,d)

(t,d)

(t,d)

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HSCombining Two-Stage and PBPK

0 5 10 15 20 25 300

50

100

150

200

250

Week

Tot

al N

umbe

r of

Pap

illom

as

TCDD Dose: 760 ng/kg

Observed papillomasExpected papillomas

0 5 10 15 20 25 300

20

40

60

80

100

120

140

160

Week

Tot

al N

umbe

r of

Pap

illom

as

TCDD Dose: 355 ng/kg

Observed papillomasExpected papillomas

0 5 10 15 20 25 300

10

20

30

40

50

60

70

Week

Tot

al N

umbe

r of

Pap

illom

as

TCDD Dose: 166 ng/kg

Observed papillomasExpected papillomas

0 5 10 15 20 25 300

10

20

30

40

50

60

Week

Tot

al N

umbe

r of

Pap

illom

as

TCDD Dose: 121 ng/kg

Observed papillomasExpected papillomas

Num

ber

of S

kin

Pap

illo

mas

Dioxin Dose

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Mathematically……

),(])()()(1[

),(),()(

)(),()(),(

,1

2

tsPststst

tsPtsPst

sttsPsttsP

ikiii

kiiki

iikiik

),()]()()([

),(),()(

)(),()(),(

,1

2

tsPststst

tsPtsPst

sttsPstds

tsdP

ikiii

kiiki

iikiik

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Colonic Crypts

0.33 birth/day0 deaths/day9 Stem Cells

2112 Cells

• 2250/crypt• ? stem cells/crypt

• Tc=24h• ~ 150 proliferative cells

• Tc>36h• 5-6 transit generations

•T=36-96h

0 birth/day0.4 deaths/day

956.5 Cells

0.66 births/day0 deaths/day

72 Cells

36 Cells

18 Cells

9 Cells

4.5 Cells

576 Cells

288 Cells

144 Cells

Steady State Size

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Two-Stage Model with Controlled Replication

Malignant

Cells

Stage 2

μ1

μ1

1st level

daughter

Birth β0

Normal

Stem Cells

Stage 0

Stemcells

δ0

Death δ0

1st leveldifferentiated

daughter

Terminal

cells

β0

β0

δ0

Initiated

Stem cells

Stage 1

μ0

Terminallydifferentiatedcells

fixed at 2

Replicatingcells(Capable of independentgrowth)

fixed at 3

μ0

δ1

β1

Death δ1

1st level

daughter

Terminal

cells

β1

β1

δ1

1st leveldifferentiated

daughter

6 steps in initiated cells

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Five Stage Colon Cancer Model

0 50 100 150 200 250 300 350 400 450 5005

10

15

20

25

30

35

40

Iteration Number

Rat

es (

scal

ed)

0 100 200 300 400 500 600 700 800 9000.9985

0.999

0.9995

1

Age (months)

Can

cer

Su

rviv

al

0.4 0.6 0.8 1 1.2 1.4 1.6

x 10-6

0

10

20

30

40

50

60

70

Mutation Rate

Fre

qu

ency

16 18 20 22 24 26 28 30 32 34 360

10

20

30

40

50

60

Birth Rate

Fre

qu

ency

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Concerning model

Your concern model 5-3, 13-3 4-stage model. Alpha% cells start from stage 1.

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Uses of Stochastic Models

Cancer– New methods

• Observable tumors

– Physiologically realistic models

– Better statistical approaches

• MCMC

– Hypothesis testing– Prediction of risk

Other Endpoints– Development– Spermatogenesis

• Controlled differentiation

– Pharmacology• Stochastic processes

Other Stuff– Data analysis– Graph theory

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nvir

onm

enta

l Exp

osur

esPopulation

Organism

Organ System

Tissue/Organ

Cellular

Biochemical

Molecularand

Genetic

HealthRisks

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