Day1.Lecture1.Applications of PBPK Modeling

1

Applications of Physiologically Based Pharmacokinetic Modeling

A Course on Physiologically Based Pharmacokinetic (PBPK) Modeling and its Applications

March 28th - April 1st, 2011

Center for Human Health AssessmentThe Hamner Institutes for Health Sciences

‐5

‐4

‐3

‐2

‐1

0

1

2

3

0 50 100 150

Ln Conc (uM)

Time (min)

Hepatic Clearance

Copyright 2011 by The Hamner Institutes for Health Sciences. May not be reproduced without permission

Plasma Protein Binding

Estimated Renal Clearance

Reverse Dosimetry

ExposureTissue Dose

Dose toCritical Biological Units

Inhalation

Exposure Effect

Organ

Biologically Based Dose-Response Modeling

Pharmacokinetics

Inhalation

Ingestion

Dermal

Cancer

OtherToxicity

Pharmacodynamics

Tissue

Macromolecules

Cells PBPK BBDR

2

Role of PBPK Modeling

The purpose of a PBPK model is to define the

relationship between an external measure of

(administered) exposure/dose and an internal

measure of (biologically effective) exposure/dose in ( g y ) p /

both the experimental animal and the human

Physiologically Based Pharmacokinetic ModelCI CXQP

Lung

QC

CA

QC

Liver

Fat

QL

QF

QRCVR

CVF

CVL

Rapidly Perfused(brain, kidneys, etc.)

Slowly Perfused(muscle, bone, etc.)

QSCVS

3

Physiologically Based Pharmacokinetic ModelBasis of Description

• Model structure– anatomy

QCPlasma

iv dose

– metabolism / transport processes• Model parameters

– physiological data (organ weights, blood flows)– biochemical data (partition coefficients,

metabolism)• Model equations

– system of mass-balance differential equations– one equation for each tissue– connected by equation for blood

QKidKidney

QRapRapidly Perfused

QSknSkin

QSlw

LLMLLLLALL PCKPCVPCCQdtdA ///// max

– connected by equation for blood– E.g., metabolizing tissue (liver):

Slowly Perfused

QLiv

Liver

VMax, KM, KMI

oral dose

• Chemical risk assessment

Applications of PBPK Modeling

• Chemical risk assessment

• Drug development research and evaluation

• Interpretation of human biomonitoring data

• In vitro to in vivo extrapolationIn vitro to in vivo extrapolation

• Evaluation of early-life susceptibility

4

• Assess the biological determinants that govern the

How are PBPK Models Used in Human Health Risk Assessment?

Assess the biological determinants that govern the kinetic behavior

• Calculate tissue dose metrics for risk assessment calculations

• Support extrapolation across dose-routes, between species, from high to low dose levels, and over various dosing scenarios

• Assess mechanisms of response (PD) based on their relationship with dose metrics for target tissues

Use of PBPK Modeling in Human Health Risk Assessment by

EPA and Other Agencies

Methylene Chloride (EPA OSHA ATSDR Health Canada)Methylene Chloride (EPA, OSHA, ATSDR, Health Canada)

2-Butoxy Ethanol (EPA, Health Canada)

Vinyl Chloride (EPA)

Chloroform (Health Canada)

Perchlorate (EPA)

Acrylamide (EPA)

Trichloroethylene (EPA)

Perchloroethylene (EPA)

5

Example of the Use of a PBPK Model to Improve Dosimetry in a Risk Assessment:

Vinyl Chloride

• Cross-species correspondence of a rare tumor type:liver angiosarcoma in mouse, rat, and human

• Carcinogenic at doses with no evidence of enhanced cellproliferation, receptor interaction, or cytotoxicity

Mode of Action Information

• Mutagenic; metabolized to reactive intermediatesassociated with DNA adduct formation and mistranscription

• Expect linear dose-response below the experimental range

Vinyl ChlorideVinyl ChlorideVinyl Chloride

Metabolism of Vinyl Chloride

ChloroethyleneEpoxide

P450

Epoxide Hydrolase

DNA AdductsCO2

H2O

GSH



P450

Epoxide Hydrolase

DNA AdductsDNA AdductsCO2CO2

H2O

GSH

Chloroacetaldehyde Tissue AdductsGlutathioneConjugates

GSHChloroacetaldehydeChloroacetaldehyde Tissue AdductsTissue Adducts

GlutathioneConjugatesGlutathioneConjugates

GSH

Dose metric: concentration (AUC) of chloroethylene epoxide

6

QPQC

QF

Lungs

Fat

CI CX

CVF

CA

QPQC

QF

LungsLungs

FatFat

CI CX

CVF

CA

PBPK Model for Vinyl Chloride

QS

QR

QL

Rapidly Perfused Tissues

Slowly Perfused Tissues

Liver

CVR

CVS

CVL

CA

CA

VMAX2KM2

VMAX1KM1

CAKZER

KA

QS

QR

QL

Rapidly Perfused TissuesRapidly Perfused Tissues

Slowly Perfused TissuesSlowly Perfused Tissues

LiverLiver

CVR

CVS

CVL

CA

CA

VMAX2KM2

VMAX1KM1

CAKZER

KA

Reactive MetabolitesCO2 Glutathione Conjugate

Tissue/DNA Adducts

KCO2 KGSMKM2KM1

KFEE

GSH

KGSM

KS KO

KA

KB

Reactive MetabolitesReactive MetabolitesCO2CO2 Glutathione ConjugateGlutathione Conjugate

Tissue/DNA AdductsTissue/DNA Adducts

KCO2 KGSMKM2KM1

KFEE

GSHGSH

KGSM

KS KO

KA

KB

Dose metric: production of reactive metabolites per gram liver

(Clewell et al. 2001)

Model Parameterization

Physiological Parameters (from literature) i l bl d fl– tissue volumes, blood flows

– alveolar ventilation, cardiac output

Partition coefficients (measured in vitro)– Rodent: blood:air and tissue:air– Human: blood:air

Metabolism (estimated by fitting in vivo data)– Rodent: closed chamber gas uptake, disposition studies

H l d h b t k– Human: closed chamber gas uptake

7

10000

Estimation of Metabolism ParametersGas Uptake Studies in Male F344 Rats

100

1000

Cha

mbe

r C

once

ntra

tion

(ppm

)

1

10

0 1 2 3 4 5 6

Hours

C

250 ppm550 ppm1250 ppm3200 ppm

8000

10000

Estimation of Metabolism ParametersRadiolabel Studies in Male Sprague-Dawley Rats

4000

6000

8000

Tot

al A

mou

nt M

etab

oliz

ed (

mg)

0

2000

0.1 1 10 100 1000 10000

Concentration (ppm)

T

8

10

KM1=0.1

KM1=1.0

Estimation of Metabolism ParametersHuman Inhalation Study – Subject A

1

Cha

mbe

r C

once

ntra

tion

(ppm

)

0.1

0 0.1 0.2 0.3 0.4 0.5

Hours

10

KM1=0.1

KM1=1 0

Estimation of Metabolism ParametersHuman Inhalation Study – Subject B

1

Cha

mbe

r C

once

ntra

tion

(ppm

)

KM1=1.0

0.1

0 0.1 0.2 0.3 0.4 0.5

Hours

C

9

Human risk estimates (per million) for lifetime exposure

to 1 ppb vinyl chloride in air based on the incidence

Cross-Species and Cross-Route Correspondence Using PBPK Dose Metric

of liver angiosarcoma in animal bioassays

Animal Bioassay Study 95% UCL Risk / million / ppb

Males Females

Maltoni - Mouse Inhalation 1.52 3.27

Maltoni - Rat Inhalation 5.17 2.24

Feron - Rat Diet 3.05 1.10

Maltoni - Rat Gavage 8.68 15.70

Comparison of Cancer Risk Estimates for Vinyl Chloride

Basis Inhalation Drinking Water

Old EPA -- Animal(mg/kg/day -- BSA)

PBPK -- Animal

(1 ug/m3)

84.0 x 10-6

2.7 x 10-6

(1 ug/L)

54.0 x 10-6

1.1 x 10-6

PBPK -- Human (Epidemiology)

1.7 x 10-6

10

Applications of PBPK Models in Drug Development Research and Evaluation

• Integrate information from different studies– Different routes of exposure– Different species– Different dosing regimens

• Provide a validated platform for predictive simulation of alternative dosing methods drug-drug interactions (DDI)

• Improve understanding of PD by relating effect to dose at target tissue or binding to target protein (PBPK/PD)

• Support more accurate estimation of equivalent human dosing to achieve same dose to target protein as in test animals g p

• Predict fetal exposure and lactation transfer

• Estimate variability of PK across special populations– Obese, Elderly, Infants, Diseases – Polymorphisms

Use of PBPK Modeling to Optimize the Dosage Regimen of

an Antiparasitic Prodrug

Zhixia (Grace) Yan

Division of Pharmacotherapy and Experimental TherapeuticsUNC Eshelman School of Pharmacy

11

• Prodrug X demonstrated reversible hepatotoxicityin humans

• Prodrug X is rapidly metabolized to the active metabolite Y in the livermetabolite, Y, in the liver

• The active metabolite, Y accumulated significantly in the liver

C ld th t i it h b id d Could the toxicity have been avoided using a more rational dosage regimen?

Dosage Optimization Strategy

• Characterize the hepatobiliary disposition of pafuramidine and furamidinepafuramidine and furamidine.

• Develop and validate a whole-body physiologically-based pharmacokinetic (PBPK) model for pafuramidine and furamidine in rats.

• Develop a whole-body PBPK model for f idi d f idi i hpafuramidine and furamidine in humans.

• Optimize dosage regimen based on the human PBPK model.

12

1

PBPK Predicted Prodrug X/Active metabolite YDisposition in Human Plasma

lasm

a 0.1

1X

Y

0 001

0.01

0.1

Co

nce

ntr

atio

n in

P(μ

M)

0.001

0.01

0 6 12 18 24

0.001

0 48 96 144 192 240

Terminal t1/2 (h) Observed Predicted

X 11 19

Y 14 64 (2.5 d)

Time (h)

3x

An Optimized Dosage Regimen for Efficacy and Safety

10001000

tion

100 mg X twice daily40 mg Y once daily

LiverAUC0 14d ↓ 5x

0.1

1

10

100

0.1

1

10

100

dine

Con

cent

rat

(μM

)

LiverAUC0-14d ↓ 5x

NOAEL

0.001

0.01

0 2 4 6 8 10 12 140.001

0.01

0 2 4 6 8 10 12 14

Time (d)

Fura

mid

Ceff,min

Plasma

13

Application of PBPK Modeling for the Interpretation of Human Biomonitoring Data

Chemical concentrations Chemical concentrations

Margin of safety

Chemical concentrations in human blood from biomonitoring studies

Chemical concentrations in animal blood in

toxicity studies

Pharmacokinetic modeling

Pharmacokinetic Modeling

Forw

ard dosi

Reverse dosi

Human exposures(Chemical concentrations in

environment)

Animal exposures(Administered doses in

toxicity studies)

Traditional risk assessment

metry

metry

Exposure Reconstruction Using a PBPK ModelIraqi woman exposed during pregnancy

to grain contaminated with methylmercury

Linking Internal Dose to Health Outcome:

450 6MaternalE

42 µg/kg/day

200

250

300

350

400

MeH

g in H

air (p

pm)

3

4

5

MeH

g in B

lood

(pp

m)

Maternal hair

Maternal blood

Infant blood

Exposure 108 days

(Clewell et al., 2000)

0

50

100

150

0 200 400 600 800

Days

Me

0

1

2

Me

Pregnancy

14

QSAR In VitroKinetics

PartitioningP i l

Application of PBPK Modeling for In Vitro to In Vivo Extrapolation

PBPKModel

In VitroDynamics

PartitioningMetabolism

etc.

PotentialTarget Tissues

Target TissueResponses

In VivoExposure Profile

In VivoHumanToxicity Estimate

Nature of Toxicity

In VivoDose-Response

IVIVE Example:Impact of CYP2C9 Polymorphism on Coumaden (Warfarin) Internal Dose

QCPlasma

iv dose

QKidKidney

QRapRapidly Perfused

QSknSkin

(Gentry et al., 2002)

QSlwSlowly Perfused

QLiv

Liver

VMax, KM, KMI

oral dose

15

IntrinsicClearance

Allele Reference Mean CV Mean CV (VmaxC/Km)

CYP2C9*1 Haining et al ., 19961 1.61 1.85 0.87

Km (mg/L)Vmax (mg/hr/kg3/4)

Metabolic Parameters for (S)-Coumaden for Three CYP2C9 Alleles

Takahashi et al ., 1998b2 2.13 0.0036 0.81 0.12 2.6

Sullivan-Klose et al ., 19962 1.01 0.046 3.57 0.078 0.28

Rettie et al ., 19943 3.2 1.26 2.5Rettie et al ., 19944

3.2 1.05 3

CYP2C9*2 Sullivan-Klose et al ., 19962 1.26 0.031 3.85 0.056 0.33

Rettie et al ., 19943 0.21 0.52 0.4

Rettie et al ., 19944 0.36 0.65 0.55Rettie et al ., 19995

1.1 0.036 1.85 0.17 0.59

CYP2C9*3 Haining et al ., 19961 0.31 9.24 0.034

Takahashi et al ., 1998b2 0.51 0.22 3.2 0.16 0.16,Sullivan-Klose et al ., 19962

1.37 0.044 28.4 0.059 0.048

1 baculovirus/insect cell system, purified enzyme2 yeast expression, microsomes3 Hep G2 cells, cell lysate4 Hep G2 cells, particulate preparation5 expressed in insect cells, purified enzymes

(Haber et al., 2002)

Average Prevalence of CYP2C9 Alleles in the U.S. Population

Prevalence

S1 homozygous 78%

S1/S2 heterozygous 12%


S2 homozygous 1%

(Haber et al., 2002)


S3 homozygous 0.5%

16

Simulation of impact of genetic polymorphism on Coumaden internal dose

A . C Y P 2 C 9 * 1 A l l e l e

1

2

3

4

5

asm

a C

onc

entr

atio

n (

mg

/L) A . C Y P 2 C 9 * 1 A l l e l e

1

2

3

4

5

asm

a C

onc

entr

atio

n (

mg

/L)

0

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0

H o u r s

Pla

B . C Y P 2 C 9 * 2 A l l e l e

0

1

2

3

4

5

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0

H o u r s

Pla

sma

Co

ncen

trat

ion

(m

g/L

)

C . C Y P 2 C 9 * 3 A l le le5

g/L)

0

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0

H o u r s

Pla

B . C Y P 2 C 9 * 2 A l l e l e

0

1

2

3

4

5

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0

H o u r s

Pla

sma

Co

ncen

trat

ion

(m

g/L

)

C . C Y P 2 C 9 * 3 A l le le5

g/L)


0

1

2

3

4

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0

H o u r s

Pla

sma

Con

cent

ratio

n (m

g

0

1

2

3

4

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0

H o u r s

Pla

sma

Con

cent

ratio

n (m

g

Simulation of Impact of Genetic Polymorphism on Warfarin Internal Exposure

200

250

AU

C

Case 2

Case 3

Normal population

Total population

Simulation of impact of genetic polymorphism on coumaden internal dose

50

100

150

Fre

que

ncy

of (

S)-

War

farin

AF

requ

ency

0

0

50

10

0

15

0

20

0

25

0

30

0

35

0

40

0

45

0

50

0

55

0

60

0

65

0

70

0

75

0

80

0

85

0

90

0

95

0

100

0

(S)-Warfarin AUC


(S)-Coumaden AUC

17

Application of PBPK Modeling to Investigate Age-Dependent Susceptibility

• An age-dependent PBPK model was developed to

simulate the physiological and biochemical changes in

humans associated with growth and aging.

• All physiological and biochemical parameters in the

model change with time based on empirical data; only

the chemical specific parameters remain constant.

• This model was used to simulate blood concentrations• This model was used to simulate blood concentrations

of nicotine and its metabolite cotinine for a constant

daily oral dose of 1 mg/kg/day nicotine from birth to 75

years

Age-Dependent Internal Exposure to Ingested Nicotine (1 ug/kg/day)

4 . 0 E - 4 2 . 5 E - 3

N i c o t i n e

C o t i n i n e

1 . 0 E - 4

2 . 0 E - 4

3 . 0 E - 4

Blo

od

Co

nc.

of

Nic

otin

e (

mg

/L)

5 0 E 4

1 . 0 E - 3

1 . 5 E - 3

2 . 0 E - 3

Blo

od

Co

nc.

of

Co

tinin

e (

mg

/L)

0 . 0 E + 0

0 2 0 4 0 6 0 8 0

A g e ( y e a r s )

0 . 0 E + 0

5 . 0 E - 4

18

• Clewell, H.J., Andersen, M.E., and Barton, H.A. 2002. A consistent approach for the application of pharmacokinetic modeling in cancer and noncancer risk assessment. Environmental Health Perspectives 110:85-93.

• Clewell, H.J., Gearhart, J.M., Gentry, P.R., Covington, T.R., VanLandingham, C.B., Crump, K.S., and Shipp, A.M. 1999. Evaluation of the uncertainty in an oral Reference Dose for methylmercury due to interindividual variability in pharmacokinetics Risk Anal 19:547-

References

methylmercury due to interindividual variability in pharmacokinetics. Risk Anal 19:547-558.

• Clewell, H.J., Gentry, P.R., Covington, T.R., Sarangapani, R., and Teeguarden, J.G. 2004. Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on tissue dosimetry. Toxicol. Sci. 79:381-393.

• Clewell, H.J., Gentry, P.R., Gearhart, J.M., Allen, B.C.,and Andersen, M.E. 2001. Comparison of cancer risk estimates for vinyl chloride using animal and human data with a PBPK model. Science of the Total Environment 274(1-3):37-66.

• Clewell, H.J., Reddy, M.B., Lave, T., and Andersen, M.E. 2007. Physiologically based pharmacokinetic modeling In: Gad SC ed Preclinical Development Handbook Johnpharmacokinetic modeling. In: Gad, SC, ed. Preclinical Development Handbook. John Wiley and Sons, Hoboken, NJ.

• Clewell, H. J., Tan, Y. M., Campbell, J.L., and Andersen, M.E. 2008. Quantitative interpretation of human biomonitoring data. Tox. Appl. Pharmacol., 231:122-133.

• Gentry, P.R., Hack, C.E., Haber, L., Maier, A., and Clewell, III, H.J. 2002. An Approach for the Quantitative Consideration of Genetic Polymorphism Data in Chemical Risk Assessment: Examples with Warfarin and Parathion. Toxicol Sci 70:120-139.

• Rowland M, Balant L, and Peck C. 2004. Physiologically Based Pharmacokinetics in Drug Development and Regulatory Science: A Workshop Report, AAPS PharmSci. 6(1):E6.

Documents

Day1.Lecture1.Applications of PBPK Modeling