Trevor johnson aaps 2014

Pediatric PBPK: Developing the models

Refining the System Parameters

Dr Trevor Johnson

Principal Scientist

Simcyp Limited

[email protected]

mailto:[email protected]

© Copyright 2014 Certara, L.P. All rights reserved.

Pediatric-PBPK model building

2

© Copyright 2014 Certara, L.P. All rights reserved. 3

Separating Systems & Drug Information

Systems

Data

Drug

Data

Trial

Design

Age

Weight

Tissue Volumes

Tissue Composition

Cardiac Output

Renal elimination

Plasma Protein

Enzymes

Ontogeny

MW

LogP

pKa

Protein binding

BP ratio

In vitro

Metabolism

Permeability

Transport

Solubility

Dose

Route

Frequency

Co-administered drugs

Populations studied

Mechanistic IVIVE approach to predict CL

Whole body PBPK model

Prediction of drug PK (PD) in population of interest


Systems data: The Complexity of Covariate Effects

Age

(Distribution in Population)

Ethnicity Disease

Sex


Genotypes


Height

Weight

Body Surface

Area

LiverVolume

Heart Volume

BrainVolume

LiverWeight

MPPGLHPGL

Enzyme &Transporter AbundanceIntrinsic

Clearance

Body Fat

CardiacOutput

CardiacIndex

SerumCreatinine

Renal Function

Plasma Proteins

&Haematocrit

(Updated after Jamei et al., 2009)


Ven

ou

s B

loo

d

Art

eri

al

Blo

od

LungLung

AdiposeAdipose

BoneBone

BrainBrain

HeartHeart

KidneyKidney

MuscleMuscle

SkinSkin

LiverLiver

SpleenSpleen

GutGut

Portal Portal

VeinVein

PO IV

Ve

no

us B

loo

d

Art

eri

al

Blo

od

LungLung

AdiposeAdipose

BoneBone

BrainBrain

HeartHeart

KidneyKidney

MuscleMuscle

SkinSkin

LiverLiver

SpleenSpleen

GutGut

Portal PortalVeinVein

PO IV

Drug Elimination Drug Distribution

Current Paediatric Models

Pae

dia

tric

in

pu

ts

Drug specific

inputs:

MW, Dose, t,

Vmax /CYP, Km, fu,

fu mic , B/P, Q gut ,

fu gut

Genotype

Population:

N Subject of Known Age/Sex

Population

Specific

Inputs

Liver:

weight/blood flow

Liver CYP Content

per mg of microsomal protein

Microsomal protein

per gram of liverGut Surface Area

and CYP Content

Plasma Proteins

Renal Function

Vmax gut

CLint

gut

Fgut

Vmax liver

CLint

liver

CLH

and FH

CL or CLpo CLR

Drug specific

inputs:

MW, Dose, t,

Vmax /CYP, Km, fu,

fu mic , B/P, Q gut ,

fu gut

Genotype

Population: N Subject of Known Age/Sex

PopulationSpecificInputs

Liver:weight/blood flow

Liver CYP + UGT ContentPer mg of Microsomal Protein

Microsomal Proteinper gram of liverGut Surface Area

and CYP Content

Plasma Proteins

Renal Function

Vmax gut

CLint gut

Fgut

Vmax liver

CLint liver

CLH and FH

CL or CLpo CLR

Retrograde model

From adult in vivo data

Ontogeny

Du

od

en

um

Je

jun

um

I

Je

jun

um

II

Ile

um

I

Ile

um

II

Ile

um

III

Ile

um

IV

Co

lon

Segregated Blood Flows

Stomach

Emptying

Luminal

Transit

Drug Absorption

Linking the Current Models


In vitro

Vmax/Km

or *CLuint

CLuint per

g Liver

System parameters in prediction of pediatric drug CL

CLuint per

Liver

Scaling

Factor 2

Scaling

Factor 3

Liver

Weight

MPPGL

Scaling

Factor 1

CYP Abundance

UGT Abundance

Ontogeny

* Or from in vivo retrograde model

QH.fuB.CLuint

QH + fuB.CLuint

CLH =Liver blood flow

HSA and AAG

Hematocrit

Renal Clearance


Liver Volume: Changes with Age

Liver Volume = 0.722 * BSA1.176

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30

Age (y)

Liv

er

Vo

lum

e (

L) Simcyp

In vivo

Adult

Analysis based on 5036 subjects


Maturation of Renal Clearance

y = 87.674x - 14.497

R 2 = 0.9988

0

50

100

150

0 0.5 1 1.5 2BSA (m2)

GF

R (

ml/m

in)

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140

Rh

od

in, D

e C

ock

, H

ayto

n (

ml/

min

)

Johnson (ml/min)

De Cock

Rhodin

Hayton

Line of unity

0

20

40

60

80

100

120

140

160

0 5 10 15 20

GFR

(m

l/m

in)

Age (yr)

Rhodin et al 2009

Johnson et al 2006

De Cock et al 2014

Rubin et al 1949

Hayton 2000

923 subjects

63 subjects

1760 subjects

921 subjects

Johnson et al 2006


CYP Ontogeny

Age (y)

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP3A4

Updated 2009Johnson 2006

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP1A2

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP2B6

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP2C8

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP2C9

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP2C19

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP2D6

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP2E1

Stevens et al 2008

Croom et al 2009

Hines 2007Hines 2007

Hines 2007

Gu et al 2002Tateishi et al 1997

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

CYP2A6

Fra

cti

on


How certain are we about ontogeny ?

10


Potential problems in determining CYP ontogeny

• Does in vitro relate well to in vivo? (co-factors, endogenous

substrates, hormones)

• Genotype - phenotype relationships

• Lack of information and lumping of age bands

• Availability and handling of clinical samples

– Ethics

– Drug and nutritional history

– Disease

– How liver samples obtained e.g. post-mortem time, storage.

– Specificity of antibodies and probe substrates in vitro.

• Moving towards optimised in vivo confirmed or derived

ontogeny models.

11


Clin Pharmacol Ther 2008

Figure 1. Changes in CYP2D6 (a) and CYP3A4 (b) activity relative to adult values. The data of

Blake et al, corrected for the development of renal function, are indicated by the diamonds.

The simulated change in in the activity of each enzyme (solid line) was derived from in vitro

data on hepatic enzyme expression and increase in liver weight with age.

0

0.2

0.4

0.6

0.8

1

0 4 8 12Age (Months)

CY

P2

D6

ac

tiv

ity

(D

M/D

X

rati

o r

ela

tiv

e t

o a

du

lt

0

0.2

0.4

0.6

0.8

1

0 4 8 12Age (Months)

CY

P3

A4

ac

tiv

ity

(D

X/3

HM

rati

o)

rela

tiv

e t

o a

du

lt

(A) (B)

CYP2D6 - Bottom-Up Approach Meets Top-Down

Clin Pharmacol Ther 2007


UGT2B7- Bottom up meets Top down again (but not every time!!)

• Take home message is that pattern of ontogeny appears to be reasonable

except for early neonates

• But under-prediction of CL across age band with morphine.

Bottom up

Top down

Bodyweight (kg) Bodyweight (kg)

Cle

ara

nce (

L/h

)

Cle

ara

nce (

L/h

)


p-PBPK prediction accuracy

Overall picture is one of under-prediction with possible reasons

−Ontogeny profile incorrect

−Other pediatric physiology incorrect - LW, LBF and fu - robust data

−Clinical studies not representative / misinterpreted?

Leong et al CPT 2012


Shifting the focus: “Systems-focused” modelling

15


Clinical data: Decompose to rebuild

16

Taking away the

known effects of:

• Protein Binding

• Red Cell Partitioning

• Renal Elimination

• Liver Size

• Liver blood flow

• Renal clearance


Midazolam iv data

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Rel

ativ

e ex

pre

ssio

n

Age (y)

CYP3A ontogeny

Latest in vivo

In vitro ontogeny

3.9

3.9

PMA

MAP*1CYP3A4

9.371

Revised CYP3A ontogeny based on in vivo data

Correct for effects of ventilation and disease


Revised CYP1A2 ontogeny based on in vivo data

18

5.75.7

5.7

PMA6.45

PMA*1.6CYP1A2

0.8exp*0.8CYP1A2 196)(PMA*0.001

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 5 10 15 20 25 30

Re

lati

ve e

xpre

ssio

n

Age (y)

CYP1A2 ontogeny

in vivo ontogeny

in vitro ontogeny

Caffeine and theophylline data


Performance of CYP1A2 and 3A in vivo vs in vitro ontogeny model

19

Independent data sets


Requires validation by other CYP2C9 probes such as s-

warfarin and diclofenac at different age groups.

20

0.1

1.0

0 5 10 15 20

Fra

cti

on

of

ad

ult

s

Age (years)

In vitro

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

-10 0 10 20 30 40C

Lin

t(u

l/m

in/m

g o

f m

ic)

rati

o p

ae

d:a

du

ltAge (years)

In vivo

• CYP2C9 (ibuprofen)

– Patent Ductus Arteriosus

– Cystic Fibrosis

– Febrile Children

Expansion of in vivo-based ontogeny function: CYP2C9


0.1

1.0

0 5 10 15 20 25

Fra

cti

on

of

ad

ult

s

Age (years)

In vitro

0.1

1.0

10.0

-10 0 10 20 30 40

CLi

nt

(ul/

min

/mg

of

mic

) ra

tio

pa

ed

:ad

ult

Age (years)

• In vivo-based CYP2C19 function requires to be validated with

other CYP2C19 probes (care with formulations, disease, demographics)

In vivo

Zane NR et al. Higher CYP2C19 functional activity in children is not entirely

explained by higher gene or protein expression. ISSX meeting San Francisco Oct 2014

Expansion of in vivo-based ontogeny functions: 2C19

• CYP2C19 (pantoprazole)

– GERD


Relative Importance of Pathways: “Ratio of Ratios”!

Pathway A in Pediatrics

Pathway A in Adults

Pathway B in Pediatrics

Pathway B in Adults

Relative Ontogeny =

0.1

1.0

10.0

4 Days 36 Days 1 Year 10 Years

Ra

tio

X(a

du

lt/P

ae

d):

CY

P1

A2

(A

du

lts/P

ae

d)

Age

X vs CYP1A2Renal (male)

CYP2D6

CYP3A4 CYP2B6

0.5

2.0

3.0

8.0

20.0

40.0

0.3

1 Day

0.01

0.10

1.00

1 Day 4 Days 36 Days 1 Year 10 Years

Ra

tio

X(a

du

lt/P

ae

d):

CY

P2

9 (

Ad

ults/P

ae

d)

Age

X vs CYP2C9

CYP1A2

CYP3A4

CYP2B6

CYP2D6

CYP2E1

CYP2C8

Renal

CYP2C18/19

0. 040. 05

0.20

0.400.500.60

2.00

3.00

J Clin Pharmacol

2013; 53: 857–865


Evaluating and using p-PBPK models

If compound does not work in adult PBPK model –

STOP!

23


Ve

no

us

B

loo

d

Art

eri

al

Blo

od

Lung

Adipose

Bone

Brain

Heart

Kidney

Muscle

Skin

Liver

Spleen

Gut

Portal

Vein

POIV

Additional

Organ

Pancreas

Ve

no

us B

loo

d

Art

eri

al

Blo

od

Lung

Adipose

Bone

Brain

Heart

Kidney

Muscle

Skin

Liver

Spleen

Gut

Portal

Vein

POIV

Additional

Organ

Pancreas

Healthy volunteer Pediatric

Learn,

Confirm,

ModifyORLearn,

Confirm,

Modify

The current reality: The learn and confirm approach


Learn and confirm: p-PBPK examples

25

Clin Pharmacokinet, 2014; 53, 89-102.

Biopharm Drug Dispos 2014 online.


Evaluating p-PBPK Care in selection of clinical studies

1. N = 2

2. Substrate file not predicting properly in adults

3. Premature neonates GA = 29 wks, PNA = 7

wks

4. Using mean values when massive outlier

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748495051

Cle

aran

ce L

/kg/

hr

Trial Groups

Trial Clearance Totals for 50 Groups of 2 Individuals out of a Population of 100

Trials Median

Median of Total Population

95th Percentile of Total Population

5th Percentile of Total Population

Data range

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1 to <6 year 6 to <12 year 12 to <17 years

CL

(L/k

g/h

)

Oral omeprazole Mean Andersson 2000

Geo mean Andersson 2000

Simuated mean

Simulated Geo mean

Clinical data range

1 - <6y 0.49 to 3.35 L/kg/h

6 - <12y 0.22 to 32 L/kg/h

12 to 17y 0.1 to 1.68 L/kg/h


The ultimate goal

• More than one way of using pediatric PBPKs to better understand the

system.

• Learn and confirm approach often used.

• Compare like with like if comparing models e.g. same / similar input

parameters.

• Ultimate goal is to move to right


Conclusions

28

When a thing was new, people said that it was not true; when its

truth could not be denied, people said it was not important;

when its importance could not be denied, people said that it was

not new. William James

• Pediatric PBPK models have the potential to improve

drug development. Especially under ~2 years

But over 2 years for DDI / Complex PK / Bridging formulations

• Pediatric PBPK models still evolving and some system

parameters are known unknowns. Transporter ontogeny

Intestinal UGT and other enzymes

• Collaborative approach between academia, drug

industry and regulators in establishing best practice in

application of this approach.


Acknowledgements

• Prof Amin Rostami-Hodjegan

• Prof Geoff Tucker

• Dr Khaled Abduljalil

• Dr Farzaneh Salem

• Dr Goahua Lu

• Dr Alice Ke

• Mr Felix Stader

29

Science

Trevor johnson aaps 2014