Pediatric PBPK: Developing the models
Refining the System Parameters
Dr Trevor Johnson
Principal Scientist
Simcyp Limited
© 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
© Copyright 2014 Certara, L.P. All rights reserved. 4
Systems data: The Complexity of Covariate Effects
Age
(Distribution in Population)
Ethnicity Disease
Sex
(Distribution in Population)
Genotypes
(Distribution in Population)
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)
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved. 6
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
© Copyright 2014 Certara, L.P. All rights reserved. 7
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
© Copyright 2014 Certara, L.P. All rights reserved. 8
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
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
How certain are we about ontogeny ?
10
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved. 12
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
© Copyright 2014 Certara, L.P. All rights reserved.
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
)
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
Shifting the focus: “Systems-focused” modelling
15
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
Performance of CYP1A2 and 3A in vivo vs in vitro ontogeny model
19
Independent data sets
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved. 21
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
© Copyright 2014 Certara, L.P. All rights reserved. 22
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
© Copyright 2014 Certara, L.P. All rights reserved.
Evaluating and using p-PBPK models
If compound does not work in adult PBPK model –
STOP!
23
© Copyright 2014 Certara, L.P. All rights reserved. 24
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
© Copyright 2014 Certara, L.P. All rights reserved.
Learn and confirm: p-PBPK examples
25
Clin Pharmacokinet, 2014; 53, 89-102.
Biopharm Drug Dispos 2014 online.
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
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.
© Copyright 2014 Certara, L.P. All rights reserved.
Acknowledgements
• Prof Amin Rostami-Hodjegan
• Prof Geoff Tucker
• Dr Khaled Abduljalil
• Dr Farzaneh Salem
• Dr Goahua Lu
• Dr Alice Ke
• Mr Felix Stader
29
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