Pediatric & Pregnancy PBPK modelling: Clinical & Drug Development Applications

Paediatric and Pregnancy
PBPK modelling: Clinical
and Drug Development
Applications
Dr Trevor Johnson
Principal Scientist
Simcyp Limited
trevor.johnson@certara.com

© Copyright 2018 Certara, L.P. All rights reserved.
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

Model Qualification and Reporting
3

Parameter and model certainty: Paediatric Body Size and Variability
Weight based on HeightHeight based on Age
0
50
100
150
200
250
0 5 10 15 20
Height(cm)
Age (y)
Male plus CV
Median
10th Percent
90th Percent
0.4th Percent
99.6 Percent
Simulated
0
20
40
60
80
100
120
0 5 10 15 20
Weight(kg)
Age (y)
Male plus CV
Median
10th
90th
0.4th
99.6th
Simulated
Correlated Monte Carlo modelUncorrelated Monte Carlo model
BSA from HT and WT
Dubois and Dubois >15kg
Haycock et al <15kg

Parameter certainty: 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)
LiverVolume(L)
Simcyp
In vivo
Adult

Oral
Emphasis on performance verification
PBPK AUC – blue, Cmax - red
Oral

Parameter Uncertainty:CYP3A4 and UGT2B7
Ontogeny

CYP3A4 in vivo ontogeny vs in vitro
Salem et al, Midazolam IV data Upreti and Wahlstrom, Sulfentanil IV data
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 5 10 15 20 25
Fraction
Age (y)
In vitro
Salem
Upreti

CYP3A ontogeny – middle out analysis
9

Latest CYP3A4 ontogeny
10
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 5 10 15 20
Fractionofadult
Age (y)
CYP3A onotgeny
Brussee No
MPPGL
Salem 2018
Similar to Upreti and Wahlstrom 2016 – methodology????
Verification data to follow – Paediatric substrates ?

Effects of underlying disease on CYP3A ontogeny
11

UGT2B7 Ontogeny
Strassburg et al 2002
Zaya et al 2006
Pacifici et al 1990
Pacifici et al 1982
Choonara et al 1989
Bhatt et al 2017
Leiden Collaboration – Top down vs bottom up ontogeny for UGT2B7
- Morphine,
- Zidovudine
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 2 4 6 8 10 12 14 16 18 20
Fraction
Age (y)
UGT2B7

UGT2B7 ontogeny ‘Top down’ vs ‘Bottom up’
Bottom up
Top down
• 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.
Bodyweight (kg) Bodyweight (kg)
Clearance(L/h)
Glucuronidationclearance(L/h)

Ontogeny of OCT1
14
Prasad et al., CPT 2016; 100: 362
OCT1, OATP1B3 & P-gp - significantly lower in neonates & infants than
adolescents & adults

Morphine CL and UGT & OCT1 ontogeny
UGT2B7 ontogeny UGT2B7 and OCT1 ontogeny
Observed data from Bouwmeester et al 2004, Anand et al 2008, Knibb et al 2009, Lynn & Slattery 1987
Based on work by Emoto et al CPT Pharmacometrics Syst Pharmacol 2018; 7: 464-473.

Introducing UDPGA
16
UGT2B7, OCT1 and UDPGA ontogeny
Tao Lui, PhD thesis 2017. Learn and confirm paradigm to optimize pharmacotherapy in
neonatal abstinence syndrome using pharmacometrics. School of Pharmacy and
medicine., University of Maryland.

Case study 1: GBT440
• Used for the treatment of Sickle Cell Disease
• Low clearance drug (4-6 L/h)
• Half-life of ~75 h in healthy subjects
• Half-life of ~ 36 hours in subjects with SCD
• Cleared by oxidation (74%), reduction (19%) and UGT-
mediated metabolism (8%)
• Main oxidative enzyme – CYP3A4 (74% of oxidation)
• fu=0.002; B:P ratio =33
17

PBPK modelling strategy: from adult to paediatric
Review of in vitro and clinical data to develop PBPK
model in healthy adults
Verify PBPK model in healthy adults using
independent clinical data sets
Verify (and refine if necessary) PBPK model in adults
with SCD using clinical data sets
Verify PBPK model in adolescents with SCD using
clinical data sets
Integrate physiological
changes related to SCD
Integrate age-related
changes
Integrate age-related
changes including
enzyme and
haematocrit ontogenies
Predict exposure of GBT440 in children aged 9
months up to 12 years of age

GBT440: simulated blood exposures in healthy adults
19
Cmax AUC (0,24)
(µg/mL) (µg/mL.h)
Simulated (n=240) 139 3077
Observed (n=24) 160 3472
S/O 0.87 0.89
A loading dose of 900 mg GBT440 on days 1 and 2 and 600 mg QD on days 3 to 7 (linear
and log-linear plots are on the left and right).
Solid black line is the mean and dashed lines are the 5th and 95th percentiles of the
simulated population. Circles are observed data.

Key system parameter changes in patients with SCD
• Coagulation, platelet and adhesion markers are increased in patients with SCD.
• Changes in protein binding may occur as a result of lower albumin levels.
• Moreover, sickled red blood cells are prone to haemolysis. Thus, haematocrits are
significantly lower in patients with SCD than in healthy subjects (21% versus 40%;
Connes et al., PLOS 2013; 8(11): 1-5.
• Integration of the lower haematocrit within the PBPK model for GBT440 led to a
reduction in the B:P ratio from 33.16 to 15.5 (consistent with observed data).
20

GBT440: simulated blood exposures in adults with SCD
21
A). single oral dose of 900 mg GBT440. B). Multiple oral doses of GBT440
(500 mg BID) for 28 days
Solid black line is the mean and dashed lines are the 5th and 95th percentiles of the simulated
population. Circles are observed data.
Cmax AUC t1/2
(µg/mL) (µg/mL*h) (h)
Simulated (n=60) 34.6 2144 39.4
Observed (n=6) 36 2480 38.8
S/O 0.96 0.86 1.02
A B
Cmax AUC (0,24)
(µg/mL) (µg/mL.h)
Simulated (n=60) 95 2147
Observed (n=6) 116 2430
S/O 0.92 0.83

GBT440: simulated blood exposures in children (6 to < 12 years) with SCD
22
A single oral dose of 600 mg GBT440 (linear and log-
linear plots are on the top and bottom).
Solid black line is the mean and dashed lines are the 5th
and 95th percentiles of the simulated population. Circles
are observed data.
Cmax AUC
Trial (n=6) (µg/mL) (µg/mL.h)
1
2
3
4
5
6
7
8
9
10
51.8
55.9
44.5
61.0
54.5
51.6
59.8
61.9
56.6
59.0
3344
2279
2560
3405
2693
3077
3666
2464
2185
2007
Population (n=60) 55.4 2716
Observed (n=6) 47.3 2785
S/O 1.17 0.98

Predicted whole blood exposures in paediatrics with SCD
23
Predicted mean blood concentrations of GBT440
following administration of multiple oral doses of
GBT440 (900 mg QD) in:
• infants aged 9 months to 2 years (black)
• children aged 2 to 5 years (orange)
• children aged 6 to 11 years (red)
• adolescents aged 12 to 17 years (blue)
• adults (green) with SCD
The solid and dashed black lines represent
simulations using the CYP3A4 ontogeny
profiles based on Simcyp and Upreti and
Wahlstrom (2016), respectively.
CYP3A4 ontogeny profiles based on
Simcyp (black circles) and Upreti and
Wahlstrom (J Clin Pharmacol 2016) (grey
circles)

Dose projections in paediatrics with SCD
24
Predicted mean blood concentrations of GBT440
following administration of multiple oral doses of
GBT440 (dose equivalent to 900 mg QD in
adults) in:
• infants aged 9 months to 2 years (black)
• children aged 2 to 5 years (orange)
• children aged 6 to 11 years (red)
• adults (green) with SCD
The solid and dashed black lines represent
simulations using the CYP3A4 ontogeny profiles
based on Simcyp and Upreti and Wahlstrom
(2016), respectively.
Dose equivalent Cmin Cmax AUC(0,24) Cmin Cmax AUC(0,24)
Populations (n=70) (900 mg in adults)
(ng/mL)(ng/mL)
(ng/mL.h)
Ratios (relative to
adult)
9 months to 2 years - Simcyp
ontogeny
200 73.7 118 2330 1.25 1.34 1.31
9 months to 2 years - Upreti
ontogeny
200 43.6 87.9 1590 0.74 1.00 0.89
2 to 5 years 300 67.7 112 2190 1.15 1.27 1.23
6 to 11 years 400 55.0 89.5 1754 0.93 1.02 0.99
12 to 17 years 900 70.0 109 2180 1.19 1.24 1.22
adults 900 58.9 87.9 1780 1.00 1.00 1.00

Conclusions
• PBPK modelling allows mechanistic investigation of exposure differences in
healthy adults versus adults with SCD (disease-related changes)
• PBPK modelling allows mechanistic investigation of exposure differences in adults
with SCD versus children with SCD (age-related changes)
• PBPK modelling can be used to assess dose projections in children
25

Predicting DDI in Paediatrics

Relative Importance of Pathways: “Ratio of Ratios”!
Pathway A in Paediatrics
Pathway A in Adults
Pathway B in Paediatrics
Pathway B in Adults
Relative Ontogeny =
0.1
1.0
10.0
4 Days 36 Days 1 Year 10 Years
RatioX(adult/Paed):CYP1A2(Adults/Paed
Age
X vs CYP1A2
Renal(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
RatioX(adult/Paed):CYP29(Adults/Paed)
Age
X vs CYP2C9
CYP1A2
CYP3A4
CYP2B6
CYP2D6
CYP2E1
CYP2C8
Renal
CYP2C18/19
0. 04
0. 05
0.20
0.40
0.50
0.60
2.00
3.00

Anticipated Effects
Drug metabolised by two enzymes inhibitor of minor enzyme introduced
Assuming in adult that
fm3A4=0.5 vs fmx = 0.5 when 3A4 is inhibited, …

Regulatory examples
29
Drug
Key theme
(Impact
level)
Key question(s) Brief description Internal impact Qualification dataset FDA response EMA response
Eribulin (NDA
submission)
Pediatric
(Low –
moderate)
What is the starting
dose of eribulin in
children
A PBPK model was
developed for eribulin and
used to perform
simulations with the
Simcyp pediatric
population. Model
predicted that the starting
dose in 6 – 12 year old
patients should be half of
the therapeutic doses in
adults. CL characteristic
CYP3A metabolism, but
mainly biliary excretion
(which was converted into
HLM CLint with the
retrograde calculator)
PBPK confirmed
results from traditional
population-based
scaling approaches to
set the starting dose
for the pediatric
program
Clinical PK data.
Results of the first
pediatric trial showed that
the model predicted the
clearance of 12 – 18 year
old patients very well.
Clearance of 6 – 12 year
old was slightly over
predicted, but within
acceptable range.
No comment.
Starting dose was
accepted.
No comment.
Starting dose
was accepted.
Quetiapine
(late
development)
Pediatrics
(medium)
Bridging formulations.
Quetiapine XR and
Quetiapine
formulations and
extrapolating from
adult to pediatric
Could we set a dose for
the XR formulation in
children without
performing a trial based
on existing preclinical and
clinical exposure data?
Inform dose selection
in children
Internal compound file Accepted Not submitted
Emflaza (late
development)
Pediatrics
(medium)
Effect of CYP3A4
perpetrators in
pediatric population
PBPK model built in adult
population with DDIs
CYP3A4 verified with
clinical data. Pediatric PK
data showed no change
in PK compared to adults
Dose adjustments with
CYP3A4 perpetrators
in line with adult
adjustments
DDI CYP3A4 in adult
population.
A case could be made to
support same dose
adjustments in pediatrics
as in adults.
Accepted

Case study 2: Deflazocort (Emflaza)
• PBPK model for 21 desDFZ
• Model verification
• Prediction of concentration-time profiles in adults
• Predicted DDI liability clarithromycin and rifampicin in adults
• Predicted exposure in children 4 to 11y and adolescents 12
to 16y
• Model application
• Predicted DDI liability in children (clarithromycin, fluconazole,
rifampicin, efavirenz)
30
Deflazocort 21-desacetyl deflazocort (21-desDFZ)
Esterases
CYP3A4
6β hydroxyl DFZ and other metabolites

Model verification (Adults)
31

Model verification (Paediatrics)
32
Children Adolescents

Model application
33
Adult values
Simulations
Cmax AUC
2.1 4.2
2.25 3.37
0.22 0.15
0.06 0.08
600mg
500mg
Obs
Obs

Acceptance criteria for PBPK models
• In vitro data prediction of CL or Vss typically 2 – 5 fold
• More fitted model e.g. in vivo CL data to fit Clint via retrograde model; 0.75 to 1.5
or 0.8 to 1.25-fold?
• Depends on variability of observed data – link success to the measure of variation
in the observed values
36

Feto-Placental-Maternal PBPK model
37
Venous
Blood
Arterial
Blood
Lung
Adipose
Bone
Brain
Heart
Kidney
Muscle
Skin
Liver
Spleen
Gut
Portal Vein
IV Dose
EHC
Generic
Pancreas
PO Dose
Tissue
Fetal-placental blood
Venous
Blood
Liver
Gut
Spleen
Pancreas
CLmet
Heart
Muscle
Skin
Kidneys
Lungs
Bone
Brain
Adipose
Amniotic
Fluid
Maternal
Placental Blood
Arterial
Blood
CLplacenta
Intracellular
(Placental tissue)
Maternal placental
permeability limited placenta model
CLPDM
CLPDF
Fetal placental

Summary
• Paediatric PBPK models have the potential to improve drug
development and not just in under 2 years
• Increasing use of p-PBPK in regulatory submissions
– Use p- PBPK where the question justifies its use and where you can
verify the model
– Model verification what is really needed?
– Novel use of p-PBPK in clinical trials
• Paediatric PBPK models, more understanding of the system,
some parameters are known unknowns.
 Some transporter ontogeny
 Co-factor ontogeny and effects on drug metabolism
 Intestinal UGT and other enzymes
 Biologics area: FcRn receptor ontogeny
• Collaborative approach between academia, drug industry and
regulators in continuing to establish best practice.
38

Acknowledgements
• Prof Amin Rostami-Hodjegan
• Dr Masoud Jamei
• Dr Khaled Abduljalil
• Dr Farzaneh Salem
• Dr Jennifer Bonner
• Dr Xian Pan
• Ms Amita Pansari
39

Pediatric & Pregnancy PBPK modelling: Clinical & Drug Development Applications

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