Simulating individual variability in pharmacokinetics as a
risk factor for drug toxicity
Iain Gardner
Head of Translational DMPK Science
Simcyp Limited
New Perspectives in DMPK: Informing Drug Discovery,
Royal Society of Chemistry, London
11 February 2014
Outline of the presentation
• Integrating population variability into PK simulations
– creation of virtual populations
• Sources of inter-individual variability in Pharmacokinetics
– Drug Clearance
• Using virtual populations to predict risk factors for drug
toxicity
– Cardiac Safety
© Copyright 2014 Certara, L.P. All rights reserved.
The Challenge of Population Variability
Environment
Disease
Genetics
(possibly 2D6 poor
metaboliser!?!)
Cross Section of Patients in the Royal Hallamshire Hospital
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Prediction of human PK (PD) in virtual individuals
In vitro
CLuint
In vitro
system
CLuint per
g Liver
Scaling
Factor
(MPGGL, HPGL)
Liver
weight
CLuint per
Liver
Simcyp approach
Combine in vitro-in vivo extrapolation
(IVIVE) and PBPK approaches
in virtual individuals to predict
drug concentration and effect
Identifying relevant DISTRIBUTION of
values for demographical, biological,
physiological and genetic parameters in
target population & the COVARIATIONS
between the parameters in target
POPULATION
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Separating Systems & Drug Information
Systems
Data
Drug
Data
Trial
Design
Mechanistic
IVIVE & PBPK
Population PK (/PD)
&
Covariates of ADME
&
Study Design
© Copyright 2014 Certara, L.P. All rights reserved.
Separating Systems & Drug Information
Systems
Data
Drug
Data
Age
Weight
Tissue Volumes
Tissue Composition
Cardiac Output
Tissue Blood Flows
[Plasma Protein]
MW
LogP
pKa
Protein binding
BP ratio
In vitro
Metabolism
Permeability
Transport
Solubility
Trial
Design
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
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Building virtual populations
Genotypes
(Population Distribution)
Body Fat
Ethnicity
Disease
Sex
(Population Distribution)
Heart
Volme
Liver
Volume
Serum
Creatinine
Age
(Population Distribution)
Height
Body Surface
Area
Renal
Function
Brain
Volume
Weight
Cardiac
Output
Cardiac
Index
Liver
Weight
© Copyright 2014 Certara, L.P. All rights reserved.
MPPGL
HPGL
Intrinsic
Clearance
Enzyme
Abundance
Frequency
Demographic Features of Healthy and Disease Populations
HV
Disease
Age
Defined by real data
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Age Distribution in Target Population
6000
Addicts
4500
Frequency
3000
1500
0
800
CVD
600
400
200
0
Age Category
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MALE
FEMALE
CLEARANCE:
In Vitro – In Vivo
Extrapolation
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Scaling Factors in Human IVIVE
?
In vitro
CLuint
HLM
rhCYP
µL.min-1
106
cells
µL.min-1
mg protein
µL.min-1
pmol CYP
X
?
Scaling CLuint per
Factor 2
Liver
Scaling
Factor 1
In vitro
system
HHEP
CLuint per
g Liver
X
HPGL
X
MPPGL
CYP
X
abundance
MPPGL
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X
Liver
Weight
Sources of Variability: MPPGL and Donor Age
Paediatric
Adult
100
50
0
0
20
MPPGL (mg.g-1)
MPPGL (mg.g-1)
150
40
60
Age (years)
Barter et al. 2007 Curr Drug Met
Barter et al. 2008 Drug Met Disposition
50
40
40 mg.g-1
80
29 mg.g-1
30
20
10
0
1
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25
45
Age (years)
65
CYP Abundance Variability
- Genetics (Ethnicity)
PLUS
-Age (Ontogeny)
- Environment (Ethnicity)
- Sex / Co-medications
pmol CYP/mg microsomal prt
33.1%
P450 Relative Abundance
(average of 167 individuals with CYP3A5 *1 allele within a
1000 randomly selected Caucasian Population)
CYP1A2
12.7%
11.0%
CYP2A6
4.1%
2.5%
CYP2B6
4.7%
CYP2C8
CYP2C9
CYP2C18
CYP2C19
CYP2D6
15.0%
CYP2E1
0.2%
CYP3A4
3.2%
11.9%
1.6%
CYP3A5
Different Individuals
500
90
400
300
200
60
30
100
0
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0
PREDICTION OF CLEARANCE (Oral)
Central
tendency
&
Measure of
variability
Predicted CL (L/h)
10000
Howgate et al (2006)
sld
1000
omp
sim
dex
mdz
buf
chlr
100
tlt
clz
s-mph
zlp
phen
theo
10
cyc
tlb
trz
1
dic
apz caf
s-wrf
0.1
0.01
0.01
met
0.1
1
10
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100
1000
10000
Observed CL (L/h)
Drug Distribution
Trial
Design
System
Data
Mechanistic
IVIVE & PBPK
Drug
Data
Fu and Fut
Tissue Volumes
Predict Drug
Distibution (Pt:p)
and Vss
LogP, Log Dvo:w to
predict K:P
Tissue Composition
(VEW, VIW, VNL, VNP)
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Example: Midazolam pharmacokinetics (simulations in 10 trials of 10 individuals)
Can describe Midazolam Pharmacokinetics
using in vitro metabolism data together with
systems physiology data
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Midazolam Tissue concentrations (mean and 5 and 95 percentiles)
Concentrations in the tissues can also be linked to
Pharmacodynamic or Toxicological effects
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Cardiotoxicity
• Cardiac side effects major cause of drug withdrawal
(regardless of the development level – from pre-clinical up to
the post-approval)
– E.g. Torsade de points and terfenadine
• Various mechanisms and effects involved
– pro-arrhythmia, cardiac cell toxicity
• Drug interactions – important element causing serious
adverse events
– E.g. astemizole (CYP related)
• PK variability important for safety assessment
– E.g. tolterodine (CYP 2D6 mediated metabolism – genetic variability)
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Screening for cardiac safety
• Effects of new compounds on IKR/Herg extensively screened
for in drug discovery/development
–
–
–
–
QSAR models
IKR binding
HERG inhibition
Purkinje fiber studies
• Cardiac safety also often investigated in vivo
– Pre-clinical studies
– Thorough QT study in humans (~$1000000)
• Can cardiotoxicity also be assessed using mechanistic in
silico models?
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19
Links to PD: Assessment of Proarrhythmic Potency
Toxicology Mechanisms and Methods, 2012
J. of Cardiovasc. Trans. Res. (2012)
hERG
in silico
(QSAR)
hERG
in vitro
(measured)
multiple drugs
other ion
other
currents
inion
silico
other
ion
currents
in silico
(QSAR)
currents in silico
(QSAR)
(QSAR)
other ion
otherinion
currents
othervitro
ion
currents
in vitro
(measured)
currents in vitro
(measured)
(measured)
HUMAN HEART
LEFT VENTRICULAR
CELL MODEL
virtual
population
demography
physiology
genetics
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action
potential
(APD90)
pseudoECG
(QTc)
contractility
electromechanical
window
cell
metabolism
cell viability
Structure of left ventricular cell model
• molecular structure –––> QTc prolongation/TdP
– mechanistic/physiological
𝑑𝑉 𝐼𝑖𝑜𝑛 + 𝐼𝑠𝑡𝑖𝑚
=
𝑑𝑡
𝐶𝑚
HUMAN HEART
LEFT VENTRICULAR
CELL MODEL
O’Hara and Rudy PLoS computational Biology 7, 2011
Ten Tusscher et al. AmJPhys-HeartPhys 286, 2004
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21
Variability matters
• Clinical evidence
Group
Parameter
age
Demography
Anatomy/
physiology
gender
plasma ions concentration
(K+, Ca2+)
cardiomyocyte size
(volume, area)
heart wall thickness
cells heterogeneity
across heart wall
heart rate
sex hormones
Genetics
common polymorphisms
(ion channels)
mutations (ion channels)
Influence on ECG
↑ age - ↑ QT/QTc
Females have longer QT/QTc as compared to
males.
↑ K+ - ↓ QT/QTc
↑ Ca2+ - ↓ QT/QTc
↑ size - ↑ QT/QTc
Reference
Pham 2002
James 2007
Etheridge 2003
Covis 2002
Pacifico 2003
↑ thickness - ↑ QT/QTc
Jouven 2002
M cells presence influence the T wave and ECG
Antzelevitch 2010
in general.
Harris 2003
↑ RR - ↑ QT
Malik 2002
↑ testosterone - ↓ QT/QTc
Pham 2002
↑ progesterone - ↓ QT/QTc
Sedlak 2012
Usually slight modification of ECG, may act as
a genetic modifier (with different mutation
Crotti 2005
both protective effect and QTc prolongation
were observed).
Etheridge 2003
↑ QT/QTc
McPate 2005
Polak 2013 DDT accepted
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Building virtual populations – cardiac safety assessment
© Copyright 2014 Certara, L.P. All rights reserved.
Example 1: Dolasetron – formulation dependent effect
DOLASETRON – CARDIOLOGICALLY ACTIVE
Plasma concentration negligible after po dosing
iv formulations – withdrawn from the
market due to the TdP reports
po formulation – considered as safe
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24
Example 1: Dolasetron – Simcyp PK prediction
iv 2.4 mg/kg
Parent
Dolasetron
Hydrodolasetron
Metabolite
Dempsey 1996
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25
Example 1: Dolasetron – Simcyp PK prediction
po 2.4 mg/kg
Parent
Dolasetron
Hydrodolasetron
Metabolite
Dempsey 1996
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26
Example 1: Dolasetron – CSS PD effect prediction
200
Max APD90 ~ 370 ms
150
100
iv 2.4 mg/kg
50
0
-50
0
500
1000
1500
2000
2500
3000
-100
-150
200
Max APD90 ~ 300 ms
150
100
50
po 2.4 mg/kg
0
0
500
1000
1500
2000
2500
3000
-50
-100
-150
Dempsey 1996
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27
Example 2 – Ranolazine: IVIVE at the population level
CVT-2738
Ranolazine
• IKr inhibitor (in vitro)
• multiple other ionic currents inhibition (in vitro)
• pharmacologically/electrically active metabolites
• clinically – large variability
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Ranolazine and CVT-2738 plasma concentrations
Ranolazine
CVT-2738
2500
700
600
Systemic Concentration (ng/mL)
Systemic Concentration (ng/mL)
2000
1500
1000
500
500
400
300
200
100
0
0
12
24
36
48
Time (h)
60
72
84
96
0
0
12
24
36
48
60
72
84
Time (h)
Reasonable description of the plasma concentrations after multiple dosing
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96
Electrophysiology inputs to the model
Ionic
current
Ranolazine
[IC50]/n
Source
CVT-2738
[IC50]
Source
IKr
12/1
Measured
1.19
QSAR predicted
IKs
1900/1
Measured
-
ICa
311/1
Measured
26.38
QSAR predicted
INa peak
INa late
428/1.63
Measured
-
-
6.86/0.71
Measured
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PRO-ARRHYTHMIC POTENCY - IVIVE at the population level - RESULTS
OBSERVED
PREDICTED
100
y = 0.0068x + 4.779
Change in QTc from baseline (msce)
80
60
40
20
0
-20
-40
0
500
1000
1500
2000
Ranolazine concentration (ng/ml)
© Copyright 2014 Certara, L.P. All rights reserved.
2500
3000
Summary
• Using extrapolated in vitro data coupled with PBPK models it
is possible to simulate the pharmacokinetics of many drugs
• By incorporating known physiological co-variates it is
possible to make simulations in virtual populations rather
than an “average” individual
• Concentrations of drugs in tissue compartments of the PBPK
model can be linked to mechanistic models to predict side
effects/toxicity
• Physiological variability can also be included within the
toxicity models
• Acknowledgements
– Sebastian Polak, Amin Rostami
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