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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
© Copyright 2014 Certara, L.P. All rights reserved.
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
© Copyright 2014 Certara, L.P. All rights reserved.
CLuint per Liver
CLuint per g Liver
In vitro system
In vitro CLuint
Prediction of human PK (PD) in virtual individuals
Scaling Factor
(MPGGL, HPGL)
Liver weight
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
© Copyright 2014 Certara, L.P. All rights reserved.
Mechanistic IVIVE & PBPK
Systems Data
Drug Data Trial
Design
Population PK (/PD) &
Covariates of ADME &
Study Design
Separating Systems & Drug Information
© Copyright 2014 Certara, L.P. All rights reserved.
Separating Systems & Drug Information
Systems Data
Drug Data
Trial Design
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
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.
Genotypes (Population Distribution)
Ethnicity Disease Body Fat Serum
Creatinine
Sex (Population Distribution)
Age (Population Distribution)
Height Brain Volume Body Surface
Area Heart Volme
Weight
Liver Volume
Cardiac Output
Cardiac Index
MPPGL HPGL
Enzyme Abundance
Liver Weight
Intrinsic Clearance
Renal Function
Building virtual populations
© Copyright 2014 Certara, L.P. All rights reserved.
Freq
uenc
y
Age
HV Disease
Defined by real data
Demographic Features of Healthy and Disease Populations
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0
1500 3000 4500 6000 Addicts
0
200
400
600
800 CVD
Age Distribution in Target Population
MALE FEMALE
Freq
uenc
y
Age Category
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CLEARANCE: In Vitro – In Vivo
Extrapolation
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? CLuint per
Liver
X
CLuint per g Liver
In vitro system
HLM
In vitro CLuint
µL.min-1
mg protein
HHEP µL.min-1
106 cells
Scaling Factors in Human IVIVE
Scaling Factor 1
Scaling Factor 2
X HPGL
MPPGL X
Liver Weight
?
rhCYP µL.min-1
pmol CYP MPPGL X X CYP
abundance
© Copyright 2014 Certara, L.P. All rights reserved.
Sources of Variability: MPPGL and Donor Age
Barter et al. 2007 Curr Drug Met Barter et al. 2008 Drug Met Disposition
0 20 40 60 80 0
50
100
150
Age (years)
MP
PGL
(mg.
g-1 )
Paediatric Adult
40 mg.g-1
29 mg.g-1
0 10 20 30 40 50
1 25 45 65 Age (years)
MP
PG
L (m
g.g-
1 )
© Copyright 2014 Certara, L.P. All rights reserved.
CYP Abundance Variability
Different Individuals
- Genetics (Ethnicity)
PLUS
-Age (Ontogeny) - Environment (Ethnicity)
- Sex / Co-medications 0
100
200
300
400
500
0
30
60
90 pm
ol C
YP/m
g m
icro
som
al p
rt
P450 Relative Abundance(average of 167 individuals with CYP3A5 *1 allele within a
1000 randomly selected Caucasian Population)
0.2%3.2%1.6%
15.0%
11.9%
33.1%
12.7% 11.0%4.1%
2.5%4.7%
CYP1A2CYP2A6CYP2B6CYP2C8CYP2C9CYP2C18CYP2C19CYP2D6CYP2E1CYP3A4CYP3A5
© Copyright 2014 Certara, L.P. All rights reserved.
0.01
0.1
1
10
100
1000
10000
0.01 0.1 1 10 100 1000 10000 Observed CL (L/h)
Pred
icte
d C
L (L
/h)
s-wrf
caf
zlp
cyc
mdz sld
tlt clz
s-mph
dex omp
chlr
trz
apz
tlb
buf
theo phen
met
dic
sim
PREDICTION OF CLEARANCE (Oral)
Central tendency
&
Measure of variability
Howgate et al (2006)
© Copyright 2014 Certara, L.P. All rights reserved.
Drug Distribution
Tissue Volumes
Predict Drug Distibution (Pt:p)
and Vss
Tissue Composition (VEW, VIW, VNL, VNP)
Fu and Fut
LogP, Log Dvo:w to predict K:P
Mechanistic IVIVE & PBPK
Drug Data
Trial Design
System Data
© Copyright 2014 Certara, L.P. All rights reserved.
Example: Midazolam pharmacokinetics (simulations in 10 trials of 10 individuals)
Can describe Midazolam Pharmacokinetics using in vitro metabolism data together with systems physiology data
© Copyright 2014 Certara, L.P. All rights reserved.
Midazolam Tissue concentrations (mean and 5 and 95 percentiles)
Concentrations in the tissues can also be linked to Pharmacodynamic or Toxicological effects
© Copyright 2014 Certara, L.P. All rights reserved.
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)
© Copyright 2014 Certara, L.P. All rights reserved.
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?
19
© Copyright 2014 Certara, L.P. All rights reserved.
Links to PD: Assessment of Proarrhythmic Potency
J. of Cardiovasc. Trans. Res. (2012) Toxicology Mechanisms and Methods, 2012
cell metabolism
cell viability
contractility
electro-mechanical
window
mu
ltip
le d
rug
s
demography
physiology
genetics
virtual population
action potential (APD90)
pseudoECG (QTc)
HUMAN HEART LEFT VENTRICULAR
CELL MODEL
hERG in vitro
(measured)
hERG in silico (QSAR)
other ion currents in silico
(QSAR)
other ion currents in vitro
(measured)
other ion currents in silico
(QSAR)
other ion currents in vitro
(measured)
other ion currents in silico
(QSAR)
other ion currents in vitro
(measured)
© Copyright 2014 Certara, L.P. All rights reserved.
• molecular structure –––> QTc prolongation/TdP
21
– mechanistic/physiological
O’Hara and Rudy PLoS computational Biology 7, 2011 Ten Tusscher et al. AmJPhys-HeartPhys 286, 2004
𝑑𝑉
𝑑𝑡=𝐼𝑖𝑜𝑛 + 𝐼𝑠𝑡𝑖𝑚
𝐶𝑚
HUMAN HEART LEFT VENTRICULAR
CELL MODEL
Structure of left ventricular cell model
© Copyright 2014 Certara, L.P. All rights reserved.
Variability matters
• Clinical evidence
Polak 2013 DDT accepted
Group Parameter Influence on ECG Reference
Demography
age ↑ age - ↑ QT/QTc Pham 2002
gender Females have longer QT/QTc as compared to
males. James 2007
Anatomy/
physiology
plasma ions concentration
(K+, Ca2+)
↑ K+ - ↓ QT/QTc
↑ Ca2+ - ↓ QT/QTc
Etheridge 2003
Covis 2002
cardiomyocyte size
(volume, area) ↑ size - ↑ QT/QTc Pacifico 2003
heart wall thickness ↑ thickness - ↑ QT/QTc Jouven 2002
cells heterogeneity
across heart wall
M cells presence influence the T wave and ECG
in general. Antzelevitch 2010
heart rate ↑ RR - ↑ QT Harris 2003
Malik 2002
sex hormones ↑ testosterone - ↓ QT/QTc
↑ progesterone - ↓ QT/QTc
Pham 2002
Sedlak 2012
Genetics
common polymorphisms
(ion channels)
Usually slight modification of ECG, may act as
a genetic modifier (with different mutation
both protective effect and QTc prolongation
were observed).
Crotti 2005
mutations (ion channels) ↑ QT/QTc Etheridge 2003
McPate 2005
© Copyright 2014 Certara, L.P. All rights reserved.
Building virtual populations – cardiac safety assessment
© Copyright 2014 Certara, L.P. All rights reserved.
Example 1: Dolasetron – formulation dependent effect
24
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
© Copyright 2014 Certara, L.P. All rights reserved. 25
Example 1: Dolasetron – Simcyp PK prediction
iv 2.4 mg/kg
Dempsey 1996
Dolasetron
Hydrodolasetron
Metabolite
Parent
© Copyright 2014 Certara, L.P. All rights reserved. 26
Example 1: Dolasetron – Simcyp PK prediction
po 2.4 mg/kg
Dempsey 1996
Dolasetron
Hydrodolasetron
Metabolite
Parent
© Copyright 2014 Certara, L.P. All rights reserved. 27
Example 1: Dolasetron – CSS PD effect prediction
iv 2.4 mg/kg
po 2.4 mg/kg
Dempsey 1996
-150
-100
-50
0
50
100
150
200
0 500 1000 1500 2000 2500 3000
Max APD90 ~ 370 ms
-150
-100
-50
0
50
100
150
200
0 500 1000 1500 2000 2500 3000
Max APD90 ~ 300 ms
© Copyright 2014 Certara, L.P. All rights reserved.
Ranolazine
• IKr inhibitor (in vitro)
• multiple other ionic currents inhibition (in vitro)
• pharmacologically/electrically active metabolites
• clinically – large variability
Example 2 – Ranolazine: IVIVE at the population level
CVT-2738
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Ranolazine and CVT-2738 plasma concentrations
0
500
1000
1500
2000
2500
0 12 24 36 48 60 72 84 96
Syst
emic
Con
cent
ratio
n (n
g/m
L)
Time (h) 0
100
200
300
400
500
600
700
0 12 24 36 48 60 72 84 96
Syst
emic
Con
cent
ratio
n (n
g/m
L)
Time (h)
Ranolazine CVT-2738
Reasonable description of the plasma concentrations after multiple dosing
© Copyright 2014 Certara, L.P. All rights reserved.
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 428/1.63 Measured - -
INa late 6.86/0.71 Measured
Electrophysiology inputs to the model
© Copyright 2014 Certara, L.P. All rights reserved.
PRO-ARRHYTHMIC POTENCY - IVIVE at the population level - RESULTS
OBSERVED PREDICTED
y = 0.0068x + 4.779
-40
-20
0
20
40
60
80
100
0 500 1000 1500 2000 2500 3000
Ch
ang
e in
QT
c fr
om
bas
eli
ne
(m
sce
)
Ranolazine concentration (ng/ml)
© Copyright 2014 Certara, L.P. All rights reserved.
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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