In Vitro In Vivo Extrapolation and its Applications in Predicting PK Population Variability

In Vitro In Vivo
Extrapolation and its
Applications in Predicting
PK Population Variability
Alice Ke, PhD
Consultant & Scientific Advisor
Simcyp Limited
Outline
• Clearance concept
• In Vitro In Vivo Extrapolation (IVIVE)
• Linking PBPK and IVIVE, accounting for variability
• Transporters
• Industry/Regulators views
• Future prospects
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Well-stirred liver model
James R. Gillette, Ann N Y Acad Sci. 1971
Commentary: A physiological approach to hepatic clearance
Wilkinson and Shand , CPT, 1975
Pang and Rowland, JPK Biopharm 1977
fB,Out = Unbound drug in venous blood /
Whole emergent blood concentration
• Unbound concentration of drug in blood cells equates to the unbound
concentration in plasma.
• Emergent venous blood is in equilibrium with that in the liver.
Rowland, Benet and Graham, JPK Biopharm 1973
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Yang et al., DMD, 2007
In Vitro - In Vivo Extrapolation (IVIVE)
Mechanistic Models
Scaling factors
in vitro
CLuint
In vitro
CLuint
In vitro
system
in vivo
CLuint
CLuint per
g Liver
Scaling
Factor
(MPGGL, HPGL)
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Liver
weight
CLuint per
Liver
Accuracy of IVIVE approaches for human CL or CLint
System
AFE
Ref
HLM
2.3
Obach DMD 27, 1350, 1999
HLM
6.2
Ito, Pharm Res, 22, 103, 2005
HLM
2.3
Stringer, Xeno, 38, 1313, 2008
HLM
2.2
Ring, J Pharm Sci, 100, 490, 2011
HLM
5
Hallifax, Pharm Res, 27, 2150, 2010
HLM
2
Jones, Clin Pk, 50, 311, 2011
Heps
2.4
De Buck DMD, 35, 1766, 2007
Heps
5.2
Stringer, Xeno, 38, 1313, 2008
Heps
5
Heps
7.6
Hallifax 2011
Naritomi DMD 31, 580, 2003
Heps
Recombinant CYP
1.53 PT
2.15 WS
Stringer DMD, 37,1025, 2009
Generally many literature studies shows under-prediction from in vitro systems.
Can be corrected using an empirical scaling factor.
Need to understand for your in vitro system if this is necessary.
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IVIVE predictions – Improvements over years
• Non-specific binding (Obach, DMD, 1999, Riley et al., DMD, 2005)
• Recombinant CYPs and ISEF values (Galetin et al., DMD, 2004; Proctor
et al. Xenobiotica, 2004)
• In vitro modelling to account for hepatic uptake (Soars et al., DMD, 2007)
• Adding BSA and HAS-FAS to HLM (Rowland et al., DMD, 2008)
• Accounting for the difference in drug ionization in extracellular and
intracellular tissue water (Berezhkovskiy, J Pharm Sci, 2011)
• Integrating uptake, metabolism, biliary excretion, and sinusoidal efflux
(Umehara and Camenisch, Pharm Res, 2012)
• Incorporating ionisation and protein binding (Poulin et al., J Pharm Sci,
2012)
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Gut wall metabolism
‘Qgut’ , a minimal model
Fg =
' Q gut '
' Q gut '+ fu gut .CLu int −gut
' Q gut ' =
CL perm ⋅ Q villi
CL perm + Q villi
Predicted Fg
1
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
Observed Fg
Gertz et al., DMD, 2010
Yang et al., CDM, 2007
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Special populations
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
Solubility
Dose
Administration route
Frequency
Co-administered drugs
Populations
Mechanistic IVIVE linked PBPK models
Prediction of drug PK (PD) in population of interest
Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012
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Demographic Features of Healthy and Disease Populations
Frequency
Randomly Generated
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
The Complexity of Covariate Effects as Applied to CL
Renal
Function
Ethnicity
Plasma
Proteins
&
Haematocrit
Disease
Serum
Creatinine
Age
(Distribution in Population)
Height
Body
Surface
Area
Weight
Liver
Volume
Cardiac
Output
MPPGL
HPGL
Cardiac
Index
Liver
Weight
Intrinsic
Clearance
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Converting CLuint to CLH
(1.407 +0.0158×age - 0.00038×age2 + 0.0000024×age3)
MPPGL= 10
CLuint = CLuint. MPPGL.Liver Weight
(whole liver)
Liver Weight = Liver Volume × Liver Density
Liver Volume = 0.722.BSA 1.176 (L/m2)
0.00718×Ht 0.725×Wt 0.425
f (age)+x
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Converting CLuint to CLH
CLuint = CLuint×MPPGL×Liver Weight
fuB =
QH×fuB×CLuint
CLH =
QH + fuB ×CLuint
QH = %CO
fu
CB/Cp
CB/Cp = (E:P)×HC + (1- HC)
CO=f (age, BSA)
HC=f (age)+f (sex)
0.00718×Ht 0.725×Wt 0.425
f (age)+x
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Revised in vivo ontogeny functions for CYP1A2 and 3A4
(Leong et al., CPT 2012; 91: 926-931)
1.8
1.4
1.2
1
in vitro
0.8
In vivo v14.1
0.6
0.4
CYP3A4 ontogeny
1
Relative expression
1.6
Relative expression
1.2
CYP1A2 ontogeny
0.8
0.6
In vitro
0.4
In vivo v14.1
0.2
0.2
0
0
0
5
10
15
Age (y)
20
25
0
5
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10
15
Age (y)
20
25
Fraction of Adult Value
UGT Ontogeny
1.2
1
0.8
UGT1A1
0.6
UGT1A4
0.4
UGT1A6
UGT1A9
0.2
UGT2B7
Strassburg et al 2002
Burchell et al 1989
Onishi et al 1997
Leakey et al 1987
Coughtrie et al 1988
Miyagi and Collier 2007
Zaya et al 2006
Pacifici et al 1990
Pacifici et al 1982
Choonara et al 1989
0
0
5
10
15
20
Age (y)
Leiden Collaboration – Top down vs bottom up ontogeny for UGT2B7
- Morphine
- Zidovudine
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UGT2B7 ontogeny ‘Top down’ vs ‘Bottom up’
Clearance (L/h)
Glucuronidation clearance (L/h)
Top down
Bodyweight (kg)
Bodyweight (kg)
Bottom up
• 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.
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Maturation of Renal Clearance
150
100
921 subjects
50
923 subjects
y = 87.674x - 14.497
R 2 = 0.9988
0
0
0.5
1
BSA (m2)
1.5
1760 subjects
2
63 subjects
160
Rhodin et al 2009
140
Rhodin, De Cock , Hayton (ml/min)
Johnson et al 2006
140
De Cock et al 2014
120
GFR (ml/min)
GFR (ml/min)
Johnson et al 2006
Rubin et al 1949
Hayton 2000
100
80
60
40
20
0
0
5
10
Age (yr)
15
De Cock
Rhodin
120
Hayton
100
Line of unity
80
60
40
20
0
20
0
20
40
60
80
100
Johnson (ml/min)
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120
140
Maturation of Biliary Clearance Appears to be Rapid
Azithromycin
Ceftriaxone
Digoxin
Buprenorphine
Johnson et al Drug Metab Dispos. 2016
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Variation in Protein Binding (fu)
1.4
50
1.2
40
1
AAG (g/L)
Albumin (g/L)
AAG g/L =
Alb = 1.1287Ln(Age) + 33.746
60
30
20
0.887 × Age D
0.38
8.89 0.38 + Age D
0.33
0.8
0.6
0.4
10
0.2
0
0
0.1
1
10
100
1000
10000 100000
0.1
1
100
1000
10000 100000
Age (days)
Age (days)
fu =
10
1
[P]
1+
KD
[P]
KD = Dissociation Constant
KD =
1
[P] = Serum Protein Concentration
−1
fu
1
fu =
In absence of changes
 [P ]
(
1 − fu pop * )
in dynamics of binding:
1+ 
×

[
]
P
fu


pop *
pop *


*pop is the population under investigation i.e paediatric
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Developing and testing a Geriatric population
1
Male - 66 to 96 y
Height (males
66 to 96 y)
In house testing
200
0.8
180
160
0.6
140
Height (cm)
Elderly : Young CL ratio
1.2
Obs
0.4
Pred
0.2
120
100
y = 0.0012x2 - 0.4357x + 196.38
R² = 0.0475
80
60
0
40
20
0
50
75
100
Age (years)
CYPs
Parkinson et al 2004
Liver weight (Male)
2500
y = 478573x-1.346
Liver weight (g)
2000
1500
Clinical
Simulated
1000
Power (Simulated)
500
0
60
400
70
Albumin (g/L)
60
50
40
Simulated
30
Veering
Verbeeck
20
Kidney weight (g)
Albumin (males)
70
Kidney weight (Female)
300
200
Power (Simulation)
0
70
80
90
100
y = 6566.4x-0.792
R² = 1
Simulation
100
10
60
90
Clinical
Campion
0
80
Age (y)
60
70
Age (y)
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80
Age (y)
90
100
100
Scaling from in vitro: drug data vs systems data
Liver
In vitro data
Jmax/Km or
CLuint T
CLuint, T
per
g Liver
HHEP
In vitro
CLuint, T
SF 1:
REF/RAFHHEP
CLuint, T
per Liver
Intestine
SF 2:
HPGL
SF 3:
Liver Weight
Caco-2, MDCK- II,
LLC-PK1 etc.
Jmax/Km or
CLuint T
CLuint, T
In Jejunum I
Kidney
In vitro data
Jmax/Km or
CLuint T
PTC
In vitro
CLuint, T
SF 1:
REF/RAFPTC
Brain
In vitro data
Jmax/Km or
CLuint T
SF 1:
REF/RAFJejunum I
CLuint, T
per Kidney
Replacement / Additional Organ
SF 2:
PTCPGK
H-BMv
In vitro
CLuint, T
SF 1:
REF/RAFH-BMv
CLuint, T
per
g Kidney
Scaling via the
Permeability and
Surface area
product
SF 3:
Kidney Weight
CLuint, T
per
g Brain
SF 2:
H-BMvPGB
CLuint, T
per Brain
@ BBB
Jmax/Km or
CLuint T
CLu, T
per whole
organ
User needs to scale to whole organ!
SF 3:
Brain Weight
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SF: Scaling Factor
Translating in vitro effective concentrations to concentrations at the site
of action
• Mechanistic, multi-compartmental tissue models (brain, kidney, liver, lung
and intestine) are available
• Enable more reliable estimates of intracellular tissue concentrations
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22
Modelling in vitro assays – a must to do!
2 Compartment Model
Active Uptake
Incubation
Medium
Volume
CLint · Km · [S]EC(t)
Km + [S]EC(t)
Intracellular
Volume
CLPD· [S]EC(t)
[S]IC
Free [S]EC
fucell
CLPD · [S]IC(t)
Baker et al., Xenobiotica, 2007; Soars et al., Mol Phar, 2009; Poirier et al., Mol Pharm , 2009; Menochet et al., J Pharm Exp
Ther, 2012
5 Compartment Model - Transwell
Heikkinen et al., 2010 Mol Pharmaceutics
Korzekwa et al., 2012 DMD
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PBPK Impact on 19 US Drug Labels in Last 2 Years
Olysio (Simerprevir)
Xarelto (Rivaroxaban)
Edurant (Rilpivirine)
Imbruvia (Ibrutinib)
Opsumit (Macitentan)
Hepatitis C
Thrombosis & Embolism
HIV infection
Lymphoma and Leukemia
Pulmonary Hypertension
Zykadia (Ceritinbi)
Odozmzo (Sonidegib)
Farydak (Panobinostat)
Revatio (Sildenafil)
Bosulif (Bosutinib)
Lung Cancer
Basal Cell Carcinoma
Multiple myeloma
Pulmonary Hypertension
Myelogenous Leukemia
Lynparza (Olaparib)
Movantik (Naloxegol)
Tagrisso (Osimertinib)
Iclusig (Ponatinib)
Cerdelga (Eliglustat)
Advanced Ovarian Cancer
Opioid Induced Constipation
Metastatic NSCLC
Chronic Myeloid Leukemia
Gaucher Disease
Jevtana (Cabazitaxel)
Cotellic (Cobimetinib)
Lenvima (Lenvatinib)
Aristada (Aripiprazolel)
Prostate Cancer
Metastatic Melanoma
Thyroid cancer
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Schizophrenia
Quantitative IVIVE of Tissue Toxicity Supported by European Commission
7th FP Predict-IV Grant
Hamon et al., Toxicology in Vitro, 2015
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25
Summary
•
In a systems pharmacology paradigm, the bottom-up approach to
modeling and simulation of the ADME processes of a chemical, is a
valuable tool in integrating available prior information and improving
decision making.
•
Improvement in the in vitro systems which can act as surrogates for in
vivo reactions relevant to ADME
•
Advances in the understanding of the extrapolation factors
•
Advances in the development of mechanistic models of the human
body
•
Facilitate predicting PK characteristics in a wide range of healthy or
disease populations accounting for age, sex, ethnicity, genetic, etc
variability
•
Moving towards PBPK coupling with systems biology models to predict
toxicity endpoints/biomarkers and their associated variability from in
vitro data
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26