In Vitro In Vivo Extrapolation and its
Applications in Predicting PK Population Variability
Alice Ke, PhDConsultant & Scientific Advisor
Simcyp Limited
© Copyright 2013 Certara, L.P. All rights reserved.
Outline
• Clearance concept
• In Vitro In Vivo Extrapolation (IVIVE)
• Linking PBPK and IVIVE, accounting for variability
• Transporters
• Industry/Regulators views
• Future prospects
© Copyright 2013 Certara, L.P. All rights reserved.
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 Yang et al., DMD, 2007
© Copyright 2013 Certara, L.P. All rights reserved.
In Vitro - In Vivo Extrapolation (IVIVE)
in vitro CLuint
in vivo CLuint
Scaling factors
Mechanistic Models
CLuint per Liver
CLuint per g Liver
In vitrosystem
In vitroCLuint
Scaling Factor
(MPGGL, HPGL)
Liver weight
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Accuracy of IVIVE approaches for human CL or CLint
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.
System AFE RefHLM 2.3 Obach DMD 27, 1350, 1999HLM 6.2 Ito, Pharm Res, 22, 103, 2005HLM 2.3 Stringer, Xeno, 38, 1313, 2008HLM 2.2 Ring, J Pharm Sci, 100, 490, 2011HLM 5 Hallifax, Pharm Res, 27, 2150, 2010HLM 2 Jones, Clin Pk, 50, 311, 2011Heps 2.4 De Buck DMD, 35, 1766, 2007Heps 5.2 Stringer, Xeno, 38, 1313, 2008Heps 5 Hallifax 2011Heps 7.6 Naritomi DMD 31, 580, 2003HepsRecombinant CYP 1.53 PT
2.15 WSStringer DMD, 37,1025, 2009
© Copyright 2013 Certara, L.P. All rights reserved.
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)
© Copyright 2013 Certara, L.P. All rights reserved.
Gut wall metabolism
‘Qgut’ , a minimal model
gutintgutgut
gutg CLu.fu'Q'
'Q'F
−+=
villiperm
villipermgut QCL
QCL'Q'
+
⋅=
Yang et al., CDM, 2007
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1Observed Fg
Pred
icte
d F g
Gertz et al., DMD, 2010
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Special populations
AgeWeight
Tissue VolumesTissue Composition
Cardiac OutputTissue Blood Flows
[Plasma Protein]
Systems Data
Drug Data
TrialDesign
MWLogPpKa
Protein bindingBP ratio
In vitro MetabolismPermeability
Solubility
Dose Administration route
FrequencyCo-administered drugs
Populations
Prediction of drug PK (PD) in population of interest
Mechanistic IVIVE linked PBPK models
Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012
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Demographic Features of Healthy and Disease Populations
Freq
uenc
y
Age
Randomly Generated
HVDisease
Defined by real data
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Age Distribution in Target Population
0
1500300045006000 Addicts
0
200
400
600
800 CVD
MALEFEMALE
Freq
uenc
y
Age Category
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The Complexity of Covariate Effects as Applied to CL
Age(Distribution in Population)
Ethnicity Disease
Height
CardiacIndex
MPPGLHPGL
Plasma Proteins
&Haematocrit
SerumCreatinine
Body Surface
AreaWeight
LiverVolume
CardiacOutput
IntrinsicClearance
Renal Function
LiverWeight
Converting CLuint to CLH
© Copyright 2013 Certara, L.P. All rights reserved.
CLuint. MPPGL.Liver WeightCLuint =
MPPGL= 10 (1.407 +0.0158×age - 0.00038×age2 + 0.0000024×age3)
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
(whole liver)
Converting CLuint to CLH
© Copyright 2013 Certara, L.P. All rights reserved.
fuB =fu
CB/CpQH×fuB×CLuint
QH + fuB ×CLuintCLH =
CLuint×MPPGL×Liver WeightCLuint =
CB/Cp = (E:P)×HC + (1- HC)
HC=f (age)+f (sex)
QH = %CO
CO=f (age, BSA)
0.00718×Ht 0.725×Wt 0.425
f (age)+x
© Copyright 2013 Certara, L.P. All rights reserved.
Revised in vivo ontogeny functions for CYP1A2 and 3A4(Leong et al., CPT 2012; 91: 926-931)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 5 10 15 20 25
Rela
tive
expr
essi
on
Age (y)
CYP1A2 ontogeny
in vitro
In vivo v14.1
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20 25
Rela
tive
expr
essi
on
Age (y)
CYP3A4 ontogeny
In vitro
In vivo v14.1
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UGT Ontogeny
Strassburg et al 2002Burchell et al 1989Onishi et al 1997Leakey et al 1987Coughtrie et al 1988Miyagi and Collier 2007Zaya et al 2006Pacifici et al 1990Pacifici et al 1982Choonara et al 1989
Leiden Collaboration – Top down vs bottom up ontogeny for UGT2B7- Morphine - Zidovudine
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20
Frac
tion
of A
dult
Valu
e
Age (y)
UGT1A1
UGT1A4
UGT1A6
UGT1A9
UGT2B7
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UGT2B7 ontogeny ‘Top down’ vs ‘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.
Bottom up
Top down
Bodyweight (kg) Bodyweight (kg)
Cle
aran
ce (L
/h)
Glu
curo
nida
tion
clea
ranc
e (L
/h)
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Maturation of Renal Clearance
y = 87.674x - 14.497R 2 = 0.9988
0
50
100
150
0 0.5 1 1.5 2BSA (m2)
GFR
(ml/m
in)
921 subjects
Johnson et al 2006
0
20
40
60
80
100
120
140
160
0 5 10 15 20
GFR
(ml/
min
)
Age (yr)
Rhodin et al 2009Johnson et al 2006De Cock et al 2014Rubin et al 1949Hayton 2000
0
20
40
60
80
100
120
140
0 20 40 60 80 100 120 140
Rhod
in, D
e Co
ck ,
Hayt
on (m
l/m
in)
Johnson (ml/min)
De CockRhodinHaytonLine of unity
923 subjects
63 subjects
1760 subjects
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Maturation of Biliary Clearance Appears to be RapidAzithromycin Ceftriaxone
Digoxin Buprenorphine
Johnson et al Drug Metab Dispos. 2016
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Variation in Protein Binding (fu)
Alb = 1.1287Ln(Age) + 33.746
0
10
20
30
40
50
60
0.1 1 10 100 1000 10000 100000
Age (days)
Albu
min
(g/L
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0.1 1 10 100 1000 10000 100000Age (days)
AA
G (g
/L)
33.0D
38.0
38.0D
g/L Age89.8Age887.0AAG+
×=
DK]P[1
1fu+
= KD = Dissociation Constant[P] = Serum Protein Concentration 1
fu1
]P[KD
−=
( )
−×+
=
*
*
*
1][
][1
1
pop
pop
pop fufu
PP
fuIn absence of changes in dynamics of binding:
*pop is the population under investigation i.e paediatric
© Copyright 2013 Certara, L.P. All rights reserved.
Developing and testing a Geriatric population
y = 0.0012x2 - 0.4357x + 196.38R² = 0.0475
0
20
40
60
80
100
120
140
160
180
200
50 75 100
Heig
ht (c
m)
Age (years)
Male - 66 to 96 y Height (males 66 to 96 y)
y = 478573x-1.346
0
500
1000
1500
2000
2500
60 70 80 90 100
Live
r wei
ght (
g)
Age (y)
Liver weight (Male)
Clinical
Simulated
Power (Simulated)
y = 6566.4x-0.792
R² = 1
0
100
200
300
400
60 70 80 90 100
Kidn
ey w
eigh
t (g)
Age (y)
Kidney weight (Female)
Clinical
Simulation
Power (Simulation)
0
10
20
30
40
50
60
70
60 70 80 90 100
Albu
min
(g/L
)
Age (y)
Albumin (males)
Simulated
Veering
Verbeeck
Campion
Parkinson et al 2004 CYPs
0
0.2
0.4
0.6
0.8
1
1.2
Elde
rly :
Youn
g C
L ra
tio
In house testing
Obs
Pred
Scaling from in vitro: drug data vs systems data
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Jmax/Km orCLuint T
SF 1:
H-BMvIn vitroCLuint, T
CLuint, Tper
g Brain
SF 2: SF 3:
CLuint, Tper Brain @ BBB
REF/RAFH-BMv H-BMvPGB Brain Weight
BrainIn vitro data
Jmax/Km orCLuint T
SF 1:
HHEPIn vitroCLuint, T
CLuint, Tper
g Liver
SF 2: SF 3:
CLuint, Tper Liver
REF/RAFHHEP HPGL Liver Weight
LiverIn vitro data
served.
In vitro dataJmax/Km or
CLuint T
SF 1:
PTCIn vitroCLuint, T
CLuint, Tper
g Kidney
SF 2: SF 3:
CLuint, Tper Kidney
REF/RAFPTC PTCPGK Kidney Weight
Kidney
Caco-2, MDCK- II, LLC-PK1 etc.Jmax/Km or
CLuint T
SF 1:
CLuint, T
In Jejunum I
Intestine
REF/RAFJejunum I
Scaling via the Permeability and
Surface area product
Replacement / Additional Organ
Jmax/Km orCLuint T
CLu, Tper whole
organ
User needs to scale to whole organ!
SF: Scaling Factor
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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
22
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Modelling in vitro assays – a must to do!
2 Compartment Model
Intracellular Volume
Free [S]EC
Incubation Medium Volume
Active UptakeCLint · Km · [S]EC(t)
Km + [S]EC(t)
CLPD· [S]EC(t)
CLPD · [S]IC(t)
[S]IC
fucell
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 Schizophrenia
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Quantitative IVIVE of Tissue Toxicity Supported by European Commission 7th FP Predict-IV Grant
25Hamon et al., Toxicology in Vitro, 2015
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Summary
• In a systems pharmacology paradigm, the bottom-up approach tomodeling and simulation of the ADME processes of a chemical, is avaluable tool in integrating available prior information and improvingdecision making.
• Improvement in the in vitro systems which can act as surrogates for invivo reactions relevant to ADME
• Advances in the understanding of the extrapolation factors
• Advances in the development of mechanistic models of the humanbody
• Facilitate predicting PK characteristics in a wide range of healthy ordisease populations accounting for age, sex, ethnicity, genetic, etcvariability
• Moving towards PBPK coupling with systems biology models to predicttoxicity endpoints/biomarkers and their associated variability from invitro data
26