Use of in vitro metabolism data in PBPK models

Use of In Vitro Metabolism Data in
PBPK Models
A Course on Physiologically Based Pharmacokinetic (PBPK)
Modeling and In Vitro to In Vivo Extrapolation
April 19 - 23, 2010
Center for Human Health Assessment
Program in Chemical Safety Sciences
Copyright 2010 by The Hamner Institutes for Health Sciences. May not be reproduced without permission
Overview
Metabolism in PBPK Model
In Vitro Systems
IVIVE - Scaling
IVIVE Examples: Use of parameters
derived in vitro into PBPK models
Summary
1
Value of In Vitro Metabolism Data
Application of the PBPK model for risk assessment by linking
an external dose with the target tissue concentration (internal
dose)
Metabolism is an important determinant for chemical
biokinetics in the body, hence one of the key components of a
PBPK model
Extending the limited human biokinetic information including
metabolism by in vitro studies using preparations from human
tissues
PBPK model as a tool to integrate in vitro metabolism
information in a proper context
Describing Metabolism in the PBPK model
Simplified representation of the kinetics of
metabolism
The rate and the product maybe more important
than the specific metabolizing enzyme for the
purpose of PBPK modeling and risk assessment
What are your goals?
– Tissue dose & response in the target
– Interpreting biomonitoring data
– Variability in human population
2
Incorporation of In Vitro Data to PBPK model
Identification of key metabolism pathway(s) in vitro &
measurement of rates of metabolism
In vitro kinetic model: Simplified representation of the
whole metabolism kinetics
IVIVE of in vitro kinetic parameters
Incorporate the scaled metabolism kinetic parameters
in PBPK model
In Vitro Systems for Metabolism Study
Precision-cut
Tissue slice
Homogenation
& centrifugation
at 9000g
S9
Integrated metabolism
(Phase I &II, soluble &
membrane bound)
Intercellular relationship
Collagenase
perfusion
Hepatocytes
Microsomal + cytosolic
Lower metabolic activity
Integrated Metabolism
Currently the closest system to
in vivo
Ultracentrifugation
Cytosol
Expressed Enzymes
SULTs, GSTs, NATs
Epoxide
hydrdolases
Esterases
Microsomes
CYPs & UGTs
Most widely used
Easy to use, low cost,
longer storage possible
Enzymes of interest
rCYPs & rUGTs & others
Lack of in vivo environment
3
Choice of In Vitro system
Precision-cut
Tissue slice
Collagenase
perfusion
Homogenation
& 9000g
Fewer confounding factors
Integrated metabolism
(Phase I &II, soluble & membrane
bound)
Intercellular relationship
Hepatocytes
Easier application &Microsomal
interpretation
+ cytosolic
S9
Integrated Metabolism
Currently the most close system to
in vivo
Lower metabolic activity
Major contributor known
Closer to true in vivo
Ultracentrifugation
Lack of in vivo environment &
Characterization of multi-enzyme
SULTs, GSTs, NATs
Cytosol
complicated extrapolation
to in vivo
& multi-step metabolism
Epoxide hydrdolases
Esterases
Microsomes
Expressed/purified Enzymes
Most widely used
Easy to use,
low cost, longer
Availability
issue
CYPs & UGTs
storage possible,
Complexity
Enzymes of interest
Lack of in vivo environment
Can we talk?
Nifed
Metabolism
in vivo
ipine
initial
1 uM
initial
10 uM
3
2
0
-1
-2
-3
-4
-5
150
Ln Co
)
nc (uM
1
100
50
0
Time
(min)
Metabolism
in vitro
4
Basis for Extrapolation of In Vitro
Metabolism data to In Vivo
The overall rate of enzyme-catalyzed reaction is
directly proportional to the total amount of enzyme
present in the system
Vapp = kapp[S][E]T
Therefore, data generated with an in vitro system
can be extrapolated to in vivo whole body by relating
the total enzyme present in the system
Kinetics of Metabolism
Most chemical metabolism is explained by saturable M-M kinetics
Michaelis-Menten Kinetics
V = Vmax[S]
(KM+[S])
Vmax/2
Typical Michaelis-Menten plot with substrate
[S] vs. velocity (ν) (Lipscomb et al., 2004)
Unit for Vmax
Needs scaling !
= mass metabolite/time/ enzyme mass
Unit for KM
= concentration
Same unit in vitro & in vivo
No scaling required
5
Application as Intrinsic Clearance
At low [S], M-M eq becomes
V =
Vmax [S]
KM
CLint
At the level of environmental exposure relevant for human, in vivo
chemical concentration is usually below KM leading to essentially first-order
metabolism for most situations
In practice, CLint is often measured instead of Vmax and KM based on
measurement of the disappearance of the parent chemical at low
concentration (hopefully below KM)
Compatible in high-throughput screening methods
Most relevant for chemicals that are active in the parent form
More detailed metabolic description required if
- non M-M (atypical) kinetics (e.g., substrate inhibition)
- metabolite(s) toxic entity
Free [S] in the System!
[S] represents FREE (unbound) chemical
concentration !!
[S] = nominal[S] x fu
fu: fraction of the unbound chemical in the system
Fraction of unbound in vitro – in vitro kinetics include
binding to protein & cellular components & non-biological
components of the system (binding to plastic, evaporation,
chemical degradation, etc).
Fraction of unbound in vivo – tissue partitioning & protein
binding in blood
6
PBPK Equations with Metabolism
dAliver/dt = Qliver
(Vmax
dAliver/dt = Qliver
(CLint
(CART - CL/PL )
CL/PL ) /(KM+CL/PL)
(CART - CL/PL )
CL/PL )
Biological Scaling of the
Enzyme Content!
Rate of metabolism =
product/time/ unit enzyme
Rate of metabolism =
product/time/ whole body
7
Typical Units for Specific Activity of Enzymes
in vitro
System
Enzyme Activity
per Unit of Enzyme content*
Expressed Enzyme
pmol/min/pmol enzyme
Subcellular Fractions
(Microsomes, Cytosol, & S9)
nmol/min/mg protein
Hepatocytes
nmol/min/ 106 hepatocytes
Liver
µmol/min/g liver
Whole Body
µmol/min/whole liver
in vivo
*Based on the assumption that the content of enzyme present in each system is
proportional to the amount of the functional unit of the given system. Enzyme
activity is expressed as the amount of product formed/unit time (e.g., nmol/min).
Scaling Factors for Hepatocytes
Hepatocellularity
= number of hepatocytes in intact liver
number of hepatocytes/g liver (HPGL)
IVIVE of hepatocyte data to intact liver
Vmaxin vivo (nmol/min/body) =
Vmaxhepatocyte (nmol/min/ # of cells) x HPGL ( # of cells/g liver) x Liver weight (g)
Calculation step
Unit
nmol/hr/106 cells
Vmax in vitro
x 137
106cells/g liver
HPGL
(Arias et al., 1982)
x 0.026 x 70 x 103
g
Liver weight
2.6 % of BW, BW=70kg
19.0
/ 103
µmol/nmol
nmol to µmol
= 4737
µmol/hr
Vmax in vivo
= 242
µmol/hr/kg BW0.7
Vmax scalar in PBPK model
(for 70kg human)
Example: Oxidation of furan
to cis-2-butene-1,4-dial in
human hepatocytes
(Kedderis & Held, 1996)
8
Scaling Factors for Microsomes
Relationship
between intact liver,
isolated microsomes,
and some CYP
isoforms (Lipscomb &
Kedderis, 2002).
Content of microsomal protein (MSP)
in intact liver
mg MSP/g liver
(MPPGL)
IVIVE of microsomal data to intact liver
Vmaxin vivo (nmol/min/body) =
Vmaxmic (nmol/min/mg MSP) x MPPGL (mg MSP/g liver) x Liver weight (g)
Calculation step
1589
Unit
pmol/min/mg MSP
Vmax in vitro
x 20.8
mg MSP/g liver
MPPGL (Lipscomb et al., 1998)
x 1820
g
Liver weight
mmol/nmol
pmol to mmol conversion
x 109
/ 131.46
mg/mmol
Molecular weight
x 60
min/hr
min to hr conversion
= 474
mg/hr
Vmax in vivo
mg/hr/kg BW
Vmax scalar in PBPK model
(for 70kg human)
= 6.8
Oxidation of
Trichloroethylene in human
hepatic microsomes
(Lipscomb et al., 1998)
HPGL & MPPGL
Scaling Factors for the Extrapolation of In Vivo Metabolic Drug Clearance From In
Vitro Data: Reaching a consensus on values of human microsomal protein and
hepatocellularity per gram of liver (Barter et al., 2007)
Human MPPGL - 32 mg/g (95% CI; 29-34 mg/g)
Human HPGL - 99 x 106 cells/g (95% CI; 74-131 mg/g)
9
Scaling Factors for Expressed Enzymes
Intrinsic activity of the expressed enzyme is not equivalent to
the one in vivo (endogenous) due to
- Differences in accessory proteins (e.g., NADPH cytochrome
P450 reductase & cytochrome b5)
- Different lipid environment (e.g, different degree of
microsomal binding)
Use of Relative Activity Factor (RAF) to convert the activity
level of a specific enzyme in the expressed system to the
activity level of this enzyme in the endogenous system (e.g.,
liver microsomes)
- Enzyme-selective marker substrates in both endogenous
and recombinat systems (Proctor et al., 2004 ; Lipscomb &
Poet, 2008)
Scaling Factors for Expressed Enzymes
Relative Activity Factor (RAF)
RAF =
RAF =
Vmax endogenous microsomes
Vmax expressed enzyme
pmol/min/mg endogenous MSP
pmol/min/mg recombinant system MSP
or
RAF =
pmol/min/mg endogenous MSP
pmol/min/pmol recombinant enzyme
Intersystem Extrapolation Factor (ISEF)
ISEF =
Vmax endogenous microsomes/CYPabundance
Vmax expressed enzyme
Intersystem Extrapolation Factor (ISEF) : combination of RAF and specific
content information to address interindividual variability in enzyme
expression (e.g., CYPs)
Both RAF and ISEF can be expressed with CLint especially when incorporating
KM difference between the recombinant and endogenous systems
Variability in RAFs depending on the probe substrate
(Proctor et al., 2004; Lipscomb & Poet, 2008)
10
Scaling Factors for Expressed Enzymes
Expressed enzyme system
product formed/time/pmol enzyme or product formed/time/mg MSP
CYPs : Specific content
information available
Use of ISEF
UGTs & others: Specific
content information NOT
available
Use of RAF
Vmax in vivo
Vmax in vivo
= ISEF x VmaxrCYP
= RAF x VmaxrEnz
x CYPabundance
x MPPGL
x MPPGL
x Liver weight
x Liver weight
CYP Abundance
CYP abundance at the population level
pmol CYP isoform/mg MSP
Not all the CYPs are evaluated, hence not all the CYP-mediated metabolic
pathways are covered
(Rostami-Hodjegan & Tucker, 2007; Inoue et al., 2006)
11
Addressing Variability in Humans
If variability information is known for enzyme
expression: Use of pooled sample for representative
mean behavior of the population + known information
for variability in enzyme expression (e.g., pooled HLM or
hepatocytes + CYP abundance data)
If variability information is not available: Use of
individual HLMs or hepatocytes from multiple donors
from the population of interest (e.g., UGTs & esterases
etc.) to provide a starting point for Monte-Carlo Analysis
Example for IVIVE of Expressed CYPs to intact liver: 1Hydroxylation of Estragole
RAFP450 =
VmaxHLM (pmol/min/mg HLM MSP)
VmaxP450, Gen (pmol/min/mg Gentest MSP)
• RAFP450 corresponds to the RAF of a P450 enzyme in
different individual human subjects.
• Interindividual variability in overall 1-hydroxylation rate can
be reflected from the variation in RAFP450 s in individuals.
(Punt et al., 2010)
12
Example for IVIVE of Expressed CYPs to intact liver: Estragole hydroxylation
Overall 1-hydroxylation rate by an individual HLM
v = (Vmax1A2, Gen x RAF1A2) xCE/(KM1A2 +CE)
+ (Vmax2A6, Gen x RAF2A6) xCE/(KM2A6 +CE)
+ (Vmax2C19, Gen x RAF2C19)xCE/(KM2C19 +CE)
+ (Vmax2D6, Gen x RAF2D6) xCE/(KM2D6 +CE)
+ (Vmax2E1, Gen x RAF2E1) xCE/(KM2E1 +CE)
Calculated 1-hydroxylation rate by CYP1A2 in
HH837 at 1000µM estragole
= (Vmax1A2, Gen x RAF1A2)xCE/(KM1A2 +CE)
= 2.44 x 65 x1000/(11+1000)
= 157
(Punt et al., 2010)
Example for IVIVE of Expressed CYPs to intact liver: Estragole hydroxylation
We will see how the variation in metabolism would affect the PBPK output
for target tissue dosimetry in the examples.
(Punt et al., 2010)
13
Scaling Factors for Other In Vitro Systems
Cytosol and S9
- Similar approach as microsomes based on protein
amount in the system
- Lack of information on scaling factors for cytosol & S9
• Cytosolic protein about 4 fold higher than
microsomal protein (Bjorntorp et al., 1965)
• S9 fraction contains ~ 5 fold more cytosolic proteins
than microsomes (Komatsu et al., 2000)
Tissue Slice
- Direct application to nmol/min/g liver or
nmol/min/mg protein in homogenate
Summary of Scaling Processes
System
Vmax in the System
Scaling factors
to whole body
Expressed
Enzyme
pmol/min
/pmol enzyme
(ISEF xCYPabundance or RAF) x
MPPGL x LWb
Microsomesa
nmol/min
/mg protein
MPPGL x LW
Hepatocytes
nmol/min
/ 106 hepatocytes
HPGL x LW
Liver
nmol/min/g liver
LW
Whole Body
nmol/min/whole liver
a Scaling
based on subcellular fraction protein amount also applies for
cytosol & S9.
b Liver weight.
14
Other Issues
Extrahepatic metabolism:
Lack of scaling factor data
Simultaneous description with uptake mechanism often
necessary (portals of entry)
Homogenous vs heterogeneous tissues: Localized
distribution of metabolic enzymes compared to the liver
- Cell type specific expression
- Spatial distribution of enzymes
Need to know what your in vitro system represents &
how to describe it in the PBPK model
Ready for IVIVE for PBPK model?
Vin vitro =
Vmaxin vitro x [S]in vitro
(KM + [S]in vitro)
dAliver/dt = Qliver x (CART – CVL) -
Vmaxin vivo x CVL
(KM + CVL)
15
Example1: Estragole PBPK model
Backgrounds:
Estragole
– Hepatocarcinogenic in animals
– formation of the ultimate genotoxic metabolite 1’-sulfooxyestragole
through CYPs & SULTs
Evaluation of species differences in bioactivation of
estragole between the human and rat observed in vitro
using PBPK model
Evaluation of human interindividual variation in
bioactivation of estragole using PBPK model
(Punt et al, 2009 & 2010)
Bioactivation
Detoxication
DNA adducts
17-ß-HSD2 (2)
+ NAD+
CYPs
+ NADPH
CYPs
+ NADPH
CYPs
+ NADPH
CYPs (1)
+ NADPH
SULTs
+PAPS
UGTs
+ UDPGA
(1) 1-hydroxylation mainly via CYP1A2 & 2A6 at dose level
relevant for human exposure (Jeurissen et al., 2007)
(2) NAD+ dependent oxidation of 1-hydroxy mediated by
17ß-hydroxysteroid dehydrogenase type 2
(Punt et al, 2009)
16
Human PBPK model structure for Estragole
(Punt et al, 2009 )
Kinetic Analysis of In Vitro Data:
CYPs mediated Detoxication &
Bioactivation
Estragole as a substrate in human
microsomes
• NADPH cofactor
• M-M kinetics observed
1’-hydroxyestragole
Estragole-2,3-oxide
3-hydroxyanethole
4-allylphenol
(Punt et al, 2009)
17
Kinetic Analysis of In Vitro Data: Fate of the proximate carcinogen, 1hydroxyestragole
1-Hydroxyestragole as a substrate in Human
Microsomes or S9
UDPGA cofactor
M-M kinetics for 1-hydroxyestragole
glucuronide
NAD+ cofactor
M-M kinetics for 1-oxoestragole
PAPS cofactor
M-M kinetics for 1-Sulfoxyestragole
Human
KM = 708 µM
Vmax = 0.3 nmol/min/mg MSP
KM = 354µM
Vmax = 4.9 nmol/min/mg MSP
KM = 727 µM
Vmax = 7.4pmol/min/mg
S9 protein
Human
(Punt et al, 2009)
Scaling in vitro values to in vivo: Understanding the role of the balance
between bioactivation & detoxication in estragole toxic metabolite formation
Species Differences in Bioactivation &
Detoxication In Vitro!
Scaling factors
(Punt et al, 2009)
18
Would the observed species differences in metabolism in vitro be
translated into in vivo species differences in bioactivation of
estragole between the rat and the human?
Internal dose metric for
interspecies comparison:
amount of carcinogenic
metabolite formed in the
liver
Less than 2-fold!
typical human dietary exposure
(Punt et al, 2009)
Where to look for inter-individual variability?
Formation of the carcinogenic
metabolite is highly dependent on
the kinetics of formation of 1hydroxy metabolite & subsequent
oxidation to 1-oxo- metabolite as
well as the kinetics of 1sulfooxyestragole.
(Punt et al, 2009)
19
Interindividual Variation in formation of the
proximate carcinogen
Based on individual enzyme kinetic data for 1-hydroxylation (activation pathway, the
example we used for application of RAFs) & 1-oxoestragole formation (detoxication
pathway, NAD+ dependent oxidation) from 14 donors
about two fold
difference
greater variation in 1-hydroxylation than
oxidation of 1-hydrdoxymetabolite
Interindividual Variation in
formation of the proximate
carcinogen
(Punt et al, 2010)
1-Hydroxyestragole
concentration in the liver
about two fold
difference
1-Hydroxyestragole AUC in
the liver
Monte-Carlo Simulation on the Variability in
Human Liver Levels of 1-Hydroxyestragole
Based on variability information from 14 individuals PLUS
known CYP variability at population level
(Punt et al, 2010)
PBPK predicted target tissue
dosimetry of the proximate
carcinogen at human dietary
exposure level to estragole
20
Example 2: Furan PBPK model
Backgrounds:
• Furan
• A volatile solvent
• Hepatotoxic & hepatocarcinogenic
• Bioactivation to cis-2-butene-1,4-dial via CYP2E1
• Evaluation of the effect of enzyme induction on
target tissue dosimetry of furan and other CYP2E1
activated VOCs using a PBPK model
• Importance of hepatic blood flow limitation
(Kedderis et al., 1993 & 1998)
Furan Metabolism Kinetics In Vitro
PBPK model for Furan Inhalation
In Vitro Metabolism Model for Furan
Bioactivation by Freshly Isolated
Hepatocytes.
Furan to cis-2-butene-1,4-dial is shown
(Kedderis et al., 1993 & 1998).
21
Kinetic parameters for furan biotransformation in
hepatocytes: Impact of Vmax differences
10x
Vmax
Simulation of the effect of
increasing Vmax on the liver
concentration of the toxic
metabolite of furan in humans.
(Kedderis et al., 1998)
43
Hepatic blood flow limitation on furan
bioactivation
<<
Near or less than KM

Rate of furan
metabolism far exceeds
the rate of furan delivery
to the liver!
Metabolism rate = Vmax/KM
(Kedderis & Held, 1996)
22
Hepatic blood flow limitation for other VOCs that are rapidly
bioactivated by CYP2E1
 The hepatic blood flow limitation will dampen or eliminate the effects of
interindividual differences in enzyme expression due to differences in genetics
(polymorphisms) or enzyme induction
(Kedderis, 1997)
Summary
A PBPK model provides a tool to integrate
in vitro metabolism data in proper in vivo
context
The combined application of human in
vitro systems and PBPK analysis of the data
can provide useful insights for the
development of human health risk
assessment.
23
Understanding the Metabolism of the
Chemical is Essential
Identification of key metabolism pathway(s) in vitro &
Measurement of rates of metabolism
In vitro kinetic model: the simplified representation of the whole
metabolism kinetics
IVIVE of in vitro kinetic parameters
Incorporate the scaled metabolism kinetic parameters in PBPK
model
The Key for the Extrapolation of In Vitro to
In Vivo is Proper Scaling!
Identification of key metabolism pathway(s) in vitro & Measurement
of rates of metabolism
In vitro kinetic model: the simplified representation of the whole
metabolism kinetics
IVIVE of in vitro kinetic parameters
Incorporate the scaled metabolism kinetic parameters in PBPK
model
24
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Inoue, S, Howgate, EM, Rowland-Yeo, K, Shimada, T, Yamazaki, H, Tucker, GT, and Rostami-Hodjegan, A (2006),
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