The document discusses challenges in extrapolating in vitro transporter data to in vivo predictions using physiologically-based pharmacokinetic (PBPK) models. It analyzes in vitro data for the P-gp substrate digoxin from three studies, finding that parameters like Km can be estimated more accurately using a modelling approach. Proper extrapolation requires considering factors like transporter expression levels, tissue-specific abundances, and milieu differences between in vitro and in vivo systems. Transporters play an important role in processes like absorption, distribution, and drug-drug interactions that must be captured through in vitro-in vivo correlations.

1. Challenges of transporter IVIVE
Digoxin example
Maïlys De Sousa Mendes 04/04/2019
2019 PBPK Symposium - Paris

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What do Transporters do?
High  Effect of transporters
CELLExtracellular
space
[Cu] [Cu]
Passive permeability
Equilibrative transport
e.g. SLC (ENTs)
[Cu] [Cu] Concentrative Uptake transport
e.g. SLC (PEPTs, OCTs, OATs, OATPs)
CL, V,
tissue levels
[Cu] [Cu]
Efflux transport
e.g. ABC (P-gp, BCRP, MRPs) and some SLC (OATPs, MATEs)
tissue levels
Low  Effect of transporters

X
Passive membrane permeability
0.0
0.5
1.0
0.01 0.1 1 10 100 1000
fa
Dose [mg]
Intestinal uptake
example
0.0
0.5
1.0
0.01 1 100 10000
fa
Dose [mg]
Efflux

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Clinical Effects of Transporters:
Non-Linear Absorption
Non-Linear Clearance and Vss
Drug-Drug Interactions (Inhibition and Induction)
Species Differences
Pharmacodynamics
Toxicity
Why are Transporters Important?
3

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In Vitro - In Vivo Extrapolation (IVIVE) of Transporter
In vitro
assays
In vitro
parameters
In vivo
parameters
In vivo PK
Analyse of the raw data:
What are the assumptions
IVIVE
depends on
the parameter
and the organ
PBPK model
Physchem
Distribution
Metabolism
solubility
Verification:
Saturation?
DDI?
Genetic?
4
Which in-vitro assay?
Optimal design?

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In-Vitro Parameters
The key parameters we need from in vitro assays to
describe the effects of transporters are:
Passive: Papp, CLPD or PS
Active: Km, Jmax or CLint, T
Analysis of in vitro data is complex and it may be necessary to use modelling and simulation to
account for phenomena not seen in standard in vitro metabolic systems (e.g. disappearance CLint)
We assume saturable Michaelis-Menten kinetics:
 Is this the correct assumption for all transporters?
 Is this correctly built into the model, i.e. has the passive
permeation been accounted for as well.
5

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Things to Consider with in vitro Transporter Data
Membrane
Transporter
Transport direction
C initial, donor
Concentration
not relevant Relevant
Concentration!
b) How to measure intracellular concentrations (and/or fucell) for efflux transporters?
Things to consider:
c) Inside-out vesicles? Need to maintain sink conditions.
d) Ratio of inside-out versus right-site out vesicles in the assay
a) Where and what is the relevant concentration?
6

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Transwell Assays: Conventional analysis (to get P-gp Jmax and Km)
AB
BA
Apical
Basolateral
Apparent passive permeability (Papp)
dt
dA
J  Where dA/dt is the amount transported per time
interval and represents the slope of the conc.-time
curve.Flux
[S]SA
J
appP  Where J is calculated via linear regression analysis of
cumulative drug permeation as a function of time.
Apparent permeability
92
94
96
98
100
102
1 10 100 1000
Papp,AB
Apical Concentration
Papp,max
Papp, min
Kmapp
92
94
96
98
100
102
1 10 100 1000
Papp,AB
Apical Concentration

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Transwell Assays
• Conventional analyse assumptions
o Kinetic parameters are determined on the basis of media concentrations.
• Wrong concentration for active efflux which is dependent on binding within the cell.
o Sink conditions are assumed, therefore need to be maintained experimentally
o Passive permeability not separately accounted for in the analysis
• Consequences: example of effect of changing transporter expression on Km
(Tachibana et al., 2010)
Apparent correlation between P-gp protein expression and
Km(app) value observed for three P-gp substrates
(Shirasaka et al., 2008)
quinidine
verapamil
vinblastine
Reanalysis of the same experimental data using mathematical model: Km
values defined for the intracellular concentration were almost the same
among the cells expressing different P-gp levels
8

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How is the Km influenced?
Passive permeation!
concentrationKm
concentration
Km not changed
concentration
Km change
Concentration in the donor wellConcentration at the
‘transporting’ binding side!
Jmax(pmol/min/mg)
Jmax
Changed
Depending on the data analysis techniques used, different conclusions can be drawn from the same data

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• Modelling can help make the most out of the data obtained from in vitro
systems used to study transporters.
• Know your in vitro system:
• Modelling of the ‘relevant’ concentration is necessary
• Transporter abundance and activity
• Fixed parameters (system parameters: Hepatocyte volume per well, No.
hepatocytes per well, Volume incubation media)
• Be aware of what parameters should be fitted:
• Passive permeability, Km, CLint,T, fucell, etc.
Modelling of In Vitro Assays Helps!

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Three compartment model – Transwell® Apparatus
Bidirectional passive permeability across apical and basolateral membranes
Active apical efflux driven by drug concentration in cell
Drug concentration dynamically modelled (dC/dt) in each compartment
Green = Drug parameters
Blue = System parameters
Substrate in apical media
[S]ap, Vap
Passive Permeability
Substrate in basolateral media
[S]bl, Vbl
Substrate in cell
[S]cell, Vcell
Active Efflux
11
Simcyp In Vitro Analysis (SIVA)
toolkit

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Digoxin example
12
• Drug properties
o Digoxin is a probe P-gp substrate
o MW = 780 g/mol
o Neutral within the physiological pH range, logP = 1.26
o Low metabolism
o Elimination: renal and biliary excretion
• 3 in vitro datasets:
o Troutman and Thakker 2003
o Neuhoff 2005
o Meng et al. 2016

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Troutman and Thakker 2003
13
Experimental details:
• Caco-2 cells 12 wells
• 8 concentrations (0.1-500 uM)
• Papp calculated when linear rate and sink
conditions are respected

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Troutman and Thakker 2003 – SIVA analysis
Conventional
Absorptive (AB)
Conventional
Secretory (BA)
SIVA analysis
Mean ± SD Mean ± SD Mean (CI)
PPD (106 cm/s) 7.74 ± 0.56 6.49 ± 1.3 20.6 (19.2-22.1)
Km (µM) 1150 ± 179 177 ± 9.2 50.7 (2-99)
Jmax (pmol/min) 718 ± 2.38 434 ± 97.4 570 (388-752)
CLint,T (µL/min) 0.62 2.45 11.24
14
• Lower Km obtained when fitting the in vitro data
• Higher CLint,T
Passive diffusion
Active

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Neuhoff 2005 (PhD thesis)
Experimental details:
• Caco-2 cells, 12 wells
• 4 concentrations (0.059, 1, 10, 100 µM)
• Apical and basolateral volumes of 0.5 and 1.5 mL
• Raw data available
• Samples collected at 5,15, 25, 50, 80, and 120 min
• Sampling of A-B experiments was conducted by moving the Transwell insert to a
new well containing blank buffer and retaining the previous well
• Sampling of B-A experiments was conducted by replacement of 400 µl of apical
buffer.
• The impact of sampling on the concentrations measured was accounted for in the
model.
15

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Neuhoff 2005- Estimation of km using SIVA
16
Note: Km estimated
closed to the maximal
intracellular estimated
Estimate 5th 95th
PPD (10^-6 cm/sec) 34.1 33.34 34.87
Km,u (µM) 24.97 0 66
Jmax (pmol/min) 404.46 0 816.5
AIC
817.8

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Neuhoff 2005- fixed km
17
Km fixed to 50.7 µM
Good confidence interval for the other
parameter and good fit of the data
Estimate 5th 95th
PPD (10^-6
cm/sec) 33.87 33.12 34.6
Km,u (µM) 50.7* - -
Jmax
(pmol/min) 766.8 678 855
AIC
826.6

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Meng et al. 2016
Kinetic of different P-gp substrate in Caco-2 cells
Active transporter fitted (T0)
Modelling approach:
-the drug in the membrane interact with P-gp
-the drug in the membrane is estimated with a coefficient partition
-basolateral (and additional uptake transporter) are assumed
bidirectional
Digoxin: concentrations 0.1, 0.3, 1, 3, and 10 µM
They found out that digoxin needed a basolateral transporter
(bidirectional) to fit the data
P-gp:
Other transporters:
18

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Meng et al. 2016 – SIVA Estimation of Km
19
Reasonably well fitted but CI out!
Rq: Km obtained by
Meng et al. 1.18 uM
Km estimated close to the
maximal intracellular estimated
Estimate 5th 95th
PPD (10^-6 cm/sec) 29.9 28.2 31.6
Km,u (µM) 2.1 0 429.3
Jmax (pmol/min) 13724.2 0 2.75E+06
AIC
440.6

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Fixing Km to 50.7 µM obtained fromTroutman and Thakker 2003
20
Concentrations too low to saturate the transporter
Good fit, reasonable CI
Lower AIC (better fit of the data)
A basolateral transporter doesn’t seem needed
Estimate 5th 95th
PPD (10^-6 cm/sec) 39.7 38.0 41.3
Km,u (µM) 50.7* - -
Jmax (pmol/min) 645.1 569.3 720.9
AIC
352.2

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Summary- In vitro parameter
Troutman and
Thakker 2003
Neuhoff 2005 Meng et al. 2016
PPD (10-6 cm/s) 20.6 34.1 34.6 29.9 39.7
Jmax (pmol/min) 570 404.46 766.8 13724 645.12
Km (µM) 50.7 24.97 50.7* 2.1 50.7*
CLint,T (uL/min) 11.24 16.19 15.12 6535 12.72
Transport expression method WB, REF (2.03) to
jejunum
Abundance per well (pmol) 0.0353 0.95
Jmax (pmol/min/pmol Transporter) 16147 14600 686.29
CLPD (mL/min/million cells) 0.023072 0.0072 0.00953
21
*Fixed
/ Abundance per well
xSA/cell density
x60/1000000

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In vitro parameter - conclusion
• In vitro setting:
o Several concentrations with at least 2 concentration above the Km (100, 200 µM
and 300 µM)
o Too high concentration will lead to solubility issue and should be avoid (below
500-800 µM)
• Next:
o Meng et al. paper was the only one with direct measurement of absolute
abundance
o The concentrations used were to low to estimate areliable Km value therefore
the Km was fixed based on Troutman and Thakker dataset
o No saturation is seen in vivo and the simulated intracellular concentration are
below 0.01µM
22

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In Vitro - In Vivo Extrapolation (IVIVE) of Transporter
In vitro
assays
In vitro
parameters
In vivo
parameters
In vivo PK
Analyse of the raw data:
What are the assumptions
IVIVE
depends on
the parameter
and the organ
PBPK model
Physchem
Distribution
Metabolism
solubility
Verification:
Saturation?
DDI?
Genetic?
23
Which in-vitro assay?
Optimal design?

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In Vitro - In Vivo Extrapolation (IVIVE) transporter scalars
Relative Scalars Activity-Abundance Organ-Based
Relative Expression/Activity
Factor (REF/RAF)
•Protein* (relative, absolute)
•mRNA
•In Vivo/In Vitro Expression or
Activity Ratio - CLint-T, Jmax
Inter-System Extrapolation Factor-
Transporters (ISEF-T)
Disconnection between the in-vitro
and in-vivo abundance-activity
correlation
•Sequestration/redundancy
•In vitro-In vivo milieu
disconnection
•Hepatocytes per gram of liver
(HPGL)
•Total Membrane Protein Per Intestine
(TMePPI)
•Proximal tubule cells per gram of
kidney (PTCPGK)
•Human Brain Microvessels Per Gram
of Brain
(H-BMvPGB)
Harwood et al., 2013 Biopharm Drug Dispos., 34(1):2-28
Variability
* Neuhoff et al., 2013

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Scaling to in vivo: Gut
25
Caco-2, MDCK- II, LLC-PK1
etc.
Jmax/Km or CLuint T
CLuint, T
In Jejunum I
Intestine
ISEF,TJejunum I
Abundance
pmol
transporter
per filter
Correction
pmol/mg total
membrane protein
Absolute scaling approach
Scaling needs to account for:
- Transporter expression/activity in vitro
- Transporter expression/activity in vivo
- Regional differences

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Input units:
Scaling to in-vivo : Liver
HEK-293,
HepaRG,
Oocytes
Jmax/Km
CLuint T Scaling
Factor 2
HHEP
CLuint, T
CLuint, T
per
g Liver
Scaling
Factor 3
Scaling
Factor 4
pmol transporter
? ? ?
µL.min-1
pmol transporter
106 hepatocytes
• Scaling via absolute abundance
HPGL
Hepatocytes per
gram of liver
Liver
Weight
Cluint, T
per
Liver
?
ISEF, THHEP
Scaling
Factor 1
CLuint, T
106HEP
P-gp: 0.201 pmol/106
hepatocytes
0
50
100
150
25 45 65
HPGL(x106cells.g-1) Age (years)
0
10
20
30
40
50
1 25 45 65
MPPGL(mg.g-1)
Age (years)
BSA (m2)
Johnson et al., 2005
Barter et al., 2007
Burt HJ et al., Drug Metab Dispos. 2016
Absolute transporter protein abundance data from
a meta-analysis of 9 published studies that used
LC-MS/MS or quantitative immunoblotting
methods.

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𝐶𝐿𝑖𝑛𝑡 =
𝑗=1
𝑛
𝐼𝑆𝐸𝐹, 𝑇𝑗 ∙ 𝐽max,𝑗 ∙ 𝑋𝑗
𝐾 𝑚,𝑗
∙ 𝐻𝑃𝐺𝐿 ∙ 𝐿𝑖𝑣𝑒𝑟 𝑊𝑒𝑖𝑔ℎ𝑡
IVIVE with abundance data for transporters:
Absolute transporter abundance in
the target population
pmol transporterj per million hepatocytes
j transporter
Scalars in units of pmol of transporter
(as for CYP and UGT scaling)
Rate of transport
pmol/min/pmol transporterj

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Scaling to in-vivo: Kidney
HEK-293,
CHO etc.
Jmax/Km
CLuint T
SF 1
HPTC
CLuint, T
CLuint, T
per
g Kidney
SF 2 SF 3
CLuint, T
per
Kidney
REF/RAFPTC PTCPGK
Proximal tubule
cells per gram
of kidney
Kidney WeightInput Units
So far fitted IVIVE scalars:
Metformin OCT2 = 3
Metformin MATEs = 3
Cimetidine OCT2 = 3
Cimetidine MATEs = 3
Burt et al., 2016
Only relative scaling approach available
REF of 3 used for P-gp as well

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PBPK model input
Organ/Tissue Gut
Jmax (pmol/min/pmol Transporter) 686.290
Km (µM) 50.700
ISEF,T 1.000
Ptrans,0 (10-6
cm/s) 39.7
Organ/Tissue Liver
Jmax (pmol/min/pmol Transporter) 686.290
Km (µM) 50.700
System User
ISEF,T 1.000
CLPD(mL/min/106
cells) 0.00953
Organ/Tissue Kidney
Jmax (pmol/min/million cells) 645.120
Km (µM) 50.700
System User
RAF/REF 3.000
CLPD basal (blood-to-cell) (mL/min/106
) 0.00953
29
Transporter inputs from Meng et al., the rest is the Simcyp digoxin PBPK model values
No change in the transporter
activity was assumed for the gut
and the liver
Assumes that the permeability per
million cells is the same in the caco-2
cells, liver and the kidney

30. © Copyright 2019 Certara, L.P. All rights reserved.
In Vitro - In Vivo Extrapolation (IVIVE) of Transporter
In vitro
assays
In vitro
parameters
In vivo
parameters
In vivo PK
Analyse of the raw data:
What are the assumptions
IVIVE
depends on
the parameter
and the organ
PBPK model
Physchem
Distribution
Metabolism
solubility
Verification:
Saturation?
DDI?
Genetic?
30
Which in-vitro assay?
Optimal design?

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Performance verification
31
Ritonavir Inhibits P-gp Kirby
et al., 2012
DDI –P-gp Inhibition
Values above bars 90% CI Geomean
0
0.5
1
1.5
2
CmaxRatio(Geomean)
Observed Predicted
PK profile
0
5
10
15
20
25
30
35
40
0 20 40 60
SystemicConcentration
(ng/mL)
Time (h)
Infusion 1mg
Johnson et al. 1987
0
1
2
3
4
5
6
7
0 4 8 12 16 20 24
SystemicConcentration
(ng/mL)
Time (h)
Hayward et al. 1978, Oosterhuis et al. 1991
0.1
1
10
100
0 20 40 60
SystemicConcentration
(ng/mL)
Time (h)
Infusion 1mg
0.1
1
10
0 4 8 12 16 20 24
SystemicConcentration
(ng/mL)
Time (h)
Oral dose 1mg Oral dose 1mg
0
0.5
1
1.5
2
AUCRatio
Observed Predicted

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Performance verification
Rifampicin Induces P-gp (x3.5)
Greiner et al., 1999
DDI –P-gp Induction
Turnover data for transporter are sparse
Intestinal P-gp was induced by 3.5 fold
after rifampicin administration (Westernblot
of biopsy)
32Taipalensuu et al., 2004
P-gp in Caco-2 using Digoxin:
Linear correlation between activity and
abundance?
Limits? REF of 3.5 to simulate
induction

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Conclusion
• Knowing your in vitro system is important
• Be aware of the assumptions made when analysing your data
• Modelling your in vitro data can help
• Each transporter needs to be scaled according to its location and function
• Absolute abundance of transporter is available in certain tissues and can help the
IVIVE
33
IVIVE of transporter is still a work in progress and understanding better their
mechanisms as well as the difference between in vitro and in vivo system, the impact
of a disease or the interspecies difference is required

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Thanks
• Sibylle Neuhoff
• Matthew Hardwood
• Howard Burt
34

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Questions?Questions?
35

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Appendix 1: P-gp Ki shift
PAGE 2017
In-vitro P-gp inhibition data required an
average fold decrease of 94-fold (19.5
fold considering only lowest in vitro
values for each inhibitor) to recover the
invivo interaction with digoxin.
36