Medicine Design
Navigating Transporter Sciences in
Pharmacokinetics Characterization using
Extended Clearance Classification System
(ECCS) Manthena Varma Med Design, Pfizer, Groton CT
This document provides an outline of a presentation and is incomplete without the accompanying oral commentary and discussion. Conclusions and/ or potential strategies contained herein are NOT necessarily endorsed by Pfizer management. Any implied strategy herein would be subject to management, regulatory and legal review and approval before implementation.
SSX India-2018
Oct 11th 2018
Medicine Design 2
MATE
Transporters and Enzymes – Drug Clearance
Major proteins
- Uptake transporters
- Efflux transporters
- CYPs
- UGTs
- Other enzymes
Medicine Design 3
Permeability cut off =
• 5 X 10-6 cm/sec
Ionization:
• Acids/Zwits vs Bases/Neutrals
Molecular Weight cut off:
• 400 [Class 1/3]
Extended Clearance Classification System [ECCS] Clearance mechanism (rate-determining step)
• Varma et al., Pharm Res. 2015, 3785-3802 • El-Kattan and Varma. Drug Metab Dispos. 2018, 729-739. • Varma et al., Adv Drug Deliv Rev. 2017 Jul 1;116:92-99. • Varma et al., Clin Pharmacol Ther. 2017, 33-36 • El-Kattan et al., Pharm Res. 2016, 3021-3030. • Varma and El-Kattan. J Clin Pharmacol. 2016, S99-S109.
Introduction
Medicine Design
ECCS Class Intestinal Transporters Hepatic uptake transporters
Renal secretory transporters
IA P-gp, BCRP (OAT2)
1B P-gp, BCRP OATP1B1, OATP1B3
2 P-gp, BCRP
3A P-gp, BCRP (PEPT1) OAT1, OAT3
3B P-gp, BCRP (PEPT1, OATP2B1) OATP1B1, OATP1B3 OAT1, OAT3
4 P-gp, BCRP (OATP2B1) OAT1, OAT3, OCT2, MATEs
Lumen
BCRP
Enterocytes
- SLC transporters
- ABC transporters
Blo
od
Inte
stin
e
Basolateral
Apical
P-gp
OAT1
OAT3
OCT2
OATP1B3
OATP1B1
Lum
en
Kid
ney
Urine
Liv
er
Hepatocyte
P-gp
BCRP
Bile
CYPs/others
CYPs/others
R
OH
R
Bases/NeutralsAcids/Zwitterions
Hig
h P
erm
eab
leL
ow
Perm
eab
le
Class 1
Class 3
Class 2
Class 4
Metabolism
Renal clearance
Class 1A Class 1B
Metabolism Hepatic uptake
MW ≤400 MW >400
Class 3A Class 3B
MW ≤400 MW >400
Renal
clearance
Hepatic uptake
(or) Renal
clearance
R
OH
R
R
OH
R
ECCS Class and Drug Transporters
Varma et al. CPT 2017, 33-36.
MATEs
Medicine Design
Transporter Victim DDIs with probe inhibitors Clinical probe inhibitors
Varma et al. CPT 2017, 33. El-Kattan & Varma, DMD, 2018, 729.
0.5
5
AU
C r
atio (
+ c
yclo
sporine)
B) DDIs with cyclosporine, an OATP1B1/1B3 and P-gp/BCRP probe inhibitor
1x1.25x
2x
5x
1x1.25x
2x
5x
ECCS 1A 1B 2 3A 3B 4
20 -
1 -
20 -
1 -
N= 2 4 7 1 6 3
ECCS 1A 1B 2 3A 3B 4
N= 2 7 14 1 10 9
Lumen
BCRP
Enterocytes Blo
od
Inte
stin
e
Basolateral
Apical
P-gp
OATP1B3
OATP1B1
Liv
er
Hepatocyte
P-gp
BCRP
Bile
CYPs/others
CYPs/others
Lumen
BCRP
Enterocytes Blo
od
Inte
stin
e
Basolateral
Apical
P-gp
OATP1B3
OATP1B1
Liv
er
Hepatocyte
P-gp
BCRP
Bile
CYPs/others
CYPs/others
A) DDIs with rifampicin, an OATP1B1/1B3 probe inhibitor
R
OH
R
Bases/NeutralsAcids/Zwitterions
Hig
h P
erm
eab
leL
ow
Perm
eab
le
Class 1
Class 3
Class 2
Class 4
Metabolism
Renal clearance
Class 1A Class 1B
Metabolism Hepatic uptake
MW ≤400 MW >400
Class 3A Class 3B
MW ≤400 MW >400
Renal
clearance
Hepatic uptake
(or) Renal
clearance
R
OH
R
R
OH
R
Medicine Design
Transporter Victim DDIs with probe inhibitors Clinical probe inhibitors
B) DDIs with cimetidine, an OCT2/MATEs probe inhibitor
A) DDIs with porbenecid, an OAT1/3 probe inhibitor
0.5
1
1.5
2
2.5
3
AU
C r
atio
(+
pro
be
ne
cid
)
ECCS 1A 1B 2 3A 3B 4
N= 0 0 13 16 9 6
1x
1.25x
2x
0.5
1
1.5
2
2.5
3
AU
C r
atio
(+
cim
etid
ine)
ECCS 1A 1B 2 3A 3B 4
1x
1.25x
2x
N= 0 0 3 6 0 16
Blood
MATE2-K
OAT1
OCT2
OAT3
Passive
diffusion
Luminal
MATE1
Nep
hron
Blood
MATE2-K
OAT1
OCT2
OAT3
Passive
diffusion
Luminal
MATE1
Nep
hron
0
Varma et al. CPT 2017, 33. El-Kattan & Varma, DMD, 2018, 729.
ECCS and renal transporters
Bases/NeutralsAcids/Zwitterions
Lo
w P
erm
eab
le
Class 3
Class 3A
OAT1
OAT2
OAT3
OATPs
OAT3
Class 3B
MW ≤400 MW >400
Class 4
OAT2
OAT3
OCT2
MATEs
Medicine Design
Compound Uptake
ESF Source
valsartan 5 Poirier A, et al, (2009) JPKPD 36
fexofenadine 10 Poirier A, et al, (2009) Mol Pharm 6
pravastatin 3.7 Watanabe T, et al, (2009) JPET 328
pravastatin 31 Varma MV, et al, (2012) Pharm Res 29
repaglinide 12 Gertz M, et al, (2013) Pharm Res 30
repaglinide 17 Varma MV, et al, (2013) Pharm Res 30
glyburide 2 Varma MV, et al, (2014) AAPS J 16
rosuvastatin - Jamei M, et al. (2014) Clin Pk 73
cerivastatin 30 Varma MV, et al, (2015) DMD 1108
Montelukast 22 Varma MV, et al, (2016) CPT
7
Jones HM et al. DMD, 2012, 1007.
• SF are estimated by fitting clinical
data
• Can we use such ESF to predict
PK of other compounds?
No SF
with SFs
PK profile prediction
In vitro – in vivo translation (PBPK)
1 or 5-C liver
“Middle-out” PBPK reports
Medicine Design
In vitro – in vivo translation (intrinsic hepatic CL)
8
)CLCL(CL
)CLCL().CLCL . (SFCL
bileint,CYPint,passive
bileint,CYPint,
passiveactiveactivehint,
Varma et al. JPET, 2014, 214.
Kimoto et al. JPS, 2017
CLuptake, CLpssive, CLbile
CLmet
‘Bottom-up’
Empirical
SF of 10.6
‘Middle-out’
Atorvastatin Pravastatin
Bosantan Rosuvastatin Cerivastatin Valsartan Fluvastatin Pitavastatin Repaglinide Glyburide
ECCS 1B ECCS 3B
IVIVE
No uptake
‘no consideration
to uptake’
Medicine Design 9
In vitro – In vivo Extrapolation for
Metabolic CL compds
Heps HLM SF ~4-5
needed to
recover
CLint.
Skew in
IVIVE
Medicine Design
Challenging Class 1B compds: Montelukast case
1 1 0 1 0 0
0
5 0
1 0 0
1 5 0
T im e (m in )
Ce
ll a
cc
um
ula
tio
n
(pm
ol/
mg
)
C on
tro
l (3 7
C )4
C
+ Rif
a mp
icin
(3
M)
+ Rif
a mp
icin
(1 0
M)
+ C y c losp
or i
ne A
(1 0
M)
+ Rif
a my c in
SV
(2 0
M)
+ Rif
a my c in
SV
(1 m
M)
+ Ge m
fib
roz il
(10 0
M)
+ Ge m
-glu
(1 0 0
M)
+ MK
5 7 1 (5 0
M)
0
5 0
1 0 0
1 5 0
2 0 0
% o
f c
on
tro
l U
pta
ke
CL
int
(0.5
-5m
)
ECCS 1B 1
10
100
1000
0 3 6 9 12 15 18 21 24
Mo
nte
luka
st p
lasm
a co
nc.
(n
g/m
L)
Time (h)
0.01
0.1
1
10
0 3 6 9 12 15 18 21 24
Rif
amp
icin
fre
e p
lasm
a co
nc.
(µ
M)
Time (h)
1
10
100
1000
10000
0 3 6 9 12 15 18 21 24
Mo
nte
luka
st p
lasm
a co
nc.
(n
g/m
L)
Time (h)
1
10
100
1000
10000
100000
0 3 6 9 12 15 18 21 24
Pit
avas
tati
n p
lasm
a co
nc.
(n
g/m
L)
Time (h)
0.1
1
10
0 3 6 9 12 15 18 21 24
Rif
amp
icin
fre
e p
lasm
a co
nc.
(µ
M)
Time (h)
0
0.5
1
1.5
2
Control + Rifampicin
Mo
nte
luka
st C
L (m
L/m
in/k
g)
0
5
10
15
20
25
Control + Rifampicin
Pit
avas
tati
n C
L (m
L/m
in/k
g)
Interaction with rifampicin in Cynomolgus monkeys
(D) (E) (F)
Interaction with rifampicin in rats
(A) (B) (C)
1
10
100
1000
0 3 6 9 12 15 18 21 24
Pit
avas
tati
n p
lasm
a co
nc.
(n
g/m
L)
Time (h)
0
5
10
15
20
25
30
Control + Rifampicin
Mo
nte
luka
st C
L (m
L/m
in/k
g)
0
5
10
15
20
Control + Rifampicin
Pit
avas
tati
n C
L (m
L/m
in/k
g)
*P=0.011 *P<0.001
*P=0.012 *P=0.005
Weak in vitro activity CL reduced with Rifampicin in
rat and monkey
Montelukast
Varma et al., CPT 2017, 406-415.
Case example
Medicine Design
Montelukast clinical DDIs - PBPK modeling
1
1
Montelukast DDIs
Hepatocyte
Bile
Blood
Other
organs
CYP3A4
Passive
Extracellular space
CYP2C8 OATP1B1
Gem Gem-Glu Clari
ECCS 1B
Gem Gem-Glu
Varma et al., CPT 2017, 406-415.
Case example
Medicine Design
ECCS Class Intestinal Transporters Hepatic uptake transporters
Renal secretory transporters
IA P-gp, BCRP (OAT2)
1B P-gp, BCRP OATP1B1, OATP1B3
2 P-gp, BCRP
3A P-gp, BCRP (PEPT1) OAT1, OAT3
3B P-gp, BCRP (PEPT1, OATP2B1) OATP1B1, OATP1B3 OAT1, OAT3
4 P-gp, BCRP (OATP2B1) OAT1, OAT3, OCT2, MATEs
Lumen
BCRP
Enterocytes
- SLC transporters
- ABC transporters
Blo
od
Inte
stin
e
Basolateral
Apical
P-gp
OAT1
OAT3
OCT2
OATP1B3
OATP1B1
Lum
en
Kid
ney
Urine
Liv
er
Hepatocyte
P-gp
BCRP
Bile
CYPs/others
CYPs/others
R
OH
R
Bases/NeutralsAcids/Zwitterions
Hig
h P
erm
eab
leL
ow
Perm
eab
le
Class 1
Class 3
Class 2
Class 4
Metabolism
Renal clearance
Class 1A Class 1B
Metabolism Hepatic uptake
MW ≤400 MW >400
Class 3A Class 3B
MW ≤400 MW >400
Renal
clearance
Hepatic uptake
(or) Renal
clearance
R
OH
R
R
OH
R
ECCS Class and Drug Transporters
Varma et al. CPT 2017, 33-36.
MATEs
Medicine Design
0
20
40
60
80
100
120
R-w
arfa
rin
PH
H U
pta
ke
Warfarin uptake by Human Hepatocytes
Both R/S-warfarin behave like
cGMP – Known to be selective
for OAT2
OATPs inh All SLCs OAT2 inh
0
20
40
60
80
100
120
S-w
arfa
rin
PH
H U
pta
ke
0
20
40
60
80
100
120
cGM
P P
HH
Up
take
R-Warfarin S-Warfarin cGMP
0
2
4
6
8
10
12
NTC
P
OA
T2
OA
TP1B
1
OA
TP1B
3
OA
TP2B
1
OC
T1R-w
arfa
rin
Up
take
rat
io
0
1
2
3
4
5
NTC
P
OA
T2
OA
TP1B
1
OA
TP1B
3
OA
TP2B
1
OC
T1S-w
arfa
rin
Up
take
rat
io
R-Warfarin S-Warfarin
OAT2 selectivity was
confirmed using singly
transfected HEK293 cells
Case example
14
Pfizer Confidential
10
100
1000
0 24 48 72 96 120 144 168
R-w
arfa
rin
conc
. (ng
/mL)
Time (h)
10
100
1000
0 24 48 72 96 120 144 168
S-w
arfa
rin
conc
. (ng
/mL)
Time (h)
1
10
100
1000
0 50 100 150 200 250 300 350 400
S-w
arfa
rin
conc
. (ng
/mL)
Time (h)
10
100
1000
0 24 48 72 96 120 144 168
R-w
arfa
rin
conc
. (ng
/mL)
Time (h)
10
100
1000
0 24 48 72 96 120 144 168S-
war
fari
n co
nc. (
ng/m
L)
Time (h)
(A) (B)
(C) (D)
(E)
Control
+Fluconazole
Control
+Fluconazole
CYP2C9*1/*1*1/*3
*2/*3
*3/*3
0
200
400
600
800
1000
0 4 8 12
S-w
arfa
rin
conc
. (ng
/mL)
Time (h)
0
200
400
600
800
1000
0 4 8 12
R-w
arfa
rin
conc
. (ng
/mL)
Time (h)
10
100
1000
0 24 48 72 96 120 144 168
R-w
arfa
rin
conc
. (ng
/mL)
Time (h)
10
100
1000
0 24 48 72 96 120 144 168
S-w
arfa
rin
conc
. (ng
/mL)
Time (h)
CYPs only
OAT2-CYPs interplay
CYPs only
OAT2-CYPs interplay R/S-warfarin PK analysis
Fluco
Ki µM
7.92
-
2
Other organs
Hepatocyte
Bile
Blood
Extracellular space
OATP1B1/1B3/2B1
OAT2
OCT1/NTCP
R-warfarin
S-warfarin
R-warfarin
S-warfarin
CYP2C19
CYP2C9
Case example
Bi et al. Mol Pharma 2018, 1284-1295.
Medicine Design
Co
nt r o
l
10
µM
CS
A
20
µM
RI F
30
µM
Ke
t op
r of e
n
10
0µ
M K
et o
pr o
f en
30
0µ
M K
et o
pr o
f en
0. 1
µM
HB
V p
ep
t i de
1µ
M H
BV
pe
pt i d
e
50
0µ
M Q
ui n
i di n
e
1m
M R
I F S
V
0
5 0
1 0 0
1 5 0
Up
ta
ke
ra
tio
(%
of
co
nt
ro
l)
0 1 2 3 4 5
0
1
2
3
4
T i m e ( m i n )
Up
ta
ke
ra
tio
N T C P
O A T 2
O A T P 1 B 1
O A T P 1 B 3
O A T P 2 B 1
O C T 1
0 2 0 4 0 6 0 8 0
0
1 0
2 0
3 0
4 0
5 0
[ T o l b u t a m i d e , M ]
OA
T2
up
ta
ke
ra
te
(pm
ol/
min
/m
g-
pr
ot
ein
)
K m = 1 9 . 5 4 . 3 M
V m a x = 4 7 . 8 4 . 0 ( p m o l / m i n / m g - p r o t e i n )
**
*
*
*
*
Co
nt r o
l
10
µM
CS
A
20
µM
RI F
30
µM
Ke
t op
r of e
n
10
0µ
M K
et o
pr o
f en
30
0µ
M K
et o
pr o
f en
0. 1
µM
HB
V p
ep
t i de
1µ
M H
BV
pe
pt i d
e
50
0µ
M Q
ui n
i di n
e
1m
M R
I F S
V
on
ic e
0
5 0
1 0 0
1 5 0
2 0 0
Up
ta
ke
ra
te
(%
of
co
nt
ro
l)
0 1 2 3 4 5
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
T i m e ( m i n )
Up
ta
ke
(p
mo
l/m
g p
ro
te
in)
C o n t r o l ( 3 7 C )
+ R i f S V ( 1 m M )
0 2 0 4 0 6 0 8 0 1 0 0
0
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
[ T o l b u t a m i d e , M ]
Up
ta
ke
ra
te
(pm
ol/
min
/m
g-
pr
ot
ein
)
K m = 3 9 . 3 6 . 6 M
V m a x = 4 2 6 . 5 3 0 ( p m o l / m i n / m g - p r o t e i n )
P a s s i v e C L = 1 . 0 1 l / m i n / m g
PHH
Time-course
Transport
kinetics
Phenotyping
HEK-OAT2 cells
MW – 270
pKa – 5.2
RRCK – 31
Tolbutamide
•Low CLmet
• IVIVE disconnect with CLmet
•CYP2C9 clinical probe
HEKs
Medicine Design
1
10
100
0 12 24 36
Tolb
utam
ide
conc
. (µg
/mL)
Time (h)
1
10
100
0 12 24 36 48
Tolb
utam
ide
conc
. (µg
/mL)
Time (h)
OAT2-CYP2C9 interplay
CYP2C9 only
OAT2-CYP2C9 interplay
CYP2C9 only
Sensitivity analysis
Green point – WT
Blue points - *1/*3,
*2/*3, *3/*3 variants
Bi et al. J Pharmacol Exp Ther. 2018, 390-398.
Case example
Tolbutamide: OAT2-CYP2C9 interplay
Hepatocyte
Bile
Blood
Extracellular space
PSactive
PSPassive
CLmet
CLbilePSbaso-efflux
fu,cell
fu,b
17
Pfizer Confidential
PHH +RifSV
HLM +UDPGA/NAD
PH
HEKs OAT2/OAT
P1B1 selectivity
R
OH
R
Bases/NeutralsAcids/Zwitterions
Hig
h P
erm
eab
leL
ow
Perm
eab
le
Class 1
Class 3
Class 2
Class 4
Metabolism
Renal clearance
Class 1A Class 1B
Metabolism Hepatic uptake
MW ≤400 MW >400
Class 3A Class 3B
MW ≤400 MW >400
Renal
clearance
Hepatic uptake
(or) Renal
clearance
R
OH
R
R
OH
R
OAT2 Substrate activity
Uptake-determined CL CL prediction for
low MW, high
permeability A/Z
(ECCS 1A)
PSactive
PSpd
CLmet
Metabolism-determined CL Extended CL model
(Transporter-Enzyme interplay)
PSactive
PSpd
CLmet
0.01
0.1
1
10
0.01 0.1 1 10
Pred
icte
d C
L
Observed CL
0.01
0.1
1
10
0.01 0.1 1 10Pr
edic
ted
CL
Observed CL
0.01
0.1
1
10
0.01 0.1 1 10
Pred
icte
d C
L
Observed CL
ECCS (n=36)
(n=25)
(n=25) (n=19)
(n=19)
Approach
18
Pfizer Confidential
Data set ECCS 1A drugs - List of drugs
17.8 334 4.1 A OATP 1B1, OAT2 CYP 2C9/UGT 0.002
6.14 305 4 A OATP 1B1, OAT2 UGT 0.02
5.82 332 4.5 A OATP 1B1, OAT2 - 0.11
4.24 317 3.8 A OATP 1B1 CYP 2C9 0.011
18.5 296 4.4 A OAT2 CYP 2C9, UGT2B7 0.004
34.2 242 4.4 A OAT2 UGT2B7 0.005
16.7 323 6 A OAT2 CYP 2C9 0.05
29.3 206 4.4 A OAT2 CYP 2C9 0.012
22.12 356 4.4 A OAT2 CYP 2C9 0.012
26.9 335 3.9 A OAT2 - 0.035
18 254 4.1 A OAT2 CYP 2C9, UGT2B7 0.021
23.9 351 3.8 A OAT2 CYP 2C9/3A4 0.008
23.3 356 6.1 A OAT2 CYP 2C8 0.012
32.9 331 2.2 A OAT2 CYP 2C9, UGT2B7 0.024
31.9 137 7.9 A OAT2 - 0.515
27.3 357 6.3 Z OAT2 CYP 2C8 0.003
24.2 308 5 A OAT2 CYP 2C9 0.013
8.38 253 5.6 A OAT2 CYP 2C9 0.325
24.2 308 5 A OAT2 CYP 2C9 0.013
31.3 270 5.1 A OAT2 CYP 2C9 0.027
20.9 273 5 A OAT2 UGT 0.002
6.84 366 5.9 Z - - 0.96
22.1 230 4.3 A OAT2 UGT2B7 0.026
11.1 258 7.8 A OAT2 - 0.4
19.9 376 3.3 Z OAT2 CYP 3A4 0.16
Th alid om id e
Tiag ab in e
Tolcap on e
Clin afloxacin
Nap roxen
P ralid oxim e
Ros ig litazon e
R-W arfarin
S u lfam eth oxazole
S -W arfarin
Tolb u tam id e
In d om eth acin
Is oxicam
Ketop rofen
Meloxicam
P iog litazon e
P iroxicam
Diclofen ac
Fen op rofen
Gliclazid e
Ib u p rofen
En tacap on e
Flu ores cein
Nateg lin id e
Brom fen ac
Up ta ke
T ra n s p o rte rsMa in E n z y m e
c
P la s m a fre e
f ra c tio n
( f u , p )d
Dru g
P e rm e a b ilit
y (10- 6
c m /s )a
Mo le c u la r
we ig h tAc id ic p Ka Io n iz a tio n
0 .0 0 .5 1 .0 1 .5 2 .0
0 .0
0 .5
1 .0
1 .5
F lu o re s c e in
T im e
(m in )
To
tal
Ce
ll A
mo
un
t
(pm
ol/
mg
)
0 .0 0 .5 1 .0 1 .5 2 .0
0
1 0
2 0
3 0
4 0
5 0
M e lo x ica m
T im e
(m in )
To
tal
Ce
ll A
mo
un
t
(pm
ol/
mg
)
0 .0 0 .5 1 .0 1 .5 2 .0
0
1 0
2 0
3 0
4 0
P ra lid o x im e
T im e
(m in )
To
tal
Ce
ll A
mo
un
t
(pm
ol/
mg
)
0 .0 0 .5 1 .0 1 .5 2 .0
0
5 0
1 0 0
1 5 0
2 0 0
F e n o p ro fe n
T im e
(m in )
To
tal
Ce
ll A
mo
un
t
(pm
ol/
mg
)
0 .0 0 .5 1 .0 1 .5 2 .0
0
1 0
2 0
3 0
K e to p ro fe n
T im e
(m in )
To
tal
Ce
ll A
mo
un
t
(pm
ol/
mg
)
Emi Kimoto
Black – Control
Red – +RifSV 1mM
Blue – on Ice
Kimoto et al. JPET, 2018, inpress
Mark Niosi & Jian Lin Sumathy & Laurie
19
Pfizer Confidential
0.1
1
10
100
1000
10000
0.1 1 10 100 1000 10000
Pre
dic
ted
CLi
nt
(mL/
min
/kg)
Observed CLint (mL/min/kg)
Extended CL (Uptake + metabolism)
Circles - OAT2 substrates with Hep uptake in PHH Triangles - OAT2/OATP1B1 substrates with Hep uptake in PHH
Square - no Hep uptake in PHH
IVIVE OAT2-mediated uptake IVIVE to Hepatic CLint
1x 3x Bias
𝐶𝐿ℎ,𝑖𝑛𝑡 =𝐶𝐿𝑢𝑝𝑡𝑎𝑘𝑒 ∙ 𝐶𝐿𝑚𝑒𝑡
𝐶𝐿𝑝𝑎𝑠𝑠𝑖𝑣𝑒 + 𝐶𝐿𝑚𝑒𝑡
David Tess
0.1
1
10
100
1000
10000
0.1 1 10 100 1000 10000
Pre
dic
ted
CLi
nt
(mL/
min
/kg)
Observed CLint (mL/min/kg)
0.1
1
10
100
1000
10000
0.1 1 10 100 1000 10000
Pre
dic
ted
CLi
nt
(mL/
min
/kg)
Observed CLint (mL/min/kg)
Stats †
n 13 CI 90%
DI 90% (±fold) 3.91 [2.6 - 6.9]
Bias ‡ (fold) 0.9 [0.6 - 1.4]
Kimoto et al. JPET, 2018, inpress
Medicine Design
Transporter phenotype per class
Bases/Neutrals Acids/Zwitterions
Hig
h P
erm
ea
ble
Class 1
Class 1A
OAT2 OATPs
Class 1B
MW ≤400 MW >400
Lo
w P
erm
ea
ble
Class 3
Class 3A
OAT1
OAT2
OAT3
OATPs
OAT3
Class 3B
MW ≤400 MW >400
Class 2
Class 4
OAT1
OAT3
OCT2
Medicine Design
3A
4
2C
9
Re
na
l Tr
an.
OA
TP
Effl
ux
Tran
. U
GT
QSA
R
Re
nal
H
ep
atic
U
pta
ke
Me
tab
ol
Lead Identification and Optimization
Physiologically Based Pharmacokinetics (PBPK) to project human PK/target conc and DDI Liabilities
Lead Development
Invigoration of Human Based In Vitro Reagents as Driver for Accurate Human Clearance and
DDI Prediction
Varma, Lai and El-Kattan. Adv Drug Deliv Rev 2017, 92-99.
ECCS
Acknowledgments
Transporter Sciences Group Yi-an Bi
Emi Kimoto
Sumathy M
Sarah Lazzaro
Mark West
Chester Costales
Bo Feng
Manthena Varma
Larry Tremaine
David Rodrigues
Project leads & ADME CoE
Dennis Scott George Chang James Gosset John Litchfield Li Di Amit Kalgutkar Anthony Carlo Matt Troutman Jian Lin Mark Niosi
Theunis Goosen
Systems Modeling & Simulations group
David Tess
Rui Li
Tristan Maurer 22
ECCS team Manthena Varma
Stefanus Steyn
Charlotte Allerton
Ayman El-Kattan
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