Recent Advances in Drug Transporter and Enzymology Sciences … · 2019-11-04 · NEDMDG Fall...
Transcript of Recent Advances in Drug Transporter and Enzymology Sciences … · 2019-11-04 · NEDMDG Fall...
NEDMDG Fall Meeting September 13, 2017
Recent Advances in Drug Transporter and Enzymology Sciences to Enable Drug Design
Larry Tremaine
Predict Efficacious Concentration
Predict PK
Predict DDI Predict Variability
Target Ki
fu,plasma
fu,in vitro
BL/PL
Kp,uu,target
CLint,met
fmet
CYP IC50
UGT IC50
Transporter IC50
ftransport
CLint,u
Predict Dosing Regimen
fu,target
PD Response
CLint,txp
CYP kinact/KI
Predict Safety
Animal CLrenal
The Parameters We Measure and the Predictions to Which They Contribute
Predict Human
Metabolites
Metabolite ID
TK
Courtesy of S. Obach
ADME Properties and Assays in Drug Design
ADME Property Measurements/Parameters Assays
CL - metabolic Hepatic metabolism, reaction phenotyping
HLM, suspension HHEPs or HHEP relay +/- chemical inhibitors
DDI - victim Fm, Ft Chemical inhibitors with HHEPs, HLM
CL - renal Ppb, active secretion Rat SSS, OATs-HEK RAF
CL – hepatic uptake Active uptake, passive uptake, transporter phenotyping
SCHH, PHH +/- inhibitors, OATPs-HEK RAF; NHP SSS
Ceff, tissue distribution
Ppb; Cbu/Cpu Tissue exposure; fu,cell
In silico; equilibrium dialysis; Pgp and BCRP-MDCK; Kpuu;
CL – biliary Biliary excretion SCHH +/- Ca2+
DDI - perpetrator Competitive DDI TDI Induction
HLM HLM SCHH, PXR reporter assay
Absorption - fafg Permeability; Efflux transporters; CYP3A metabolism
RRCK (low canine Pgp expression) Pgp and BCRP MDCK HLM, preclinical species PK
Genesis of Predicting Human CL from In Vitro Data at Pfizer
PK Predictions – A Long History At Pfizer
PDM CONSISTENTLY re-evaluated our PK-predictions to improve and evolve our Science
There has been many teams over the years
Major Challenge Projected clinical PK
parameters with LIMITED Human IV
Data
Courtesy S. Steyn Obach et al.,JPET (1997) 283:46-58 Hosea et al., (2009) J Clin Pharmacol 49:513-533
Changing Philosophy to Predicting Human PK
0
20
40
60
80
100
120
1 10 100
% o
f com
poun
ds
Fold error
rat sss
dog sss
NHP SSS
HLM PBPK Vss
HHEPS PBPK Vss
No single method accurately predicts ALL compounds (2006-2010 Data Set) If several approaches agreed, then high “confidence in prediction”
Extended Clearance Classification System Predictability
7
Bases/NeutralsAcids/Zwits
Hig
h P
erm
eabl
eLo
w P
erm
eabl
e
Class 1
Class 3
Class 2
Class 4
Metabolism
Renal
Class 1A Class 1BMetabolism Hepatic uptake
MW ≤400 MW >400 n=172
n=32
n=29 n=14
Renal5%
Metabolism95%
Renal75%
Metabolism25%
Renal10%
Metabolism90%
Renal14%
Hepatic uptake
86%
Class 3A Class 3BRenal Hepatic uptake
(or) Renal
MW ≤400 MW >400
n=24n=36
89%
Obtained data from Obach et al. + other papers.
N = 789
Identified rate-determining clearance step using Pfizer/Novartis dataset
N = 313
Utilized phys chem/ in vitro properties to classify compounds based on their rate determining clearance step
Varma MV, Steyn SJ, Allerton C, El-Kattan AF. Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS). Pharm Res. 2015, 3785-3802
Permeability cut off = 5 X 10-6 cm/sec Molecular Weight cut off:
400 [Class 1] Ionization:
Acid+Zwit vs Base+Neutral
Prevalence and importance of non-P450 metabolism
2 0 0 52 0 0 6
2 0 0 72 0 0 8
2 0 0 92 0 1 0
2 0 1 12 0 1 2
2 0 1 32 0 1 4
2 0 1 52 0 1 6
0
5
1 0
1 5
2 0
2 5
Dru
gs
N o M a jo r M e ta b o lite sP 4 5 0N o n -P 4 5 0
1 09
8
1 2
7
1 3
1 7
1 4
1 8 1 7
6 6
M Cerny DMD 2016, 44:1246–1252
FDA–Approved Oral and Intravenous Drugs: 2006–2015
Clearance Panel – Identify major CL mechanism, appropriate in vitro systems and generate rate constants
HHeps+/-ABT
Empirical Classification based on Physiochemical Properties
Transporters Acids
OATPs, OATs
CYP Dominated
Non-CYP Dominated
rCYP CLint UGT CLint
AO CLint
Other Conj.
Other Ox.
Hydro-lysis
Tier 1
Tier 2
No Metabolism
HHeps Type of metabolism
Transporters Bases OCTs
ECCS Class 2 and 1A ECCS Class 1B, 3 and 4
Current Chemistry Space - Metabolic HHEP CLia
From Jan, 2016 – June 2017 (18 months) approximately 7000 compounds submitted to 4 hr suspension HHEP for metabolic CL Approximately 1000 compounds have Clia <3 ul/min/million cells.
~ 50% Low
Clia<12
~ 30% High Clia>20
~ 20% Moderate 12<Clia<20
Courtesy of A. Carlo
Extending Clmetabolic Dynamic Range – Hepatocyte relay method for low clearance
Compounds Enzyme In vivo Relay MethodDiazepam CYP3A, 2C19 15 15
Tolbutamide CYP2C9 4.9 6.9Theophylline CYP1A2 2.1 2.5
Timolol CYP2D6 36- 49 14Disopyramide CYP3A 5.9 4.6
S-Warfarin CYP3A, 2C9 4.5 5.1Zolmitriptan CYP1A2 and MAO 13 3.2*
CLint (mL/min/Kg)
Transfer Supernatant
* Under-predict due to extra-hepatic contributions of MAO
Di et al., DMD (2012) 40:1860-1865 Di et al., DMD (2013) 42:2018-2023
Assays Incubation Time CL int
Detetion Limit
Tier 2a HHEP 4 Hours 7.5 mL/min/Kg
HHEP Relay 20 Hours – 0.5Mcells/ml 20 Hours – 2.0Mcells/ml
1.46 mL/min/Kg 0.36 mL/min/Kg
IVIVE for Metabolic CL using Global Lot of HHEPs
CLb,obs ≤ 10 mL/min/Kg
HHEP Relay
HHEP suspension
Hepatocyte lot DCM apparent intrinsic clearance (𝑪𝑪𝑪𝑪𝐢𝐢𝐢𝐢𝐢𝐢,𝒂𝒂𝒂𝒂𝒂𝒂) measures should be scaled by a factor of 1.0 regardless of assay conditions (4 hr suspension or relay)
IVIVE for CLb,obs ≤ 10 mL/min/Kg • Most applicable clearance range for oral drugs. • 70% of data within this cutoff (need high n’s for high confidence) • Cutoff enables application of single scalar regardless of assay type.
All Data, N = 35 compounds
Courtesy S. Steyn
Metabolic Phenotyping - Reporting Limitations of Substrate Depletion Assays (CYP)
• Highlighted values represent compound CL required to enable capture of majority of CYP activity under given conditions (sum of ALL contributing isoforms)
– i.e. 90% of one enzyme, 90% of 2 enzymes (40% CYP1, 50% CYP2)
• Increased sensitivity comes with increased reagent consumption and assay cost
Current limit of parent
depletion
Increasing sensitivity & resource
Courtesy of A. Doran and S. Obach
Assay HLM HHEP HLM HHEP Relay HHEP Relay Incubation time 60 min 4 H 60 min 20 H 20 H Concentration 0.75 mg/mL 0.5e6 cells/mL 2.0 mg/mL 0.5e6 cells/mL 2e6 cells/mL Lowest Reportable CLi,a,s 7.0 mL/min/kg 7.5 mL/min/kg 2.6 mL/min/kg 1.5 mL/min/kg 0.36 mL/min/kg
Measured CLint,a,s 150 95% 94% 98% 99% 100% 40 83% 90% 94% 96% 99% 30 77% 86% 91% 95% 99% 20 65% 79% 87% 93% 98% 10 30% 58% 74% 85% 96% 9 22% 53% 71% 83% 96% 7 0% 40% 63% 79% 95% 6 -- 30% 57% 75% 94% 5 -- 16% 48% 70% 93% 4 -- 0% 35% 63% 91% 3 -- -- 13% 50% 88% 2 -- -- 25% 82% 1 -- -- 64%
0.8 -- -- 55% 0.6 -- -- 40% 0.4 -- -- 10%
Maximum Observable Inhibition
Standard Screening Assays
Pre-incubation with Inhibitor
Remove Inhibitor Incubate with Substrate
Transfer Supernatant
Next Relay
Novel HHEP Relay - Reaction Phenotyping Assay
MBIs to avoid the impact of inhibitor depletion during long incubation
MBIs were removed from incubation after inactivation to increase selectivity by
minimizing reversible inhibition (CYP, AO, UGT, SULT, CES, Transporters)
Add Substrate
Courtesy of L. Di
Yang X, Atkinson K, Di L., DMD (2016) 44(3):460-5
Experimental Conditions for HHEP Relay Phenotyping
CYP Pre-Incub. Time (min)
Inactivators and Concentrations Substrates Metabolites
1A2 15 1 µM Furafylline Phenacetin Acetaminophen
2B6 15 10 µM Phencyclidine Bupropion OH-Bupropion
2D6 15 1.8 µM Paroxetine Dextromethorphan Dextrorphan
2C8 30 100 µM Gemfibrozil Glucuronide Amodiaquine N-Desethylamodiaquine
2C9 30 15 µM Tienilic Acid Diclofenac 4′-OH-Diclofenac
2C19 15 8 µM Esomeprazole Mephenytoin 4′-OH-Mephenytoin
3A 15 25 µM Troleandomycin Midazolam 1′-OH-Midazolam
3A4 15 2 µM CYP3cide* Midazolam 1′-OH-Midazolam Pan-CYP 30 1 mM ABT & 15 µM
Tienilic Acid All Above All Above
* Genotyped HHEP CYP3A5 EM
Courtesy of L. Di
Yang X, Atkinson K, Di L., DMD (2016) 44(3):460-5
HHEP Relay Phenotyping Assay Validation
Reported fm : 0.5 3A, 0.5 2C19
Relay fm: 0.56 3A, 0.42 2C19
1
10
100
1000
0 5 10 15 20 25
% R
em
ain
ing
Time (hour)
Diazepam (3A + 2C19)
1
10
100
1000
0 5 10 15 20 25
% R
em
ain
ing
Time (hour)
Diazepam + TAO
1
10
100
1000
0 5 10 15 20 25
% R
em
ain
ing
Time (hour)
Diazepam + Esomeprazole
Courtesy of L. Di
Yang X, Atkinson K, Di L., DMD (2016) 44(3):460-5
Assay Radiometric Mass SpecIncubation time 30 min 30 minConcentration 2 mg/mL pt HLM 2 mg/mL pt HLMSubstrate Conc 1 uM 1 uMQuantititive limit 200 - 100000 dpm (0.2%) 0.1 nM (LLOQ) Lowest Reportable CLi,a,s 0.03 mL/min/kg 0.0015 mL/min/kg
Measured CLint,a,s4 100% 100%
0.4 99% 100%0.04 93% 96%0.01 70% 85%
0.004 25% 63%0.002 25%
Maximum Observable Inhibition
Metabolite Formation
Increased Sensitivity of Metabolite Formation Assays (CYP)
• Affords increased dynamic range due to differences in bioanalytical endpoint (change in metabolite response), decreased reagent usage.
• Availability of radiolabel compound offers additional opportunities for assessing low CL phenotyping.
Parent depletion limit
Increasing sensitivity
Courtesy of A. Doran and S. Obach
An Overview of the Isolation Process Walker, et al, DMD, 2014 42:1627-1639
Metabolite Biosynthesis and NMR quantitation
• Transporters are important determinants of clearance (DDI) & exposure at the site of efficacy / toxicity
• Common target tissues: Brain, Liver, Kidney & Intestine
Opening design space Transporter space as a key gap and opportunity
Courtesy of M. Varma
Varma, MV et al. Pharm Res (2015), 32(12), 3785-3802. El-Kattan, AF et al. Pharm Res (2016), 33(12), 3021-3030 Varma, MV. et al. Adv Drug Deliv Rev (2017), epub Ahead of Print.
~ 20 % of design effort in complex disposition space High potential for exposure asymmetry (opportunity and/or liability)
Active Renal Excretion A relevant space with room for improvement
Courtesy of M. Varma
Predicting CLrenal via Single Species Scaling
• No mechanistic description of renal reabsorption or active renal secretion
from Rat CLr from Dog CLr
Correction for plasma protein binding Correction for plasma protein binding and kidney blood flow
Paine et al., DMD 2011, 1008
Courtesy of M. Varma
OAT1 Substrate selectivity
------------ OAT2 Substrate selectivity --------------------
--------------------------- OAT3 Substrate selectivity ----------------------------------
0 2 40
5
1 0
1 5
2 0
T e n o fo v ir
T im e(m in )
To
tal
Ce
ll A
mo
un
t(p
mo
l)
W T
h O A T 1
h O A T 3
0 2 40
2
4
A c y c lo v ir
T im e(m in )
To
tal
up
tak
e(p
mo
l/mg
of
pro
tein
)
W T
h O A T 1
h O A T 3
h O A T 2
0 2 40
2
4
6
G a n c ic lo v ir
T im e(m in )
To
tal
up
tak
e(p
mo
l/mg
of
pro
tein
)
W T
h O A T 1
h O A T 3
h O A T 2
0 2 40
1
2
3
O s e lta m iv ir a c id
T im e(m in )
Ce
ll u
pta
ke
(pm
ol/m
g p
rote
in)
W T
h O A T 1
h O A T 3
0 .0 0 .5 1 .0 1 .5 2 .00
2 0
4 0
6 0
8 0
1 0 0
F u r o s e m id e
T im e(m in )
Ce
ll u
pta
ke
(pm
ol/m
g p
rote
in)
W T
h O A T 1
h O A T 3
0 2 40
2
4
6
8
1 0
B e n z y lp e n ic i ll in
T im e(m in )
Ce
ll u
pta
ke
(pm
ol/m
g p
rote
in)
W T
h O A T 1
h O A T 3
Time-course of uptake by OATs-transfected HEK293 and wild-type cells
Mathialagan et al., DMD 2017, 409-417 RAFOAT1=0.64
RAFOAT2=7.3 RAFOAT3=4.1
CLrenal - OAT scaling via single transfect cells
Mathialagan et al., DMD 2017, 409-417
• Descriptions of both renal reabsorption and active renal secretion
Courtesy of M. Varma
Active Renal Excretion Towards a fit for purpose approach to clearance prediction
Courtesy of M. Varma
Mathialagan et al., DMD 2017, 409-417
Hepatobiliary Transport A relevant space with room for improvement
Giacomini KM & Huang S-M (2013) CPT Transporters in Drug Development and Clinical Pharmacology
Varma, MV et al. Pharm Res (2015), 32(12), 3785-3802. El-Kattan, AF et al. Pharm Res (2016), 33(12), 3021-3030 Varma, MV. et al. Adv Drug Deliv Rev (2017), epub Ahead of Print.
Sirianni GL & Pang (1997) J Pharmacokin Biopharm 25(4) 449 - 470
~ 15% of design effort in hepatobiliary transport disposition space High potential for exposure asymmetry (opportunity and/or liability)
Courtesy of M. Varma
In vitro tool – Primary human hepatocytes
26
Suspension Heps (Oil spin method)
Plated human hepatocytes (PHH)
Sandwich-cultured human hepatocytes (SCHH)
Before 2004
Circa 2005
2015-16
UNC/Pfizer collaboration Bi et al. DMD 2006 2011/12
SCHH-PBPK model Jones et al. DMD 2012
Adopted from Sugiyama et al.
2014 Middle-out PBPK Li et al. DMD 2012
2012-14 SCHH-DDI M&S Varma et al. 2012
Milestones
Courtesy of M. Varma
Mechanistic PBPK Modeling for Prediction of Hepatic Transporter-Mediated Disposition Using SCHH Data
Jones, et al. DMD (2012) 40-1007-17
Hepatic Transporter Disposition -Current best practice (SCHH/PBPK)
v3 – Li (2014), J Pharmacokinet Pharmacodyn, 41(3), 197-209 – Trained with six ECCS 1 compounds devoid of biliary clearance
• Use SCHH inputs with the following training set. – Bosentan, Cerivastatin, Fluvastatin, Glyburide, PF-05186462 (NAV1.7), &
Repaglinide
– Population fit of clearance scaling factors (𝑆𝑆𝑆𝑆) • Log-normal inter compound variability on scaling factors (𝑆𝑆𝑆𝑆)
– Account for imprecision in compound inputs – Provides an method to determine uncertainty in prospective predictions by
bootstrapping the scaling factor distribution space
0.1
1
10
0.1 1 10
Pred
ictio
ns
Observations
Model SFpass SFact
SFmet
v3 (populati
on)
0.35 27 0.19
90% CI 0.064-2.1
14-49
0.065-0.56
CV 110% 39% 66%
Transporters in vitro HHEP systems do not represent in vivo physiological activity levels
Courtesy of T. Maurer
Hepatic Transporter Phenotyping IC50 OF SELECTIVE INHIBITORS in HEK293 CELL LINES
Yi-An Bi ISSX 2017 poster
Challenging Class 1B compds: Montelukast case
Con tro l (
3 7 °C )
4 °C
+ R ifam
p icin
(3 µ
M)
+R ifam
p icin
(10 µ
M)
+ C y c lo sp
o r in e A
(10 µ
M)
+R ifam
y c in S
V (2
0 µM
)
+R ifam
y c in S
V (1
mM
)
+ Gem
fibro
z il (
1 0 0 µM
)
+Gem
-glu
(10 0 µ
M)
+MK 5 7 1 (5
0 µM
)
0
5 0
1 0 0
1 5 0
2 0 0
% o
f c
on
tr
ol
Up
ta
ke
CL
int
(0
.5-5
m)
ECCS 1B 1
10
100
1000
0 3 6 9 12 15 18 21 24
Mo
nte
luka
st p
lasm
a c
on
c. (
ng
/mL)
Time (h)
1
10
100
1000
10000
0 3 6 9 12 15 18 21 24
Mo
nte
luka
st p
lasm
a c
on
c. (
ng
/mL)
Time (h)
0
0.5
1
1.5
2
Control + Rifampicin
Mo
nte
luka
st C
L (m
L/m
in/k
g)
Interaction with rifampicin in Cynomolgus monkeys
(D) (
Interaction with rifampicin in rats
(A) (
0
5
10
15
20
25
30
Control + Rifampicin
Mo
nte
luka
st C
L (m
L/m
in/k
g)
*P=0.011
*P=0.012
CL reduced with Rifampicin in rat and monkey
Varma et al., CPT (2017) 101:406-415
Montelukast – recommended as substrate for CYP2C8 clinical DDI studies
PK prediction strategy
LUNG
BRAIN
KIDNEY
ADIPOSE
HEART
MUSCLE
OTHERS
SKIN
VEN
OU
S B
LOO
D
ARTE
RIA
L B
LOO
D
LUNG
BRAIN
KIDNEY
ADIPOSE
HEART
MUSCLE
OTHERS
SKIN
VEN
OU
S B
LOO
D
ARTE
RIA
L B
LOO
D
GUT
• CLbile from SCHH • Other ADME inputs from
Enzymology and HDO
Rat/Monkey DDI with Rif
Cyno SS scaling
No
Assume no active uptake in human for
PK predictions
Yes
ECCS
Uptake inhibitable by Rif SV
Class 1B Class 3B
Tier 1 PHH assay
Yes
Phenotyping Characterize transport mechanisms using HEK panel, selective inhibitors, etc.
No
0.1µM conc.*
Renal CL
Class 3B
Class 1B
PBPK modeling
Multiple replicates for dose projections
* Compounds with analytical sensitivity issues will be run at 1µM conc. as Tier 2.
Courtesy of M. Varma
OATP-HEK Uptake and steady state unbound tissue-to-plasma exposure of Glucokinase Activators PF-049 and PF-051 determined in rats post–4-day infusion.
A Ghosh et al. DMD 2014;42:1599-1610
Copyright © 2014 by The American Society for Pharmacology and Experimental Therapeutics
Time (sec)
PF-049 PF-051
Glucokinase Activators
Drug Disposition and Trafficking in Cells – Factors Contributing to Asymmetric Distribution
Mitochondria pH 8
-167 mV
Influx
Passive Diffusion
Lysosomes pH 4.7
+ 19 mV
Cytosol: pH 7.1, -38 mV
D
M1 M2 …
Proteins
Metabolism
Binding
Lipids
pH Gradient
Membrane Potential
Medium/Blood pH 7.4
63%
28%
0.8%
Nucleus pH 7.2
-9.2 mV 8%
Courtesy of L Di
Introduction of Kpuu
Pharmacology and ADMET
Cell-Based
In Vitro
Kpuu = 𝐅𝐅𝐅𝐅𝐅𝐅𝐅𝐅 𝐂𝐂𝐅𝐅𝐂𝐂𝐂𝐂𝐅𝐅𝐅𝐅𝐅𝐅𝐅𝐅 𝐌𝐌𝐅𝐅𝐌𝐌𝐢𝐢𝐌𝐌𝐌𝐌
Steady State
Blood Liver
In Vivo
Kpuu = 𝐅𝐅𝐅𝐅𝐅𝐅𝐅𝐅 𝐓𝐓𝐢𝐢𝐓𝐓𝐓𝐓𝐌𝐌𝐅𝐅𝐅𝐅𝐅𝐅𝐅𝐅𝐅𝐅 𝐁𝐁𝐂𝐂𝐁𝐁𝐁𝐁𝐌𝐌
Tissue exposure, PD, TI & DDI
CLint = CLint′
Kpuu EC50 = EC50
’ × Kpuu IC50 = IC50’× Kpuu
Courtesy of L Di
Predict In Vivo Hepatic Kpuu (IVIVE) in Rat
Compounds
In Vivo Rat Liver-
to-Plasma Kpuu
(IV Infusion)
In Vitro
Suspension Rat
Hepatocyte Kpuu
Fold Difference
in Vivo Kpuu /in
Vitro Kpuu
Cerivastatin 29 ± 8.5 21 ± 2.0 1.4
Fluvastatin 44 ± 8.7 21 ± 1.5 2.1
Rosuvastatin 57 ± 9.5 35 ± 0.6 1.6
Pravastatin 2.2 ± 1.5 2.9 ± 0.3 0.8
PF-04991532 5.7 ± 1.6 7.1 ± 0.1 0.8
PF-05187965 2.4 ± 0.6 4.2 ± 0.2 0.6
Good IVIVE for Substrates of Oatps in Rat K Riccardi et al., DMD, (2017), 45:576-580
Approaches to Predicting Human Hepatic CL
Drug Distribution to Brain - physiology of the central barriers
Courtesy of P. Trapa
Title
1 1 0 1 0 0 1 0 0 0
0 .0 1
0 .1
1
1 0
M D R 1 (B o rs t) E ff lu x R a tio
Ag
g.
rod
en
t A
UC
b,u
/p,u
E R = 2 .5
A U C b ,u /p ,u = 0 .3
1 1 0 1 0 0 1 0 0 0
0 .0 1
0 .1
1
1 0
M D R 1 (N IH ) E ff lu x R a tio
Ag
g. r
od
ent
AU
Cb
,u/p
,u
E R = 6 .0
A U C b ,u /p ,u = 0 .3
Borst and NIH MDR1-MDCK Efflux Ratio Correlation with Rodent Brain Penetration (Cbu/Cpu)
Courtesy of B Feng
Title
1 1 0 1 0 0
1
1 0
1 0 0
M D R 1 (B o rs t) E ff lu x R a tio
MD
R1
(N
IH)
Eff
lux
Ra
tio
E R = 2 .5
E R = 6 .0
Borst MDR1 Assay vs. NIH MDR1 Assay
The two MDR1 assays do not correlate Courtesy of B Feng
Title
MDR1 mRNA P-gp Protein
N IHB o rs
tN IH
B o rst
N IHB o rs
t0
5
1 0
1 5
2 0
Rat
io n
orm
aliz
ed b
y B
ors
t
TEER
Key Properties for Borst vs. NIH MDR1 Cells
Courtesy of B Feng
Translational Strategy for Brain Penetration
Exptl. Parameters – Compound Specific
binding: in vitro equilibrium dialysis
intrinsic passive brain permeability: in silico
active transport at the brain/choroid plexus: analysis of in vitro cell lines (MDR1 and BCRP)
compound specific
Output
extracellular fluid (ECF)
intracellular fluid (ICF)
CSF
plas
ma
BBB
BCSFB
species specific
expression levels: measured from tissues and cell lines or activity from IVIVC at steady state
physiology:
from literature
bulk flow active transport passive diffusion
Trapa PE et al. (2016) J Pharm Sci 105: 965 - 971
Key assumptions Steady-state
Passive flux >> bulk ESF flow Only PgP (ER1) & BCRP (ER2)
2 approaches • Model based estimation against neuroPK data • Relative protein expression
Brain Penetration Exhaustive translational model qualification
Courtesy of P Trapa
Efflux Transporter expression-level changes across species can impact distribution
Trapa PE et al. (2016) J Pharm Sci 105: 965 - 971
• Mechanistic Approach to Clearance IVIVE – CLhepatic mature but challenged by expanded chemical space – Incorporation of hepatic- and renal-transporter mediated CL
• Phenotyping – Lower and non-CYP metabolic CL require novel approaches to
phenotyping – Drug transporter phenotyping is still evolving and lacks clinical markers
• Drug Distribution – Greater appreciation of intracellular pH, membrane potential and
uptake and efflux transporters influencing tissue Kpuu and BBB permeation
Summary
• IVIVE relationships are limited by lack of human PK – Cliv; Vd; non-CYP PK; fafg; Cbu/Cpu – Consideration of preclinical species
• Reagent / technology advances continually needed – HHEP relay costs impacts capacity
• Co-cultures, immortalized cell lines and microphysiological systems
– Hepatic transporter cell model(s) needed with greater dynamic range • Can we identify an Industry-wide approach?
• Non-traditional modalities will continue to increase – E.G. – Antibody drug conjugates, biologics, gene therapy, parenteral
delivery
Gaps and Horizon
Acknowledgements Enzymology (and Preclinical PK) Transporter Sciences
Group PDM Project Reps / M&S and Biotransformation
Atkinson, Karen Lin, Jian Bi, Yi-An El-Kattan, Ayman
Cerny, Matt Lin, Zhiwu Costales, Chester Kalgutkar, Amit
Cianfrogna, Julie Lovering, Elaine Tseng Eatmadpour, Soraya Li, Rui
Dantonio, Aly Niosi, Mark Feng, Bo Maurer, Tristan
Di, Li Novak, Jon Kimoto, Emi Scott, Dennis
Eng, Heather Nulick, Kelly Lazzaro, Sarah Steyn, Stefan
Fahmi, Odette Obach, R Scott Mathialagan, Sumathy Tess, David
Doran, Angela Orozco, Christine Rodrigues, Dave Trapa, Patrick
Goosen, Theunis Riccardi, Keith Tylaska, Laurie Walker, Greg
Johnson, Nat Ryu, Sangwoo Varma, Mathena
Kosa, Rachel Strelevitz, Tim Vildhede, Anna
Lapham, Kim Yang, Xin West, Mark
Additional Thanks to Colleagues in Hit Design Lead Optimization, NonReg Bioanalytical and Comparative Medicine
Thank You for Your Attention
Approaches to Predicting Human Hepatic CL