ECCS] in Projecting ADME Behavior and Drug-Drug ... · Medicine Design The Application of the...
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Medicine Design
The Application of the Extended Clearance Classification System
[ECCS] in Projecting ADME Behavior and Drug-Drug
Interactions in Early Discovery and Development: Industrial
Perspective
Ayman F. El-Kattan, B.Pharm, Ph.D.
Pfizer Inc. Cambridge Laboratories
Medicine Design
Thanks for being a great team
ECCS Team Manthena Varma Stefan Steyn Charlotte Allerton Ayman El-Kattan
Medicine Design
Acknowledgments
3
Professor Yuichi Sugiyama Reiken, Japan
Professor Leslie Benet UCSF, US
Medicine Design
Outline
The genesis of ECCS
ECCS, rate determining step, and clearance driving enzymes and transporters
ECCS and human ADME
ECCS and Drug-Drug Interactions
4
ECCS Applications and its impact on PFE drug discovery and beyond
Concluding remarks
Medicine Design 5
Keep the end in mind
• Css,u: Free efficacious concentration
• CLint: intrinsic clearance (mL/min/kg)
• fa: Fraction absorbed
• fg: Fraction escapes intestinal first pass effect
• t: Dosage interval (hr)
DMPK scientists role is fundamentally focused on ACCURATE PREDICTION of dose, efficacious free systemic exposure, fraction absorbed/escapes intestinal first pass effect, and clearance of NMEs to enable successful testing of mechanism of action in clinical development.
𝑫𝒐𝒔𝒆 =𝑪𝒔𝒔.𝒖 ∙ 𝝉 ∙ 𝑪𝑳𝒊𝒏𝒕
𝒇𝒂 ∙ 𝒇𝒈
Medicine Design
Department commitment to continue to improve human PK prediction
1995 – 1998 19 Compounds
T½
1998 – 2000 20 Compounds
T½
1998 – 2002 21 Compounds
Dose
1998 – 2005 50 Compounds
T½; CL/F
2006 – 2010 115 Compounds
T½; CL/F
Human PK Prediction Teams
2007-2008 21 Compounds
Profile based on PBPK
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Medicine Design
2006 – 2010 Team Conclusion:
‘NO single prediction method clearly outperforms’
7
Potential Drivers Acids & Transporter substrates increase
Generally low-permeable chemical space
Increasing alternative metabolic pathways like UGTs, AOs.
Medicine Design
2012 PK Predictions Team
Assemble next PK-Predictions team with task:
Provide an alternative framework for prediction of human PK, to enable clearer assessment of risk in our predictions.
8 Pfizer Internal Use
Medicine Design
‘Provide an alternative framework…’
Use Biopharmaceutics Drug Disposition Classification System (BDDCS) as a framework (also Phys Chem. bins, high low HLM, CYP & Non-CYP) for categorizing compounds route of elimination towards improving human PK prediction and moving out of empirical allometry to comprehensive mechanistic models.
Medicine Design
Provide alternative framework: First attempt BDDCS application
Wu and Benet 2005
Medicine Design
ROC [Receiver Operating Characteristic Curve] analysis is a better (statistical) approach over picking one compound and relating to that (for eg. Metoprolol). MDCK-LE Papp > 5 x 10-6 cm/s → almost all drugs are completely absorbed. Majority of high permeability compounds have extent of high metabolism (>70% metabolism).
High Permeability =
High Metabolism (>70%)
Low Permeability =
Low Metabolism (<70%)
Wu and Benet, 2005
Hu
man
fa (
%)
Pe
rme
abili
ty
Define permeability cut-off: ROC analysis
High Permeability Absorption / High Extent of Metabolism
Molecules
Poor Permeability and Absorption / Low Extent of Metabolism
Molecules
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Sensi
tivity
(1-Specificity)
Fa cut-off @ 80%
Fa cut-off @ 90%AUC' = 0.87
Papp = 5 x10-6 cm/s
11
Permeability (*10-6 cm/s)
Medicine Design Varma … El-Kattan, JMC 2009 Varma …..El-Kattan, MP 2012
n = 199
% D
ose
exc
rete
d in
Uri
ne
(
Hu
man
)
MDCK-LE Permeability (x10-6 cm/s)
High Passive Permeable
Low Passive Permeable
if, Freabs = 0
Renal CL is likely a predominant mechanism
if, Freabs = 1
No renal CL expected
High Permeability + High Extent of Metabolism
Poor Permeability + High Renal Recovery
Ionization of Renally Eliminated Molecules
Physicochemical and in vitro drivers of renal elimination
Pe
rme
abili
ty
Scatter Plot
BCS Class
0
50
100
150
200
250
300
1 2 3 4
Define solubility cut-off: ROC analysis
26 3
2 18
High Sol
Low Sol Exp
erim
enta
l
200 µg/mL
Solubility Cut-off
Solu
bili
ty (
µg/
mL)
Class I & III Class II & IV
Sensitivity 93% (n = 49) Specificity 86%
Solubility data: Validation against BCS and BDDCS classes. Good predictability Solubility Cut-off @ 200 µg/mL BCS Class
I II III IV
Established BDDCS Cut Off Values, we are ready for prime time! Are we?
Permeability Cut off Value
5x10-6 cm/s
Solubility Cut off Value
200 mg/mL
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Critical assessment of BDDCS : Solubility as a driver for drug CL
Is SOLUBILITY a valid parameter that does influence
drug clearance!
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Critical assessment of BDDCS : Sole emphasis on extent
SDS LD Preclinical Development
Phase I Phase II Phase III Phase IV
Medicine Design
Ob
ject
% ∆
AU
C
CYP450 Inhibitor OATP Inhibitor
BDDCS Class 2 BDDCS Class 2 BDDCS Class 1 BDDCS Class 1
Varma, M…..El-Kattan, A. Pharm Res 2015
Impact of CYP and OATP inhibitors on plasma exposure of extensive metabolism molecules: data extracted from UW DDI database
Medicine Design
Sirianni and Pang, Clin Pk 1997, 449 + Liu and Pang, DMD 2005, 1 + Shitara et al, EJPS 2006, 425 + Varma, t-DDD 2013
if, high biliary/met CL
and significant CLup
Uptake Limited
if, CLmet+CLbile << CLpass
and CLup << CLpass
Rapid Equilibrium Extended CL
Extended CL
If all the terms are appreciable relative to each other
Extended Clearance Concept
18
Medicine Design
Extent vs Rate: What truly determine the accuracy of your clearance prediction?
10
100
1000
10000
10 100 1000 10000
CL m
et (m
L/m
in/k
g)
In Vivo CLint (mL/min/kg)
Clint.HLM
Modified from Varma, M. et al., JPET, 2014
Flu
Rep
Ato
Pit
Cer
Bos
Gly
Pra Val
Ato: Atorvastatin Bos: Bosentan Cer: Cerivastatin Gly: Glyburide
Flu: Fluvastatin Pit: Pitavastatin Pra: Pravastatin Rep: Repgalanide
Ros: Rosuvastatin Val: Valsartan
CLmet
The Biased Emphasis on Extent of Metabolism vs Rate For NMEs with Active Hepatic Uptake
Underestimation
Ros
10
100
1000
10000
10 100 1000 10000
CL u
p o
r C
L met
(mL/
min
/kg)
In Vivo CLint (mL/min/kg)
PS.inf.Scal
Clint.HLM
CLmet
CLup
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Medicine Design
Cla
ssif
y Th
e C
lear
ance
Pat
hw
ay
3A
4
2C
9
Re
na
l Tr
an.
OA
TP
Effl
ux
Tran
. U
GT
QSA
R
Re
nal
B
iliar
y H
epat
ic
Up
take
M
eta
bo
l
Lead Identification and Selection
Physiologically Based Pharmacokinetics (PBPK) to project human PK/target conc and DDI Liabilities
Lead Development
The urgent call for a shift in our approach for drug ADME and dose prediction: Mechanism based approach driven by extended clearance concept
Medicine Design
One size fits all is NO longer an option! Now is the time of change!
Relying only on HLM and human hepatocyte as the SOLO TOOLS for predicting human clearance (along with single species allometric scaling) is NO longer a valid approach! Now is the time of change!
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ECCS
Ion
izat
ion
Pe
rme
abili
ty
Mo
lecu
lar
We
igh
t
“Extended Clearance Classification System” [ECCS]…...
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ECCS utility in defining CL rate determining step
Include compounds according to the following filters: o MW ≤ 700 Dalton [Small Molecule Druggable Space]
o In absence of experimental data, cMDCK-LE confidence ≥ 0.6
o Contribution of rate determining step is > 70% of total clearance
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hOATP and rOatp1b2 datasets High MW – low permeability Acids/Zwitterions compounds predominantly show hepatobiliary excretion. OATP substrates are mostly high MW Acids/Zwitterions compounds. NOT NECESSARILY WITH LOW PERMEABILITY. Hepatic uptake is the rate-determining step.
144/188 (77%) 54/60 (90%) M
DC
K-L
E P
erm
(x1
0-6
cm
/s)
Mwt (Dalton)
Avg
of
do
se in
rat
bile
%
Acid Base Neutral Zwitter
45
40
35
30
25
20
15
10
5
0
MDCK-LE Perm (x10-6 cm/s) Mwt (Dalton)
Hepato-biliary physicochemical and in vitro drivers of disposition
Ion
izat
ion
Pe
rme
abili
ty
Mo
lecu
lar
We
igh
t
24
Medicine Design
Permeability cut off = 5 X 10-6 cm/sec
Molecular Weight cut off= 400 [Class 1/3]
Ionization= Acids/Zwits vs Bases/Neutrals
Varma, M…..El-Kattan, A. Pharm Res 2015
Extended Clearance Classification System [ECCS] CL rate determining process
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26 26 Varma, M…..El-Kattan, A. Pharm Res 2015
Bases/NeutralsAcids/Zwits
Hig
h P
erm
eab
leL
ow
Perm
eab
le
Class 1
Class 3
Class 2
Class 4
Metabolism
Renal
Class 1A Class 1B
Metabolism Hepatic uptake
MW ≤400 MW >400 n=172
n=32
n=29 n=14
Renal5%
Metabolism95%
ECCS 2
Renal75%
Metabolism25%
ECCS 4
Renal10%
Metabolism90%
ECCS 1A
Renal14%
Hepatic uptake
86%
ECCS 1B
Class 3A Class 3B
Renal Hepatic uptake
(or) Renal
MW ≤400 MW >400
n=24
n=36
89%
Medicine Design
ECCS, rate determining step, and clearance driving enzymes and transporters
27
Medicine Design
ECCS Class 1A CL rate determining step, main enzymes/transporters and prediction tools
Varma, M…..El-Kattan, AF. Pharm Res 2015 El-Kattan, AF. et al. Pharm Res 2016
Acids/Zwitterions
HLM Hepatocytes
Clearance Prediction Tool(s) of Choice ECCS Class 1A
N = 37
ECCS Class 1A Main Metabolizing Enzymes
N = 37
Medicine Design
ECCS Class 1B CL rate determining step, main enzymes/transporters and prediction tools
29
Acids/Zwitterions
Sandwich Culture Human Hepatocytes
Clearance Prediction Tool(s) of Choice ECCS Class 1B
Eg. bosentan, cerivastatin,
atorvastatin, repaglinide, fluvastatin
ECCS Class 1B Main Metabolizing Enzyme/Uptake Transporters
Varma, M…..El-Kattan, AF. Pharm Res 2015 El-Kattan, AF. et al. Pharm Res 2016 Jones, H et al., 2012, Rui L et al, 2014
Medicine Design
ECCS Class 2 CL Rate Determining Step and CL Prediction Tools
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Bases/Neutrals
ECCS Class 2 CL rate determining step, main enzymes/transporters and prediction tools
ECCS Class 2 Main Metabolizing Enzymes
N = 104
Varma, M…..El-Kattan, AF. Pharm Res 2015 El-Kattan, AF. et al. Pharm Res 2016 30
Medicine Design
Acid/Zwitterion
Low
Pe
rme
abili
ty
ECCS Class 3A CL rate determining step, main enzymes/transporters and prediction tools
Clearance Prediction Tools of Choice ECCS Class 3A
ECCS Class 3A Main Uptake/Efflux Transporters
Varma, M…..El-Kattan, AF. Pharm Res 2015 El-Kattan, AF. et al. Pharm Res 2016
N = 14
N = 8
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Medicine Design 32
Sandwich Culture Human Hepatocytes
Clearance Prediction Tools of Choice ECCS Class 3B
ECCS Class 3B CL rate determining step, main enzymes/transporters and prediction tools
N = 14
N = 8
Acid/Zwitterion
Low
Pe
rme
abili
ty
ECCS Class 3B Main Uptake/Efflux Transporters
32
Medicine Design
ECCS Class 4 CL rate determining step, main enzymes/transporters and prediction tools
33
Varma, M…..El-Kattan, AF. Pharm Res 2015 El-Kattan, AF. et al. Pharm Res 2016
Clearance Prediction Tools of Choice ECCS Class 4
ECCS Class 4 Main Uptake/Efflux Transporters
Base/Neutral
Low
Pe
rme
abili
ty
33
Medicine Design
ECCS and Human ADME
Medicine Design
Average and Median Compound fa based on ECCS Classification
Mo
dif
ied
fro
m A
. El-
Ka
tta
n e
t a
l. P
ha
rm R
es, 2
01
6
ECCS
f a
Medicine Design
Mo
dif
ied
fro
m E
l-K
atta
n, A
. et
al.,
Ph
arm
Re
s 2
01
6 f g
ECCS
Average and Median Compound fg based on ECCS Classification
Medicine Design
Mean percentage contribution of all measured UGT and CYP metabolizing enzyme proteins in the small intestine
37
UGT1A1 17%
UGT1A3 1%
UGT1A4 4%
UGT1A5 0%
UGT1A6 2%
UGT1A7 14%
UGT1A8 11%
UGT1A9 12%
UGT1A10 8%
UGT2B7 0%
UGT2B10 9%
UGT2B11 0%
UGT2B15 0%
UGT2B17 22%
UGT2B18 0%
CYP2C9 12%
CYP2C19 1% CYP2D6
1%
CYP2J2 1%
CYP3A4 62%
CYP3A5 23%
UGT1A1 = 8.5 pmol/mg protein Highest UGT expressed enzyme in small intestine
CYP3A4 = 66.2 pmol/mg protein Highest CYP expressed enzyme in small intestine
SimCYP v.15.1
Medicine Design
Average and Median Compound fg based on Major Metabolizing Enzymes
38
f g
Modified from El-Kattan, A. et al., Pharm Res 2016
ECCS
Impact of Grape Fruit Juice
Medicine Design
Average and Median Compound fh based on ECCS Classification
39
100 %
90 %
80 %
70 %
60 %
50 %
40 %
30 %
20 %
10 % 1A 1B 2 3A 3B 4 18 11 103 14 16 25
95 % 95 % 79 % 98 % 98 % 97 % 91 % 82 % 72 % 98 % 94 % 93 %
Count Median Avg
f h
El-K
atta
n, A
. et
al.,
Ph
arm
Re
s 2
01
6
ECCS
Medicine Design
Limiting factors of oral bioavailability
Medicine Design
ECCS and Drug-Drug Interactions
Medicine Design
Weighted prevalence estimates of the most commonly used therapeutic classes among older adults in the united states
Medicine Design
Examples on reported DDI for medications given to seniors
Precipitant Disease Object %AUC*
Itraconzole Dyslipidemia Lovastatin 3540
Cyclosporine Dyslipidemia Pravastatin 2183
Grapefruit Juice Dyslipidemia Simvastatin 1514
Ketoconazole Congestive Heart Failure Conivaptan 982
Repaglinide Diabetes Gemfibrozil/Itraconazole 1830
Pioglitazone Diabetes Gemfibrozil 366
Midazolam Anxiety Tipranavir /Ritonavir 2590
Budesonide COPD Ketoconazole 581
Nebivolol Hypertension Bupropion 621
Eletriptan Migraine Ketoconazole 488
*: Extracted from University of Washing Drug Interaction Database
Medicine Design
Rate determining step and DDI
Rate Determining Clearance
P e
r c e
n t
C h
a n g
e A
U C
5000 %
4500 %
4000 %
3500 %
3000 %
2500 %
2000 %
1500 %
1000 %
500 %
0 %
Hep.uptake Metabolism Renal
17 130 30
138 % 142 % 56 %
471 % 462 % 70 %
Count
Median
Avg
% A
UC
44
El-K
atta
n, A
. et
al.,
Ph
arm
Re
s 2
01
6
Medicine Design ECCS
5000 %
4500 %
4000 %
3500 %
3000 %
2500 %
2000 %
1500 %
1000 %
500 %
0 %
1A 1B 2 3A 3B 4 15 13 110 9 13 21
51 % 255 % 156 % 75 % 87 % 63 % 96 % 576 % 495 % 109 % 292 % 109 %
Count Median Avg
% A
UC
The relationship between ECCS and (% AUC) in presence of inhibitor
45
El-K
atta
n, A
. et
al.,
Ph
arm
Re
s 2
01
6
Medicine Design
Impact of Rifampicin on the systemic exposure based on ECCS Classification
Rifampicin
Potent OATP inhibitor (single dosing) Potent CYP3A4 inducer (chronic dosing)
Active hepatic uptake [rate determining step]
Metabolism [rate determining step]
Renal [rate determining step]
Pie charts depict the percentage of interactions per ECCS class in the no (GREEN), low (yellow), moderate (PINK), and high (RED) DDI magnitude categories.
Medicine Design
The Relationship Between Main Transporter and (% AUC) in Presence of Inhibitor
El-K
atta
n, A
. et
al.,
Ph
arm
Re
s 2
01
6
Medicine Design
The Relationship Between Main Metabolizing Enzyme and (% AUC) in Presence of Inhibitor
Substrates for CYP enzymes are more prone to higher DDI (%AUC ) with the current
inhibitors available in Market
El-K
atta
n, A
. et
al.,
Ph
arm
Re
s 2
01
6
Medicine Design
ECCS Applications and its impact on PFE
drug discovery and beyond
Medicine Design
3A
4
2C
9
Re
na
l Tr
an.
OA
TP
Effl
ux
Tran
. U
GT
QSA
R
Re
nal
H
epat
ic
Up
take
M
eta
bo
l
Lead Identification and Candidate Selection
Physiologically Based Pharmacokinetics (PBPK) to project human PK/target conc and DDI Liabilities
Lead Development
ECCS as a Basis for Clearance Profiling Strategy (CPS)
Medicine Design
0.2 2 20
Repaglinide AUC ratio
PBPK Predicted
Observed
+ Keto 200mg (CYP3A4 inh.)
+ CSA 100mg (OATP inh.)
+ Gem 900mg (OATP & CYP2C8 inh.)
+ Itra 100mg (CYP3A4 inh.)
+ Gem 600mg + Itra 100mg (OATP & CYP3A4+2C8 inh.)
+ Clari 250mg CYP3A4 inh.)
+ Gem 600mg (OATP & CYP2C8 inh.)
+ Rif 600mg (24h)(CYP3A4 ind.)
+ Rif 600mg (12h)(CYP3A4 ind.)
+ Rif 600mg (1h)(CYP3A4 ind. & OATP inh)
+ Rif 600mg (0h)(CYP3A4 ind. & OATP inh)
1 10
Integration of Multiple Mechanisms is Key: Case Example - Repaglinide DDI predictions [ECCS 1B]
Varma et al. Pharm Res 2013, 1188 Varma et al. DMD 2013, 966
CYP3A induction and
OATP inh.
CYP3A inh.
OATP inh.
DD
I Co
mp
lexi
ty In
ten
sifi
es
wit
h In
terp
lay
Interplay inh.
Medicine Design
Transporter DDI Timing: Call to shift Screening earlier & Lead Identification and development
Rate Determining Clearance
P e r c
e n t C
h a n g e A
U C
5000 %
4500 %
4000 %
3500 %
3000 %
2500 %
2000 %
1500 %
1000 %
500 %
0 %
Hep.uptake Metabolism Renal 17 130 30
138 % 142 % 56 %
471 % 462 % 70 %
Count
Median
Avg
53
Clearance determined by metabolism (> 70%). Excreted as metabolites (> 70%). Absorption is not permeability limited.
Clearance determined by OATPs (> 70%). Excreted as metabolites (> 70%. Absorption is not permeability limited.
Clearance determined by renal (> 70%). Excreted as parent in urine (> 70%). Absorption is permeability limited.
Clearance determined by renal/OATPs (> 70%). Excreted as parent in urine /bile (> 70%). Absorption is permeability limited.
Clearance determined by renal (> 70%). Excreted as parent in urine (> 70%). Absorption is permeability limited.
Clearance determined by metabolism (> 70%). Excreted as metabolites (> 70%). Absorption is not permeability limited.
ECCS
Validate the ECCS Classification
The Roadmap to ECCS Application in Drug Industry
Medicine Design
Concluding remarks
ECCS is based on readily available pillars and consistent with the mechanistic principles of extended clearance and renal elimination.
ECCS enables efficient use of resources in early drug discovery by moving screening paradigm from one size fits all philosophy to clearance driven screening approach.
ECCS predicts both extent of metabolism and CL rate determining step therefore has a better ability to predict clearance rate determining step and DDI compared to other classification systems i.e. BDDCS.
Unlike BDDCS, ECCS is able to discern biliary from renal elimination for Class 3A/4.
ECCS is a valuable classification system in shedding light on the driver of bioavailability for NMEs.
ECCS is a GUIDANCE and exceptions will always happen, they create great opportunities for us to expand our knowledge in drug disposition.
54
Medicine Design
Together Everyone Achieves More = TEAM
David Tess Stefan Steyn Shinji Yamazaki Manthena Varma Ayman El-Kattan
Tristan Maurer Doug Sprachlin Susanna Tse Jenny Liras
Dennis Scott Tristan Maurer Larry Tremaine Susanna Tse Jenny Liras Charlotte Allerton Tess Wilson David Rodrigues
Stefan Steyn Manthena Varma Charlotte Allerton Ayman El-Kattan
Medicine Design
Your are Cordially Invited to Attend URI Transporter Workshop
Transporters in Drug Discovery and Development Driving
Knowledge from Laboratory to Label
July 31-August 2, 2017
University of Rhode Island
College of Pharmacy