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

Ayman.El-Kattan@pfizer.com

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

6

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

Medicine Design

Critical assessment of BDDCS : Solubility as a driver for drug CL

Is SOLUBILITY a valid parameter that does influence

drug clearance!

Medicine Design

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

19

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!

21

Medicine Design 22

ECCS

Ion

izat

ion

Pe

rme

abili

ty

Mo

lecu

lar

We

igh

t

“Extended Clearance Classification System” [ECCS]…...

22

Medicine Design

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

23

Medicine Design 24

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

25

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

30

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

31

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