predicting pharmacokinetics in humans: the … pharmacokinetics in humans: the bottom-up approach...

© 2001-2009 Simcyp Limited

1

Predicting Pharmacokinetics in Humans:

The Bottom-Up Approach

Jiansong Yang

[email protected]

SOT RASS TELECON

February 11, 2009


2

Agenda

3.00 Background – PK Modeling

3:10 The Bottom-Up Approach

4:00 Simcyp Rat

4:15 Discussion

4:30 End


3

Cost-Effectiveness of New Drug Development

Source of

the Problem

Is it possible to optimise

the clinical studies that are

needed and eliminate the

studies that are doomed to

fail?

Description & Prediction


4

In Vitro – In Vivo Extrapolation (IVIVE)

CLINICALPRE-CLINICAL

Ki

Kinact

LogPED50


5

Simcyp: What We Do

Regular worldwide workshops and

seminars on PK & IVIVE for key players in

drug development.

Develop and update a user- friendly,

comprehensive, mechanistic platform for

integration of ADME models &

databases

Provide consultancy services.

Provide advice / help to reach consensus

on IVIVE & ADME issues / Identify areas of

further research - (defining common / specific

projects in the form of focus groups)0 50 100 150 200 250 300

2.10 4

4.10 4

6.104

8.10 4

0

Time (hour)

Am

ount of

CY

P3A

4 in

the G

ut

( Pm

ol /g

ut) 50 mg

100 mg

200 mg

400 mg

600 mg

800 mg

0 50 100 150 200 250 3000 50 100 150 200 250 300

2.10 4

4.10 4

6.104

8.10 4

0

2.10 4

4.10 4

6.104

8.10 4

0

Time (hour)

Am

ount of

CY

P3A

4 in

the G

ut

( Pm

ol /g

ut) 50 mg

100 mg

200 mg

400 mg

600 mg

800 mg


Integration of demographic, physiological and genetic information with in vitro drug absorption, metabolism and transport data to simulate and predict in vivo drug absorption, disposition and drug-drug interactions invirtual patient populations.

Interindividual Variability

6


Physiological

Bioavailability = Release, Dissolution,

Stability, Permeability, Efflux/ or Uptake

Transporters, Gut Wall and Hepatic First

pass Metabolism, ...

CL = Unbound Fraction, Efflux or Uptake

Transporters, Enzymes, Blood Flow,

Induction, Inhibition, ......

V = Unbound Fraction, Blood Flow, Efflux

or Uptake Transporters, Organ Size, .....

PK Modeling: Bottom-Up

7


C=Cie-kit

Empirical

Bioavailability = AUCpo / AUCiv

CL = DOSE /AUC

V = D / C0

..........

Just Parameters in Equations:

Posterior Analysis of Their Covariation with

Individual Characteristics

PK Modeling: Top-Down

8


“TOP DOWN”

“BOTTOM UP”

Demography, Physiology, Genetics,

In Vitro Data

POPPK

PBPK/IVIVE

Confirming

Learning

Plasma Data

Top-Down vs Bottom-Up

9

© 2001-2009 Simcyp LimitedBottom-Up Approach in Risk Assessment

10

Clewell HJ & Andersen ME (1996) Use of physiologically-based

pharmacokinetic modeling to investigate individual versus

population risk. Toxicology, 111: 315–329

Clewell HJ (1997) Investigation of the potential impact of

benchmark dose and pharmacokinetic modelling in noncancer

risk assessment. J Toxicol Environ Health, 52: 475–515

Kedderis GL, Lipscomb JC (2001) Application of in vitro

biotransformation data and pharmacokinetic modeling to risk

assessment. Toxicol Ind Health. 17:315-21.

Lipscomb, J.C., Teuschler, L.K., Swartout, J. and Kedderis, G.L.

(2002) Incorporation of human interindividual enzyme expression

and biotransformation variance into human health risk

assessments. Toxicological Sciences 66 (Suppl):154±55


AgeWeightHeightSexGeneticsRaceDisease

Organ sizeBlood flowEnzymesTransportersPlasma proteinHaematocritTransit timepH

MWtpKaLog DH-bondingSolubilityPermeabilityMetabolismfu, fuB, fuinc

POPULATION SPECIFICFACTORS

SYSTEM SPECIFICFACTORS

DRUG SPECIFICFACTORS

++

CL,V,t½,DDI PD

Systems Pharmacokinetics

11Jamei et al. EOMT 2009

© 2001-2009 Simcyp LimitedDemography

12

Fre

qu

ency

0

1500

3000

4500

6000MALEFEMALE

Fre

qu

ency

(IN

TH

OU

SA

ND

S)

0

200

400

600

800MALEFEMALE

Age Category

Addicts

CVD

Demographic

features of ethnic

groups and disease

populations differ,

and this should be

considered when

simulating drug

behaviour:

Addicts/AIDS

CVD (general)

Angina

Arrhythmia

Diabetes

Heart Attack

Schizophrenia

Rheumatoid Arthritis

Stroke

© 2001-2009 Simcyp LimitedFactors Affecting Drug Absorption

13

Physicochemical &

Pharmaceutical issues

Physiological issues

Disintegration

De-aggregation

Dissolution

Solubility

Precipitation

Permeability

Intra-gut degradation

Gastric emptying

Intestinal mobility

pH

Intestinal metabolism

Disease state

P-gp and other transporters

Intestinal blood flow

Food effects

GI-tract fluid secretion, re-

absorption and motility


R distributionpH distributionPermeability distribution CYPs distributionBlood flow distribution

Pgp

MetabolismEnterocytes

PBPK Distribution

ModelPortal Vein Liver

Faeces

Dissolved Drug

Dissolution / Precipitation

Absorption / Efflux

FineParticles

Degradation

/ Super-Saturation

ColonJejunum I & IIDuodenum Ileum I Ileum II Ileum III Ileum IVStomach

Release

SolidDosage

The Advanced Dissolution, Absorption & Metabolism (ADAM) Model

14

© 2001-2009 Simcyp LimitedIntestinal Transit Time

15

0

50

100

150

200

250

52 92 135 165 207 250 288 325 365 400 447 493 570 More

Intestinal Transit Time (min)

Fre

qu

ency

0%

20%

40%

60%

80%

100%

120%

Yu et al. (1998)

0

50

100

150

200

250

52 92 135 165 207 250 288 325 365 400 447 493 570 More


Fre

qu

ency

0%

20%

40%

60%

80%

100%

120%

0

50

100

150

200

250

52 92 135 165 207 250 288 325 365 400 447 493 570 More


Fre

qu

ency

0%

20%

40%

60%

80%

100%

120%

Yu et al. (1998)

© 2001-2009 Simcyp LimitedVariability in pH Profile in the GI Tract

16

Wilding et al. (Presented at PKUK 2004)Ven = stomach

Duo = duodenum

Pro = proximal small intestine

Mid = mid small intestine

Dis = distal small intestine

Cae = caecum

Asc = ascending colon

Tra = transverse colon

Des = descending colon

R/S = sigmoid colon or rectum

Fae = faeces

Modified from: Fallingborg et al. (1989) Aliment. Pharmacol. Therap. 3:605-613

1.5

6.46.6

6.9

7.3

5.7 5.6 5.7

6.6 6.5 6.4

0

1

2

3

4

5

6

7

8

9

Ven Duo Pro Mid Dis Cae Asc Tra Des R/S Fae

pH

© 2001-2009 Simcyp LimitedEffects of Food on Drug Absorption

17

FDA (2002) Guidance for Industry Food-Effect Bioavailability and

Fed Bioequivalence Studies

Delay gastric emptying

Stimulate bile flow

Change gastrointestinal (GI) pH

Increase splanchnic blood flow

Change luminal metabolism of a drug substance

Physically or chemically interaction with a dosage form or a

drug substance

Food can alter the absorption process by various means

including interactions between the drug, its formulation, and

the gastrointestinal (GI) tract.

Overall, the effects of food can vary depending on the composition and size of

the meal.

© 2001-2009 Simcyp LimitedDistribution

18

Ven

ou

s B

loo

d

Art

eri

al

Blo

od

Lung

Adipose

Bone

Brain

Heart

Kidney

Muscle

Skin

Liver

Spleen

Gut

Portal Vein

PO IV

© 2001-2009 Simcyp LimitedCovariates of Determining Tissue Volumes

19

Age Sex Weight Height

Adipose

Bone

Brain

Gut Heart Liver Lung Muscle Skin

Spleen

Plasma

Erythrocytes

Kidney

© 2001-2009 Simcyp LimitedModels to Predict Tissue Volumes

20

Price et al., 2003

Male = (-90.7 * BH(m) + 178.1) * BW(kg) / 1040;

Female = (-97.5 * BH(m) + 181.2) * BW(kg) / 1040;

Volume of Brain (L) for subjects aged 0-19 years

Male = 9.22 * BW(kg)0.853 / 1040;

Female = 9 * BW(kg)0.855 / 1040;

Volume of Heart (L) in Adults

Male = (22.81 * BH(m) * BW0.5 - 4.15) / 1040;

Female = (19.99 * BH(m) * BW0.5-1.53) / 1040;

Volume of Heart (L) for Pediatrics

© 2001-2009 Simcyp LimitedDrug Distribution in Tissues

21

KtP-off

P

PPlasma Water

EW

IW

EW: Extracellular Water

IW: Intracellular Water NP: Neutral Phospholipids

NL: Neutral Lipids AP: Acidic Phospholipids

pH=7.4

pH=7.4

pH=7

-ve

NP

NL

+ve

AP

+ve

+ve

KtNP-on

KtNP-off

KtNL-on KtNL-off

KtEW-in

KtIW-in KtIW-out

KtEW-out

KP-off

KP-on+ve

P+ve KtP-on

KtP-off

KtAP-on KtAP-off

Elimination

© 2001-2009 Simcyp LimitedPredicting Drug Distribution

22

In vitro drug dependent

data on lipophilicity

(logP, LogD) + Tissue and

plasma binding (fu, fut)

Tissue Volumes (Vt) +

Compositions (Vnlt, Vphlt, Vwt)

(generated in Simcyp)

Predict Pt:p and VSS

One-compartment

distribution model

Csys, Cpv, Cliv

Physiologically-based

distribution model

Tissues and plasma

concentrations

1 2

Blood Flows (Qt)

(generated in Simcyp)

© 2001-2009 Simcyp LimitedPrediction of Metabolism

23

In vitro

system

rhCYP

HLM

HHEP

In vitro

CLuint

µL.min-1

pmol P450 isoform

µL.min-1

mg mic protein

CLuint per

g Liver

µL.min-1

106 cells

CLuint per

Liver

Scaling

Factor 1

Scaling

Factor 2

X Liver

WeightX

X

X

HPGL

pmol P450 isoform

mg mic proteinX MPPGL

MPPGL

© 2001-2009 Simcyp LimitedHepatic Clearance

24

Plasma proteinaffinity

Partition toRed cells

Plasma proteinconcentration

Haematocrit

Hepatocellularity(106 cells/G liver)

Microsomal protein(mg/G liver)

CYP abundance(pmol/G liver)

CLuH,int perhepatocyte

CLuH,int per mgmicrosomal protein

CLuH,int perSpecific CYP

Disease

Age

Sex

Environment

Genetics

Hepatic blood flow

Liver weight

CLuH.int/G liver

fuB

DiseaseAge

Sex

Genetics

Body Size Environment

Rostami-Hodjegan and Tucker, 2007

© 2001-2009 Simcyp LimitedCYP Abundance Variability

25

Different Individuals

- Genetics (Ethnicity)

PLUS

-Age (Ontogeny)

- Environment (Ethnicity)

- Sex / Co-medications

P450 Relative Abundance

(average of 167 individuals with CYP3A5 *1 allele within a

1000 randomly selected Caucasian Population)

0.2%

3.2%1.6%

15.0%

11.9%

33.1%

12.7% 11.0%4.1%

2.5%

4.7%

CYP1A2

CYP2A6

CYP2B6

CYP2C8

CYP2C9

CYP2C18

CYP2C19

CYP2D6

CYP2E1

CYP3A4

CYP3A5

© 2001-2009 Simcyp LimitedRate per pmol of “Each Enzyme”

26

Knowing:

Weight LiverMPPGL)(rhCYPK

abundanceCYP)(rhCYPVmax]h/L[CL

n

1j

n

1i ijm

jij

h

Proctor et al. Xenobiotica 2004

the abundance of each CYP isoform per mg of microsomal

protein

the isoform(s) responsible for specific metabolic routes

Americans/Europeans

Japanese/Chinese

© 2001-2009 Simcyp LimitedCYP Abundances: Caucasian vs Japanese

27

0

5

10

15

20

2C18 2J2 2D6 2C19 2B6

CY

P (

pm

ol/m

g)

0

50

100

150

2A6 2C81A2 2E1 3A52C9 3A4

Caucasian

Japanese

© 2001-2009 Simcyp LimitedEthnic Differences in Clearance

28

Caucasian (CC) vs Japanese (JP)

(Geometric mean ± 90% confidence interval, *p<0.05)

0.1

1

10

CL

po

Re

lati

ve

Dif

fere

nc

e(f

old

Mean

(geo

) in

Cau

casia

n)

Caucasian

Japanese

CC>JP* CC<JP*CC JPCC>JP* CC<JP*CC≈JP

© 2001-2009 Simcyp LimitedPrediction of CLpo (Caucasian vs Japanese)

29

10/11 8/11

(Geometric mean ± 90% confidence interval)

Caucasian

0.01

0.1

10

100

0.01 0.1 1 10 100

Predic

ted C

Lpo

(m

L/m

in/k

g)

1

Japanese

100

AlprazolamCaffeineChlorzoxazoneCyclosporineMidazolamOmeprazoleSildenafilTolbutamideTriazolamS-warfarinZolpidem

0.01 0.1 1 10

Observed CLpo (mL/min/kg)

Inoue S et al. Prediction of in vivo drug clearance from in vitro data. II: Potential inter-

ethnic differences. Xenobiotica, 2006, 36: 499-513

© 2001-2009 Simcyp LimitedLiver Cirrhosis

30

0

100

200

300

400

29.5 39.5 49.5 59.5 69.5

Median age (y)

Incid

en

ce p

er

100,0

00

Demographics

CYPs e.g CYP3A4

0

20

40

60

80

100

CP-A (6) CP-B (21) CP-C (21)*

% o

f co

ntr

ol

Cardiac output &

Blood FlowLiver volume

0

20

40

60

80

100

CP-A=46 CP-B=53 CP-C=28

% o

f co

ntr

ol

Albumin

0

10

20

30

40

50

60

Control CP-A (94)CP-B (133)CP-C (100)

Alb

um

in (

g/L

)

0

40

80

120

160

Control CP-A(112) CP-B(110)CP-C(120)

GF

R (

ml/m

in) Renal function

-150 -100 -50 0

shift in gastric emptying T1/2 in cirrhosis

-150 -100 -50 0-150 -100 -50 0

shift in gastric emptying T1/2 in cirrhosis

-150 -100 -50 0

Gastric emptying

Ascites

© 2001-2009 Simcyp LimitedPredicting the Effect of Liver Cirrhosis

31

CAFFEINE (oral)

0

5

10

15

20 ***

Fo

ld in

cre

ase in

exp

osu

re

(CL

rati

o [

co

ntr

ols

:cir

rho

tics])

DICLOFENAC (oral)

0

1

2

3

4

Fo

ld in

cre

ase in

exp

osu

re

(CL

rati

o [

co

ntr

ols

:cir

rho

tics])

METOPROLOL (oral)

0.0

2.5

5.0

7.5

10.0

Fo

ld in

cre

ase in

exp

osu

re

(CL

rati

o [

co

ntr

ols

:cir

rho

tics

])

MIDAZOLAM (oral)

0

3

6

9

12

***

Fo

ld in

cre

ase in

exp

osu

re

(CL

rati

o [

co

ntr

ols

:cir

rho

tics])

© 2001-2009 Simcyp LimitedIVIVE & Allometric Scaling

32

In vitro human data

In vivo animal data

IVIVE

Allometric Scaling

In vivo human PK

Ward & Smith 2004

© 2001-2009 Simcyp LimitedWhy Simcyp Rat?

33

IVIVE in rat (Simcyp Rat)

IVIVE in human (Simcyp)

Yes No

?Additional Data

What shall I do with the in vivo animal data?

© 2001-2009 Simcyp LimitedWhat Rat?

34

Create your own rat

A random selection of rat studies were taken from DMD (2005-

2008)

• 75% used Sprague-Dawley; 20% used Wistar

• 90% used males only; 10% used both males and females

Sprague-Dawley data used where available.

Male

250 g

No variability

© 2001-2009 Simcyp LimitedRat PBPK: An Example

35

1

10

100

1000

10000

0 2 4 6 8 10

Observed

Predicted

1

10

100

1000

10000

0 2 4 6 8 10

Observed

Predicted

1

10

100

1000

10000

0 2 4 6 8 10

Observed

Predicted

plasma

lung muscle

heart

Predicted vs. observed (Igari et al. 1982) tissue distribution of diazepam in rat

(1.25 mg/kg, iv)

1

10

100

1000

0 2 4 6 8 10

Observed

Predicted

Time (h)

Co

nc

(ng

/ml)

Time (h)

Co

nc

(ng

/ml)

Time (h)

Co

nc

(ng

/ml)

Co

nc

(ng

/ml)

Time (h)

© 2001-2009 Simcyp LimitedConclusion

36

“Everything should be made as simple as

possible, but not simpler.”

http://images.google.co.uk/imgres?imgurl=http://www.packing.be/upload/Albert_Einstein_Imagination.jpg&imgrefurl=http://www.packing.be/&h=602&w=451&sz=65&hl=en&start=7&um=1&tbnid=vhmwBPMMGUPnUM:&tbnh=135&tbnw=101&prev=/images?q=albert+einstein&ndsp=18&svnum=10&um=1&hl=en&sa=N

© 2001-2009 Simcyp LimitedAcknowledgement: Team Simcyp

37

Geoff TuckerMohsen Aarabi

Khalid Abduljalil

Malidi Ahamadi

Lisa Almond

Steve Andrews

Adrian Barnett

Zoe Barter

Kim Crewe

Helen Cubit

Duncan Edwards

Kevin Feng

Cyrus Ghobadi

Matt Harwood

Phill Haywood

Eleanor Howgate

Masoud Jamei

Trevor Johnson

James Kay

Susan Lundie

Steve Marciniak

Chris Musther

Helen Musther

Sibylle Neuhoff

Sebastian Polak

Karen Rowland-Yeo

David Turner

Hua Wang

Jiansong Yang

Mark Baker

Hege Christensen

Gemma Dickinson

Shin-Ichi Inoue

Hisakazu Ohtani

Helen Perrett

Muhmut Ozdemir

Maciej SwatKohn Boussery

Aurel Allabi

Linh Van

& .... Many others

http://images.google.co.uk/imgres?imgurl=http://www.pollyticks.com/media/1/20060531-Iranian flag copy.jpg&imgrefurl=http://www.middleeastinfo.org/forum/index.php?s=d4c0ad335b63feee9051132e13b626f8&showtopic=13779&h=257&w=450&sz=25&tbnid=ByFgIRROcRIBXM:&tbnh=73&tbnw=127&prev=/images?q=iranian+flag&um=1&start=2&sa=X&oi=images&ct=image&cd=2

http://images.google.co.uk/imgres?imgurl=http://www.theodora.com/flags/new10/poland_eagle.gif&imgrefurl=http://www.geographic.org/flags/new1/poland_flags.html&h=253&w=426&sz=9&tbnid=vZ9iu_tP4u7FhM:&tbnh=75&tbnw=126&prev=/images?q=polish+flag&um=1&start=3&sa=X&oi=images&ct=image&cd=3

© 2001-2009 Simcyp LimitedAcknowledgement: Consortium Members

38

Amgen

AstraZeneca

Daiichi-Sankyo

Eli Lilly

Genentech

GlaxoSmithKline

J&J

Lundbeck

Pharmaceutical Companies:

Universities

Aberdeen (UK); Manchester (UK); Uppsala (Sweden); Lisbon (Portugal);

Showa (Japan); China Pharmaceutical (China); Buffalo (USA); Gothenburg

(Sweden); Missouri (USA); Méditerranée (France), Groningen (Netherlands);

Washington (USA); Sheffield (UK) ….

Regulatory/Governmental Organisations

MPA (Sweden); NAM (Finland); Norwegian Medicines Agency; ECVAM (Italy)

MEB (Netherlands); FDA (USA); Framework 6, 7 (EU)

Merck

Novartis

Nycomed (Altana)

Ousuka

Pfizer

Roche

Sanofi-aventis

Servier

Takeda

UCB

Wyeth

predicting pharmacokinetics in humans: the … pharmacokinetics in humans: the bottom-up approach...

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