An Introduction to:In Vitro-In VivoExtrapolation (IVIVE)

IN CONFIDENCE © 2001-2009

An Introduction to:In Vitro - In Vivo Extrapolation (IVIVE)

Senior Scientific Advisor, Head of M&S Honorary Lecturer, University of Sheffield

The University of Greenwich, 29th Oct 2009, UK

[email protected]

Masoud Jamei


Current: Geoff Tucker, Amin Rostami-Hodjegan, Mohsen Aarabi, Khalid Abduljalil, Malidi Ahamadi, Lisa Almond, Steve Andrews, Adrian Barnett, Zoe Barter, Kim Crewe, Helen Cubitt, Duncan Edwards, Kevin Feng, Cyrus Ghobadi, Matt Harwood, Phil Hayward, Masoud Jamei, Trevor Johnson, James Kay, Kristin Lacy, Susan Lundie, Steve Marciniak, Claire Millington, Himanshu Mishra, Chris Musther, Helen Musther, Sibylle Neuhoff, Sebastian Polak, Camilla Rosenbaum, Karen Rowland-Yeo, Farzaneh Salem, David Turner, Kris Wragg

Previous: Aurel Allabi, Mark Baker, Kohn Boussery, Hege Christensen, Gemma Dickinson, Eleanor Howgate, Jim Grannell, Shin-Ichi Inoue, Hisakazu Ohtani, Mahmut Ozdemir, Helen Perrett, Maciej Swat, Linh Van, Hua Wang, Jiansong Yang & .... Many others

Acknowledgement: The Team

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

http://www.woodlands-junior.kent.sch.uk/customs/xmas/calendar/flags/swedish_flag.jpg

http://upload.wikimedia.org/wikipedia/commons/9/94/Flag_of_the_Comoros.svg

http://images.google.co.uk/imgres?imgurl=http://www.britishcouncil.org.br/champions/blog/upload/06052008074302_India_flag.gif&imgrefurl=http://www.britishcouncil.org.br/champions/blog/c_article_display.asp?idb=18&v=501852426&ida=124&usg=__zvdNmf8QAd9HxkyQAhqDMLcPlQU=&h=240&w=360&sz=7&hl=en&start=3&um=1&tbnid=HTXuvEKJGmKqYM:&tbnh=81&tbnw=121&prev=/images?q=india+flag&hl=en&um=1


Grants Received by Simcyp


Simcyp Background

“Simcyp” stands for simulating CYPs (a super family of metabolising enzymes).

The Simcyp® Population-Based ADME Simulator is a platform and database for „bottom-up‟ mechanistic modelling and simulation of the ADME processes of drugs and drug candidates in healthy and disease populations.

Simcyp is a spin-out company of the University of Sheffield founded in 2001.

Simcyp activities and future developments are guided by a consortium of pharmaceutical companies (the Simcyp consortium).


Pharmacology is the study of how drugs interact with living organisms to produce a change in function. The field encompasses drug composition and properties, interactions, toxicology, therapy, and medical applications and antipathogenic capabilities.

Pharmacokinetics (PK) is a branch of pharmacology dedicated to the determination of the fate of substances administered externally to a living organism.Or, what the body does to a substance.

Pharmacodynamics (PD) is the study of the biochemical and physiological effects of drugs on the body, the mechanisms of drug action and the relationship between drug concentration and effect.Or, what the substance does to the body.

Source: Wikipedia

Pharmacology, PK and PD


In Vitro - In Vivo Extrapolation (IVIVE)

in vitro

In vitro (Latin: within the glass) refers to the technique of performing a given procedure in a controlled environment outside of a living organism.

In vivo (Latin for "within the living") refers to experimentation using a whole, living organism as opposed to a partial or dead organism.

in vivo

Mechanistic approach

Drug fate in body


CLINICALPRE-CLINICAL

Ki

Kinact

LogPED50

One Source of the Problem


Hoffman J M et al. Radiology 2007;245:645-660

A Timeline of Traditional Drug Discovery and Development


Hoffman J M et al. Radiology 2007;245:645-660

Estimate of the Total Investment required to “launch”

Windhover's in vivo: the business and medicine report, Bain drug economics model, Nov 2003


PK is often divided into several areas including, but not limited to, the extent and rate of Absorption, Distribution, Metabolism and Excretion (ADME).

Absorption is the process of a substance entering the body through mouth.

Distribution is the dispersion or dissemination of substances throughout the fluids and tissues of the body.

Metabolism is the irreversible transformation of substances and its daughter metabolites.

Excretion is the elimination of the substances from the body. In rare cases, some drugs irreversibly accumulate in a tissue in the body.

Source: Wikipedia

The biological, physiological, and physicochemical factors influence the rate and extent of ADME of drugs in the body.

ADME


UNWANTEDRESPONSE

TOXICITY

DESIREDRESPONSE

THERAPY

NO RESPONSE

NO CHANGE

Drugin Tablet

Drug at Receptor

Drugin Faeces

Drug in Gut

Release

Tabletin Faeces

Drugin Tablet in Gut

ComplianceComprehension

Excretion

Drug in Blood

Absorption

Drug in Tissues Distribution

DrugMetabolites

Drug inUrine, Bile, Milk

ExcretionMetabolism

Excretion

Metabolite at Receptor

Food, environment, genetic, race, gender, etc effects!

DrugMetabolites

Metabolism

ADME: The Roadmap to Site of Effect

DrugMetabolites

Metabolism


C=Cie-kit

Empirical

1

2

Compartmental Physiological

GT Tucker (Basic PK Course)

PK Models

Different PK models:


Mechanistic IVIVE & PBPK

SystemsData

Drug

DataTrial

Design

Population Pharmacokinetics &

Covariates of ADME

Combining Physiological and Drug-dependent Data

(Jamei et al., 2009)


Environment Disease

Genetics

The Challenge of Population Variability


Age(Distribution in Population)

Ethnicity Disease

Sex(Distribution in Population)

Genotypes(Distribution in Population)

Height

Weight

Body Surface

Area

LiverVolume

Heart Volume

BrainVolume

LiverWeight

MPPGLHPGL

Enzyme Abundance

IntrinsicClearance

Body Fat

CardiacOutput

CardiacIndex

SerumCreatinine

Renal Function

Relationships Between Covariates Affecting ADME

(Jamei et al., 2009)


Covariates of Determining Tissue Volumes

Age Sex Weight Height

Adipose

Bone

Brain

Gut Heart Liver Lung Muscle Skin

Spleen

Plasma

Erythrocytes

Kidney


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 M&F aged 0-19 (including adult F)

Price et al., 2003

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 others

Models to Predict Tissue Volumes

ICRP

Age (year)

00.20.40.60.81

1.21.41.6

0 5 10 15 20 25

Bra

in V

olu

me (

L)

ICRP

Age (year)

00.20.40.60.81

1.21.41.6

0 5 10 15 20 25

Bra

in V

olu

me (

L)

Predicted

Male Female

Predicted


In many cases, pharmacological action, as well as toxicological action, is related to plasma concentration of drugs. Consequently, through the study of PK parameters, we will be able to individualise therapy for patients.

van de Waterbeemd and Gifford 2003, Drug Discovery

Volume of distribution

ClearanceAbsorption

Half-lifeOral

bioavailability

Dosing regimen: How often?Dosing regimen: How much?

Dosing Regimen and PK Parameters


From Moore & Dalley, 5th Ed

Oral Absorption and the GI Tract


Physicochemical &Pharmaceutical issues

Disintegration

De-aggregation

Dissolution

Solubility

Precipitation

Permeability

Intra-gut degradation

Physiological issues

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

Factors Affecting Solid Drug Absorption


Portal Vein

Liver

To Siteof

Action

MetabolismMetabolism

To Faeces

Gut Wall

Gut Lumen

Fa FG FH

Rowland and Tozer 1995

Oral Absorption and First-Pass Effect


Foral = fa . FG . FH

Fraction of dose released from formulation and permeates through gut wall

Fraction escaped metabolismin enterocytes

ReleaseSolubilityStabilityTransitPermeability

MetabolismPermeabilityBindingBlood Flow

Fraction escaped metabolism in hepatocytes

MetabolismTransportBindingBlood Flow

Oral Bioavailability


disintegration

deaggregation

dissolution

reaggregation

AbsorptionSolution

precipitation

dissolution

Solid Drug Absorption


Dissolution Rate

Solid drug

kt,n-1AS,n-1 kt,nAS,n

PrecipitationRate

Absorption Rate

Dissolved drug

kt,n-1AD,n-1 kt,nAD,n

ReleaseRate

Drug in formulation

kf,n-1AF,n-1 kf,nAF,n

Absorbed drug

To portal vein

Transport RateLuminal

Degradation

Gut Wall Metabolism

AF,n : the amount of solid mass trapped in the formulation and not available for dissolution

AS : the amount of solid mass available for dissolution

AD : dissolved drug

Jamei et al. (2009) AAPSJ

Breakdown / Dissolution Stages


dt

AdAkAk

dt

dA

dt

dA nF,

1n,S1n,tn,Sn,t

n,dissn,S

nentgutnTnnDntnDntn

ndissnDCfuCLuAkAkka

dt

dA

dt

dA,,int1,1,,,ndeg,

,,k

nentgutnTnGnentnentndissn

nent

nentCfuCLuCLuCQAka

Vdt

dC,,int,int,,,

,

, 1

Jamei et al. (2009) AAPSJ 11:225

Some Differential Equations

)t(V

AC

h

1

)t(r

1D)t(rπ4

dt

dA

n,lumen

n,D

n,S

eff

2n,diss


Pgp

Metabolism

Enterocytes

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

R distributionpH distributionPermeability distribution CYPs+Pgp distributionBlood flow distribution

Release

SolidDosage

Jamei et al. 2009After Agoram 2001

Advanced Dissolution Absorption & Metabolism


Fluid Dynamics in the GI-tract

Rsec, j

jjjjAbsjjjj

VKtVKRVKtdt

dV ,Resec,11

Rsec, j: Fluid secretion rate into jth gut segment (1/h)

KRe-Abs, j

Ktj Vj

Vj: Volume of fluid in jth segment (mL)

Ktj: Transit rate constant in jth segment (1/h)

KRe-Abs, j: Fluid re-absorption rate constant from jth segment (1/h)

Ktj-1 Vj-1

Vj


Inter-individual Variability & fa

fa vs Peff and Tsi (R=1.7 cm)

Peff (cm/h)Tsi (h)0

510

0

2

40

50

100

f a(%

)M Jamei et al, 2009

0

50

100

150

200

250

52 135 207 288 365 447 570

Intestinal Transit Time (min)

Fre

qu

en

cy

0%

20%

40%

60%

80%

100%

120%

Yu et al. (1998)

Probability distribution fitting Sensitivity Analysis


The Clearance (Cl) of a drug is the volume of plasma from which the drug is completely removed per unit time. The amount eliminated is proportional to the concentration of the drug in the blood.

Q x CAQ x CV

Q(CA - CV)

Rate of Extraction=E = (CA-CV)/CA

Mass Balance

Clearance = QE

Clearance (CL)


QH.fuB.CLuint

QH + fuB.CLuint

CLH =

fuB.CLuint

QH + fuB.CLuint

EH =

Metabolism mainly happens in the liver but it can happen in the gut and to much lesser degree in the kidney.

Can we find Cluint from in vitro assays?How?

Intrinsic hepatic (gut) clearance (CLint): The ability of the liver (gut) to remove xenobiotic from the blood in the absence of other confounding factors (e.g., QH).

Metabolism in the liver


In vitrosystem

rhCYP

HLM

HHEP

In vitroCLuint

µL.min-1

pmol P450 isoform

µL.min-1

mg mic protein

CLuint per g Liver

µL.min-1

106 cells

Scaling Factors for Hepatic Clearance

CLuint per Liver

Scaling Factor 1

Scaling Factor 2

X LiverWeight

X

X

X

HPGL

pmol P450 isoformmg mic protein

XMPPGL

MPPGL


Total CYP in gut

Microsomal Protein(mg/g liver)

Hepatocellularity(106/g liver)

Overall CYPs(pmol/g liver)

CLint per CYP

Liver Weight

Liver Blood Flow

CLint Liver

Gut Blood Flow

Gut Surface Area

Gut Wall Permeability

CLint Gut

fuB

CLpo

MPPGL

CYP/mg x MPPGL

HPGL

CLint per Specific CYP

CLint per mg of Microsomal Protein

CLint perHepatocyte

CLH

fa, FGGenetic/Environmental/race/age/sex/disease

considerations

Overal CYPsin gut

IVIVE - Metabolism


Knowing:

htLiver WeigMPPGL)(rhCYPK

abundanceCYP)(rhCYPVmax]/[

n

1j

m

1i ijm

jij

int

hLCLu

Proctor et al. Xenobiotica 2004

the abundance of each CYP isoform per mg of microsomal protein

the isoform(s) responsible for specific metabolic routes

][

maxint

SKm

VCL

Americans/Europeans

Japanese/Chinese

Rate per pmol of “Each Enzyme”

CYP1A2

CYP2A6

CYP2B6

CYP2C8

CYP2C9

CYP2C18

CYP2C19

CYP2D6

CYP2E1

CYP2J2

CYP3A4

CYP3A5


[S]

[E]

[E·S]

[P]

[E·I]

[I] [PI]

Rsys

kdegrad

Induction

kinact

[E·MI]

Accelerated Deactivation

Mechanistic Model for Expressing Enzyme Pool


Sub Inh 1 Inh 2 Inh 3

Comp, MBI, Ind

Comp, MBI, Ind

Comp, MBI, Ind

Inh1 MetSub Met

Comp, MBI, Ind

Comp, MBI, Ind

Comp, MBI, Ind

Mutual Interactions: Drugs/Metabolites/Self-Induction/Inhibition


Vss knowing distribution into individual tissues is (Sawada et al., 1984):

Vp = volume of plasma; Vt = tissue (t) volume

t

:pttepss PVP:EVVV

Predicting Volume of Distribution (Vss)

ssp

sst

ptpC

CPK

,

,

:

ssp

sse

C

CPE

,

,:

Tissue : Plasma partition coefficient

Erythrocyte : Plasma partition coefficient


Gut Lumen

LiverSystemic

CompartmentQPV+HA

QPV QPV

QHA

CLH

PO

IV

Renal Clearance

Minimal Physiologically-Based PK Model

Gut Metabolism

Faeces

Portal Vein

Gut Wall

1-fa

fa 1-FG

FG

Hepatic Clearance

FH

CLR


Physiological parameters including: • tissue volumes,• tissue compositions,• blood flow to each organ/tissue,• Enzyme abundances and distributions,• Transporters abundances and distributions

Whole Body Physiologically-based PK Parameters

Physiologically-based pharmacokinetics (PBPK) models need different sets of parameters which can be divided into:

Drug-dependent parameters including:• Physicochemical and blood/plasma binding data (MW, LogP, pKa, fu,

B:P, etc),• Absorption data (fa, ka, permeability, solubility, particle size, etc),• Metabolism data (CL, CLint, etc),• Distribution data (tissue:plasma ratios (Kp))• Transport data (Jmax, Km, REF, CLPD, etc)


VenousBlood

ArterialBlood

Lung

Adipose

Bone

Brain

Heart

Kidney

Muscle

Skin

LiverSpleen

GutPortal Vein

PO DoseIV Dose

Full PBPK Model with Time-Dependent Volume


KtP-off

P

PPlasma Water

EW

IW

EW: Extracellular WaterIW: 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

Ktel

Multicompartment Mammillary Model


TPR

NPNLIWEWpu PRKa

Y

fPfPf

Y

XfK

7.03.0

Y

aAPKa

Y

fPfPf

Y

XfK TAPNPNL

IWEWpu

7.03.0

Strong bases (pKa ≥ 7) and Zwitterions (pKa ≥ 7)

Other compounds (Zwitterions pKa < 7, neutrals, acids and weak bases)

Rodgers and Rowland 2006, 2007

Prediction of Tissue to Plasma Partition Coefficients


Active and Passive Transport

The drug movement across the cell membrane can be either passiveor/and active.

Extracellular fluid

Intracellular fluid

Capillary bloodQT QT

Phospholipid bilayer

Perfusion-limited penetration (permeability is NOT rate limiting) Permeability-limited penetration (permeability is rate limiting)

For most drugs the capillary membrane is very permeable and diffusion to the interstitial fluid is very fast (Gibaldi and Perrier 1975).

http://cellbiology.med.unsw.edu.au/units/science/lecture0803.htm


> 50 human ABC transporters are identified;

7 sub-families (A-G)

> 360 human SLC transporters;

48 sub-families

http://www.humanabc.bio.titech.ac.jp/http://www.bioparadigms.org/slc/menu.asp

Known Human Transporters!


Ho and Kim, 2005

Tissues Transporters


EW: Extracellular Water

IW: Intracellular Water NP: Neutral Phospholipids

NL: Neutral Lipids AP: Acidic Phospholipids

KtP-off

P

PCapillary blood

EW

IW

pH=7.4

pH=7.4

pH=7

-ve

NP

NL

+ve

+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

Ktel

AP

Permeability-limited Liver Model - Hepatobiliary Transporters

Tight junction

Bile

OATP1B1 OATP1B3 OCT1

P-gp

MRP3

BCRP

MRP2

Sinusoidal membrane

Canalicularmembrane


Tune design parameters to fit observations

Parameter Estimation Module

Simcyp simulation

Trial and Error

Tune design parameters to fit observations

Parameter Estimation (PE) Module


Parameter Estimation Process

During a parameter estimation process the design parameters are changed, according to a specific algorithm, to get the model outputs as close as possible to the observed DVs.

Design parameters: Vss, CL, fu, BP, …Model: one-compartment absorption and/or PBPK modelDVs: plasma concentrations

t1t2 t3

0

1

2

3

C(t)


Least Squares (LS) Objective Function

C(θ, t)

e (t1)

t1 t20 20 40 60 80

0

1

2

3

t3

e (t2)

e (t3)

ni

1i

2

iii

ni

1i

2

iiWLS t,C)t(ywmin)t(ewminˆ

ni

1i i

2

ii

y

)t,(fy

ni

1i2

i

2

ii

y

)t,(fy

ni

1i i

2

ii

)t,(f

)t,(fy

ni

1i2

i

2

ii

)t,(f

)t,(fy


Direct/random search methods (Hooke-Jeeves,

Nelder-Mead, …);

Genetic Algorithms (GA);

Combined Algorithms:

Begin with a global optimisation method (GA) and

then switch to a local optimisation method; e.g.,

HJ or NM.

Optimisation Algorithms


Genetic Algorithms

Randomly Assigned Candidates

Evaluate Candidates

Rank Candidates

Reproduce New Candidates

Recombination and Mutation

Select a New Set of Candidates

Set of Candidate Parameters


Maximum Likelihood (ML) Estimation

C(θ, t)

t1t20 20 40 60 80

0

1

2

3

t3

In a population, the model parameters and observations are different for different subjects and we are interested in predicting individual as well as population parameters.

l(θ|y1)

l(θ|y2)

2

2

2

,exp

2

1|

i

i i

i

tCyy

Assuming normal distribution of parameters N(C(θ, t1), σ12)

Likelihood function:

l(θ|y3)


Maximum a Posterior (MAP) Objective Function

MAP estimation is a Bayesian approach in the sense that it can exploit an additional information on the supplied experimental data.

P

j

2

j2

j

2

jjN

1i

2b

i102b

i10

2

iiMAP )ln(

)())t,(fbb(ln

))t,(fbb(

))t,(fy()(O 2

2

Consequently if the user has prior knowledge regarding the experimental data then the MAP should in theory provide more accurate estimations of the design parameters than the Maximum Likelihood which only requires experimental measurements.

Where β={b0, b1, b2} vector defines the variance model:

Additive β={b0, 0, 1}

Proportional β={0, b1, 1}

Combined β={b0, b1, 1}

MAP differs from ML in that MAP assumes the parameter θ is also a random variable which has a prior distribution p(θ)


Expectation-Maximisation (EM) Algorithm

E-step:

Determining the conditional expectation using Monte Carlo (MC) sampling and updating MC pool for each individual after each iteration

M-step:

Maximise this expectation with respect to θ and updating population parameters and variance model parameters

In order to determine the ML or MAP estimations we need to use an optimisation algorithm.

The Expectation-Maximisation (EM) algorithm is one of the most popular algorithms for the iterative calculation of the likelihood estimates.

The EM algorithm was first introduced by Dempster et al (Dempster, Laird et al. 1977) and was applied to a variety of incomplete-data problems and has two steps which are the E-step and the M-step.


Useful Simulations vs Accurate Predictions

Rostami-Hodjegan & Tucker, Drug Discovery Today: Technologies, V4, Dec 2004


Regular Hands-on Workshops to give update on latest IVIVE activities applied to ADME to ALL key players in the drug development scene (e.g. scientists in regulatory agencies, different sections of industry)

Gathering Data / Reaching Consensus on Common IVIVE & ADME Parameters / Identifying Areas of Further Research (defining specific projects in the form of

focus groups)0 50 100 150 200 250 300

2.10 4

4.10 4

6.10 4

8.10 4

0

Time (hour)

Am

ou

nt

of

CY

P3A

4 i

n t

he

Gut

(Pm

ol/

gut)

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

6.10 4

8.10 4

0

2.10 4

4.10 4

6.10 4

8.10 4

0

Time (hour)

Am

ou

nt

of

CY

P3A

4 i

n t

he

Gut

(Pm

ol/

gut)

50 mg

100 mg

200 mg

400 mg

600 mg

800 mg

Continuous Development and Update of a user friendly and mechanistic platform for easier integration of ADME models & databases (simulation of candidate drugs in virtual populations)

- Intelligent Workforce - Reliable Data - Enabling Tools

3 Pillars of Successful Knowledge Management


Washington – April

Leiden - May

Sheffield – September

La Jolla – November

Organising IVIVE Workshops


For academic and research institutions leading the field of IVIVE, ADME, Pharmaceutics and Modelling and Simulation

‘Most Informative Scientific Report’

• Awarded to lead author• Receives bursary towards scientific meeting / sabbatical at Simcyp

‘Most Innovative Teaching Application’

• Awarded to course leader• Receives contribution towards computer hardware or software /

sabbatical at Simcyp

Annual Simcyp IVIVE Awards Academic (Research & Teaching)


Publications: Peer Reviewed Articles

1. Johnson TN, Boussery K, Tucker GT, Rostami-Hodjegan A. Prediction of the increased exposure to drugs in liver cirrhosis: A systems biology approach integrating prior information on disease with in vitro data on drug disposition, Clin Pharmacokin2009 (in press)

2. Johnson TN, Kerbusch T, Jones B, Tucker GT, Rostami-Hodjegan A, Milligan P. Assessing the efficiency of mixed effects modelling in quantifying metabolism based drug-drug interactions: Using in vitro data as an aid to assess study power, Pharm Stats 2009 (Epub ahead of print)

3. Van LM, Sarda S, Hargreaves JA and Rostami-Hodjegan A. Metabolism of Dextrorphan by CYP2D6 in Different Recombinantly Expressed Systems and its Implications for the In Vitro Assessment of Dextromethorphan Metabolism, J Pharm Sci 2009, 98(2): 763-71

4. O‟Mahoney B, Farre Albaladejo M, Rostami-Hodjegan A, Yang J, Cuyas Navarro E, Torrens Melich M, Pardo Lozano R, Abanades S, Maluf S, Tucker GT and De La Torre Fornell R. The consequences of 3,4-methylenedioxymethamphetamine (MDMA, Ecstasy) induced CYP2D6 inhibition in humans, J Clin Psychopharm 2008, 28(5): 523-9

5. Barter Z, Chowdry J, Harlow JR, Snawder JE, Lipscomb JC and Rostami-Hodjegan A. Co variation of human microsomalprotein per gram of liver with age: Absence of influence of operator and sample storage may justify inter laboratory data pooling, Drug Metab Dispos. 2008, 36(12): 2405-9

Research Articles Published/In Press

Review Articles Published/In Press

1. Almond LM, Yang J, Jamei M, Tucker GT, Rostami-Hodjegan A. Towards a quantitative framework for the prediction of DDI‟s arising from Cytochrome P450 induction, Curr Drug Metab 2009, 10(4): 420-432

2. Jamei M, Turner D, Yang J, Neuhoff S, Polak S, Rostami-Hodjegan A, Tucker GT. Population-based Mechanistic Prediction of Oral Drug Absorption, The AAPS Journal 2009, 11(2): 225-237

3. Jamei M, Dickinson GL, Rostami-Hodjegan A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of „bottom-up‟ vs „top-down‟ recognition of covariates, DMPK 2009, 24(1): 53-75

4. Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp population-based ADME simulator, Expert Opinion on Drug Metabolism & Toxicology 2009, 5(2): 211-223


Publications: Others

1. Rostami-Hodjegan A. Translation of in vitro metabolic data to predict in vivo drug-drug interactions: IVIVE and modeling and simulations, in “Enzymatic- and Transporter-Based Drug-Drug Interactions: Progress and Future Challenges” (Eds. Sandy K. Pang, David A. Rodrigues and Raimund M. Peter), Springer, 2009, In press

2. Rostami-Hodjegan A. Predicting Inter-individual Variability of Metabolic Drug-Drug Interactions: Identifying the Causes and Accounting for them Using Systems Approach, in “Enzyme Inhibition in Drug Discovery and Development: The Good and the Bad” (Eds. Chuang Lu and Albert P. Li), Wiley, 2009, In press

3. Yang J. Simulation of population variability in pharmacokinetics, in “Systems Biology in Drug Discovery and Development” (Eds. Daniel L. Young and S. Michelson), Wiley, In press

Book Chapters in Press

Commentary Articles

1. Toon S, „Model Making – Virtual Reality‟, International Clinical Trials, November 2008

2. Toon S, „R&D in a Virtual World‟, Applied Clinical Trials, 17(10):82, October 2008


1. Wong H, Chen JZ, Chou B, Halladay JS, Kenny JR, La H, Marsters JC, Plise E, Rudewicz PJ, Robarge K, Shin Y, Wong S, Zhang C, Khojasteh SC. Preclinical assessment of the absorption, distribution, metabolism and excretion of GDC-0449 (2-chloro-N-(4-chloro-3-(pyridin-2-yl)phenyl)-4-(methylsulfonyl)benzamide), an orally bioavailable systemic Hedgehog signalling pathway inhibitor. Xenobiotica. 2009 Sep 2. [Epub ahead of print]

2. Polasek TM, Polak S, Doogue MP, Rostami-Hodjegan A, Miners JO. Assessment of inter-individual variability in predicted phenytoinclearance, Eu J Clin Pharm, 2009 (in press)

3. Gibson CR, Bergman A, Lu P, Kesisoglou F, Denney WS, Mulrooney E. Prediction of Phase I single-dose pharmacokinetics using recombinant cytochromes P450 and physiologically based modelling, Xenobiotica 2009, 39(9): 637-648

4. Foti RS, Pearson JT, Rock DA, Wahlstrom JL, Wienkers LC. In vitro inhibition of multiple cytochrome P450 isoforms by xanthone derivatives from mangosteen extract, Drug Metabolism & Disposition 2009, 37(9): 1848-55

5. Fahmi OA, Hurst S, Plowchalk D, Cook J, Guo F, Youdim K, Dickins M, Phipps A, Darekar A, Hyland R, Obach RS. Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: predictions of compounds as precipitants of interaction, Drug Metabolism & Disposition 2009, 37(8): 1658-1666

6. Thelingwani RS, Zvada SP, Hughes D, Ungell AL, Masimirembwa CM. In vitro and in silico identification and characterisation of thiabendazole as a mechanism-based inhibitor of CYP1A2 and simulation of possible pharmacokinetic drug-drug interactions, Drug Metabolism & Disposition 2009, 37(6): 1286-1294

7. Hyland R, Osborne T, Payne A, Kempshall S, Logan YR, Ezzeddine K, Jones B. In vitro and in vivo glucuronidation of midazolam in humans, British Journal of Clinical Pharmacology 2009, 67(4): 445-454

8. Ping Z, Ragueneau-Majlessi I, Zhang L, Strong JM, Reynolds KS, Levy RH, Thummel KE, Huang SM. Quantitative Evaluation of Pharmacokinetic Inhibition of CYP3A Substrates by Ketoconazole: A Simulation Study, J Clin Pharmacol 2009, 49: 351-359

9. Emoto C, Murayama N, Rostami-Hodjegan A, Yamazaki H. Utilization of estimated physicochemical properties as an integrated part of predicting hepatic clearance in the early drug-discovery stage: Impact of plasma and microsomal binding, Xenobiotica 2009, 39(3): 227-235

10. Badwan A, Remawi M, Qinna N, Elsayed A, Arafat T, Melhim M, Hijleh OA, Idkaidek NM. Enhancement of oral bioavailability of insulin in humans, Neuro Endocrinology Letters, 30(1): 74-78

11. Grime KH, Bird J, Ferguson D, Riley RJ. Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods, European Journal of Pharmaceutical Sciences 2009, 36(2-3): 175-191

Publications: Growing Independent Research

Applications of Simcyp


Publications: Growing Awareness

Espie P, Tytgat D, Sargentini-Maier Maria-Laura, Pogessi I, Watelet JB. Physiologically based pharmacokinetics (PBPK), Drug Metabolism Reviews 2009, 41(3): 391-407

Peters SA, Ungell AL, Dolgos H. Physiologically based pharmacokinetic (PBPK) modeling and simulation: Applications in lead optimization, Current Opinion in Drug Discovery & Development 2009, 12(4): 509-518

Grimm SW, Einolf HJ, Hall SD, He K, Lim HK, Ling KH, Lu C, Nomeir AA, Seibert E, Skordos KW, Tonn GR, Van Horn R, Wang RW, Wong YN, Tang TJ, Obach RS. The conduct of in vitro studies to address time-dependent inhibition of drug-metabolizing enzymes: a perspective of the Pharmaceutical Research and Manufacturers of America (PhRMA), Drug Metabolism & Disposition, 37(7): 1355-1370

Chu V, Einolf HJ, Evers R, Kumar G, Moore D, Ripp S, Silva J, Sinha V, Sinz M. In vitro and in vivo induction of cytochromep450: a survey of the current practices and recommendations: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective, Drug Metabolism & Disposition 2009, 37(7): 1339-1354

Summerfield S, Jeffrey P. Discovery DMPK: changing paradigms in the eighties, nineties and noughties. Expert Opinion on Drug Discovery 2009, 4(3): 207-218

Bouzom F, Walther B. Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modelling, Fundamentals of Clinical Pharmacology 2008, 22(6): 579-587

Referring to Simcyp

Book Chapters

Zhao P, Zhang L and Huang SM, Complex Drug Interactions: Significance and Evaluation, in “Enzyme and Transporter Based Drug-Drug Interactions” Eds. Sandy K. Pang, David A. Rodrigues and Raimund M. Peter) , Springer, 2009, In press Prakash C and Vaz ADN, Drug Metabolism: Significance and Challenges, in “Nuclear Receptors in Drug Metabolism” (Ed. W Xie), John Wiley & Sons, 2009, 1-42


Thanks for Your Attention

Any Questions?

Date post:	25-Jan-2015
Category:	Documents
Upload:	mjamei
View:	6,827 times
Download:	0 times

An Introduction to:In Vitro-In VivoExtrapolation (IVIVE)

Documents