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

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

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

M.Jamei@Simcyp.com

The University of Greenwich, 29th Oct 2009, UK

IN CONFIDENCE © 2001-2009

Acknowledgement: The Team

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


Grants Received by Simcyp


Simcyp Background

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

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).

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.


Pharmacology, PK and PD

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

In Vitro - In Vivo Extrapolation (IVIVE)

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.

Mechanistic approach

Drug fate in body
in vitro in vivo


One Source of the Problem

PRE-CLINICAL CLINICAL

Ki
ED50
LogP
Kinact


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”

Hoffman J M et al. Radiology 2007;245:645-660
Windhover's in vivo: the business and medicine report, Bain drug economics model, Nov 2003


ADME

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.

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

Source: Wikipedia

ADME: The Roadmap to Site of Effect
Drug Food, environment,
Tablet Compliance
in Faeces
in Tablet
Comprehension genetic, race, gender,
Excretion
etc effects!
Drug Drug
Drug in Tablet in Gut Release Metabolites
in Faeces

Drug in Gut Absorption Drug
Excretion Metabolites
Metabolism

Drug in Drug in Blood Metabolism
Urine, Bile, Milk Drug
Metabolites
Excretion Drug in Tissues Distribution
Metabolism

Drug at Receptor Metabolite at Receptor

NO DESIRED UNWANTED
RESPONSE RESPONSE RESPONSE

NO CHANGE THERAPY TOXICITY


PK Models

Different PK models:

1
C=Cie-kit

2
Empirical
Compartmental Physiological

GT Tucker (Basic PK Course)

Combining Physiological and Drug-dependent Data

Drug
Data
Systems Trial
Data Design

Mechanistic
IVIVE & PBPK

Population Pharmacokinetics
&
Covariates of ADME
(Jamei et al., 2009)

The Challenge of Population Variability

Environment Disease

Genetics


Relationships Between Covariates Affecting ADME

Genotypes
(Distribution in Population) Renal
Function
Body Ethnicity Disease
Fat Serum
Creatinine

Sex Age
(Distribution in Population) (Distribution in Population)

Height Brain
Heart Body Volume
Volume Surface
Area
Weight MPPGL
Cardiac HPGL
Liver Cardiac
Output Index Enzyme
Volume Abundance
Liver Intrinsic
Weight Clearance
(Jamei et al., 2009)

Covariates of Determining Tissue Volumes

Age Sex Weight Height

Adipose Erythrocytes

Brain Plasma

Bone Spleen

Gut Heart Kidney Liver Lung Muscle Skin


Models to Predict Tissue Volumes
Price et al., 2003
Volume of Brain (L) for M&F aged 0-19 (including adult F)

 Male = (-90.7 * BH(m) + 178.1) * BW(kg) / 1040;
 Female = (-97.5 * BH(m) + 181.2) * BW(kg) / 1040;

Volume of Heart (L) in Adults

 Male = 9.22 * BW(kg)0.853 / 1040;
 Female = 9 * BW(kg)0.855 / 1040;

Volume of Heart (L) for others

 Male = (22.81 * BH(m) * BW0.5 - 4.15) / 1040;
 Female = (19.99 * BH(m) * BW0.5-1.53) / 1040;

1.6 Male 1.6 Female
1.4 1.4
Brain Volume (L)

1.2 Brain Volume (L) 1.2
1 1
0.8 0.8
0.6 ICRP 0.6 ICRP
0.4 Predicted 0.4 Predicted
0.2 0.2
0 0
0 5 10 15 20 25 0 5 10 15 20 25
Age (year) Age (year)

Dosing Regimen and PK Parameters

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.

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

Oral
Half-life
bioavailability

Volume of
Absorption Clearance
distribution

van de Waterbeemd and Gifford 2003, Drug Discovery

Oral Absorption and the GI Tract

From Moore & Dalley, 5th Ed

Factors Affecting Solid Drug Absorption

 Physicochemical &  Physiological issues
Pharmaceutical issues
 Disintegration  Gastric emptying
 De-aggregation  Intestinal mobility
 Dissolution  pH
 Solubility  Intestinal metabolism
 Precipitation  Disease state
 Permeability  P-gp and other transporters
 Intra-gut degradation  Intestinal blood flow
 Food effects
 GI-tract fluid secretion, re-
absorption and motility


Oral Absorption and First-Pass Effect

Gut Lumen
Portal Vein

Gut Wall
Liver

Fa FG FH To Site
of
Action

Metabolism
Metabolism

To Faeces

Rowland and Tozer 1995


Oral Bioavailability

Fraction escaped metabolism
Fraction of dose released
in enterocytes
from formulation and Fraction escaped
permeates through gut wall metabolism in
hepatocytes

Foral = fa . FG . FH
Release
Solubility
Metabolism Metabolism
Stability
Permeability Transport
Transit
Binding Binding
Permeability
Blood Flow Blood Flow


Solid Drug Absorption

dissolution
Solution Absorption

precipitation

dissolution
disintegration

deaggregation

reaggregation


Breakdown / Dissolution Stages
kf,n-1AF,n-1 Drug in kf,nAF,n
AF,n : the amount of solid mass trapped formulation
in the formulation and not available for
dissolution
Release
Rate

kt,n-1AS,n-1 kt,nAS,n
AS : the amount of solid mass available Solid drug
for dissolution

Precipitation Dissolution Rate
Rate
kt,n-1AD,n-1 kt,nAD,n
AD : dissolved drug Dissolved
drug

Transport Absorption Rate
Luminal Rate
Degradation

Absorbed drug

Gut Wall To portal vein
Jamei et al. (2009) AAPSJ
Metabolism

Some Differential Equations

dAS ,n dAdiss,n dAF, n
  kt ,n AS ,n  kt ,n  1 AS ,n  1 
dt dt dt

 k deg,n  kan  kt ,n AD,n  kt ,n1 AD,n1   nCLuintT , n fu gutCent, n
dAD,n dAdiss,n

dt dt

dCent, n
dt

1
Vent, n
ka An diss, n  Qent, nCent, n  CLuintG , n  CLuintT ,n  fu gutCent, n 

dAdiss,n  1 1  AD ,n 
 4πr ( t )D2
  C S ,n  
dt  r( t ) h  Vlumen,n ( t ) 
 eff  

Jamei et al. (2009) AAPSJ 11:225


Advanced Dissolution Absorption & Metabolism
Stomach Duodenum Jejunum I & II Ileum I Ileum II Ileum III Ileum IV Colon

Solid
Dosage

Release

Fine
Particles

Dissolution / Precipitation / Super-Saturation

Dissolved
Drug
Degradation
Pgp Absorption / Efflux
Faeces

Enterocytes
Metabolism

R distribution
pH distribution
PBPK Distribution
Permeability distribution Portal Vein Liver Model
CYPs+Pgp distribution
Blood flow distribution After Agoram 2001 Jamei et al. 2009

Fluid Dynamics in the GI-tract

Rsec, j

Ktj-1 Vj-1
Ktj Vj
Vj

KRe-Abs, j

Rsec, j: Fluid secretion rate into jth gut segment (1/h)
KRe-Abs, j: Fluid re-absorption rate constant from jth segment (1/h)
Vj: Volume of fluid in jth segment (mL)
Ktj: Transit rate constant in jth segment (1/h)

dV j
 Kt j 1V j 1  Rsec, j  K Re  Abs, jV j  Kt jV j
dt

Inter-individual Variability & fa

fa vs Peff and Tsi (R=1.7 cm)
250 120%

100%
200 100
80%
150

fa (%)
60% 50
Frequency

100
40%

50 0
20%
4
0 0% 10
52 135 207 288 365 447 570 2 5
Peff (cm/h)
Intestinal Transit Time (min) 0 0 Tsi (h)

Yu et al. (1998) M Jamei et al, 2009

Probability distribution fitting Sensitivity Analysis


Clearance (CL)

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.
Mass Balance
Q x CA Q x CV

Rate of Extraction=
E = (CA-CV)/CA
Q(CA - CV)
Clearance = QE


Metabolism in the liver

Metabolism mainly happens in the liver but it can happen in
the gut and to much lesser degree in the kidney.
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).
fuB.CLuint
EH =
QH + fuB.CLuint

QH.fuB.CLuint
CLH =
QH + fuB.CLuint

Can we find Cluint from in vitro assays?
How?

Scaling Factors for Hepatic Clearance

In vitro CLuint per
CLuint g Liver

In vitro Scaling Scaling CLu per
int
system Factor 1 Factor 2 Liver

HLM µL.min-1
MPPGL
mg mic protein X

HHEP µL.min-1 Liver
X HPGL X
106 cells Weight

rhCYP µL.min-1 pmol P450 isoformX MPPGL
X mg mic protein
pmol P450 isoform

IVIVE - Metabolism

CLint per CYP/mg x MPPGL Overall CYPs fuB
Specific CYP (pmol/g liver)
Liver Weight
CLint per mg of MPPGL Microsomal Protein
(mg/g liver) CLint Liver
Microsomal Protein

CLint per HPGL Hepatocellularity
Hepatocyte (106/g liver) Liver Blood Flow

CLH
CLpo
fa, FG
Genetic/Environmental/rac
e/age/sex/disease
considerations
Gut Blood Flow

Gut Surface Area

Total CYP in gut Overal CYPs
CLint per CYP in gut
CLint Gut

Gut Wall Permeability


Rate per pmol of “Each Enzyme”

Knowing:
 the abundance of each CYP isoform per mg of microsomal
protein
 the isoform(s) responsible for specific metabolic routes
 n  m Vmax (rhCYPj )i  CYP jabundance 
CLuint [ L / h]       MPPGL Liver Weig ht
 j1  i 1
  K m (rhCYPj )i 

Proctor et al. Xenobiotica 2004 Vmax
Americans/Europeans CLint 
CYP1A2
Km  [ S ]
CYP2A6

CYP2B6

CYP2C8

CYP2C9

CYP2C18

CYP2C19

CYP2D6

CYP2E1

CYP2J2

CYP3A4

CYP3A5
Japanese/Chinese


Mechanistic Model for Expressing Enzyme Pool

[S] [P]

[E·S]
Rsys
[E]
Induction
kdegrad [E·I]

[I] kinact [PI]

[E·MI]

Accelerated Deactivation


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

Comp, MBI, Ind

Comp, MBI, Ind
Comp, MBI, Ind

Sub Sub Met Inh 1 Inh1 Met Inh 2 Inh 3

Comp, MBI, Ind

Comp, MBI, Ind

Comp, MBI, Ind


Predicting Volume of Distribution (Vss)

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

Vss  Vp  Ve  E : P   Vt  Pt:p
t

Vp = volume of plasma; Vt = tissue (t) volume
Ce, ss
Erythrocyte : Plasma partition coefficient E:P
C p , ss
Ct ,ss
Tissue : Plasma partition coefficient K p  Pt: p 
C p ,ss


Minimal Physiologically-Based PK Model

1-fa
PO Gut Lumen Faeces
fa 1-FG
Gut Wall Gut Metabolism

Portal Vein

FG
QPV QPV
QHA

FH Systemic
Liver IV
QPV+HA Compartment
CLH Hepatic
CLR Renal
Clearance
Clearance

Whole Body Physiologically-based PK Parameters

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

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

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)


Full PBPK Model with Time-Dependent Volume

Lung

Adipose
Bone
Brain
Heart
Venous Arterial
Kidney Blood
Blood
Muscle
Skin
Liver
Spleen
Portal Vein
Gut
IV Dose PO Dose


Multicompartment Mammillary Model
Plasma Water KKtP-off
P-on
P +ve
P KP-off pH=7.4
KtEW-in KtEW-out
KtP-off
+ve
P
KtP-on +ve
EW pH=7.4
KtIW-in KtIW-out

KtNP-on
+ve

KtNP-off KtAP-on KtAP-off
NP

Ktel KtNL-on KtNL-off +ve

AP
-ve
NL
IW pH=7

EW: Extracellular Water NL: Neutral Lipids AP: Acidic Phospholipids
IW: Intracellular Water NP: Neutral Phospholipids

Prediction of Tissue to Plasma Partition Coefficients

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

K pu  f EW
X
  f IW    
 
  P  f NL  0.3P  0.7 f NP   Ka AP AP T  a 


 
Y   Y   Y 

Other compounds (Zwitterions pKa < 7, neutrals, acids and weak
bases)
X   P  f NL  0.3P  0.7 f NP 
K pu  f EW   f IW      KaPR PR T 
Y   Y 

Rodgers and Rowland 2006, 2007


Active and Passive Transport
QT QT
Capillary blood

Extracellular fluid

Phospholipid bilayer

Intracellular fluid

For most drugs the capillary membrane is very permeable and diffusion to
the interstitial fluid is very fast (Gibaldi and Perrier 1975).
The drug movement across the cell membrane can be either passive
or/and active.

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

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


Known Human Transporters!

> 50
human ABC transporters are identified;
7 sub-families (A-G)

> 360
human SLC transporters;
48 sub-families

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


Tissues Transporters

Ho and Kim, 2005

Permeability-limited Liver Model - Hepatobiliary Transporters

Capillary blood KP-on
KtP-off
P +ve
P KP-off pH=7.4
KtEW-in KtEW-out
KtP-off
+ve
P
KtP-on +ve
EW pH=7.4
Sinusoidal OATP1B1 OATP1B3 OCT1 MRP3
KtIW-in KtIW-out
membrane
Tight junction
KtNP-on
P-gp
+ve

KtNP-off KtAP-on KtAP-off
NP MRP2
KtNL-on KtNL-off Bile
Ktel
+ve
BCRP
AP
-ve
NL
IW pH=7

EW: Extracellular Water NL: Neutral Lipids AP: Acidic Phospholipids Canalicular
IW: Intracellular Water NP: Neutral Phospholipids membrane

Parameter Estimation Module

Tune design parameters
to fit observations
Simcyp simulation

Trial and Error

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 model
DVs: plasma concentrations

3

2

C(t)
1

0
t1 t2 t3


Least Squares (LS) Objective Function
3

2 e (t1) e (t2)
C(θ, t)

1 e (t3)

0
0 t1 20 t2 40 t3 60 80
i n i n
WLS  min  w i e( t i )  min  w i y( t i )  C, t i 
ˆ 2 2

i 1 i 1

in
yi  f (, t i )2 in
yi  f (, t i )2 in
yi  f (, t i )2 in
yi  f (, t i )2

i 1 yi

i 1 yi2
i 1 f (, t i )

i 1 f (, t i ) 2


Optimisation Algorithms

 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.


Genetic Algorithms
Evaluate Candidates

Randomly Assigned Set of Candidate
Candidates Parameters

Select a New Set of
Rank Candidates
Candidates

Recombination and Reproduce New
Mutation Candidates


Maximum Likelihood (ML) Estimation

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|y2)

3 l(θ|y1)

2 l(θ|y3)
C(θ, t)

1

0
0 t1 20 t2 40 t3 60 80

Assuming normal distribution of parameters N(C(θ, t1), σ12)
   y i  C  , ti 2 
 | y   
1
Likelihood function: exp  
 i 2  2 i2 
 


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.
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.

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

 ( yi  f (, t i )) 2  P  ( j   j ) 2 
 
N
O MAP ()     ln (b 0  b1f (, t i ) b 2 ) 2     ln( j )
2

i 1  ( b 0  b1f (, t i ) )  j  j
b2 2 2
 


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}


Expectation-Maximisation (EM) Algorithm

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.
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


Useful Simulations vs Accurate Predictions

Rostami-Hodjegan & Tucker, Drug Discovery Today:
Technologies, V4, Dec 2004 IN CONFIDENCE © 2001-2009

3 Pillars of Successful Knowledge Management
- Intelligent Workforce - Reliable Data - Enabling Tools

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)

Amount of CYP3A4 in the Gut
8.10 4

Gathering Data / Reaching Consensus on Common
6.10 4 50 mg

(Pmol/gut)
100 mg

IVIVE & ADME Parameters / Identifying Areas of 4.10 4 200 mg
400 mg
600 mg

Further Research (defining specific projects in the form of 2.10 4 800 mg

focus groups) 0
0 50 100 150 200 250 300

Time (hour)

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)


Organising IVIVE Workshops
Washington – April
Leiden - May
Sheffield – September
La Jolla – November


Annual Simcyp IVIVE Awards Academic (Research & Teaching)

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


Publications: Peer Reviewed Articles

Research Articles Published/In Press
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 Pharmacokin
2009 (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 microsomal
protein 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

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

Book Chapters in Press

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

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


Publications: Growing Independent Research
Applications of Simcyp
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 phenytoin
clearance, 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 Awareness

Referring to Simcyp

 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 cytochrome
p450: 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

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


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