Assessing an individual's risk of drug-drug interactions is an important step on the way to "personalized medicine." Watch this presentation to learn how modeling and simulation will play a key role in making this vision a reality.

1. Use of Modelling and Simulation to Assess
and Manage Individualized Risk of Drug-Drug
Interaction
Helen Humphries, Senior Research Scientist at Simcyp

2. © Copyright 2015 Certara, L.P. All rights reserved.
M&S to Assess and Manage Individualized Risk of DDI
• Importance of fm to risk of DDI
• Simcyp Approach
• Tablet and Smartphone App
– Static DDI and Population Representative
• Simcyp simulator
– Dynamic simulations and inter-individual variability
• The future – Virtual twin..
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3. © Copyright 2015 Certara, L.P. All rights reserved.
• Systemic Clearance (L/h) =
Metabolic Clearance (CLmet) + Renal Clearance (CLR)
• fm = fraction of the systemic clearance metabolised via enzyme
• fm is THE most important parameter contributing to inter-individual
variability in the extent of DDI
Importance of fm to Risk of DDI
30%
CYP3A4
29%
CLR
41%
CYP2C8
North European Caucasian Population
Mean of 1000 individuals
Inhibitor of CYP2C8
e.g., VICTIM DRUG X

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Variability in DDI: Detecting Individuals at Risk
Av Subject
CL2C9 (L/h) 8
CLRenal (L/h) 2
CL (relative) 100%
fm2C9 0.80
Two Elimination Pathways
0%
100%
200%
300%
400%
Sbj Avg Sbj 2 Sbj 3
CL
Sbj2
32
2
340%
0.94
Sbj3
8
0.5
85%
0.94
0%
100%
200%
300%
400%
Sbj Avg Sbj 2 Sbj 3
Dose
Usually the dose is
adjusted to CL in the
clinic (based on
monitoring response;
e.g. Warfarin)
Subject 2 & 3 have same susceptibility
to inhibition of CYP2C9 however:
The higher maintenance dose (/CL)
can be used as an early indication of
susceptibility to strong inhibition of
CYP2C9 in Subject 2 BUT NOT in
Subject 3.

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Apps for Tablets and Smartphones
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• Static Inhibitor Concentration
• Substrate: Ratio of Inhibited to Uninhibited
Clearance
• Competitive and Mechanistic Inhibition
• Induction
“Everything should be made as
simple as possible, but not simpler”
Albert Einstein
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7. © Copyright 2015 Certara, L.P. All rights reserved.
Simcyp Main Simulator Platform
HTML Screens
•Data rich
•Editable fields
Interfaces with MS Excel
•Data rich
•Input/output data
(regulatory needs)
Teaching/e-learning
•Simplified to key Input Fields
•Interactive/Dynamic Screens
•Animation/output on screen
•PC/PDA-based
Simcyp Engine
Virtual Subject Generation
Age
Gender
Blood Flows
Organs & Tissues
Weight
Height
Albumin
AAG
Enzymes
(abundance & phenotype & turnover)
Stomach Emptying
Int Transit Time
Renal Function
Virtual Population
Blood Flows

8. © Copyright 2015 Certara, L.P. All rights reserved.
What is IVIVE? “New Tricks” for an “Old Hat”
In Vitro to In Vivo Extrapolation

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Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012
Systems Pharmacology
Age
Weight
Height
Sex
Genetics
Race
Disease
Organ size
Blood flow
Enzymes
Transporters
Plasma protein
Haematocrit
Transit time
pH
MWt
pKa
Log D
H-bonding
Solubility
Permeability
Km, Vmax
Kbinding
SYSTEM SPECIFIC
FACTORS
DRUG SPECIFIC
FACTORS
++
CL,V,t½,DDI PD
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10. © Copyright 2015 Certara, L.P. All rights reserved.
Physiologically-Based Pharmacokinetic Modelling (PBPK)
Rowland, Peck & Tucker – Physiologically-based pharmacokinetics in drug
development and regulatory science – Ann Rev Pharmacol Toxicol 51: 45-73 (2011)

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Inter-Individual Variability: IVIVE of Liver Clearance
WeightLiver×MPPGL×
K
abundanceEnzyme×V
=[L/h]CL
n
1=j
n
1=i jim,
jimax,
int ∑ ∑
j CYP
isoforms
i metabolic
pathways
Rate of metabolism
pmol/min/pmol
enzyme
e.g. for recombinant microsomal system
BSA (m2)
LV(L)
Johnson et al, 2005. Liver Transplantation. 12: 1481
Rowland-Yeo et al., 2004. BCRP. 57: 687
Barter et al, 2010. BDD. 31: 516
Barter et al, 2008. DMD. 36: 2405

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Age
(Distribution in Population)
Ethnicity Disease
Height
Cardiac
Index
MPPGL
HPGL
Plasma
Proteins
&
Haematocrit
Serum
Creatinine
Body
Surface
Area
Weight
Liver
Volume
Cardiac
Output
Intrinsic
Clearance
Renal
Function
Liver
Weight
Complex Covariate Effects: IVIVE of Clearance
QH∙fuB∙CLuint
QH + fuB∙CLuint
CLH =
CLH+CLR
fa∙FG∙FH
CLPO =
Jamei et al, 2009. DMPK. 24: 53
Relationships between covariates
based on meta-analysis of
published literature and other
data sources including
Health Survey for England data

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Importance of fm to Risk of DDI
• Factors contributing to inter-individual variability in the extent of DDI do so
through the impact on fm
• Cirrhosis Chalon et al., 2003. Johnson et al., 2010
• Japanese Inoue et al., 2006
• Chinese Barter et al., 2013
• Obesity Ghobadi et al., 2011
• Pregnancy Lu et al., 2012. Ke et al., 2012
• Paediatric Salem et al., 2013 and 2014
• Smoking Plowchalk & Rowland Yeo, 2012
• Geriatric
• Renal Impairment
– Tortorici et al., 2014. Rowland Yeo et al., 2011
– Key PBPK parameters related to kidney disease
• Renal function, Cardiac output, CYP and transporter abundance,
Gastric emptying, Albumin and Haematocrit

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Paediatric Population – Impact of Age on fm

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Importance of fm in Assessment of DDI risk
Systemic Clearance (L/h) = Metabolic Clearance (CLmet) + Renal Clearance (CLR)
fm – fraction of the systemic clearance metabolised via each enzyme
30%
CYP3A4
29%
CLR
41%
CYP2C8
Mean fm for DRUG X (control)
North European Caucasian Population
+ Gemfibrozil and Itraconazole
(Metabolites)
AUC Ratio = 6.86
(5.81-6.75) 8%
CYP3A4
38%
CLR
54%
CYP2C8
Values are geometric mean (90% CI)
Substrate for OATP1B1
CLint,T = 246 µl/min/million hepatocytes
+ Itraconazole
(Metabolites)
Competitive Inhibitor of CYP3A4
8%
CYP3A4
38%
CLR
54%
CYP2C8
AUC Ratio = 1.57
(1.48-1.59)
+ Gemfibrozil
(Gemfibrozil
1-O-β Glucuronide)
Mechanistic Inhibitor of CYP2C8
Inhibitor of OATP1B1
29%
CYP3A4
30%
CLR
41%
CYP2C8
AUC Ratio = 3.89
(3.34-3.85)
fm values are static
AUC Ratio is based on dynamic concentrations

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Individualised Risk of DDI
NEC NEC NEC NEC Subject 1 Subject 2
CYP2C8 *1/*1 *3/*3 *1/*1 *3/*3 *3/*3 *3/*3
OATP1B1 ET ET PT PT ET ET
Subject 1: 75 year old female with renal failure, 60kg weight, height 158 cm
Subject 2: 45 year old male with cirrhosis CP-C, 80kg weight, height 175 cm
AUC Ratio
Simulated data: 10 trials of 12 subjects for each population
Values are geometric mean (90% CI)

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The U.S. Food and Drug Administration (FDA) has seen a recent increase in the application of
physiologically-based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug
interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, the
predictive performance of PBPK modeling in predicting CYP-mediated DDI was evaluated.
In 21/26 (81%) and 20/26 (78%) cases, respectively, AUC and Cmax ratios were within a pre-defined
threshold of 1.25-fold.
These results suggest that, for submissions to the FDA to date, there is a
high degree of concordance between PBPK-predicted and observed
effects of CYP inhibitors on the exposure of drug substrates.
Experience on DDI - Possibility to Take a Position

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PBPK Model: Influenced Drug Label Approvals
Company Drug Name Indication
No Clinical
Studies
Revatio (Sildenafil) Pulmonary Arterial Hypertension 1
Xarelto (Rivaroxaban)
Deep Vein Thrombosis - Pulmonary Embolism – hip/knee replacement and
surgery
4
Edurant (Rilpivirine) HIV infection 1
Iclusig (Ponatinib) Chronic Myeloid Leukemia 0
Olysio (Simeprevir) Hepatitis C 7
Imbruvica (Ibrutinib) Mantle cell lymphoma & chronic lymphocytic leukemia 24
Opsumit (Macitentan) Pulmonary Arterial Hypertension 2
Movantik (Naloxegol) Opioid Induced Constipation 10
Cerdelga (Eliglustat) Gaucher Disease 12
Zykadia (Certinib) Metastatic Non-Small Cell Lung Cancer 2
Jevtana (Cabazitaxel) Metastatic hormone refractory prostate cancer 1
Bosulif (Bosutinib) Chronic, accelerated or blast phase Ph & chronic myelogenous leukemia 0
Blincyto (Blinatumomab)
Philadelphia chromosome-negative relapsed or refractory B-cell precursor
acute lymphoblastic leukemia (ALL
1
Farydak (Panobinostat) Myeloma 2
Lenvima (Lenvatinib)
Metastatic, progressive, radioactive iodine-refractory differentiated thyroid
cancer
1

19. © Copyright 2015 Certara, L.P. All rights reserved.
• Match characteristics of a real patient with his or her virtual twin
– Age, Weight, Height, Sex, Race
– Current drug dosage, Comedications
– Activity of metabolic enzymes and transporters
• Genotype
• Biomarkers
• Exploration of likely impact of co-medication and changes in organ
function
• Management of drug dosage
• Important step on the way to truly ‘personalised/stratified’ medicine
The Future – The Virtual Twin
19

20. © Copyright 2015 Certara, L.P. All rights reserved.
• Global profiling of ADME proteins (enzymes and transporters) in different
organs (individual fingerprint)
• LC-MS/MS Methods for proteomics
– AQUA, QconCAT, PSAQ and Label-Free
– Achour et al., 2014. Russell et al., 2013. Al Feteisi et al., 2015
– Harwood et al., 2013 and 2014
The Future of Proteomics-PBPK
• Use of enzyme expression patterns in blood cells
as a surrogate for organ levels (e.g., liver)
• Use of blood circulating mRNA and microRNA
as a biomarker for disease
(e.g., hepatitis in Zhang et al., PLOS One, 2014)
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21. © Copyright 2015 Certara, L.P. All rights reserved.
Mobile application
or web based interface
Physician enters data
onto tablet device
Patient
Details
Virtual Twin
+ Variances
PBPKPD
Predictor
Virtual Twin Generation
Patient characteristics
from 3rd party profiling
Data sent to PBPKPD Predictor
in the Cloud
Required metrics
returned ie predicted plasma
drug concentration – time
profile with confidence
limits, relative to known
‘therapeutic range’.
Any recommended dosage
adjustment.