The document discusses trends in early drug development, focusing on innovative trial designs, the use of genomic and other biomarkers in clinical trials, and the modeling and simulation processes in these contexts. It emphasizes the importance of adaptive trials and the integration of real-world data in regulatory decision-making to improve drug safety and effectiveness. Additionally, it highlights how personalized medicine aims to tailor treatments based on individual genetic profiles to enhance clinical outcomes.

1. © 2018 PAREXEL INTERNATIONAL CORP.
TRENDS IN EARLY
DEVELOPMENT
Dr. John Lambert
Corporate Vice President and Chief Medical
Officer, Early Phase Medical Sciences, PAREXEL
International
April 17, 2018
PAREXEL KOREA SYMPOSIUM 2018

2. © 2018 PAREXEL INTERNATIONAL CORP. / 2
AGENDA
• Introduction
• Innovative Trial Design
• Use of Genomic and other
Biomarkers in Clincal Trials –
Oncology
• Modelling and Simulation – Role
and importance
TRENDS IN EARLY
DEVELOPMENT
Source: Butler, Declan. Nature; (Jun 12, 2008): 840-842.
FROM BENCH TO BEDSIDE

3. © 2018 PAREXEL INTERNATIONAL CORP. / 3
BOSTON, MASSACHUSETTS
INNOVATIVE TRIAL DESIGNS IN
CLINICAL DRUG DEVELOPMENT

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CLINICAL RESEARCH DATA - SOURCES
Registries
EHRs
Claims
data
RCT
• FDA - RWD/ RWE supports regulatory decision-making about drug safety; less
frequently to establish drug effectiveness ; useful for the clinical devices
• EMA – Use if RCT is not feasible (time, ethics, rarity); hard endpoints (to offset bias);
conditions with known & predictable progression
Source: thelogicofscience.com

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REAL WORLD DATA (RWD) AND REAL WORLD EVIDENCE (RWE)
RWD:
Data relating to patient health status
and/or the delivery of health care
routinely collected from a variety of
sources
• Insurance claims/billing data
• Electronic health records
• National product/disease registry
data
• Patient-generated data including in
home-use settings
• Data from other sources inform on
health status (e.g. mobile devices
Uses
• Creating formularies
• Developing clinical practice
guidelines and clinical decision
support tools
• Developing a product’s benefit-
risk profile
• Monitor post-market safety and
adverse events
• Generate additional hypotheses
for continued clinical
development
*RWE
*Clinical evidence from RWD re the use/ potential benefits or risks of a drug

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Drug 1
Indication 1
• Single treatment
• Single indication
Drug 1
Indication 1
Indication 2
…..
Indication n
• Single treatment
• Multiple indications
• Genomic information
• Severity
• Lines of therapy
• Background Characteristics
Drug 1
Drug 2 Indication 1
….
Drug n
• Multiple treatments
• Fixed # treatment arms or
add/delete treatment arms
• Single indication
Drug 1 Indication 1
Drug 2 Indication 2
…. …..
Drug n Indication n
• Multiple treatments
• Fixed # treatment arms or
add/delete treatment arms
• Multiple indications
• Fixed # indications or
add/delete indications
INNOVATION - CLINICAL TRIAL DESIGN OPTIONS
Traditional
Design
Umbrella
Design
Basket Design
Platform
Design

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WHAT IS AN ADAPTIVE TRIAL?
Prospectively planned opportunity to revisit initial assumptions based on
interim data – e.g. adapt design and/or sample size based on observed treatment
effect differences or drop a dose arm that is ineffective
Most Suited For Drugs If -
1. Rapid Biomarker/PD Readout
2. Multiple Therapeutic Indications
3. Established Dose Effect/Toxicity Relationships
4. Ethical Issues Of Traditional Designs
TRADITIONAL DESIGN (CARGO) ADAPTIVE DESIGN (PASSENGER)

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CURRENT USE OF ADAPTIVE TRIALS
STAGE PHASE MARKET ADOPTION
LEARN
I and
seamless
I/II
Already significant in the use of flexible combination
protocols
II
seamless
II/III
• Probably more adoption in Phase II
• Estimates of % adoption
– ISR (2010): 20%
– ISR (2015): 30%
– Tufts (2013): 20%
– ISR (2014): 22%
• DIA adaptive design working group
– 20% adoption
– 40% sponsors examine adaptive as optionCONFIRM
III
POST-MARKETING
IV
Minimal current adoption, but significant
growth opportunity

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FIRST IN HUMAN COMBINED COMPLEX PROTOCOLS
(SMALL MOLECULE - FLEXIBLE DESIGN)
Single Dose 1
Single Dose 2
Single Dose 3
Single Dose 4
Multiple Dose 1
Multiple Dose 2
Single Dose n
Min. Intoler. Dose
Max. Toler. Dose
Food
Effect
PK?
yes
Multiple Dose Elderly
Safe Starting Dose
(Human Equivalent Dose)
NOAEL/PAD/MABEL
DDI, Human Disease
Model, Ethnicity ,others
Multiple Dose 3

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PAREXEL’S VISION AND APPROACH
STATISTICAL DESIGN
AND ANALYSIS
STUDY EXECUTIONADAPTIVE EXECUTION
ENVIRONMENT
• Sample size adjustment
based on interim effect size
• Seamless Phase II/III
designs
All Functions understand
and execute the needs of
adaptive study designs
• Project leadership
• Clinical monitoring
• Data management
PAREXEL provides key
technologies to support adaptive
trials, integrating EDC, ePRO,
IVR, supplies simulation and
forecasting
• Bayesian adaptive dose
finding design
• Two stage designs with
adaptive sample size
sequential methods

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GENOMIC AND OTHER
BIOMARKERS IN
EARLY CLINICAL
TRIALS

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THREE PILLAR FRAMEWORK
Source: Morgan P. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival., Drug Discov Today.
2011 Dec.
- PK analysis
- Free drug
exposure >
pharmacological
potency
- In vivo PK/PD with
PET receptor
occupancy or radio
ligands
- Indirect
- In vivo PK/PD with
expression of
Biomarkers
- Indirect
Framework to assess probability of successful translation
PHASE II SURVIVAL
Pillar 1
Exposure at the
target site of
action
- PK analysis
- Free drug
exposure >
pharmacological
potency
Pillar 2
Binding to the
target site
- In vivo PK/PD
with PET
receptor
occupancy or
radio ligands
- Indirect
Pillar 3
Expression of
pharmacology
- In vivo PK/PD
with expression
of Biomarkers
- Indirect (e.g.
human disease
models)
Ask the right questions at the right timeand
quantitatively
address them

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THREE PILLARS OF SURVIVAL
44 Pfizer Phase II programs between 2005-09
Source: Morgan P. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival.,
Drug Discov Today. 2011 Dec.

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BIOMARKERS IN PHASE I STUDIES:
Mechanistic
• Protein expression
• Phosphorylation of an enzyme
• Occupation of a receptor = target
engagement
• Ex vivo functional assay (LPS on PMCs)
Functional
• Cardiac output
• Forced expiratory volume in 1 sec
(FEV1)
• Heat pain thresholds (pain)
• Cognitive performance (Cogstat™)
Efficacy measures
• Blood Pressure
• Cholesterol
• Glucose
• Tumor imaging
Toxicity
• Liver function tests, liver enzymes
• Proteinuria (and other excreted markers)
• Qtc (cardiac repolarization parameter)
A biomarker is a characteristic that is objectively measured as an indicator of normal biologic
processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention
(NIH Biomarker Definition Working Group)

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A NEW TREATMENT PARADIGM
The molecular profile of an individual patient and their disease influences the effect of a
medicine; biomarker diagnostics help to target the right medicine to the right patient
Source: Adapted from Bayer Healthcare, “Personalized Medicine” https://pharma.bayer.com/en/research-and-development/research-focus/oncology/personalized-medicine/index.php
(accessed May 2015)

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BIOMARKERS & GENOMICS ENABLE DELIVERY
OF PRECISION MEDICINE
1. http://www.personalizedmedicinecoalition.org/Resources/Personalized_Medicine_at_FDA. Accessed 04 May 2017
2. Personalized Medicine Coalition. The Case for Personalized Medicine (eds. 1–4). 2008–2014; Personalized Medicine Coalition. Applications: Therapies. Accessed October 31,
2016 at http://www.personalizedmedicinecoalition.org/Education/Therapies.
3. The support of human genetic evidence for approved drug indications. Nelson MR et al. Nature Genetics 2015; 47: 856-60
4. https://www.bio.org/sites/default/files/Clinical%20Development%20Success%20Rates%202006-2015%20-20BIO,%20Biomedtracker,%20Amplion%202016.pdf
Nearly 1 in 4 of new drugs
approved by the U.S. Food
and Drug Administration
(FDA) between 2014-16 were
personalized or precision
medicines1
Over the next five years, the
proportion of personalized
medicines in clinical
development is expected to
increase to nearly 70 percent2
In 2015, a Nature Genetics
publication estimated that
selecting genetically
supported targets could
double the success rate in
clinical development3
63
28
55
83
8.4
76
46
76
94
25.9
0
20
40
60
80
100
Phase I to
Phase II
Phase II to
Phase II
Phase III to
NDA/BLA
NDA/BLA to
Approval
Phase I to
Approval
Without Bx
With Bx
Probability of Success With or
Without Selection Biomarkers
3x

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PHARMACOGENOMICS
Source: Pharmacotherapy 2011; 31 (8): 729-735
Drug response in diverse populations

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• Key cell types: Cancer, stromal and
immune present in each patient's tumor ;
over 100,000 tumor exomes sequenced
• Should be possible to design a therapeutic
strategy that improves outcomes in an
individual patient
• Functional Genomics laboratory tools/in
vivo models used to assess:
1. Mechanisms of tumorigenesis
2. Mediators of drug resistance
3. Principles of cell signaling
4. Oncogenes and tumor suppressor
5. Somatic mutation affects on protein
function
PRECISION FUNCTIONAL
GENOMICS IN CANCER CARE

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PHARMACOKINETIC/
PHARMACODYNAMIC
MODELING/SIMULATION

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PK/ PD MODELING AND SIMULATION
• The ultimate goal is to utilize internal and external data in a quantitative
manner to improve drug development strategy and decision-making
• Providing the right information at the right time in the right format. Early
buy-in from clinical and commercial to develop quantitative metrics by
which a meaningful ‘go/no go’ decision
• Reduce cost and time (e.g. use modeling instead of additional studies or
treatment arms)
• To facilitate dialogue with Regulatory agencies (Exposure Response or
PK/PD modeling is more of an expectation these days)
• Modelling of 1. Exposure (PK) to PD responses
2. Animal PK to human starting doses
3. Exposure to efficacy response
4. Exposure to toxicity/safety issues

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LEARNING AND CONFIRMING ARE QUITE DISTINCT ACTIVITIES,
IMPLYING DIFFERENT GOALS, STUDY DESIGNS, AND ANALYSIS
MODES - L.SHEINER

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WHAT IS MODELLING?
• Fit model to all subjects individually
and then calculate statistics (e.g.
mean, SD etc.)
• Combine all the data in one
mathematical model (structural
model)
• Differences between individuals are
described with random effects.
• Covariates are implemented
Traditional Approach
(sequential process)
Population Approach
(fit simultaneously)

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PK MODELING ACTIVITIES AND CAPABILITIES
Non Compartmental
analysis
Allometry
Pediatric PK
Population PK
Conc- ECG
QT Analysis
DDI
models
PK/PD
Modeling
Binary
Analysis
Complex
Pop PK
Survival
Analysis
Physiol/
Based/PK
Meta
Analysis
Disease
IVIVC
CTS
SP
Population
Pharmacokinetics
Modeling using
Sparse PK
sampling and
Population PK -
becoming routine
request by
regulatory agencies
Pediatric study
plan (PSP or
PIP) required
by regulatory
agency -
Population PK
modeling and
simulation key
component

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CASE STUDIES:
DE-RISKING QT EFFECTS
Situation
Potential QT prolongation liability
evidence
Action
Early Phase studies were designed to
support the development of
Concentration-QT models.
Based on the model and available PK
data from historical studies, dose /
effect simulations for multiple dosing
regimens, administration routes and
populations were analyzed to
determine the effect of the drug on
QT interval.
Drug concentration (ng/mL)
Modeling TQT study
Simulating Dosing Regimens in the Drug
Label
Low dose
High dose
Result
The magnitude of QT effects were
predicted, impacting risk assessments for
future clinical plans and allowing
subsequent dosing regimens to be
assessed against QT prolongation criteria.

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REQUIREMENTS FOR PK/POPULATION PK MODELING
Service Needs PAREXEL
Experienced PK Modelers/ Scientists/Analysts Yes
Biostatisticians Yes
Therapeutic Experts Yes
Clinical Pharmacologists Yes
Clinical Study Capability
- PAREXEL Phase I units (4)
- Phase II and III Services
Yes
Regulatory Experts Yes

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THANK YOU
© 2018 PAREXEL INTERNATIONAL CORP. / 26