BIO International Convention

Rare Diseases Experience as a Model to Critically Affect Innovation in Biomarker Strategy and Precision Medicine
Moderator
Candida Fratazzi MD
Speakers
Claudio Carini, PhD, FRCPath
GioraFeuerstein MD, MSc. F.A.H.A.
Mark TrusheimPhD
Colin Williams PhD

Precision Medicine The Time is now
Claudio Carini, MD, PhD, FRCPathPfizer Inc.

Drug Development is a lengthy, high- attrition process
More Spending – Less Apparent Productivity and Innovation?

What is missing?
Biomarkers

Biomarker Definition
A molecule that indicates an alteration of
the physiological state of an individual in
relationship to health or disease state,
drug treatment, toxins etc
Biomarkers are by virtue of their short
term availability predictors of long term
events

Why Biomarkers are Important in Medicine?
Staging or Severityof Disease
Patient/Subject Selection
Safety/Prediction of AE
Prognosis of TX intervention
Patient/Subject Selection
Discriminate Health from Disease Stage
Monitoring ClinicalResponse to Therapy

The Elephant in the Room
Putting it all together
Understanding
A multi
-
”
omics
”
Qualified
Biology
Strategy
Biomarkers
Genechip
Target
n
It
’
s
It
’
s
UC
UC
Efficacy
n
RT
-
PCR
PK/PD
n
It
’
s
It
’
s
IHC
RA
RA
Safety/
/Tox
It
’
s
It
’
s
n
It
’
s
It
’
s
SLE
SLE
AS
AS
Flow
Mechanism
n
cytometry
Pharmacology
n
Molecular
imaging
Disease progression
n
It
’
s
It
’
s
It
’
s
It
’
s
Classification
Protein
n
JIA
JIA
analysis
CD
CD
Precision Medicine
n
Mass
Understanding
Spectrometry
Drug PK/PD
Proteomics
profiling

Biomarkers: Potential Guides to Effectiveness and Safety
The -omics
Clinical
New study paradigms*
Experimental human biology
Imaging
An Integrated Approach
Proteomics
Pharmacogenetics
Metabonomics

Building Bridges Between Research and Clinical Development
Exchange of Information
Biomarkers - PK - PG - Experimental CP

Preclinical
Animal
Testing
Biomarkers
Compounds
Biomarkers Connect Discovery and Clinical Research
Clinical
Medicine
Clinical Research
Phases
Discovery & Preclinical Phases
Better
Qualified
Compounds
Patient
Enrichment
Strategies
Omics,
Screening
Based on
Cellular ,
Physiologic
Models,
Driven by
Target
Population
Analysis
Diagnostic
tests
Patient samples
Missing? Impact on clinical medicine
Bench to Bedside

What Patients Expect Today and More So in The Future?
BIOMARKERS
Drugs that work
Drugs that are safe
Doses that are right
for me
12

A fit-for-Purpose Biomarker
Qualified Biomarker
Clinical endpoints
Biology
A qualified biomarker must link a biomarker with biology and clinical end-points
13

Why Do we Need Biomarkers?
To treat diseases more effectively: Disease Biomarkers
Disease BM will enable the:
1. Differentiation/stratification of otherwise similar disease states
2. Better identifies which disease states are more responsible to the study drug
3. Evaluation of disease susceptibility
4. Treat high risk pts before the onset of symptoms
5. Tracking disease progression
To predict clinical efficacy: Patient Selection BM
Patient BM will provide:
1. Explain why Pts are responding differently to different drugs
2. Basis for differentiating “high responders” from “low responders”
3. To target “ high responders” who stand better chances of success

What is Personalized Medicine?
15

Major Drugs Ineffective for Many…
Beneficial to Some
16

Harmful to Others
17

Why do individuals respond differently to the same drug?
Why do individuals require different doses?
Why do some experience AE after taking the drug?
Genetic variation of the drug target gene
Genetic variation in the biochemical pathways affected by the drug
Genetic and genomic factors related to the etiology of the disorder
Other factors (age, sex, diet, environment ...)
Pharmacogeneticslooks at inherited factors that may influence these differences.

Responders
Non-Responders
Adverse Drug
Events
Current Treatments Take Little Account of Human Variation

Personalized Medicine Foresees Greater Use of Diagnostics in Therapeutic Decision Making
Responders
Non-Responders
Adverse Drug Events
Choose the RIGHT DRUGat the RIGHT DOSEfor the RIGHT PERSON
A
B
Dx
Test
C

21
Why is Personalized Medicine Important?
To avoid adverse events:
2.2 million people are hospitalized and 100,000 deaths occur each year due to adverse effects of prescription drugs
To better treat disease:
Development of predictive markers would allow for earlier treatment
To identify novel drug targets:
Current drugs are based on less than 500 targets.

Why we Need Personalized Medicine in Research and Drug Development? Safer and more Effective Drugs
22

Six Blinded Scientists Examining an Elephant
Translational Medicine: the lacking piece of the puzzle
23

24
Thank You
Questions?

Gene discovery and genomic prediction of Atherosclerosis disease states and susceptibility

Biomarkers In Drug Discovery and Development: Orphan Diseases and Orphan Therapeutics
Giora Feuerstein MD
FARMACON LLC
Washington DC
June 28, 2011

…and its getting worse
Translational Medicine in Pharmaceutical Industry:
from “nice to have” to “do or die”

Translational Medicine in Pharmaceutical Industry: from “nice to have” to “do or die”
Aims at reducing the Attrition Rate – Increase Successful Deliveries by Rigorous Science
From “Bench to Bed and Bed to Bench” (BB2)
Identification, validation and implementation of Biomarkersin lieu of clinical end-points
Harmonize and Rationalize Pre-clinical Research, Safety/ADME and Early Clinical Development

Target Validation
Biomarkers that validate the importance of the target in human disease
Biomarkers that define the direct interaction of the compound with its discrete target
Biomarkers that define consequencesof compound interaction with the target relative to PK
Biomarkers that correlatewith disease
(initiation, progression, regression, remission, relapse or modification)
Biomarkers that define likelihood of patients to respond (or not) to the compound
Target/Compound
Interaction
Pharmacodynamic Activity (PK/PD)
Disease Biomarker
& Disease Modification
Patient selection and
Stratification
Biomarkers: A Utilitarian Classification

PK/PD Biomarkers in Orphan Disease
show the way
Case Study 1: Muckle–Wells syndrome
Rare genetic disorder provides biomarkers that ‘pave the way’ for target validation
The case of anti-IL-1b development for RA
PK/PD- Biomarkers
Bayesian methodology
Target validation in M-W syndrome
Registration for Rheumatoid arthritis

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Target Validation: When Biology Trumps Chemistry
Case Study 2: Diabetes
Diabetes drugs are available but unmet medical need high
Insulin injection is the ultimate treatment in chronic diabetes
Insulin sensitivity drugs (TZDs) of limited efficacy and carry safety issues
11beta-HSD-1 inhibitors have the potential to improve insulin sensitivity
Neuro-endocrine liabilities observed with all HSD-1 inhibitors

~
-
Hypothalamus
Tissue
Plasma
~
Pituitary
[Drug]
[Drug]
~
Adrenal
[HSD-1]act
~
[cortisol]n
[cortisol]
~
Cortisol
~
IR
Translational Medicine Case Study:When Biology “trumps” the compound, The 11bHSD1 Inhibitor saga
PK
PD
Plasma AdiposeTissues
Liver

Visceral Fat
SubQ Fat
D4-Cortisol/D3-Cortisol
Translational Medicine Issue: Can 11beta-HSD-1 inhibitors reduce peripheral tissue local cortisol and reduce tissue IR w/o activating the HPA
The “Killer Study”
HSD1 inhibitors can reduce IR w/o change in plasma cortisol
Translational Medicine study (Univ Edinburgh, B walker)
Tissue cortisol production is significant
In non-diabetics:
Liver: Major source (~90%) of splanchniccortisol release into the circulation
SubQ fat: Account for ~10% of cortisol release
Visceral fat: does not appear to contribute to net cortisol output

Rare Diseases as Stratified Medicine Economic Models
Mark Trusheim
Visiting Scientist, MIT
trusheim@mit.edu
President, Co-Bio Consulting

What We Mean by Stratified Medicine
Matching therapies to patient sub-populations with clinical biomarkers
Objective: Do more good (efficacy) or avoid ill (adverse reactions)
Clinical Biomarkers -- beyond genotyping
Molecular (gene expression, proteomic, biochemical)
Imaging
Clinical observation
Patient self-reporting
Clinical Biomarkers: Any information which shows a reliable, predictive correlation to differential patient responses

The Patient Therapeutic Continuum: Stratified Medicines are not “Personalized”
Nature Reviews Drug Discovery: April 2007

Orphan Drugs Demonstrate Economic Potential
Stratified
Medicines
Increasingly
Approaching
Orphan sizes
(thousands of patients, average yearly price in $thousands)

Comparing Orphan and Stratified Medicines
Orphan
Stratified
Known mechanism & marker
Small population
Strong patient and provider networks
Modest payer impact
Known mechanism & marker
May be small or large population
Perhaps unrecognized strata and no networks
Modest payer impact for one, but large if entire field (like oncology) stratifies

When are Orphan Drugs Good Models for Stratified Economics?
Stratification creates a small population
Strong patient advocacy exists
Clinical trial and regulatory models for small populationsBUT
Market exclusivity and lower competition may not apply
Payer concern that a large stratified condition is not ‘rare’10% of all Alzheimer’s patients is a lot of patients, and cost.

Orphans Modeling Stratified Medicines: Expect Price and Profitability Premiums?
Supporting Arguments
Stratified medicines will perform substantially better for their target populations than alternative treatments (assumption)
Recently introduced therapies have commanded price premiums: biologics, stratified medicines, adjuvant therapies
Payers have formal or informal policies to “pay for performance”
Counter Arguments
Limited payer ability to afford increased costs
Diagnostics will siphon profitability
Multiple entrants in new “stratified” drug classes will lower prices
Analytical Task
Develop a Performance Differential/Price Premium curve by examining price premiums obtained in the market today by “classic” therapies LIKE ORPHAN DRUGS
Price
Premium
Performance Differential

Orphans Modeling Stratified Medicines: Development Processes
Opportunities
Strong patient and provider networks to enable clinical trials
Novel clinical trial designs to accommodate few patients available can speed development and lower costs
Potentially more rapid entry into man based on strong mechanism understanding and high need
Challenges
Need to develop and validate biomarker lower since embedded in diagnosis
Regulatory skepticism that stratification is tactic to avoid ‘gold standard’ clinical trials
Clinical
Trial
Size
Biomarker Driven Performance Differential
Potential: Lower Cost and Higher Success
Probability of
Regulatory
Approval

Orphans Modeling Stratified Medicines: Public Policy and Incentives?
Federal Research and Development Support
NIH grants for research, and even development (Bench to Bedside Awards)
Expedited regulatory pathways
Federal Financial Incentives
Market exclusivity grants to INDICATION
High value reimbursement
R&D support above
Registries to identify, monitor and involve patients and samples
Role for Disease Foundations
Awareness, network creation and dissemination
Direct research support
Expert science panels validates early, small company science

Orphan Learnings in Stratified Medicine Examples
Tysabri re-introduction for Multiple Sclerosis enabled by patient advocacy, patient registries and now, a biomarker
Rare oncology sub-populations receiving Orphan level reimbursement
Provenge autologous stem cell therapy: $93,000 for 3 course regimen
Erbitux and Vectibis: Up to $80,000 for 18 week regimen
Revlimid: Up to $10,000 per month for multiple myeloma
Gleevec: Up to $54,000 per year for CML
High market shares (>80%) are possible

Conclusions
Rare diseases and orphan drugs have blazed the trail for stratified medicine economic models
From clinical development through regulatory to reimbursement and public policy, the lessons of rare diseases are being translated to stratified medicine
However, the aggregate size of some stratified medicine markets may strain payers and induce skepticism by regulators that special treatment is appropriate
An ‘integrated stakeholder chain” from research foundations to companies, regulators, payers and advocates is critical

Information in Biomarker discovery
How effective use of information resources can support innovation
Dr. Colin Williams
Thomson Reuters

Why information?
“In 1990, all the necessary data for the concept of feedback control of p53 function were available, although this function was recognized only recently.”
Conceptual biology: Unearthing the gems, Nature 416, 373; 2001 Blagosklonny, M and Pardee, A
Knowledge differentiates organizations and delivers competitive advantage
Decreases risk
Enhances project level decision making
Focuses resource allocation
Empowers scientists to drive innovation
Creates efficiencies by preventing wasted experimentation

The information challenge
Information overload
Wide range of sources - Literature, Patents, Conferences and medical meetings, Press releases, Clinical trial reports
Google 425k hits – Her2 and biomarker
Highly variable terminology in Biology
Lack of standard or Universal criteria on Biomarker development
Variation in protocols, statistical analysis
So many parameters not enough subjects
It is estimated scientists spend ~35% time dealing with information

Mesothelioma
Mesothelioma is a form of cancer that affects the Mesothelium and can affect:
the inner surface of the chest wall where it is known as the pleura
the abdomen, where it is known as the peritoneum.
Organs within those cavities eg lung, heart
New diagnoses in US ~2,500 annually

Mesothelioma drug pipeline – 52
Compared to 1104 drugs under development for Obesity
Source: Thomson Reuters Integrity

Reported Biomarkers of Mesothelioma - 430
3,400 hits in Google search
PubMed 1143 results

Proof of efficacy with mesothelioma markers
Ranpirnase (activation of Caspase)
Cisplatin
Gemcitabine hydrochloride
Pemetrexed disodium
Troglitazone
PPAT agonist inhibitors
LenvatinibMesylate
BEZ-235 (Novartis Phase I/II breast cancer, mTor inhibitor)
Celecoxib
AZD-1152-HQPA
Do the targets associated to these drugs have a role in other disease?

The Core cell cycle, Source MetaCore

Gene Expression and Mesothelioma
Developing insights from experimental information is also a critical
Analyzing gene expression data from mesothelioma can give a glance in to the mechanism of the disease
Analyse publically available dataset from Geo (GDS2604)
Explore expression profile in responders to previously identified drugs and identify differentially expressed genes

Summary
Knowledge enhances decision making
Improve efficiency in project design through researching the area
Biomarker information is:
published in a non standard way
is fragmented way
is expanding rapidly
Disease segmentation based on molecular characteristics is going to create many new orphan diseases.
Will the ophan disease ‘model’ become the life blood of pharmaceutical research
There are many biomarkers available which can prove efficacy of a compound.
Using Mesothlioma (or other orphan diseases) as a model can prove efficacy against a target quickly.
Understanding the biological function of that target can open new indications for a therapeutic

BIO International Convention

by Boston Biotech Clinical Research on Jul 22, 2011

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