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BIO International Convention
 

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Boston Biotech Clinical Research (BBCR) Presentations at the BIO International Convention.

Boston Biotech Clinical Research (BBCR) Presentations at the BIO International Convention.

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  • A qualified biomarker must link a biomarker with biology and clinical end points.Wagner, Webster, 2007, Nature
  •  
  • SMs are therapies that are matched to patient subpopulations with the aid of clinical biomarkers that predict with some reliability patient differential response – be it in efficacy or safety. Our notion of clinical biomarkers is not limited to genotyping – also includes imaging, clinical observation, or even patient self-report (urge vs. stress incontinence, self-identified black person).

BIO International Convention BIO International Convention Presentation Transcript

  • 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
  • 26
  • 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
  •                      
    Website for this image
    health.com
    • Full-size image - Same sizex larger
    This image may be subject to copyright.
    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