Oncology Execution Team by MikeJenny

VIEWS: 5 PAGES: 38

									Overcoming Operational Barriers to
 Oncology Clinical Trials Execution

              September 2009
                  Eliav Barr
 Vice President – Oncology Clinical Research
        Merck Research Laboratories
Who am I (So That You Know My Biases)…
 Conflict of Interest
   – I am an employee of Merck Research Laboratories, a division of Merck & Co., Inc.
   – I own stock in Merck & Co., Inc.
 Current Position
   – For the past 1.5 years, I have managed the interface between Merck’s Oncology
     Clinical Research and Merck’s Clinical Research Operations Groups
   – I am not an Oncologist
 Prior Experience
   – 14 years in Industry, at U of Chicago prior to that
   – I have worked in clinical research in Acute Cardiovascular Medicine, Primary Care,
     Vaccines, and Oncology
   – I developed and ran the clinical program for GARDASIL™ (quadrivalent HPV vaccine)




                                            2
Setting the Stage
Obligatory Famous Quote: “We have met the enemy and he is us.” Walt Kelly

Heard in the Halls of Merck (and Several Medical Centers)…
• “But we’ve always done it that way…”
• “Well, I believe we do it this way because it is a [insert one of the following:
  Regulatory, HIPAA, Hospital, Technical requirement+… I think.”
• “They *insert one of the following: hospital management, Pharma research
  management, Oncology staff] will not go for this because they are set in their
  ways, and there is too much change…”

Speed and Accuracy are Critical…
“I am going to post the data I have at this point. He has been on the MK-2206 (AKT pathway
inhibitor) trial. When he started the drug, he had a cough, had terrible pain, was running
temperature, and sleeping all but 6 hours of the day. His tumors on last scan were growing
rapidly. He is now cough free, OFF ALL PAIN MEDS and is no longer running temperature.
He is up and about and is going hunting tomorrow!” Patient’s wife, via Blog
                                             3
Presentation Outline
 Oncology Differs from Other Therapeutic Areas: Unique Operational Issues
 How one Pharma Company has Chosen to Address These Issues




                                       4
Presentation Outline
 Oncology Differs from Other Therapeutic Areas: Unique Operational Issues:
   – Broad Parameters (Timelines/Success Rates)
   – Granular Issues (Operational Nuts and Bolts)
 How one Pharma Company has Chosen to Address These Issues




                                           5
Oncology Clinical Research Accounts for ~20% of all Pharma
Clinical Research – and This Proportion is Increasing




                                      Source: BioNest Partners Analyses
                                 6    www.bionest.com
More Trials Does Not Equal More Success: Productivity in Phase 2
is Declining




                                      Source: BioNest Partners Analyses
                                 7    www.bionest.com
Oncology Development Timelines are Slower than for Other Areas

                                            7.5

                                             7
Years from First-in Human to First Filing




                                            6.5

                                             6

                                            5.5

                                             5

                                            4.5

                                             4
                                                  Cardio   Inf Dis   Metabolism       Resp   Musc-Skel    Neuro            Onc


                                                                                  8            Source: CMR International
Leading Causes of Study Delay in US Clinical Trials Centers




                                  9
Key Causes of Enrollment Delays – US Sites




                                10
Success Rates: Oncology vs. Other Therapeutic Areas
                          First In Human Dose to          First Pivotal Dose to
    Therapeutic Area
                           Successful Licensure           Successful Licensure

  Alimentary/Metabolism            9%                             63%

      Cardiovascular               8%                             74%

      Anti-Infectives             13%                             69%

       Anti-Cancer                16%                             59%

     Musculo-Skeletal             15%                             87%

        Neurology                  6%                             45%

       Respiratory                 7%                             82%

                                    11             Source: CMR International
Presentation Outline
 Oncology Differs from Other Therapeutic Areas: Unique Operational Issues:
   – Broad Parameters (Timelines/Success Rates)
   – Granular Issues (Operational Nuts and Bolts)
 How one Pharma Company has Chosen to Address These Issues




                                          12
Congestion
                  Oncology                                           Other TAs

 Centralization of care – fewer sites to           Primary care approaches
  conduct studies                                   Larger patient population
 Sites often in Universities – less use of         Often arranged for rapid clinical trial
  central IRBs, more bureaucracy                     execution
 Over 1000 compounds in development




• Need to be able to cut through barriers
• Need to be flexible, nimble like in no other field
• Need to establish longer-term relationships
                                              13
Complex Clinical Studies Programs
                   Oncology                                     Other Therapeutic Areas
 Highly fragmented indication spectrum               Narrower indication spectrum
 Signal = clinical activity, not surrogate           Can get signal in Phase 1 using validated
 Adaptive trials are the rule                         surrogates (ie, cholesterol, BP, PFTs, etc.)
 Rapidly changing field  nimble clinical            Large Longitudinal Phase 3 studies are
  plans                                                the rule

 Oncology: “Lung cancer” = ~15 settings
   –   Within Lung Cancer: SCLC vs. NSCLC
   –   If choose NSCLC: neoadjuvant, adjuvant, primary therapy (depends on stage)
   –   If choose Stage 3b/4 NSCLC: 1L, 2L, 3L
   –   If choose 1L Stage 3b/4 NSCLC: separate trials for one of 3 different SoC meds
 Metabolism: “Type 2 diabetes”
   – Variability by background meds, age, severity only

 High variability/complexity in study designs, frequent amendments  Changes to EDC
  tools, changes to procedure/data manuals, must retrain site and Pharma staff
 Need highly educated Site + Merck staff: familiar with our tools and with each other
 Need speedy, flexible, intuitive, reliable EDC and specimen management tools
                                               14
Types of Schedules of Administration of Medicines
                    Oncology                                   Other Therapeutic Areas

 Medicines often have narrow therapeutic               In general, meds have large therapeutic
  window, with frequent toxicities                       index and are given chronically
 In general, medications are given in “cycles”           – Meds administered qD until study
  to allow recovery from toxicities
                                                             end
    – E.g. meds at Day 1 and 15 X 28 day cycle
                                                          – May have discontinuation, but rarely
    – Delay in cycle starts if recovery from                 dose changes
      toxicity is incomplete
 Often, medications are dosed on a mg/m2               In general, one dose is used throughout
  basis and may be adjusted each cycle                   the study
    – High variability in actual dose given               – Most common alternative: protocol-
    – Dosing may vary with prior toxicities                  required ramp up to a target dose
                                                          – Decrease in dose is less common




↑ complexity in protocol design, drug supplies, data collector development, data entry,
patient tracking, summarization
                                                  15
Data Management
                  Oncology                               Other Therapeutic Areas

 High data density and different kinds of       Central labs – allows streamlining of data
  data                                            entry
 A lot of local laboratory values requires      Complicated patients typically excluded
  significant data entry                         Once-per-day medication. Titration rare
 A long medical chart – lots of                  (and if present, allowed only at study
  complications                                   start)
 Variability in drug administration             Commonalities in data rules across TAs
 Use of unique grading systems and data
  rules




•   Need expert data coordinators expert at understanding the clinical picture
•   Need to leverage tools used by oncology field to improve site compliance
•   Need Familiarity: Sites ↔ Merck, Merck ↔ Onc Standard
•   Need flexible, intuitive tools            16
Adverse Events Reporting
                      Oncology                                                             Other Therapeutic Areas
 Oncology patients have frequent serious                                          Serious adverse events are rare
  adverse events (due to cancer, due to                                            Occurrence of cancer is a serious adverse
  non-study meds, due to study meds)                                                event
 Complex adverse event scenarios                                                  Assessment of adverse events relatively
 CTCAE grading is standard                                                         straightforward
 Patient-level review is often needed                                             Patient level review non-standard


         % age of Pts with ≥1 SAE                                                 AE management systems may need to be
45%                                                                               tailored for Oncology
40%                                                                               Must use experienced investigators and
                           Osteoporosis




35%                                                                               sites
                                                               Cholesterol
          Oncology




                                          Diabetes

                                                     Obesity




30%
                     HIV




                                                                                  Use CTCAE system as primary tool for AE
25%
                                                                                  collection
20%
15%
10%
 5%
 0%                                                                          17
Laboratory Management
                  Oncology                                 Other Therapeutic Areas

 High use of local laboratories                   High use of central laboratories
   – Need same day results - prior to each
     round of therapy
 Large number of tests/patient
 Unique tests
   – Complex readout
   – Research Use Only tests
   – Unique Specimens




• Data entry at sites, rather than a central lab feed into database → more errors, high
  burden on sites (esp. Lab Normal Ranges), high burden to Merck (reconciliation)
• Non-standard forms, high amount of data for each test
• Multiple sources of data: multiple vendors with complex requirements
• Lose ability to use central lab data to monitor study progress, safety
                                             18
Biomarkers are Critical to the Success of Oncology
                  Oncology                                     Other Therapeutic Areas
 Efficiently define most promising new               Can define activity in Phase 1 based on
  medicines/combinations in Ph 1/2a                    surrogate or registration markers
  – Improve POS of agents/combinations                  – Blood chemistries (e.g. cholesterol)
  – Rapid read-out instead of expensive, long         Assays are available and validated
    studies measuring clinical response                 – Specimens easy to access, easy to measure
  – Define responders/non-responders                    – Relationship to hard endpoints generally
                                                          defined
 Field demands personalized medicine
                                                      Personalized medicine not yet on the
  – Improve benefit/risk ratio and value               horizon
  – Requires new tumor markers co-developed
    with new targeted medicines




• Seamless research effort of a dedicated team of molecular epidemiology, pre-clinical,
  experimental medicine, and clinical research personnel
• Operations group arranged to work together with sites and the labs to ensure high
  quality specimen acquisition, handling, and testing
                                                19
How Oncology Compares Against the Ideal Clinical Trial Setting

     Parameter          Ideal Trial Setting          Oncology     Cardiovascular

    Biochemical         Validated to Predict
                                                       Rare         Common
 Surrogate Markers        of Drug Efficacy
  Clinical Surrogate    Validated to Predict
                                                     Variable       Common
       Markers            Overall Survival
                                                     OS – Yes
 Efficacy Outcomes       Easy to Measure                               Yes
                                                    PFS/RR - No

   Patient Acuity        High Event Rates               Yes            No

    Trial Designs          Standardized              Variable          Yes

 Trial Infrastructure      Standardized              Variable          Yes




                                               20
Presentation Outline
 Oncology Differs from Other Therapeutic Areas: Unique Operational Issues
 How one Pharma Company has Chosen to Address These Issues




                                       21
Challenge and Potential Solutions
    New Drug AND New Biomarker:                 Biomarker/Biopsy-Rich Phase 0 to 2a
The Right Medicine for the Right Patient                     Studies


         Fragmented Field:                       Stronger Collaboration with Sites
 How to Optimally Develop our Drugs?

                                                  Collaborative Studies Program
          Cycle Times are Long

                                                    Streamline Late Phase Trials
           Studies are Costly

                                                   Solve Data Mgmt Pain Points
              Competition:
    Patients, Sites, Staff, Equipment
                                                       Oncology Standards

       Clinical Trials are Complex:
Drug Admin, Procedures, Specimen Mgmt                    Global Footprint


 Data Management Tools are Complex:                   Using Today’s Tools:
      Each Company is Different            22   Oncology e-Communications Portal
Issue: Complexity of Early Clinical Trials

 Length driven by environmental, competitive, & process causes
         Environmental                    Competitive                      MRL Processes
    Biopsy-driven trial designs  Competitors’ preferred           MRL tools unfamiliar to
     in sub-populations            partner relationships             study sites
      – Screen to enroll rates        – Barriers to entry              – High delay/error rate
      – Patient willingness           – Timeline advantage             – Constant re-learning
 Resulting in increased overall Phase 1 to 2a timelines

                                      [FIH to Ph 2a LPE] in Oncology:
                                    MRL Programs vs. Pharma Top 25%ile
                              700
                              600
                              500
                                                             9.5 months
                       Days




                              400
                              300          576
                              200
                                                                 287
                              100
                                0
                                          Merck               Top 25%ile
                                              23
Solution: Deeper Collaboration With Key Sites
 Choose sites w/strong interest in Merck’s Ph 1/2a oncology trial designs
   – New designs, complex biomarkers, require adaptability
 Maximize efficiencies through long-term relationships
   – Mutual prioritization and commitment (pipeline view, not protocol view)
   – Less administrative time, more science time (modify internal processes to
     reduce cycle times)
   – Standardization of protocols, start-up procedures, data capture, guidelines
   – Dedicated site resource talking to dedicated MRL field staff
   – Sites meeting metrics have access to funds for investigator-initiated studies
 Acknowledge Globalization
   – Asia Pacific: growing cancer burden, different cancer types
   – Europe: high experience, high quality sites, annotated tumor banks
   – North America: high experience, high quality sites


                                         24
Issue: Where to Test our Drugs for Proof-of-Concept
 It is important to define the activity (or lack-of-activity) of drugs early
 However, cancer includes multiple settings
   – Different organs
   – Different etiologies
   – Localized vs. Metastatic
   – ±surgery,
   – ±radiation
   – Background therapy
 There are only so many scientists at Merck…
   – Can we get help in prioritization?
   – Can we get ideas for study designs, biomarkers, settings from the ‘outside’?
   – How do we ensure that the advice is right?




                                           25
How to Choose Among Multiple Studies/Indications?
Oncology requires multiple Phase 2a Studies to Evaluate Different Settings –
unique among Therapeutic Areas

      Primarily Merck                Either/Or                Primarily External
 • Ph 1 (First-in-human)     • Ph 1 (combinations)        • Phase IV studies
 • Proof-of-biology          • Proof-of-concept           • Ph I to III trials for
 • Proof-of-concept (major     (add’l indications)          uncommon tumors
   or fastest indications)   • Ph 3 (first WMA)           • Supplement Ph 3 trials in
                             • Ph 3 (sWMA)                  lead indications

                                                                           CTEP
 Core MRL Studies:                            External Partnerships        Trial
 1. Merck-Sponsored and -run                                              Groups
    studies
 2. Merck-Sponsored and CRO-run                Merck-Funded, Site
    studies                                                                OCSP
                                              Conducted Early Phase
                                         26
How to Choose Among Multiple Studies/Indications?
Oncology requires multiple Phase 2a Studies to Evaluate Different Settings –
unique among Therapeutic Areas

      Primarily Merck                Either/Or                Primarily External
 • Ph 1 (First-in-human)     • Ph 1 (combinations)        • Phase IV studies
 • Proof-of-biology          • Proof-of-concept           • Ph I to III trials for
 • Proof-of-concept (major     (add’l indications)          uncommon tumors
   or fastest indications)   • Ph 3 (first WMA)           • Supplement Ph 3 trials in
                             • Ph 3 (sWMA)                  lead indications

                                                                           CTEP
 Core MRL Studies:                            External Partnerships        Trial
 1. Merck-Sponsored and -run                                              Groups
    studies
 2. Merck-Sponsored and CRO-run                Merck-Funded, Site
    studies                                                                OCSP
                                              Conducted Early Phase
                                         27
Oncology Collaborative Studies Program - OCSP (Merck) - 1
 Definition of Oncology Collaborative Studies (Merck)
   – Phase 0, 1, 2a studies of Merck’s investigational compounds sponsored and
     conducted by a cancer center or a consortium of cancer centers
   – For each compound, trials start once Phase 1a study achieves PK/PD targets
 Why Invest in OCSP?
   – Recognition that Merck does not have a monopoly on brains and brawn
      • Allows for more creativity in developing our novel compounds
      • Allows broader access to novel biomarkers or trial approaches
      • Resource sparing (less bureaucracy and personnel involvement at Merck)
   – Recognition that site performance varies with motivation
      • Better performance if it is the site’s idea
      • Better performance if the site is in charge
 Key Guiding Principles
   – Critical part of Merck’s developmental activities – equal weight to “core” studies
   – Must be sufficiently robust so that program decisions can be made
      • Top notch science
      • Top notch execution (data quality, timelines)
                                            28
Oncology Collaborative Studies Program - OCSP (Merck) – 2
Key Operational Principles
 Only selected sites are eligible
   – Sites that have capacity/track-record for novel early phase trials
   – Sites that have entered into a pipeline-based longitudinal relationship with Merck –
     and have a proven track record of success
   – Sites that have streamlined administrative processes and reduce delays
 Study designed by site, but in collaboration with Merck scientists
   – Merck and advisory team (from sites) propose areas of interest
   – Sites design study proposals (study capsules with justification)
   – Selection of trials based on robustness of science, track record, and strategic fit
 Merck provides study grant, but site/consortium is responsible for the study
   –   Merck willing to invest in site personnel as part of larger collaborative arrangement
   –   Site responsible for conduct (IND, protocol adherence, data, regulatory matters)
   –   Mutual agreement on timelines and format for read-out (analysis plan)
   –   Merck is updated regularly
   –   Site holds database, publishes results, can use the data for internal research
       purposes (per agreement)
                                              29
Oncology Collaborative Studies Program - OCSP (Merck) - 3
 Examples of OCSPs
   – Ph 2a Study of IGF1R mAb in 1L treatment of Pancreatic Cancer (N = 100)
   – Phase 2a Study of an investigational compound to define responders and provide
     proof of concept (Ovarian Cancer) (N = 190)
   – Phase 0 study to compare the activity of standard-of-care medications and Merck’s
     compounds on markers of cell proliferation and survival in a neoadjuvant breast
     cancer setting (N = 150)
   – Phase 0 study to validate a new imaging technique against response rate and PFS in
     lung and pancreatic cancer (N = 100)
 Size of Program
   – Year over year, investment has increased from 5% (2006) to 35% (2010) of our
     Phase 1 to 2a Budget
   – To date, site performance has been excellent, consistent with predictions




                                          30
Data Management is Difficult – Need Improvements
 High complexity
   – A lot of data are collected, but how much of the data is actually important?
   – Multiple sources of data (local lab, central lab, radiology, patient care visits, etc)
 Lack of standardization
   – Each company has unique EDC system – each with its strengths and weaknesses
   – Even within a company, studies may vary with regard to standard eCRFs
   – Pharma staff may give different answers to the same question (!)
 Manual Entry/Transcription = Errors
   – At many sites, staff at site transcribe data from one database (EMR) to another
     database (company EDC)
 High error rates in most common data points
   – Free form text
   – Complex medical history




                                              31
Example: “Problem eCRFs”
     Pain Point             Root Cause              Intervention                 Did it Work?
Certain eCRFs       Ineffective site training  New CRFs                    In Process…
cause large number  CRFs may not work for      Better Training
of data errors       Oncology (therapy cycles)  Site Familiarity

 Supportive Data:

             NSCLC Study (N = 253)                             CRC Study (N = 80)

      Total Number of Queries = 25,857                Total Number of Queries =3382
            Form             Total   %age                  Form                  Total   %age
AE – (Adverse Experiences)   7668    29.7%    AE (Adverse Experiences)            714    21.1%
SM (Study Meds)              4337    16.8%    DOV (Date of Visit)                 519    15.3%
CM (Prior and Concom Meds)   3841    14.9%    TAM TL (Tumor Assessment)           316     9.3%
AECOM ( AE/Com Med Link)     2599    10.0%    SM (Study Med)                      188     5.6%
DOV (Date of Visit)          1510     5.8%    SOH (Surgical Oncologic History)    161     4.8%
All Other Modules            5902    22.9%   32 Other Modules
                                               All                               1484    43.9%
How to Solve Data Problems?
 Top to Bottom Review of eCRFs
   – Are data needed?
   – Are questions clear?
   – Beta-test with study data entry personnel
 Reliance on Standards for AE Collection
   – Can CTCAE classification system be used to allow for pull down menus – minimizing
     free form text?
 Data Entry and Handling Guidelines are not helpful
   – Currently too long, too vague, inconsistent
   – Create FAQ and example modules in a web-based tool
 EMR to EDC
   – Some sites have a robust EMR system
   – Can data be extracted directly to EDC?
   – Complexity – validation of transfer (many sites, many EMR systems)


                                          33
Global Footprint
 The US has been a mainstay of innovation/research in Oncology; however…
   – American cancer centers are congested: too many trials
   – There has been a steady increase in costs and in barriers to interaction
   – In most centers, <20% of patients participate in trials
 Cancer Centers in the European Union/Australia are Equally Outstanding
   – Lower costs generally
   – Standardization of care AND standardization of medical records facilitate trials
   – BUT: are heavily congested, and staffing can be difficult
 Developed East Asian Countries are Highly Competitive
   – Korea, Japan, Taiwan, Hong Kong, Singapore have great scientists and facilities
   – Korea, Japan, Taiwan require ethnic/national inclusion prior to licensure
   – Cost structure is equal to US (Korea, Japan) or much lower (others)
 Emerging Markets are Tomorrow’s Giants
   – Brazil, Russia, India, China, Turkey all expected to grow dramatically
   – Each already has solid research capabilities and much lower cost structure
   – Each has unique administrative limitations that lengthen cycle times – but they are
     changing to accommodate research
                                            34
Emerging Markets – Strength and Weakness




                              35
Communications
 Old style is inefficient
   – Paper
   – Phone
   – Visits/meetings
 Sites and Pharma have recognized this, and are shifting to web-based
  approaches
   – Remote monitoring
   – Ability to obtain up-to-date information on patients (without calling the site)
   – Sophisticated e-portals
      • Provide protocol information, key documents, status reports
       • Request supplies
       • Answer questions at all hours
       • Videos on specimen handling




                                            36
Challenge and Potential Solutions: Huge Room for Improvement in
the Years to Come
    New Drug AND New Biomarker:                 Biomarker/Biopsy-Rich Phase 0 to 2a
The Right Medicine for the Right Patient                     Studies


         Fragmented Field:                       Stronger Collaboration with Sites
 How to Optimally Develop our Drugs?

                                                  Collaborative Studies Program
          Cycle Times are Long

                                                    Streamline Late Phase Trials
           Studies are Costly

                                                   Solve Data Mgmt Pain Points
              Competition:
    Patients, Sites, Staff, Equipment
                                                       Oncology Standards

       Clinical Trials are Complex:
Drug Admin, Procedures, Specimen Mgmt                    Global Footprint


 Data Management Tools are Complex:                   Using Today’s Tools:
      Each Company is Different            37   Oncology e-Communications Portal
    THANK YOU!

Eliav_Barr@Merck.com

  +1-267-305-7282


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