Joga Gobburu Division of Pharmacometrics - OCP/OTS/CDER/FDA Need Slides?

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Joga Gobburu Division of Pharmacometrics - OCP/OTS/CDER/FDA Need Slides?
Joga Gobburu
Division of Pharmacometrics     Need Slides?
OCP/OTS/CDER/FDA                Joga.gobburu@gmail.com
                      Gobburu                            1
Joga Gobburu Division of Pharmacometrics - OCP/OTS/CDER/FDA Need Slides?
R&D Challenge
                                             R&D

Learn & Apply, Cases

                                      L&A          Future

Future

Obsessive focus on ‘Confirm’ goal contributed to R&D inefficiency.
R&D goals ought to be about learning, applying.
Joga Gobburu Division of Pharmacometrics - OCP/OTS/CDER/FDA Need Slides?
R&D Challenge
                                             R&D

Learn & Apply, Cases

                                      L&A          Future

Future

Obsessive focus on ‘Confirm’ goal contributed to R&D inefficiency.
R&D goals ought to be about learning, applying.
Joga Gobburu Division of Pharmacometrics - OCP/OTS/CDER/FDA Need Slides?
Joga Gobburu Division of Pharmacometrics - OCP/OTS/CDER/FDA Need Slides?
Potential Root Causes
                                 Direct drivers of declining R&D productivity
 A. Looming patent cliffs and
     high revenue requirements
                                   I Higher attrition
 B. High expectations of R&D
 C. New R&D paradigms
 D. Organizational complexity                  X                            Declining R&D
 E. Increasing competitive                                                  productivity:
     intensity                     II Higher numbers                        Flat/declining
                                      of programs                   =       output, soaring
 F. Technological
                                                                            R&D spend
     innovation/automation
 G. More complex                               X
     targets/mechanisms/
     molecules                     III Higher cost per
                                       program
 H. Regulatory scrutiny
 I. Payor/HTA pressure

Singh N. McKinsey
1990 – 2007
Category of root cause                                 Description                                  % of overall failures (n = 106)

Efficacy vs. placebo                                   ▪ Failure to demonstrate significant
                                                         difference from placebo in treatment                             45
                                                         effects

                          Confirmation of              ▪ Safety issues either raised in earlier
                          early safety                   trials or seen in similar class of on-           8
Safety vs.                concerns                       market compounds
placebo                                                                                                                          27
                          Unclassifiable               ▪ Unable to determine from outside-in
                                                         cause of safety failure                              19

                          Efficacy                     ▪ Given similar safety profile, failure to
Lack of                                                  demonstrate superior efficacy vs.                      24
differen-                                                active comparator
tiation                                                                                                                          28
                          Safety                       ▪ Given similar efficacy, failure to
                                                         demonstrate superior safety vs. active       4
                                                         comparator

Sources: Evaluate; Pharmaprojects; Factiva; literature search; team analysis                                                          6
´   Industry, regulators and academia are all in
    this together.

´   This talk is not about Pharmacometrics – but it
    is about the fundamental R&D goals. Excessive
    focus on ‘confirmation’ is curtailing innovation.
    I propose an alternative here for your
    consideration.

                         Gobburu                   7
R&D Challenge
                                             R&D

Learn & Apply, Cases

                                      L&A          Future

Future

Obsessive focus on ‘Confirm’ goal contributed to R&D inefficiency.
R&D goals ought to be about Learning, Applying.
“Currently, the practical goal of drug
development is (regulatory) approval. This goal
drives the intellectual focus: demonstrating
(confirming) efficacy. Thus, understanding
confirmatory study design (primarily how to
avoid confounding) and devising and evaluating
test statistics are seen as the intellectually
challenging tasks as, indeed, a glance at the
contemporary clinical trial or biostatistics
literature will confirm.”

                Learning versus confirming in clinical drug development
                                                  LB Sheiner, CPT, 1997
                       Gobburu                                    9
Kola I, Landis J. Nat.Rev.Drug.Disc. Aug 2004.

Gobburu                                                10
Gobburu   11
Gobburu   12
L ea r n

Apply

           Gobburu   13
Gobburu   14
´To confirm is important, but should not be the
 only goal of drug development.
´ Confirmation applies only effectiveness, but
 safety, dosing, why a trial failed, biomarker-
 endpoint relationship etc are equally important.
 Hence drug development decisions will need to
 take them into account.
´ Here is where Pharmacometrics comes in…
Decisions
                                         • Go/No‐go,
                                           Go/No‐go, trial design
                                         • Approval, Label, Policy
                                         • Personalized medicine

                     Analysis                               Information
                     • Quantitative disease
                                    disease‐‐               • Data collected in trials
                     drug ‐trial modeling
                     drug‐trial                             and studies.
                     • Simulations                          • Domain expertise

Pharmacometrics is the science of quantifying disease, drug and trial
characteristics with the goal to influence drug development, regulatory
and therapeutic decisions.
1950   1960   1970   1980   1990   2000   2010
Diverse
                                                Expertise
            FDA Data                                            Physiology

         Disease                       Drug                             Trial
          Model                        Model                           Model

         Molecule                                           Trial Design
                                               Dose
         Screening         Patient             Selection    Endpoints
                           Selection                        Policy

Gobburu, Pharmacometrics                               18
Remifentanil                    Cellcept

• One of the early MBDD       • One of the early trials
• Approved dosing not         designed prospectively by
directly studied in trials.   advanced CTS.

  Trileptal                     Firmagon

• Mono‐therapy in             • First NDA with EOP2A
pediatrics approved based     meeting.
on Pharmacometric‐            • Registration trial dose
bridging. No additional       determined at EOP2A
trials needed.                meeting.
                              • Drug currently approved.
90
                    80
Number of Reviews

                    70
                    60
                    50
                    40
                    30
                    20
                    10
                    0
´   400 projects in 2008 for 10 companies

  ´   Senior management expects volume increase

  ´   Entry-level scientists expected to have some
      pharmacometrics skills

PhRMA Survey. JCP 2010.
70                                                      Impact on Approval-ER
                         Approval
                    60   Labeling                                           analysis provided
                                                                            supportive or pivotal
Number of Reviews

                    50                                                      evidence of
                                                                            effectiveness.
                    40                                                      Impact on labeling-ER
                                                                            analysis supported D&A,
                    30                                                      Warnings,
                                                                            Intrinsic/Extrinsic
                    20
                                                                            factors sections
                    10
                    0
45                                                           45
              40                                                           40
              35                                                           35

                                                        % Reviews
  % Reviews

              30                                                           30
              25                                                           25
              20                                                           20
              15                                                           15
              10                                                           10
                                                                            5
               5
                                                                            0
               0                                                                     6mo
                    6mo
                                                                                        Trial Duration Savings
                           Trial Duration Savings

              70                                                            60
              60                                                            50
  % Reviews

                                                               % Reviews
              50                                                            40
              40
                                                                            30
              30
                                                                            20
              20
              10                                                            10
               0                                                                0
                    6mo                                400
                       Trial Duration Savings                                               Trial Size Savings

Based on 2007-08 reviews
REGULATORY
POLICY & OPPORTUNITIES
 Good Review Management Processes
 Office of Clinical Pharmacology (OCP) is expected to routinely review:
 - Does the exposure-response support evidence of effectiveness?
 - Is the proposed dosing strategy acceptable?

 Formation of Division of Pharmacometrics
 DPM was officially formed in 2009 within the OCP

 Integrated Genomics, Pharmacometrics, Clinical Pharmacology Review
 (IRP) Manual of Policies and Procedures (MAPP)
 IRP expects reviewers from the three disciplines and medical to scope the
 review questions within 45 days of a submission

 End-of-Phase IIA Meeting Guidance, MAPP
 Opportunity for industry and FDA to discuss competing development
 strategies earlier; driven by science.
Case#
  1

 ´ Sponsor was developing a drug for a life-
   threatening condition.
 ´ Few approved drugs available in US

 ´ 3 Registration trials conducted
     « ~600    patients, 3 doses
     « Mild, severe baseline disease patients

     « All 3 trials failed to meet primary endpoint

                             Gobburu                  25
Case#
  1

                                  Mild Baseline Disease                                                       Severe Baseline Disease
                            80
                                  (Unlikely Responders)                                                       (Likely Responders)
                                                                                                        80

                            60
Placebo-Subtracted Change

                                                                                                        60

                                                                            Placebo-Subtracted Change
  In Score A at Week 12

                                                                              In Score A at Week 12
                            40
                                                                                                        40

                            20
                                                                                                        20

                             0                                                                           0

                            -20                                                                         -20

                            -40                                                                         -40
                                  0   5   10      15      20   25   30                                        0   5   10      15      20   25   30
                                               Dose, mg                                                                    Dose, mg

                                                                         Gobburu                                                                     26
Case#
  1

     M=Mild
    S=Severe

   Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4   Q1 Q2 Q3 Q4 Q1
       Yr1          Yr2         Yr3           Yr4   Yr5
                          Gobburu                     27
Case#
  1

        Gobburu   28
Gobburu   29
30

Insomnia patients LPS
% change from mean placebo response
-30
              Y = 0.31x -32.5
-35
              r2 = 0.66
                                                                    PM approach and impact
-40                                                                 ▪   Sponsor developing a drug to treat insomnia
-45                                                                     held an end-of-phase 2a meeting (EOP2A)

-50
                                                                    ▪   Key questions discussed were:
                                                                        – Is the dose range selected for the Phase
-55                                                                         2b studies in insomnia patients
      -65 -60 -55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5       0            reasonable?
                                          Healthy volunteers LPS,       – What should be the duration of the Phase
                            % change from mean placebo response             2b studies?
                                                                    ▪   Analysis conducted by 1 person for
Insomnia patients WASO
31

      NDA submission – Feb 29, 2008; approval – Dec 24, 2008

      Activity          2001 02     03     04     05    06        2007   PM approach and impact

      CS02/N =                                                           ▪   Sponsor needed to determine the
      129, 6 mo                                                              dosing for a drug 7 years in
                                                                             development for advanced prostate
      CS06/N =                                                               cancer patients
      82, SD
                                                                         ▪   Key questions were:
      CS07/N =
      172, SD                                                                – Is a loading dose needed to
                                                                                suppress testosterone, and, if so
      CS12/N =                                                                  how much?
      187, 12 mo                                   Registration
                                                   trial                     – Is a maintenance dose and
      CS14/N =                                                                  suppression regimen needed?
      127, 12 mo
                                                                         ▪   Sponsor developed a mechanistic data
      CS21/N = 610                                                           model to explore dosing strategies via
                                                                             trial simulations
      EOP2A meeting
                                                                         ▪   Identified alternative dosing strategies
      CS21 dose/
                                                                             and clarified regulatory expectations
      regimen                                                                that led to approval
      not finalized

                       Mar 02 Mar 03 Mar 04 Mar 05 Mar 06 Mar 07
NOTE: Only dose-finding studies shown
R&D Challenge
                                             R&D

Learn & Apply, Cases

                                      L&A          Future

Future

Obsessive focus on ‘Confirm’ goal contributed to R&D inefficiency.
R&D goals ought to be about learning, applying.
2020
Strategy Targets
• Health technology assessment
                                                        Share Case Studies
• Novel MOAs                                            Publish, present 250
• Global drug development                               applications of
• Smarter safety testing                                Pharmacometrics.

   Process Targets               People Targets       Business Targets
   Standardize & automate        Train 500            100% protocols designed
   data, analysis, reports       Pharmacometricians   by simulations
   for 15 indications
´   Egan TD, Muir KT, Hermann DJ, Stanski DR and Shafer SL. The
    electroencephalogram (EEG) and clinical measures of opioid potency: defining
    the EEG-clinical potency relationship (‘fingerprint’) with application to
    remifentanil. Intl J Pharm Med. 2001, 15: 001-002.
´   Reigner BG, Williams PE, Patel IH, Steimer JL, Peck C and van BP. An evaluation
    of the integration of pharmacokinetic and pharmacodynamic principles in clinical
    drug development. Experience within Hoffmann La Roche Clin Pharmacokinet
    33:142-152, 1997.
´   Olson SC et al. Impact of population pharmacokinetic-pharmacodynamic analyses
    on the drug development process: experience at Parke-Davis Clin Pharmacokinet
    38:449-459, 2000.
´   Zhang L, Sinha V, Forgue ST, et al. Model-based drug development: the road to
    quantitative pharmacology. J.PKPD. 33(3):369-393, 2006.
´   Lalonde RL et al. Model-based drug development. Clin Pharmacol Ther 82:21-
    32, 2007.
´   Lee H, Yim DS, Zhou H, Peck CC. Evidence of effectiveness: how much can we
    extrapolate from existing studies? AAPS J. 2005 Oct 5;7(2):E467-74. Review.
´   Bhattaram VA et al (2005) Impact of pharmacometrics on drug approval and
    labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.
´   Hale, Michael D, et al (1998) The pharmacokinetic-pharmacodynamic
    relationship for mycophenoalte mofetil in renal transplant, Clin Pharmaco Ther,
    64, pp. 672-683
´   Firmagon’s approval history.
    www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm?fuseaction=search.
    Label_ApprovalHistory (accessed 21 May 2010).
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