Joga Gobburu Division of Pharmacometrics - OCP/OTS/CDER/FDA Need Slides?
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Joga Gobburu Division of Pharmacometrics Need Slides? OCP/OTS/CDER/FDA Joga.gobburu@gmail.com Gobburu 1
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.
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.
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
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L ea r n Apply Gobburu 13
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´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
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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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