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1 Pharmacometrics Impact on FDA Decisions & Recommendations: Past, Present & Future Bob Powell, PharmD Director (2/05-1/07), Pharmacometrics, OCP Office.

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Presentation on theme: "1 Pharmacometrics Impact on FDA Decisions & Recommendations: Past, Present & Future Bob Powell, PharmD Director (2/05-1/07), Pharmacometrics, OCP Office."— Presentation transcript:

1 1 Pharmacometrics Impact on FDA Decisions & Recommendations: Past, Present & Future Bob Powell, PharmD Director (2/05-1/07), Pharmacometrics, OCP Office of Translational Sciences Joga Gobburu, PhD Acting Director, Pharmacometrics Office of Clinical Pharmacology CDER FDA

2 2

3 3 Pharmacometrics Definition: quantitative pharmaco- statistical analysis to answer clinical drug development & regulatory questions & influence decisions People who do this work usually have background in clinical pharmacology, biostatistics and have good judgment in therapeutics, drug development and regulatory decisions

4 4 The Past

5 5 History DateCenter for Drug Evaluation & Research Director Office of Translational Sciences Director Date Biopharmaceutics to Clinical Pharmacology Director Carl Peck Tom Ludden Janet Woodcock1995-Larry Lesko 2005-Steven Galson 2006Shirley Murphy Clinical Pharmacology Focal Point Dosage Form Drug Interactions Dosage Regimen Efficacy/Safety Personalized medicine 70s80s90s00s Lewis Sheiner

6 6 History History Topics/contributions Peck-Ludden –Drug concentration development paradigm Individual PK forecasting & individualized Rx Population PK/PD applications Pharmacometrics derived evidence of efficacy/safety (e.g., Phase 2b-3) Randomized concentration-controlled trial

7 7 History History Topics/contributions Lesko-Woodcock-Galson-Murphy 95-present –1997 Population PK guidance –2001 End of Phase 2a Meeting idea emerged CDDS meeting –2002 Clinical pharmacology subcommittee emphasizing pharmacometrics solutions Drug approval decision based on PM analysis –2003 Exposure-Response guidance –2004 Implement EOP2a meetings Disease model & trial design started (Parkinsons disease) QT trial design & concentration-response analysis –2005 Office strategic plan emphasizing PM Centralized PM Data warehouse-Software environment

8 8 Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development Peck CC, Barr WH, Benet LZ, et al Clin Pharmacol Ther 51: 465, 1992

9 9 15 year Impact PM roadmap in drug development & regulatory decisions Led to FDA guidances (e.g., exposure response, population PK, EOP2a) Enabled possibility of 1 phase 3 trial + supportive evidence Indexed toxicology to likely human exposure (first in humans guidance) Enabled routine prediction of human PK/PD from preclinical data Changed information available in NDA package…[drug] preclinical NDA Enabled FDA PM to do current work

10 10 How should we improve it in 2007? Balance interest in disease, drug & safety Account for key decision points, questions & information required Decision making mechanism supported by quantitative analysis When & how sponsors & FDA communicate Extend paradigm through product life- cycle Enhanced collaboration between clinical, biostatistics, PM Translate accumulated knowledge to better support clinician recommendations & patient decisions

11 11 On their Shoulders Academics –Lewis Sheiner –Stuart Beal –Sid Riegelman –Leslie Benet –Malcolm Rowland –Tom Tozer –John Wagner –Gerhard Levy –Bill Jusko –Nick Holford –Matts Karlsson –Don Stanski –Don Rubin FDA –Roger Williams –Bill Gillespie –Raymond Miller –Bill Bachman –Ene Ette –Jerry Collins –Hank Malinowski –He Sun –Lilly Sanathanan –Stella Machado Industry –Rick Lalonde –Sandy Allerheiligan –Mike Hale –Karl Peace –Karl Metzler –Dan Weiner

12 12 The Present

13 13 My Fear-based Mental Model Failure Company Value Merger Lose Job Company Unsafe or ineffective drugs approved People hurt Lose confidence in FDA FDA Poor Decisions Insufficient Knowledge & Decision Process

14 14 My Hope-based Mental Model Safe or Effective drugs approved Health for All Gain confidence in FDA FDA Wise Decisions Sufficient Knowledge & Decision Process Leverage Point Success Health Company Value Company Promotion

15 15 Drug Development Decisions Too Biased –Marketing trumps science –Champions trumps team recommendations We can make better decisions regarding Trial design Dose response Safety signal Market Value Label set for populations, not individual patients…personalized medicine?

16 16 SIMULATE DOSING REGIMEN DOSE FREQUENCY DISEASE SEVERITY DRUG INTERACTIONS PEDIATRICS IMPACT OPPORTUNITIES- MODEL & SIMULATE KEY DECISIONS COMPANY TRIAL DESIGN (2, 3), GO/NO GO, LABELING, FORMULATION, COMBOS, PEDS FDA TRIAL DESIGN (2, 3, 4), NDA APPROVAL (BENEFIT/RISK, DOSING REGIMEN), LABELING, APPROVAL CRITERIA (GUIDANCE REVISION), FORMULATION, COMBOS, QT STUDIES, PEDIATRIC WRITTEN REQUESTS [HbA1c] Relative Risk MI & STROKE RETINOPATHY NEPHROPATHY DISEASE MODEL CLINICAL TRIAL INFO BASELINE PLACEBO EFFECT DROP-OUT RATE ADHERENCE MODEL BASED DRUG DEVELOPMENT Dose [Drug] [HbA1c] [Drug] Toxicity [HbA1c] [TIME (WEEKS)] Toxicity DRUG MODEL [Drug] Time

17 17 2 Extract Clinical Trial Information BASELINE EFFECT/ MODEL PLACEBO MODEL DROP-OUT MODEL DESIGN PATIENT DEMOGRAPHICS MECHANISM-SYMPTOMS-OUTCOMES 1 Build Disease & Drug Model TIME 4 Plug Sponsor Data, Play & Decide (Go/No Go, trial design) TRIAL DESIGN PATIENT SELECTION DOSAGE REGIMEN SAMPLE SIZE SAMPLING TIMES ENDPOINTS, ANALYSIS 3 Simulate Scenarios UPDATE 1, 2, 3: PUBLIC LIBRARY Modeling Cycle

18 18 Preclinical Phase Clinical PhasePost-NDA Phase eINDINDEOP2aEOP2NDA6 mo safetypreINDVGDS Major Development & Regulatory Decision Points ? Organization Differences Major decision points Information-time paradigm Analysis & presentation Decision process NDA information design

19 19 Preclinical Phase Clinical PhasePost-NDA Phase eINDINDEOP2aEOP2NDA6 mo safetypreINDVGDS Predict, Learn Confirm, Save Safety Model: learn at risk population, detect early or avoid risk Predict, Learn Confirm, Save Disease Model: detect change, qualify new biomarkers, simulate trial design Predict, Learn Drug Model: measure change in disease & safety over time Confirm, Save

20 20 Predict, Learn Confirm, Save Predict, Learn Confirm, Save Preclinical Phase Clinical Phase Drug Model: PK/PD Post-NDA Phase eINDINDEOP2aEOP2NDA6 mo safetypreINDVGDS Quantitative Analysis &/or Simulation Safety Model: learn at risk population, detect early or avoid risk PK/PD Bridging Pediatrics Elderly Dosage forms Disease Model: detect change, qualify new biomarkers, simulate trial design Label Update Benefit Risk Efficacy/SafetyBenefit/Risk Approval Drug Label Individual Dosing Cross-trial analysis: dose-response (efficacy/safety) Dose Ranging Confirming SS PK/PD Dose-escalation POP S Human PK/PD Prediction Simulate (S) Dosing Human proof of principle Phase 3 trial design Value Target Product Profile

21 21 FDA Pharmacometrics 5 Decision Target Activities (2007) NDA review decisions –Drug approval –Label-dosing regimen, 1° and special populations QT trial design & analysis Pediatric written requests End of Phase 2a meetings Disease model construction –Trial design –Biomarker qualification

22 22 Pivotal: Regulatory decision will not be the same without PM review Supportive: Regulatory decision is supported by PM review Impact Discipline ApprovalLabeling PM Reviewer95%100% Clin Pharmacology – Reviewer 95%100% – Team Leader 90%94% Medical Reviewer90% Impact of FDA Pharmacometrics Analyses (N = 31) Impact of FDA Pharmacometrics Analyses (N = 31) Clin Pharmacol Ther 81: , 2007

23 23 Pharmacometric Reviews Across Therapeutic Areas (2/05-6/06)

24 24 Therapeutic drug monitoring to adjust dose was an unpopular idea, but what about…..25% of population? One dose for all-Anti-infective One dose for all-Anti-infective poorly absorbed, highly active drug, Rx prevent life-threatening infection Positive control P< logistic regression

25 25 EOP2a or Type C meetings: Dose-response & trial design Phase 1-2a data analyzed for dose selection & Phase 2b/3 trial design 10 meetings total over past 2 years (e.g., antivirals, endocrine, neuro, repro, analgesia) 4-6 weeks of work, several inside meetings & sponsor meetings Post-meeting evaluation (1=worthless, 5=pivotal) –Sponsors average 4.3 –FDA average 3.2 Pause in future meetings-workload. Recommend using Type C meetings PDUFA4- EOP2a Guidance, Formal restart ?~08

26 26 Preclinical Phase Clinical PhasePost-NDA Phase eINDINDEOP2aEOP2NDA6 mo safetypreINDVGDS Mechanistic Model Clinical Trial Model Epidemiologic Model Disease Model Continuum Primary Endpoints Utility Biomarker qualification Clinical trial simulation

27 27 Disease Models (trial design & endpoints) Objectives –Use prior data plus statistical analysis & simulation to solve regulatory problems –Share solution + models of prior data publicly Collaboration: Clinical (OND), Biostatistics (OB), OCP Projects –Parkinsons disease: trial design to detect disease progression change Critical to understand disease/baseline characteristics, disease progression, placebo/drug effects, and statistical issues (Missing data, etc) –Non-small cell lung cancer: predictive value in 2D imaging for disease progression-8 NDAs –Osteoarthritis: predictive value of 2D imaging for disease progression. Large failed phase 3 trial –…..

28 28 Parkinsons Disease Progression & Clinical Trial Models Parkinsons Disease Progression & Clinical Trial Models (drug, placebo, drop-outs, baseline) Objective: to simulate trial design able to detect a change in disease progression for drugs currently in pipeline

29 29 Pediatric Written Requests FDA invites sponsor to prepare a written request for pediatric submission detailing efficacy, safety, dosing FDA agrees the protocol If sponsor complies with protocol & studies requisite patient #, 6 months additional patent exclusivity granted Too often trials fail and limited or no information gets to label even though exclusivity granted

30 30 Pediatric Case - blood thinner Sponsor required to study efficacy, safety & pk/pd in 24 patients (0-2, 2-8, 8-16 years) Completed 12, internal recommendation to deny. Data not reviewed. No information would be in label Reviewed data Difficult internal negotiation Sponsor had additional 4 patients, requested data

31 31 Effect on aPTT is concentration dependent Drug X (ng/L)

32 32 Drug X (ng/L) Effect on aPTT is concentration dependent

33 33 Drug X (ng/L) Effect on aPTT is concentration dependent

34 34 Deliverables: Consultations (Internal) QT Protocols Final Studies (quantitative assessment & report) Maintain databases for QT trial data Consultations & labels Research focus Mine database to improve standards & interpretation Preclinical to clinical prediction value OND Divisions & Teams (N=15) Sponsor Protocol, Final reportRecommendations, Risk/benefit interpretation Risk/benefit interpretation Label judgment & text QT Services & Research Team (MD, Stats, CP, Pcol) Recommendations Protocols, Final Reports QT Protocol & Final Report Process Christine Garnett, PharmD represents OCP

35 35 ICH E14 Metric: QT Assessment Intersection-Union Test 1 TIME ddQTc Mean and one-sided 95% CI 10 ms 1 Referred to as Max-Mean Approach Positive Study

36 36 Dose Mean (Lower, Upper CI) QTc Effect? C-QTcIUT/E14 X0.3 (0.1, 0.5)9.56 (3.9, 15.3)No/Yes 10X 4.3 (1.2, 7.5)6.88 (1.5, 12.2)No/Yes Conflicting Results: Does the Drug Prolong QTc? False positive rate of the primary analysis was 37%

37 37 Sponsor-FDA Sponsor-FDA Pharmacometrics Regulatory Communication NDA submissions. –Submit cross trial (2b/3) 2° analysis linking dose- response (efficacy:safety) –Contact Joga on PM components of NDAs & IND protocols/simulations –Participate in pre-NDA meetings When you want FDA PM alignment on regulatory issue, be specific in your letter (name, discipline) & send or call Submit CDISC compliant data sets

38 38 Interested for a Fellowship or Sabbatical? Contact Joga Gobburu at or (301)

39 39 The Future

40 40 Pharmacometrics Consults Activity/year 2007 est.2011 NDA 3050 Pediatric written request (protocol & report) 3050 EOP2a meetings 1240 QT protocols & reports Disease models 0.63

41 41 VISION Laying a Foundation for Change

42 42People Demand People (Industry, FDA, Academics) ~25-50 new PM people/year in 5 years Skill Attributes –Clinical pharmacology/pharmacokinetics –Biostatistics –Judgment Medicine Drug development Regulatory decisions –Influential Negotiation Presentation Training –On the job –Fellowships –Ph.D. New R&D Paradigm People Tools Library

43 43 Tools (acquire, assure, detect, analyze, influence, save) New R&D Paradigm People Tools Library 300 companies submit data Janus NCI/FDA Warehouse CDISC large data set visualization Pharmacometrics data warehouse Nonmem, S+, trial simulator, scripts visualization team-based Final report External disease data & models

44 44 Library & Components A drug (PK, PD (efficacy, safety) & disease model library needs to facilitate reproducibility and generalization (reuse) Dean Bottino, DIA/FDA 1/24/07 Drug & Disease Model Library interfaces users content actions management retrieval authors borrowers browsers internal external Model components Metadata model pages communication New R&D Paradig m People Tools Library

45 45 Trends TrendPotential Impact Aging, smart population 40 year growth –Demand to stay healthy longer –Multiple meds Transparent, dynamic, personal health information (eLabel +) –Promotes individual choices –Benefit/risk trade-offs (graphics) Risk averse (U.S., Europe, Japan) Mechanistic safety investment Fast clinical safety signal detection Information explosion + Demand for speed & efficiency IT systems supporting work & communicate with FDA Less societal trust of industry & FDATransparent, quantitative decisions Global warmingShifting disease patterns tropical infectious diseases (e.g., malaria) Disease Oriented R&D (Novartis, Lilly, Wyeth, Pfizer,…. –Learn-Confirm –Quantitative decision (M&S) Early decisions more important Biomarker qualification Cross company consortia FDA refined roles –Decision-making –Consultation –Knowledge-sharing Changes in FDA – Organization & culture – Funding

46 46 Similar information can be used to answer questions from different perspectives PerspectiveQuestions Patient- customer Which How to use Clinician Which How to use Provider- payer Relative cost/benefit Price FDA-decider Approve (Efficacy/Safety) Label- how to use Companies- drug, biologic, device Go/no go Label Other populations & indications Disease, Drug &Safety Models Decisions Benefit/risk/ Cost Benefit/risk Cost/benefit Efficacy/safety Dosing Efficacy/safety Value Market Data Analysis

47 47 Disease Benefit-Risk Network Industry Veterans Administration Mission: Improve disease & intervention decisions by sharing and analyzing impact of intervention on patient well being Share quantitative data & models: disease, intervention (drug, device, surgery), efficacy & safety Uniform standards (data, models) Local & Central Statistics & Pharmacometric Staff

48 48 Disease Model Center Academic Base Pharmacometrics Center Disease Clinical trial Efficacy Safety Medicine Pharmacy Public Health Industry Mission: Create & Share Train Ph.Ds & fellows in Pharmacometrics Disease models: Mechanistic & empirical reflecting morbidity & mortality Clinical trial information to plan a successful trial (placebo, drop-outs, baseline) Drug models for efficacy & safety Benefit/Risk research 5-10 Programs needed

49 49 5 year Direction 5 year Direction (Peck, Ludden, Lesko, Murphy, Gobburu, Powell) FDA quantitative decision Mainstream –NDA review decisions Drug approval Label-dosing regimen, 1° and special populations –QT trial design & analysis –Pediatric written requests –End of Phase 2a meetings –Disease model construction Trial design Biomarker qualification FDA EOP2a Meetings: Key to R&D productivity Model based drug development R&D framework across companies FDA IT Tools: Rapid access, analysis, report of drug & diseases data Label: Efficacy/safety Benefit/risk & graphics Closer collaboration- medical officer, biostatistics, clinical pharmacology, PM Share disease, drug & clinical trial models Training –PhD Programs-5 producing 25/year –FDA PM Fellowships: 5 new 2 year fellows/year

50 50 Acknowledgements Carl Peck Tom Ludden Office of Clinical Pharmacology –Larry Lesko –Reviewing Divisions Mehul Mehta Chandra Sahajwalla Patrick Marroum Ramana Uppoor Brian Booth Young Moon Choi Seong Jang Rashni Ramchandani –Pharmacometrics Joga Gobburu Atul Bhattaram Christine Garnett Yaning Wang Christoffer Tornoe Raj Madabushi Hao Zhu Pravin Jadhav Joo Yeon Lee Peter Lee Jenny Zheng Office of New Drugs –Norman Stockbridge –Bob Temple –Rusty Katz –Lenard Kapcala –Bob Rappaport –Doug Throckmorton –Jim Witter –Renata Albrecht Office of Biostatistics –Bob Oneill –Ohid Siddiqui –Jim Hung –Joan Buenconsejo Office of Translational Sciences –Shirley Murphy –ShaAvhree Buckman Office of Pediatrics –Lisa Mathis

51 51 While considering my breakfast this morning………. Involved Committed 2007 is the year of the Golden Pig! & Im a Golden Pig


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