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 robert.powell@fda.hhs.gov

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

The Past

History Lewis Sheiner Clinical Pharmacology Focal Point Date Center for Drug Evaluation & Research Director Office of Translational Sciences Director Biopharmaceutics to Clinical Pharmacology Director 1987- 1993 Carl Peck 1991-1995 Tom Ludden 1994-2005 Janet Woodcock 1995- Larry Lesko 2005- Steven Galson 2006 Shirley Murphy Clinical Pharmacology Focal Point Dosage Form Drug Interactions Dosage Regimen Efficacy/Safety Personalized medicine 70’s 80’s 90’s 00’s

History Topics/contributions Peck-Ludden 87-95 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

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 (Parkinson’s disease) QT trial design & concentration-response analysis 2005 Office strategic plan emphasizing PM Centralized PM Data warehouse-Software environment

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

15 year Impact 1992 → 2007 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

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

On their Shoulders Academics FDA Industry 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

The Present

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

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

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?

SIMULATE DOSING REGIMEN MODEL BASED DRUG DEVELOPMENT [HbA1c] Relative Risk MI & STROKE RETINOPATHY NEPHROPATHY DISEASE MODEL Dose [Drug] [HbA1c] Toxicity [TIME (WEEKS)] DRUG MODEL Time SIMULATE DOSING REGIMEN DOSE FREQUENCY DISEASE SEVERITY DRUG INTERACTIONS PEDIATRICS CLINICAL TRIAL INFO BASELINE PLACEBO EFFECT DROP-OUT RATE ADHERENCE IMPACT OPPORTUNITIES- MODEL & SIMULATE KEY DECISIONS COMPANY → TRIAL DESIGN (2, 3), GO/NO GO, LABELING, FORMULATION, COMBO’S, 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

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

Major Development & Regulatory Decision Points Preclinical Phase Clinical Phase Post-NDA Phase eIND preIND IND VGDS EOP2a EOP2 NDA 6 mo safety ? Major Development & Regulatory Decision Points Organization Differences Major decision points Information-time paradigm Analysis & presentation Decision process NDA information design

Preclinical Phase Clinical Phase Post-NDA Phase eIND preIND IND VGDS EOP2a EOP2 NDA 6 mo safety Predict, Learn Drug Model: measure change in disease & safety over time Confirm, Save Predict, Learn Confirm, Save Disease Model: detect change, qualify new biomarkers, simulate trial design Predict, Learn Confirm, Save Safety Model: learn ‘at risk’ population, detect early or avoid risk

Cross-trial analysis: dose-response (efficacy/safety) Preclinical Phase Clinical Phase Post-NDA Phase eIND preIND IND VGDS EOP2a EOP2 NDA 6 mo safety Drug Model: PK/PD Efficacy/Safety Benefit/Risk  Dose Ranging Confirming S PK/PD Dose-escalation POP Human PK/PD Prediction Simulate (S)  Dosing Human proof of principle Phase 3 trial design Value Target Product Profile Approval Drug Label  Individual Dosing Cross-trial analysis: dose-response (efficacy/safety) Label Update Benefit Risk  PK/PD Bridging Pediatrics Elderly Dosage forms  Quantitative Analysis &/or Simulation  Disease Model: detect change, qualify new biomarkers, simulate trial design Predict, Learn Confirm, Save  Safety Model: learn ‘at risk’ population, detect early or avoid risk Predict, Learn Confirm, Save

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

Impact of FDA Pharmacometrics Analyses (N = 31) 2005-2006 Pivotal: Regulatory decision will not be the same without PM review Supportive: Regulatory decision is supported by PM review Impact → Discipline Approval Labeling PM Reviewer 95% 100% Clin Pharmacology Reviewer Team Leader 90% 94% Medical Reviewer Clin Pharmacol Ther 81: 213-21, 2007

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

One dose for all-Anti-infective poorly absorbed, highly active drug, Rx prevent life-threatening infection Positive control P< 0.0001 logistic regression Therapeutic drug monitoring to adjust dose was an unpopular idea, but what about…..25% of population?

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

Disease Model Continuum Preclinical Phase Clinical Phase Post-NDA Phase eIND preIND IND VGDS EOP2a EOP2 NDA 6 mo safety Disease Model Continuum Mechanistic Model Epidemiologic Model Clinical Trial Model  Primary Endpoints Utility Biomarker qualification Clinical trial simulation

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 Parkinson’s disease: trial design to detect disease progression change http://www.fda.gov/ohrms/dockets/ac/cder06.html#PharmScience 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 …..

Parkinson’s 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

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

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

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

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

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

QT Protocol & Final Report Process OND Divisions & Teams (N=15) Sponsor Protocol, Final report Recommendations, Risk/benefit interpretation Christine Garnett, PharmD represents OCP QT Services & Research Team (MD, Stats, CP, P’col) Recommendations Protocols, Final Reports 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 Risk/benefit interpretation Label judgment & text

ICH E14 Metric: QT Assessment Intersection-Union Test1 10 ms “Positive Study” ddQTc Mean and one-sided 95% CI INTRODUCTION TO FIRST KEY POINT. The intention of the “thorough QT study” is to determine whether a drug has a threshold effect on cardiac repolariazation, as detected by QT prolongation. The threshold level of regulatory concern is a mean of 5 ms with an upper 95% confidence bound of 10 ms. POINT 1. The statistical hypothesis to be tested is shown. The H0 is the difference between baseline-adjusted QTc interval for drug and placebo is at least 10 ms for 1 time point. This is a positive study. If the null is rejected, then the alternative is accepted and the study is considered “negative”. POINT 2. This cartoon shows how the data is analyzed. The y-axis is double-delta QTc which is both baseline- and placebo-adjusted and the x-axis is time. Each point is the mean and upper one-sided 95% confidence interval. As long as none of the upper bound cross 10 ms, the study is negative. But if one upper bound crosses the 10 ms threshold, then the study is considered “positive.” TIME 1 Referred to as Max-Mean Approach

Conflicting Results: Does the Drug Prolong QTc? Dose Mean (Lower, Upper CI) QTc Effect? C-QTc IUT/E14 X 0.3 (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) False positive rate of the primary analysis was 37%

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 email or call Submit CDISC compliant data sets

Interested for a Fellowship or Sabbatical? Contact Joga Gobburu at jogarao.gobburu@fda.hhs.gov or (301) 796-1534

The Future

Pharmacometrics Consults Activity/year 2007 est. 2011 NDA 30 50 Pediatric written request (protocol & report) EOP2a meetings 12 40 QT protocols & reports 150 200 Disease models 0.6 3

Laying a Foundation for Change

People ↑ 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.

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

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

Trends Trend Potential 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 & FDA Transparent, quantitative decisions Global warming Shifting 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

Disease, Drug &Safety Models Similar information can be used to answer questions from different perspectives Perspective Questions Patient- customer Which How to use Clinician 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 Decisions Benefit/risk/ Cost Benefit/risk Cost/benefit Efficacy/safety Dosing Value Market Data Disease, Drug &Safety Models Analysis

Disease Benefit-Risk Network Veterans Administration Industry 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

Disease Model Center Academic Base Medicine Pharmacy Public Health Industry Pharmacometrics Center Disease Clinical trial Efficacy Safety Mission: Create & Share Train Ph.D’s & 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

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

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

“2007 is the year of the Golden Pig! While considering my breakfast this morning………. “2007 is the year of the Golden Pig! & I’m a Golden Pig” Committed Involved