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Pharmacometrics Impact on FDA Pharmacometrics Impact on FDA Decisions & Recommendations: Decisions & Recommendations:

Past, Present & FuturePast, Present & Future

Bob Powell, PharmDDirector (2/05-1/07), Pharmacometrics, OCP

Office of Translational Sciences

Joga Gobburu, PhDActing Director, Pharmacometrics

Office of Clinical PharmacologyCDERFDA

robert.powell@fda.hhs.gov

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PharmacometricsPharmacometrics

• 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

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The PastThe Past

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HistoryHistory

Date Center for Drug Evaluation &

Research Director

Office of Translational

Sciences Director

Date 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

Lewis Sheiner

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HistoryHistoryTopics/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

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HistoryHistoryTopics/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

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Opportunities for integration of pharmacokinetics, Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug developmentpharmacodynamics, and toxicokinetics in rational drug development

Peck CC, Barr WH, Benet LZ, et al

Clin Pharmacol Ther 51: 465, 1992

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15 year Impact15 year Impact1992 1992 → 2007→ 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

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How should we improve it in 2007?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

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On their ShouldersOn 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

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The PresentThe Present

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My Fear-based Mental ModelMy 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

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My Hope-based Mental ModelMy Hope-based Mental Model

Safe or Effective drugs approved

↑ Health for All

Gain confidence in FDA

FDA

Wise Decisions

Sufficient Knowledge

&Decision Process

Leverage Leverage PointPoint

↑ Success

↑ Health

↑ Company Value

Company

Promotion

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Drug Development DecisionsDrug 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?

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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, 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

[HbA1c]

Rel

ati

ve

Ris

kMI & STROKE

RETINOPATHY

NEPHROPATHY

DISEASE MODEL

CLINICAL TRIAL INFO• BASELINE• PLACEBO EFFECT• DROP-OUT RATE• ADHERENCE

MODEL BASED DRUG DEVELOPMENTMODEL BASED DRUG DEVELOPMENT

Dose[D

rug

]

[Hb

A1c

]

[Drug]

To

xicity [Hb

A1c

]

[TIME (WEEKS)]

To

xicity

DRUG MODEL

[Dru

g]

Time

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2Extract Clinical Trial Information

• BASELINE EFFECT/ MODEL

• PLACEBO MODEL

• DROP-OUT MODEL

• DESIGN

• PATIENT DEMOGRAPHICS

MECHANISM-SYMPTOMS-OUTCOMES

1Build Disease & Drug Model

TIME

4Plug Sponsor Data,

Play & Decide (Go/No Go, trial design)

• TRIAL DESIGN

• PATIENT SELECTION

• DOSAGE REGIMEN

• SAMPLE SIZE

• SAMPLING TIMES

• ENDPOINTS, ANALYSIS

3Simulate Scenarios

UPDATE

1, 2, 3: PUBLIC LIBRARY

Modeling CycleModeling Cycle

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Preclinical Phase

Clinical Phase Post-NDA Phase

eIND IND EOP2a EOP2 NDA 6 mo safetypreIND VGDS

Major Development & Regulatory Decision Points

?

Organization Differences

• Major decision points

• Information-time paradigm

• Analysis & presentation

• Decision process

• NDA information design

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Preclinical Phase

Clinical Phase Post-NDA Phase

eIND IND EOP2a EOP2 NDA 6 mo safetypreIND VGDS

Predict, LearnConfirm, Save

Safety Model: learn ‘at risk’ population, detect early or avoid risk

Predict, LearnConfirm, Save

Disease Model: detect change, qualify new biomarkers, simulate trial design

Predict, LearnDrug Model: measure change in disease & safety over time

Confirm, Save

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Predict, LearnConfirm, Save

Predict, LearnConfirm, Save

Preclinical Phase

Clinical Phase

Drug Model: PK/PD

Post-NDA Phase

eIND IND EOP2a EOP2 NDA 6 mo safetypreIND VGDS

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

BenefitRisk

Efficacy/Safety Benefit/Risk

Approval• Drug• Label

Individual Dosing

Cross-trial analysis: dose-response (efficacy/safety)

Dose

RangingConfirming

S SPK/PD

Dose-escalationPOP

SHuman PK/PD Prediction

Simulate (S) • Dosing

• Human proof of principle

• Phase 3 trial design

• Value

Target Product Profile

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FDA PharmacometricsFDA Pharmacometrics 5 Decision Target Activities (2007)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

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Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review

Impact →Discipline

Approval Labeling

PM Reviewer 95% 100%

Clin Pharmacology– Reviewer

95% 100%

– Team Leader 90% 94%

Medical Reviewer 90% 90%

Impact of FDA Pharmacometrics AnalysesImpact of FDA Pharmacometrics Analyses (N = 31)(N = 31) 2005-2006

Clin Pharmacol Ther 81: 213-21, 2007

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

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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< 0.0001 logistic regression

0 10 20 30 40Steady-State Concentration, ng/mL

0

20

40

60

80P

atie

nts

with

Clin

ical

Fai

lure

, %

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EOP2a or Type C meetings: EOP2a or Type C meetings: Dose-response & trial design 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

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Preclinical Phase

Clinical Phase Post-NDA Phase

eIND IND EOP2a EOP2 NDA 6 mo safetypreIND VGDS

Mechanistic Model

Clinical Trial Model Epidemiologic Model

Disease Model Continuum

Primary

EndpointsUtility• Biomarker qualification

• Clinical trial simulation

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Disease ModelsDisease Models (trial design & endpoints)(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

– …..

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Parkinson’s Disease Progression & Clinical Trial ModelsParkinson’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

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Pediatric Written RequestsPediatric 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

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Pediatric Case - ’blood thinner’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

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0

50

100

150

200

0.1 1 10 100 1000 10000Argatroban Concentration, ug/L

aPT

T, s

eco

nd

s

Pediatric patients - old data

Mean

Effect on aPTT is concentration dependent

Drug X (ng/L)

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0

50

100

150

200

0.1 1 10 100 1000 10000Argatroban Concentration, ug/L

aPT

T, s

eco

nd

s

Healthy Adults

Pediatric patients - old data

Mean

Drug X (ng/L)

Effect on aPTT is concentration dependent

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0

50

100

150

200

0.1 1 10 100 1000 10000Argatroban Concentration, ug/L

aP

TT

, s

ec

on

ds

Healthy Adults

Pediatric patients - old data

Mean

Pediatric Patients - New Data

Drug X (ng/L)

Effect on aPTT is concentration dependent

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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)

SponsorP

roto

col,

Fin

al r

epo

rt

Rec

om

men

dat

ion

s,

Ris

k/b

enef

it

inte

rpre

tati

on

• Risk/benefit interpretation• Label judgment & text

QT Services & Research Team (MD, Stats, CP, P’col)

Recommendations

Protocols, Final Reports

QT Protocol & Final Report ProcessQT Protocol & Final Report Process

Christine Garnett, PharmD represents OCP

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ICH E14 Metric: QT AssessmentICH E14 Metric: QT AssessmentIntersection-Union TestIntersection-Union Test11

TIME

ddQTc

Mean and one-sided 95% CI

10 ms

t allfor ms10)()(QTc :

t oneleast at for ms10)()(:

1

0

PdQTcDdH

PdQTcDdQTcH

tt

tt

1 Referred to as Max-Mean Approach

“Positive Study”

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DoseMean (Lower, Upper CI)

QTc Effect? C-QTc IUT/E14

X 0.3 (0.1, 0.5) 9.56 (3.9, 15.3) No/Yes

10X4.3 (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%

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Sponsor-FDASponsor-FDAPharmacometrics 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

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Interested for a Fellowship Interested for a Fellowship or Sabbatical?or Sabbatical?

Contact Joga Gobburu at jogarao.gobburu@fda.hhs.gov or (301) 796-1534

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The FutureThe Future

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Pharmacometrics ConsultsPharmacometrics Consults

Activity/year

2007 est. 2011

NDA 30 50

Pediatric written request (protocol & report)

30 50

EOP2a meetings 12 40

QT protocols & reports

150 200

Disease models 0.6 3

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New R&DParadigm

People

Tools

Library

VISION

Laying a Foundation for ChangeLaying a Foundation for Change

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PeoplePeople

• ↑ 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&DParadigm

People

Tools

Library

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ToolsTools (acquire, assure, detect, analyze, influence, save)(acquire, assure, detect, analyze, influence, save)

New R&DParadigm

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

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Library & ComponentsLibrary & ComponentsA 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

userscontent

actions

management

retrieval

authors

borrowers

browsers

internal external

Model componen

tsMetadata

“model pages”

communication

New R&DParadigm

People

Tools

Library

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TrendsTrends 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

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Similar information can be used to answer Similar information can be used to answer questions from different perspectivesquestions from different perspectives

Perspective Questions

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

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Disease Benefit-Risk NetworkDisease 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

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Disease Model CenterDisease Model CenterAcademic BaseAcademic Base

Pharmacometrics Center• Disease• Clinical trial• Efficacy • Safety

Medicine

PharmacyPublic Health

Industry

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

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5 year Direction5 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

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AcknowledgementsAcknowledgements

• 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

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While considering my breakfast this morning……….

Involved Committed

“2007 is the year of the Golden Pig!

& I’m a Golden Pig”