PharmacometricsPharmacometrics
Jogarao V. GobburuDirector, Pharmacometrics Services
Office of Translational Sciences / Office of Clinical PharmacologyCenter for Drug Evaluation and Research
U.S. Food and Drug Administration
NDA#1: Why did 3 Consecutive NDA#1: Why did 3 Consecutive Registration Trials Fail?Registration Trials Fail?
Severe Baseline DiseaseResponders
Mild Baseline DiseaseNon-Responders
0 5 10 15 20 25 30Dose, mg
-40
-20
0
20
40
60
80
Plac
ebo-
Sub
tract
ed C
hang
e
In S
core
A a
t Wee
k 12
0 5 10 15 20 25 30Dose, mg
-40
-20
0
20
40
60
80
Pla
cebo
-Sub
tract
ed C
hang
e
In S
core
A a
t Wee
k 12
NDA#2: Why did 2 Consecutive NDA#2: Why did 2 Consecutive Registration Trials Fail?Registration Trials Fail?
Lack of biomarker-endpoint relationship,Led to poor dose selection
Failed?Success
R&D Productivity Declines: High Late R&D Productivity Declines: High Late Phase AttritionPhase Attrition
↓ 50%
29 JULY 2005 VOL 726 309 SCIENCE www.sciencemag.org
Low Attrition In Late Development Low Attrition In Late Development Will Massively Reduce CostsWill Massively Reduce Costs
0
100
200
300
400
500
600
PCD I II III ApprovalPhase
Cos
t ($U
Sm)
From Steve Arlington,PriceWaterHouseCoopers
The Solution: SimpleThe Solution: Simple
“We are an industry with a 98% failure rate…..The only thing we have to do to double our success rate is to drop our failure rate by 2%” **
Hank McKinnell, Pfizer CEO, at http://www.bio-itworld.com, 2/14/06
** 45% of failures related to inadequate prediction of efficacy and safety. This failure rate has not changed in over 15 years.
CDER New Molecular Entity & New Biologic* Approvals by Calendar Year
13 12 10
18
9
1619
9 7 7 9
2115
129
19
35
30
1416
1817
1012
15
5
0
10
20
30
40
50
60
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004* 2005*
Calendar Year
Num
ber A
ppro
ved
Priority NME Approvals Standard NME Approvals Number of NMEs Filed
*Beginning in 2004, these figures include new BLAs for therapeutic biologic products transferred from CBER to
7 of 39 withdrawnfrom market
The Critical Path: Now that I have your attention, where do we go next?
Phacilitate R&D Leaders ForumPhacilitate R&D Leaders ForumAutumn 2006Autumn 2006October 4, 2006October 4, 2006Berlin, GermanyBerlin, Germany
Joga GobburuOffice of Clinical PharmacologyCenter for Drug Evaluation and ResearchFood and Drug Administration
Key PointsKey Points
Root Cause Analysis of High Attrition Rate Necessary
Knowledge Management, Quantitative Pharmacology and Innovative Trial Designs/Analyses Will Become Key Drivers of Drug Development Change– Organizational infrastructure will need to
change
The Original Critical Path Initiative The Original Critical Path Initiative Announced by FDAAnnounced by FDA
“…if accomplished, the new tests and tools developed under the critical path initiative will modernize the drug development process by 2010…”
SafetyMedical UtilityIndustrialization
Note: PhRMA, BIO and 21 Patient Groups Signed on to Support Critical Path
Critical Path Priority FDA Activities
Biomarkers, Disease Models Several disease models, Data mgmt tools, Conferences, Advisory Comm meetings
Clinical trial streamlining EOP2A MeetingsGuidances, eIND
Bioinformatics Data standards, VGDS
Manufacturing Quality by Design, Biologics
Specific Public Health Needs Rapid identification of pathogen→anti-biotic
At Risk Populations Innovation of pediatric products
http://www.fda.gov/bbs/topics/news/2006/NEW01336.htmlhttp://www.fda.gov/bbs/topics/news/2006/NEW01336.html
VGDS Submissions Over Time VGDS Submissions Over Time Have Been ConsistentHave Been Consistent
0
1
2
3
Num
ber o
f Sub
mis
sion
s
Q1,'04
Q2,'04
Q3,'04
Q4,'04
Q1,'05
Q2,'05
Q3,'05
Q4,'05
Q1,'06
Note: Two VGDS submitted jointly with FDA and EMEA in Q4, ’05 and Q1, ‘06
Why do late trials fail?Why do late trials fail?
Poor dose selectionInadequate designUnanticipated placebo responseInefficient data analysis– Handling missing data (or drop-outs)Truly not a drug Unanticipated toxicity
Critical Path (R&D) OpportunitiesCritical Path (R&D) Opportunities
Manage knowledge efficiently– Utilize prior information to drive future– Effective integration of data across
development programs– Useful tools to archive/summarize data
Innovative trial designs/analysis– Model-based drug development– Increased awareness among inter-
disciplinary scientists towards teamwork
OxcarbazepineOxcarbazepine: Anti: Anti--EpilepticEpileptic Adjunctive Monotherapy Adults Clinical trials Clinical trials
Children (4-16 years of age)
Clinical trial “Model Based Bridging” approach proposed by FDA
FDA/Sponsor pursued approaches to bestutilize knowledge from the positive trials toassess if monotherapy in pediatrics can be approved without new controlled trials
ParkinsonParkinson’’s Disease Databases Disease DatabaseData Source #Patients Trial DurationTrial#1
Trial#2Trial#3
Trial#4
Trial#5 IND 300 1.5yr
NDA 400 1yr + 3yr follow-up
NIH 400 1yr + follow-upNDA 900 9mo + follow-up
NDA 200 9mo + follow-up
This project aims at developing policy to discern symptomatic and disease-modifying drug effects
PLACEBO/DISEASE MODEL
Surrogate
Rel
ativ
e R
isk
Morbidity#1
Morbidity#2
Mortality
Surr
ogat
e
TIMEDose
Expo
sure
Surr
ogat
e
Exposure
Toxicity
DRUG MODEL
CLINICAL TRIAL MODEL
80 140 200
020
4060
Bl k F l80 120 160
010
30
Oth F l
Patient PopulationBaseline Body Weight TIME
% D
rop-
out
%Adherence%
Pat
ient
s
DiseaseDrugTrial
Models
Core Development Strategy
DesignMoleculeScreening
Patient Population Dose Selection
Approval Criteria
Individualization
Value of Disease
DrugTrial
Models
Core Development Strategy for Testosterone SuppressantsCore Development Strategy for Testosterone Suppressants
Knowledge Mgmt System
Reporter Gene Assay
Preclinical
Clinical Trial Simulation
Dose optimization
in cancer patients
Pivotal trial
|----*2 mo-----|*Actual execution time.- it does account for time spent accumulating resources.
|----*2 mo-----||----*2 mo-----||----*3 mo-----||---------*12 mo--------------|
- Early screening of compounds based on IC50 value.
- High thr’putmethod to filter thousands of compounds
- Based on prior experience, a few potential entities will be selected for the next phase
IC50
PKPD data
- In vitro IC50 as a guide for preclinical dose selection
- Animal modelsto measure all possible biomarkers e.g. GnRH, LH, T and Drug conc.
- Invitro and preclinical data for clinical dose and regimen selection
- Clinical development plan
- Pilot study for dose optimization thr’innovative trial designs
Eff/Safety data
From Pravin Jadhav, VCU/FDA
Sharing Knowledge to Improve Clinical Drug Sharing Knowledge to Improve Clinical Drug Development & Regulatory Decisions:Development & Regulatory Decisions:
Data/models of Diseases, Drugs, Placebo, Baseline and Dropouts
January 24-25, 2007Washington Marriott Hotel
1221 22nd Street NWWashington, DC 20037
Objectives:
• Show prior examples for the advantages of sharing information
• Present examples demonstrating the application of sharing information in Parkinson’s Disease, Diabetes, Depression & Cancer to help make decisions
• Consider how information can be shared in a library-type mechanism
• Consider future actions to progress these ideas.
How Will Critical Path Opportunities How Will Critical Path Opportunities Get Done?Get Done?
FDA will not and cannot do it alone- no budget and no FTEs in 2006- approx $6 million requested in 2007 budget- no expected huge influx of resources
Shift from uncoordinated individual projects to coordinated PPP and consortia
- FDA, NIH, CMS, academic and industry- concept of collaboration for common good- consortia are neutral and noncompetitive space- demonstrated already that it can work- sustainability and rate of progress are real risks
Summary: Critical Path to Drug Approval Summary: Critical Path to Drug Approval Is Often Unpredictable Is Often Unpredictable –– No SurpriseNo Surprise
Many possible opportunities and tools under the FDA Critical Path Initiative that have the potential to make development more efficient and predictable– Exploratory IND guidance, VGDS, EOP2A, model-
based drug development and efficient clinical trial designs ~ unprecedented flexibility for innovation
There are likely many other approaches and critical path opportunities to improve productivity. Industry should take full advantage of them.
Key PointsKey Points
Root Cause Analysis of High Attrition Rate Necessary
Knowledge Management, Quantitative Pharmacology and Innovative Trial Designs/Analyses Will Become Key Drivers of Drug Development Change– Organizational infrastructure will need to
change