disease models overview and case studies joga gobburu pharmacometrics office clinical pharmacology,...
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Disease Models
Overview and Case Studies
Joga Gobburu
Pharmacometrics
Office Clinical Pharmacology,
Office of Translational Sciences, CDER, FDA
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Pharmacometrics Survey• Between 2000-2006, 72 NDAs needed
Pharmacometrics Reviews/Analyses• For each of the Pharmacometrics Reviews,
the ‘customers’ were asked to rate the impact on approval related and labeling decisions:– Pivotal: Decision would not have been the same
without Pharmacometrics analysis– Supportive: Decision was well supported by the
Pharmacometrics analysis– No Contribution: No need for the
Pharmacometrics analysis
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Impact of Pharmacometrics Analyses 2000-2004
Bhattaram et al. AAPS Journal. 2005; 7(3): Article 51. DOI: 10.1208/aapsj070351
Impact Approval Labeling
Pivotal 54% 57%
Supportive 46% 30%
No Contribution 0 14%
Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review
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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%
DCP Reviewer 95% 100%
DCP TL 90% 94%
Medical Reviewer 90%@ 90%@
DCP=Division of Clinical Pharmacology@=survey pending in 1 case
Impact of Pharmacometrics Analyses 2005-2006
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NDA#1: Approval of monotherapy oxcarbazepine in pediatrics for treating partial
seizures using prior clinical data
FDA/Sponsor pursued approaches to best
utilize knowledge from the previous trials to
assess if monotherapy in pediatrics can
be approved without new controlled trials
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• The sponsor was pursuing an accelerated approval, for drug to prevent a life-threatening disease, based on a biomarker even though clinical endpoint analysis failed in two pivotal trials
NDA#2: Establishment of biomarker-outcome relationship allowed more efficient
future trial design
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NDA#2: Establishment of biomarker-outcome relationship allowed more efficient
future trial design
0.0 0.5 1.0 1.5 2.0
Ratio of Baseline Anti-dsDNA Levels
01
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Rel
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e R
isk
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enal
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reStudy 09
Estimated RRLL of 95% CLUL of 95% CL
Ratio of biomarker level to baseline
Hazard ratio=10.0 (95% CI 2.5-30.0)
p<0.001Rel
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isea
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NDA#3: Insights into trial failure reasons will lead to more efficient future trials
0 5 10 15 20 25 30Dose, mg
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Mild Baseline DiseaseNon-Responders
Severe Baseline DiseaseResponders
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Females seem to be more sensitive to QT prolongation
Slo
pe
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Slo
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pe
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Need/Opportunities for Innovative Quantitative Methods in Drug Development
Optimal design to show ‘disease modifying’ effects?
Good marker(s) of survival benefit in cancer patients?
Maximize the change of success of a 2yr obesity trial?
Given 85% of depression trials fail, how to improve success?
Best dose for a 26wk trial based on 12 wk data?
Providing solutions for these issues callsfor efficient use of prior knowledge
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Manage and Leverage Knowledge
Knowledge
Placebo & Disease Models
Information• Biomarker-Endpoint • Time course• Drop-out• Inclusion/Exclusion criteria (Trial)
• Parkinson’s• Obesity, Diabetes• Tumor-Survival• Rheumatologic condition• HIV• Epilepsy• Pain
We are referring to such diverse quantitative approach(es) as ‘Disease Modeling’
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Core Development Strategy for Testosterone Suppressants
Disease Model
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’put method 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 models to 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
PKPD data
From Pravin Jadhav, VCU/FDA
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Obesity
• Obesity trials are large, over 1-2 yrs and fraught with challenges due to high drop-out rate
Dr. Jenny J ZhengDr. Wei QiuDr. Hae Young Ahn
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Obesity
Baseline Body Weight
3000 patients
Model Qualification
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Mean
weig
ht ch
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Drop-out patients
Remaining patients
Patients with small weight loss drop-out
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Obesity: Time Course of Placebo Effect
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Days
Wei
gh
t L
oss
, kg
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Value to Drug Development
• Effective use of prior data for designing future registration trials
• Might lead to alternative dosing considerations– Titration vs. fixed dose– Could lead to increased trial success
• Allows of designing useful shorter duration trials for future compounds for screening and initial dose range selection
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Diabetes
• How to reliably select doses for registration trials based on abbreviated dose finding trials
• Need arose from an EOP2A meeting– Work in progress: No patient population and
drop-out models yet.
Drs. Vaidyanathan, Ahn, Yim, Zheng, Wang,
Gobburu, Powell, Sahlroot, Orloff
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Pivotal Trial Dose Selection: Anti-Diabetic
• Sponsor conducted 12 wk dose ranging trial in diabetics
• Key Regulatory Question– What is a reasonable dose range and
regimen for the pivotal trial(s)?
• Challenge– Estimate of effect size on HbA1c at 26
wks not available. Effect size on FPG available.
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FPG
HbA1c
)1(50
max
CEC
CEKout
inK
inK ' outK '
cHbAKFPGKdt
cdHbAoutin 1''
1
Hb
Alc
FP GD
rug
Conc.
Time (Week)
FPGCEC
CEKK
dt
dFPGoutin
)1(50
max
Cmt 1 Cmt 2
1st order Oral Absorption
FPG-HbA1c relationshipfrom historic studiesemployed to estimateeffects on HbA1c of thenew compound
Jusko et al
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Biological relationship between FPG-HbA1c bridged information gap
Week
Ob
serv
ed F
PG
(m
g/d
L)
-10 0 10 20 30 40
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300
Week
Ob
serv
ed H
bA
1c
(%)
-10 0 10 20 30 40
67
89
10
Week
Ob
serv
ed F
PG
(m
g/d
L)-10 0 10 20 30 40
100
120
140
160
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200
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260
Week
Ob
serv
ed H
bA
1c
(%)
-10 0 10 20 30 40
67
89
10
11Week
Ob
serv
ed
FP
G (
mg
/dL
)
-10 0 10 20 30 40
10
01
50
20
02
50
30
0
WeekO
bse
rve
d H
bA
1c
(%)
-10 0 10 20 30 40
67
89
10
11
+ =
Drug X (Sponsor) in 72 patients
Drug X (other)in 28 patients
Hybrid datasetin 100 patients
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Value to Drug Development
• More informed dose/regimen selection– Could lead to increased trial success
• Quantitative analysis was critical
• Effective use of prior data for predictions
• Supports conduct of useful shorter duration trials for future compounds
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Disease Models: Challenges
• Data Management– How to best maintain an efficient database?
• Analysis– How to best conduct meta-analysis?– Identify and fill gaps (time-varying biomarkers
in survival models)?• Inter-disciplinary collaboration
– Biologists, Pharmacologists, Statisticians, Disease Experts, Quantitative Clinical Pharmacologists, Engineers need to come together to develop these models as a team.