Transcript Disease Models Overview and Case Studies Joga Gobburu Pharmacometrics

Disease Models
Overview and Case Studies
Joga Gobburu
Pharmacometrics
Office Clinical Pharmacology,
Office of Translational Sciences, CDER, FDA
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
Impact of Pharmacometrics Analyses
2000-2004
Pivotal: Regulatory decision will not be the same without PM review
Supportive: Regulatory decision is supported by PM review
Impact
Approval
Labeling
Pivotal
54%
57%
Supportive
46%
30%
0
14%
No Contribution
Bhattaram et al. AAPS Journal. 2005; 7(3): Article 51. DOI: 10.1208/aapsj070351
Impact of Pharmacometrics Analyses
2005-2006
Pivotal: Regulatory decision will not be the same without PM review
Supportive: Regulatory decision is supported by PM review
Impact →
Discipline
PM Reviewer
Approval
Labeling
95%
100%
DCP Reviewer
95%
100%
DCP TL
90%
94%
Medical Reviewer
90%@
90%@
DCP=Division of Clinical Pharmacology
@=survey pending in 1 case
NDA#1: Approval of monotherapy
oxcarbazepine in pediatrics for treating partial
seizures using prior clinical data
Adjunctive
Monotherapy
Adults
Clinical trials
Clinical trials
Children (416 years of
age)
Clinical trial
“Model Based Bridging”
approach proposed by
FDA
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
NDA#2: Establishment of biomarkeroutcome relationship allowed more efficient
future trial design
• The sponsor was pursuing an accelerated
approval, for drug to prevent a lifethreatening disease, based on a biomarker
even though clinical endpoint analysis failed
in two pivotal trials
2
Estimated RR
LL of 95% CL
UL of 95% CL
1
1.6
0.5
Hazard ratio=10.0
(95% CI 2.5-30.0)
0
event
disease
of the
risk Risk
Relative
Relative
of Renal
Flare
3
NDA#2: Establishment of biomarkeroutcome relationship allowed more efficient
Study 09
future trial
design
p<0.001
0.0
0.5
1.0
1.5
Ratio
of biomarker
level to baseline
Ratio
of Baseline Anti-dsDNA
Levels
2.0
NDA#3: Insights into trial failure reasons will
lead to more efficient future trials
Severe Baseline Disease
Responders
Mild Baseline Disease
Non-Responders
80
40
20
0
-20
60
In Score A at Week 12
Placebo-Subtracted Change
60
In Score A at Week 12
Placebo-Subtracted Change
80
40
20
0
-20
-40
-40
0
5
10
15
Dose, mg
20
25
30
0
5
10
15
Dose, mg
20
25
30
Slope
Slope
Slope
Slope
Females seem to be more sensitive to QT
prolongation
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 calls
for efficient use of prior knowledge
Manage and Leverage Knowledge
Information
Placebo &
Disease Models
Knowledge
We are referring to such diverse quantitative
approach(es) as ‘Disease Modeling’
• Biomarker-Endpoint
• Time course
• Drop-out
• Inclusion/Exclusion
criteria (Trial)
• Parkinson’s
• Obesity, Diabetes
• Tumor-Survival
• Rheumatologic condition
• HIV
• Epilepsy
• Pain
Core Development Strategy for Testosterone Suppressants
IC50
Reporter
Gene Assay
- 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
Preclinical
Disease Model
- 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.
Clinical Trial
Simulation
- Invitro and
preclinical data
for clinical dose
and regimen
selection
- Clinical
development
plan
Dose
optimization
in cancer
patients
- Pilot study for
dose
optimization thr’
innovative trial
designs
Pivotal trial
|----*2 mo-----||----*2 mo-----||----*2 mo-----||----*3 mo-----||---------*12 mo--------------|
*Actual execution time.- it does account for time spent accumulating resources.
From Pravin Jadhav, VCU/FDA
Obesity
• Obesity trials are large, over 1-2 yrs and
fraught with challenges due to high dropout rate
Dr. Jenny J Zheng
Dr. Wei Qiu
Dr. Hae Young Ahn
0 2 4 6 8
0
80 120
180
Black Male
-1
1
3
Caucasian Female
4.2
4.8
3000 patients
-3
-2
0
1
2
-2
0
2
-3
-1
1
3
-2
Other races Female
4.4
4.8
Baseline Body Weight
Black Male
4.6
5.0
80 120 160
Other races Female
Black Female
4.4 4.8 5.2
80
140 200
Black Female
Other races Male
4.6
5.0
30
0
0 10
200
0
100
200
Caucasian Female
80 120 160
Other races Male
Model Qualification
20 40 60
400
100
200
Caucasian Male
Caucasian Male
4.4
5.0
0 50
2
4
150
6
Obesity
-2
Quantiles of Standard Normal
0 1 2
0 1 2
Patients with small weight loss drop-out
30
0.5
0
-0.5
Drop-out, %
Drop-out patients
20
-1
15
-1.5
-2
10
-2.5
5
-3
Remaining patients
0
1
0-12
2
12-24
3
Week
24-36
4
36-52
-3.5
Mean weight change, kg
25
Obesity:
Time Course of Placebo Effect
Weight Loss, kg
2.0
1.6
1.2
0.8
0.4
0.0
0
100
200
Days
300
400
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
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
Pivotal Trial Dose Selection: AntiDiabetic
• 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.
K in
FPG
E C
K out  (1  max
)
EC50  C
1st order Oral Absorption
Cmt 1
HbA1c
K'out
FPG-HbA1c relationship
from historic studies
employed to estimate
effects on HbA1c of the
new compound
Drug Conc.
K'in
Cmt 2
HbAlc
FPG
E C
dFPG
 K in  K out (1  max
)  FPG
dt
EC50  C
dHbA1c
 K 'in FPG  K 'out HbA1c
dt
Time (Week)
Jusko et al
20
30
40
Week
10
20
30
40
Drug X (Sponsor)
in 72 patients
20
30
40
20
30
40
11
10
11
Observed HbA1c (%)
10
9
8
7
Observed HbA1c (%)
30
10
=
6
6
20
Week
0
Week
10
10
200
-10
40
Week
9
8
Observed HbA1c (%)
7
0
250
300
0
+
6
-10
150
Observed FPG (mg/dL)
100
-10
9
10
8
0
7
Observed FPG (mg/dL)
-10
100 120 140 160 180 200 220 240 260
250
200
150
100
Observed FPG (mg/dL)
300
Biological relationship between FPGHbA1c bridged information gap
-10
0
10
20
30
Week
Drug X (other)
in 28 patients
40
-10
0
10
Week
Hybrid dataset
in 100 patients
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
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.