adaptive designs as enabler for personalized medicine michael krams m.d. janssen pharmaceuticals,...
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![Page 1: Adaptive designs as enabler for personalized medicine Michael Krams M.D. Janssen Pharmaceuticals, Titusville, NJ mkrams@its.jnj.com Thanks to Donald A](https://reader037.vdocument.in/reader037/viewer/2022110403/56649e595503460f94b535f8/html5/thumbnails/1.jpg)
Adaptive designs as enabler for personalized medicine
Michael Krams M.D.
Janssen Pharmaceuticals, Titusville, NJ
Thanks to Donald A Berry
MDAnderson Cancer Center, Houston, TX
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2
Traditional “mapping” is one-dimensionalCompound Indication
Desired approach is multi-dimensional Indication Target Compounds
Compound F
Compound B
Compound C
Compound A
Target A
Target B
-
Target D Target E
Compound E
Compound D
Compound GCompound J
Compound ICompound H
Compound L
Compound K
Compound M
Compound O
Compound N
Compound F
Compound B
Compound C
Compound A
Indication
Target A
Target B
Target C
Target D Target E
Compound E
Compound D
Compound GCompound J
Compound ICompound H
Compound L
Compound K
Compound M
Compound O
Compound N
Compound YIndication
Compound X
Compound YCompound Y
IndicationCompound XCompound X
Borrowing information across compounds
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• uses accumulating data to decide on how to modify aspects of the study
• without undermining the validity and integrity of the trial
Validity means• providing correct statistical
inference (such as adjusted p-values, estimates and confidence intervals)
• assuring consistency between different stages of the study
• minimizing operational bias
Validity means• providing correct statistical
inference (such as adjusted p-values, estimates and confidence intervals)
• assuring consistency between different stages of the study
• minimizing operational bias
Integrity means• providing convincing results to a
broader scientific community• preplanning, as much as possible,
based on intended adaptations• maintaining confidentiality of data
Integrity means• providing convincing results to a
broader scientific community• preplanning, as much as possible,
based on intended adaptations• maintaining confidentiality of data
Adaptive design - definition
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• Biomarkers as “necessary condition” with early readout – Can be used to adapt treatment allocation
(drop a dose or stop for futility)
Biomarkers Are Critical to enable efficient decision making within clinical trials
Time
Num
ber
of s
ubje
cts
with
info
rmat
ion
xx
Subje
cts
recr
uite
d
Sub
ject
s w
ith
late
read
out
Subj
ects
with
early
read
out
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EXAMPLE
TRIAL
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Standard Phase II Cancer Drug Trials
Experimental armExperimental armPopulationof patients
Outcome:Tumor
shrinkage?
Experimental armExperimental armPopulation
of patients
Outcome:Longer time
disease free
Standard therapyStandard therapy
RANDOMIZE
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I-SPY2 TRIAL
Experimental arm 1
Experimental arm 1
Outcome:Complete response at surgery
Experimental arm 2
Experimental arm 2
Experimental arm 3 Experimental arm 3 Experimental arm 4 Experimental arm 4 Experimental arm 5
Experimental arm 5
Populationof patients Standard therapy
Standard therapy
ADAPTIVELY
RANDOMIZE
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I-SPY2 TRIAL
Outcome:Complete response at surgery
Experimental arm 2
Experimental arm 2
Arm 2 graduates to small focused
Phase III trial
Experimental arm 1
Experimental arm 1
Experimental arm 3 Experimental arm 3 Experimental arm 4 Experimental arm 4 Experimental arm 5
Experimental arm 5
Standard therapy
Standard therapy
Populationof patients
ADAPTIVELY
RANDOMIZE
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I-SPY2 TRIAL
Outcome:Complete response at surgery
Experimental arm 1
Experimental arm 1
Experimental arm 3 Experimental arm 3 Experimental arm 4 Experimental arm 4 Experimental arm 5
Experimental arm 5
Standard therapy
Standard therapy
Populationof patients
Arm 3 dropsfor futility
ADAPTIVELY
RANDOMIZE
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I-SPY2 TRIAL
Outcome:Complete response at surgery
Experimental arm 5
Experimental arm 5
Arm 5 graduates to small focused
Phase III trial
Experimental arm 1
Experimental arm 1
Experimental arm 4 Experimental arm 4 Standard therapy
Standard therapy
Populationof patients
RANDOMIZE
ADAPTIVELY
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I-SPY2 TRIAL
Outcome:Complete response at surgery
Arm 6 isadded tothe mix
Experimental arm 6
Experimental arm 6
Experimental arm 1
Experimental arm 1
Experimental arm 4 Experimental arm 4 Standard therapy
Standard therapy
Populationof patients
RANDOMIZE
ADAPTIVELY
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I-SPY2 TRIAL
Outcome:Complete response at surgery
Arm 6 isadded tothe mix
Experimental arm 6
Experimental arm 6
Experimental arm 1
Experimental arm 1
Experimental arm 4 Experimental arm 4 Standard therapy
Standard therapy
Populationof patients
RANDOMIZE
ADAPTIVELY
Goal: Greater than 85% success rate in
Phase III, with focus onpatients who benefit
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Adaptive designs for dose-finding:Background and Case Study
Scott Berry
Gary Littman
Parvin Fardipour
Michael Krams
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16Berry et al. Clin Trials 2010; 7: 121–135
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Adaptive Study Design (1)
• Investigational drug: oral anti-diabetic– What is the impact of study drug on
a) FPG (week 12) b) body weight (week 24)
• Two-stage adaptive design– Stage 1:
• Compound M, low and medium dose• PBO• Active Comparator
– Stage 2:• Compound M very low, low, medium, high dose• PBO• Active Comparator
– Selection of dose range and study design informed by preclinical toxicology findings – Study powered to compare FPG at Week 12– Enrollment
to high dose conditional on evidence of safety&efficacy at medium dose; to very low dose conditional on evidence of efficacy at low dose
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Adaptive Study Design (2)
• Bayesian decision algorithm– The algorithm analyzes the full dose-response curves of all treatment arms utilizing
all available data during the treatment period– Every week, the algorithm provided probability estimates and recommendations to
the Data Monitoring Committee as to whether enrollment should continue or be terminated
• Three formal interim analyses to review emerging benefit-risk profile– 100 subjects with at least 4 weeks of Rx– 240 subjects randomized– 375 subjects randomized
• Additional interim analyses may be requested by the Data Monitoring Committee
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Compound M very low dose
Placebo
Active Comparator (AC)
Compound M low dose
Compound M medium dose
Compound M high dose
Earliest transition point to Stage 2 after 100 subj. treated for at least 4 weeks
The decision to initiate Stage 2 requires that Compound M medium dose be at least comparable to Active Comparator in decreasing FPG
Adaptive Study Design (3)Two stages – up to 500 subjects treated for 24 weeks
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Decision Criteria for Opening Stage 2
0
Difference in FPG changes at 12 weeks:
GO: Open very high dose
[ ]Compound M medium dose – AC
STOP for futility
[ ]Compound M medium dose – AC
Continue with Stage 1 (up to 240 randomized subjects)
][Compound M medium dose – AC
GO: Open very high dose
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Dealing with Partial Data
• Regression model to estimate week 12 data for
patients who have not yet reached that point
• Initial model based on historical data from another compound
• Model is refined as we accumulate data from the present study
• As more patients complete 12 weeks, we become more
confident in the results for two reasons– The percentage of actual observed (rather than model-based) data increases
– The model becomes better as we learn from this trial
– Therefore, we need to be cautious about making an early decision
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The real study
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Overview and times of interim analysis findings
• Review of DMC findings:– Per protocol interim analysis: 14-Sep
• No safety/tolerability issues necessitating early stop• Model on verge of futility recommendation• DMC recommended continuing stage 1 enrollment
– Weekly analyses: 21-Sep, 4-Oct• Futility threshold crossed twice
– Ad hoc Interim Analysis: 4-Oct• Baseline characteristics• Key Efficacy Results• Safety/Tolerability Conclusions
• DMC recommends stopping trial for futility on 4-Oct• Executive Steering Committee reviewed the DMC recommendation to
terminate the study for futility and agreed on 23-Oct
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Mar 29 24 Subjects
Pr(>Piog)
0
0.1
0.2
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0.5
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0.9
1
30mg 60mg
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Plac 30mg 60mg Piog
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G, C
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+1sd
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Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Apr 11 28 Subjects
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
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1
30mg 60mg
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Plac 30mg 60mg Piog
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FP
G, C
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good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Apr 30 36 Subjects
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
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Plac 30mg 60mg Piog
Dose
FP
G, C
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+1sd
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good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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May 8 37 Subjects
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
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0.8
0.9
1
30mg 60mg
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Plac 30mg 60mg Piog
Dose
FP
G, C
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+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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May 30 49 Subjects , 1 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
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0.8
0.9
1
30mg 60mg
-50
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Plac 30mg 60mg Piog
Dose
FP
G, C
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Mean
+1sd
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Raw
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Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Jun 8 57 Subjects, 1 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
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0.9
1
30mg 60mg
-50
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10
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Plac 30mg 60mg Piog
Dose
FP
G, C
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Mean
+1sd
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good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Jun 21 65 Subjects , 8 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
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0.8
0.9
1
30mg 60mg
-50
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Plac 30mg 60mg Piog
Dose
FP
G, C
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ng
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rom
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+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Jul 9 71 Subjects , 9 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
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0
10
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30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
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Mean
+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Jul 25 90 Subjects , 18 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
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0
10
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Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
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Mean
+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Aug 5 95 Subjects , 25 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
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0
10
20
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Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Aug 15 109 Subjects , 35 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
-30
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-10
0
10
20
30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Sep 5 133 Subjects , 46 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
-30
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-10
0
10
20
30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Sep 14 147 Subjects , 53 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
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0
10
20
30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
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Oct 1 171 Subjects , 61 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
-30
-20
-10
0
10
20
30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
STOP
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
37
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Oct 4 179 Subjects , 63 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
-30
-20
-10
0
10
20
30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
STOP
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
38
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Oct 15 190 Subjects , 70 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
-30
-20
-10
0
10
20
30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
STOP
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
39
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Oct 18 199 Subjects , 71 complete
Pr(>Piog)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
30mg 60mg
-50
-40
-30
-20
-10
0
10
20
30
Plac 30mg 60mg Piog
Dose
FP
G, C
ha
ng
e f
rom
ba
se
line
Mean
+1sd
-1sd
Raw
good
bad
STOP
Low Medium
Placebo Low Medium Active Comparator
Probability (> AC)
40
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Berry et al. Clin Trials 2010; 7: 121–135
Learning in real time - Early stopping
41
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42
Organizational structure
Sponsor Steering Committee
Study Team Lead Members
Blinded Endpoint Adjudication Committee
Data Monitoring Committee
Independent Statistical Center
Blinded
Unblinded
Reports Queries and requests for additional reports
Clinical data
Reports Adjudicated endpoints
Recommendations
DMC Liaison
Reports Queries and requests for additional reports
Clinical Data
Study Team