Adaptive designs as enabler for personalized medicine
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Adaptive designs as enabler for personalized medicine. Michael Krams M.D. Janssen Pharmaceuticals, Titusville, NJ [email protected] Thanks to Donald A Berry MDAnderson Cancer Center, Houston, TX. Borrowing information across compounds. Compound X. Compound X. Compound X. Indication.

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Adaptive designs as enabler for personalized medicine

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Adaptive designs as enabler for personalized medicine

Adaptive designs as enabler for personalized medicine

Michael Krams M.D.

Janssen Pharmaceuticals, Titusville, NJ

[email protected]

Thanks to Donald A Berry

MDAnderson Cancer Center, Houston, TX


Borrowing information across compounds

Borrowing information across compounds

Compound X

Compound X

Compound X

Indication

Compound Y

Compound Y

Compound Y

Indication

Compound O

Compound O

Compound N

Compound N

Compound A

Compound A

Compound B

Compound B

Target B

Target B

Compound M

Compound M

Compound C

Compound C

Target A

Target A

-

Compound L

Compound L

Target C

Indication

Compound D

Compound D

Compound K

Compound K

Target D

Target D

Compound F

Compound F

Target E

Target E

Compound G

Compound G

Compound J

Compound J

Compound E

Compound E

Compound H

Compound H

Compound I

Compound I

Traditional “mapping” is one-dimensional

Compound Indication

Desired approach is multi-dimensional

Indication TargetCompounds


Adaptive designs as enabler for personalized medicine

uses accumulating data to decide on how to modify aspects of the study

without undermining the validityandintegrityof the trial

Validitymeans

providing correct statistical inference (such as adjusted p-values, estimates and confidence intervals)

assuring consistency between different stages of the study

minimizing operational bias

Adaptive design - definition

  • Integritymeans

  • providing convincing results to a broader scientific community

  • preplanning, as much as possible, based on intended adaptations

  • maintaining confidentiality of data


Adaptive designs as enabler for personalized medicine

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

Subjects with late readout

Subjects with early readout

Number of subjects with information

Subjects recruited

x

x

Time


Adaptive designs as enabler for personalized medicine

E

X

A

M

P

L

E

T

R

I

A

L


Standard phase ii cancer drug trials

Population

of patients

Outcome:

Tumor

shrinkage?

Standard Phase II Cancer Drug Trials

Experimental arm

RANDOMIZE

Population

of patients

Experimental arm

Outcome:

Longer time

disease free

Standard therapy


I spy2 trial

ADAPTIVELY

RANDOMIZE

I-SPY2 TRIAL

Experimental arm 1

Experimental arm 2

Outcome:

Complete

response

at surgery

Population

of patients

Experimental arm 3

Experimental arm 4

Experimental arm 5

Standard therapy


I spy2 trial1

ADAPTIVELY

RANDOMIZE

I-SPY2 TRIAL

Experimental arm 1

Experimental arm 2

Outcome:

Complete

response

at surgery

Population

of patients

Experimental arm 3

Experimental arm 4

Experimental arm 5

Standard therapy

Arm 2 graduates

to small focused

Phase III trial


I spy2 trial2

ADAPTIVELY

RANDOMIZE

I-SPY2 TRIAL

Experimental arm 1

Outcome:

Complete

response

at surgery

Population

of patients

Experimental arm 3

Experimental arm 4

Experimental arm 5

Standard therapy

Arm 3 drops

for futility


I spy2 trial3

ADAPTIVELY

RANDOMIZE

I-SPY2 TRIAL

Experimental arm 1

Outcome:

Complete

response

at surgery

Population

of patients

Experimental arm 4

Experimental arm 5

Standard therapy

Arm 5 graduates

to small focused

Phase III trial


I spy2 trial4

ADAPTIVELY

RANDOMIZE

I-SPY2 TRIAL

Experimental arm 1

Experimental arm 6

Outcome:

Complete

response

at surgery

Population

of patients

Experimental arm 4

Standard therapy

Arm 6 is

added to

the mix


I spy2 trial5

ADAPTIVELY

RANDOMIZE

I-SPY2 TRIAL

Experimental arm 1

Goal: Greater than

85% success rate in

Phase III, with focus on

patients who benefit

Experimental arm 6

Outcome:

Complete

response

at surgery

Population

of patients

Experimental arm 4

Standard therapy

Arm 6 is

added to

the mix


Adaptive designs for dose finding background and case study

Adaptive designs for dose-finding:Background and Case Study

Scott Berry

Gary Littman

ParvinFardipour

Michael Krams


Adaptive designs as enabler for personalized medicine

Berry et al. Clin Trials 2010; 7: 121–135


Adaptive study design 1

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 doseconditional on evidence of safety&efficacy at medium dose; to very low dose conditional on evidence of efficacy at low dose


Adaptive study design 2

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


Adaptive designs as enabler for personalized medicine

Adaptive Study Design (3)

Two stages – up to 500 subjects treated for 24 weeks

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 designs as enabler for personalized medicine

GO: Open very high dose

STOP for futility

Compound M medium dose– AC

Compound M medium dose – AC

[

]

]

[

Compound M medium dose– AC

GO: Open very high dose

[

]

Decision Criteria for Opening Stage 2

Difference in FPG changes at 12 weeks:

0

Continue with Stage 1 (up to 240 randomized subjects)


Dealing with partial data

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


Adaptive designs as enabler for personalized medicine

The real study


Overview and times of interim analysis findings

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


Adaptive designs as enabler for personalized medicine

Mar 2924 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Apr 1128 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Apr 3036 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

May 837 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

May 3049 Subjects , 1 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Jun 857 Subjects, 1 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Jun 2165 Subjects , 8 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Jul 971 Subjects , 9 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Jul 2590 Subjects , 18 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Aug 595 Subjects , 25 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Aug 15109 Subjects , 35 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Sep 5133 Subjects , 46 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Sep 14147 Subjects , 53 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

STOP

Oct 1171 Subjects , 61 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

STOP

Oct 4179 Subjects , 63 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

STOP

Oct 15190 Subjects , 70 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

STOP

Oct 18199 Subjects , 71 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Adaptive designs as enabler for personalized medicine

Learning in real time - Early stopping

Berry et al. Clin Trials 2010; 7: 121–135


Organizational structure

Organizational structure

Sponsor Steering Committee

Study Team Lead Members

Blinded Endpoint Adjudication Committee

Study Team

Blinded

Unblinded

Adjudicated endpoints

Reports

Independent Statistical Center

Queries and requests for additional reports

Reports

Clinical data

Clinical Data

DMC Liaison

Queries and requests for additional reports

Reports

Recommendations

Data Monitoring Committee


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