Adaptive designs as enabler for personalized medicine
Download
1 / 42

Adaptive designs as enabler for personalized medicine - PowerPoint PPT Presentation


  • 172 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Adaptive designs as enabler for personalized medicine' - astra


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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


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


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


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


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 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


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



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


Mar 29 24 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Apr 11 28 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Apr 30 36 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


May 8 37 Subjects

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


May 30 49 Subjects , 1 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Jun 8 57 Subjects, 1 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Jun 21 65 Subjects , 8 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Jul 9 71 Subjects , 9 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Jul 25 90 Subjects , 18 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Aug 5 95 Subjects , 25 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Aug 15 109 Subjects , 35 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Sep 5 133 Subjects , 46 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


Sep 14 147 Subjects , 53 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


STOP

Oct 1 171 Subjects , 61 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


STOP

Oct 4 179 Subjects , 63 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


STOP

Oct 15 190 Subjects , 70 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


STOP

Oct 18 199 Subjects , 71 complete

Probability (> AC)

bad

good

Placebo Low Medium Active Comparator

Low Medium


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


ad