Ugochi Emeribe, PhD

The Era of Personalised Healthcare: Designing

Clinical Studies with Biomarkers

2

Outline

Background concepts, and retrospective analyses of Gefitinib (Iressa) trial

Classic Designs using Biomarkers Classifier, Prognostic and Predictive Biomarkers

Sample Size Calculations

Validating Biomarkers

surrogate Biomarkers

Conclusions

SPECIAL REPORT-Big Pharma's global guinea pigs

Chicago TribuneMonday, May 9, 2011 8:13 AM CDT

As drug treatments become more targeted, scientists are unraveling how small genetic variations may make one medicine suitable for a particular group of people.

AstraZeneca's lung cancer drug Iressa, for example, failed to help Western patients overall in tests but proved much more effective in Asians -- a discovery that has shed valuable new light on ways of tackling the disease worldwide.

“We are starting to understand ethnic differences through the responses seen in global trials. By cherishing our genetic diversity we can identify biomarkers like the one for Iressa. That is really exciting.” says Dr. David Kerr, president of the

European Society for Medical Oncology.

What is Personalized Health Care?

Perfect Medicine

• Effective in all patients!

• The same dose for every patient!

• No side effects!

Real Medicines• Effective only in some

patients• Dose varies for

different patients• Some patients may

develop adverse events

Matching individual patient characteristics with drugs that produce better outcomes for that patient

Herceptin is seen as the poster child for PHC

But a classic example of a drug development that did not start with PHC in mind is Gefitinib

5

Retrospective Analyses of Gefitinib Trials

EGFR Mutation- first thought to be predictive was actually prognostic

EGFR Gene Amplification- first thought to be prognostic was actually predictive

EGFR mutation status

► Median TTP for EGFR mutation +ve cases was longer (116 days, range 25-171), than that for mutation -ve cases (57 days, range 28-170)

► There was no impact on OS

IIII

I

I

I

III

II

I I

0.0

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0.4

0.6

0.8

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Progression free survival time (months)0 2 4 6 8 10 12

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Gefitinib 250/500mg and EGFR M+(n = 14)

Gefitinib 250/500mg and EGFR M–(n = 65)

II I I

I I

I

IIIIII I

I IIIIII

I IIII

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I I I I III

I

0.0

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Survival time (months)0 2 4 6 8 10 12

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Gefitinib 250/500mg and EGFR M+(n = 14)Gefitinib 250/500mg and EGFR M–(n = 65)

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EGFR gene amplification

Time (months)

0.0

N=256, E=157Cox HR=1.16 (0.81, 1.64)

p=0.42

FISH -

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10 12 14 16

Gefitinib Placebo

Proportion surviving

Interaction test: p=0.04Time (months)

0.0

N=114, E=68Cox HR=0.61 (0.36, 1.04)

p=0.07

FISH +

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10 12 14 16

Gefitinib Placebo

FISH: technique for measuring increased EGFR gene copy

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FISH positive status and clinical characteristics

Asian origin

Adeno

Histology 156 214 48 322 11 359 117 253

50

40

30

20

10

Smoking Ethnicity Gender

Other Never Ever Other Female Male

% of FISH positive patients

60

No. patients with evaluable samples:

0

Conclusions from Gefitinib trials

FISH+ status is the biomarker which is the strongest predictor of Gefitinib benefit on OS

Patients who are FISH- are unlikely to benefit from Gefitinib therapy.

Therefore, EGFR amplification is a predictive marker for benefit with Gefitinib therapy.

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Definitions

Clinical Endpoint (or Outcome) : A characteristic or variable that reflects how a patient feels, functions, or how long a patient survives.

Biomarker (or Biological marker): A characteristic objectively measured as an indicator of normal biologic or pathogenic process, or pharmacologic responses to a therapeutic intervention.

measured once before treatment

Types of Biomarkers Prognostic

Predictive

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Prognostic vs Predictive

Prognostic markers indicate that clinical outcome is independent of treatment.

Stage of disease is a prognostic marker for survival outcome.

Predictive biomarkers show treatment effect on the clinical endpoint.

High Her-2 gene copy number in advanced breast cancer is predictive for the effect of Herceptin.

Statistically, a predictive marker is a marker that interacts with treatment “significantly.”

…and will adversely impact on power

…and will adversely impact on power

•…therefore it is obvious that a biomarker targeted approach to drug development will lead to smaller, more secure and more successful developments

•losers will be dropped early and winners taken forward, resulting in more successful drug development…

How about this?

100 treatment resistant patients are offered a new drug

70 respond and 30 do not.

How do we interpret this experiment?

Which is the correct interpretation?

A - Treatment works for 70% of patients 100% of the time and for 30% of patients 0% of the time.

Or…

B - Treatment works in 100% of patients 70% of the time.

Which is the correct interpretation?

A - No within patient variability –patients are deterministically responders or non responders

B - Within patient variability –drug has some effect in all patients, but patients vary in their response –sometimes they respond, sometimes they don’t

What does this mean?•In most situations, it is impossible to know if patients respond deterministically•To know for sure requires repeat administration of drug (and control) in within-patient crossover trials

However, such trials are impossible in many settings, especially oncology, so that there is little choice but to assume interpretation A.

Suppose biomarker target identified in patients treated with drug, show target +vepatients do better than target –vepatients

% s

urvi

ving

or

prog

ress

ion-

free

Time

…suppose the same is true for patients treated with control, target +vepatients do better than target –vepatients

% s

urvi

ving

or

prog

ress

ion-

free

Time

This is an example of a prognostic biomarker

•Patients with the biomarker do better than those without it irrespective of the treatment they receive

This biomarker is not predictive for the effect of drug over control

•Using this biomarker as a basis for patient selection is unlikely to result in a positive outcome for drug

Predictive

Prognostic

Predictive vs. Prognostic

Biomarker +vepatients treated with drug do better than biomarker +vepatients treated with control

% s

urvi

ving

or

prog

ress

ion-

free

Time

•This is an example of a predictive biomarker biomarker

•+vepatients do better when treated with drug than when treated with control

•biomarker –vepatients do less well on both drug and control

So, we need to stratify on receptor status and then randomize to drug and no drug to assess the true potential of a drug

Therefore, data from a properly designed Phase II trial

could be used to assess the true value of receptor status

How about designing late Phase trials?

Just an example

C E Effect

+ve (25%) 6 months 12 months 0.50

–ve (75%) 6 months 6 months 1.0

All patients 6 months 7.5 months 0.80

No. required to screen

No. required to enroll

All patients 1000

+ve (25%) 117 468

1median follow-up of 18 months assumed

To validate biomarkers……

SensitivityPr(test +ve/true +ve)

SpecificityPr(test –ve/true –ve)

Positive Predictive ValuePr(true +ve/test +ve)

An imperfect test lessens the advantage of a biomarker strategy

Sens, Spec C E Effect size

No. Required to enroll

No. required to screen

100%, 100% 6 12 0.50 117 468

95%, 75% 6 9.4 0.64 260 613

75%, 95% 6 11 0.55 149 663

75%, 75% 6 9 0.68 317 845

Anyway, assume we have the perfect test, what happens if there is some modest effect in –ve pts?

Is a selected design still best?

Even a small effect in biomarker –vepts erodes the

advantage of a biomarker strategy

C E Effect

+ve (25%) 6 months 12 months 0.50

–ve (75%) 6 months 7.5 months 0.80

All patients 6 months 8.7 months 0.69

No. required to screen

No. required to enroll

All patients 384

+ve (25%) 117 468

Effect in –vepts = 1/3 effect in +vepts

Likely the relationship between treatment effect and biomarker

level is continuous, reflecting underlying biology

In a biomarker strategy we would need to be very confident that

(i)we had a very good test (ii)the biomarker ‘-ve’ population achieved no or very little benefit

from treatment

In late phase development, testing across the

population offers some advantages

Power the trial for an “interaction”

“Is there any statistical evidence that the treatment effect in +vepts is different to the treatment effect in -ve pts?”

If “Yes”, valid to look at +ve and -ve groups separately. Possibility of labeling in (+) pts.

If “No”, then there is no statistical rationale for looking at +ve and –ve patients separately.

Compare treatments in the overall population, irrespective of biomarker status.

An example

Treatment A is either better or worse than treatment B (qualitative interaction)

Treatment HR = 0.74

Interaction HR = HR(+vepts) / HR(-vepts)

= 0.48 / 2.85 = 0.17

If interaction effect size is better that treatment effect size

Than interaction is highly significant

Power of Interaction Test

Interaction test has very low power

So, validation of predictive biomarker is more complicated ---due to limited power of the interaction test

It is known that as inflation factor for total sample size decreases, so does interaction effect size in relation to overall treatment effect size.

Therefore, inflation factor is required to increase the sample size to ensure interaction test has the same power as the original sample size calculated for overall treatment effect.

Design can provide confirmatory evidence in either all

patients or the subset of biomarker +ve patients

Alternatively, patient selection adaptive designs can

identify those patients most likely to benefit

Interim Continuance

Ran

dom

ize

The need for surrogate endpoints

In many settings, the primary clinical endpoint takes large, long term trials

Breast cancer recurrence, cardiac events, osteoporotic fracture, death from prostate cancer

To reduce time and expense and to bring effective medicines to patients quickly requires use of surrogate endpoints

Statistical definition of Surrogacy

“A response variable for which the test of the null hypothesis of no relationship to the treatment groups under comparison is also a valid test of the corresponding hypothesis based on the true endpoint.” by Prentice, (1989)

The Problem with Prentice

Criteria based on Prentice’s definition are problematic

Cannot prove the null

The ‘%’ effect retained is not a true percentage, and CI for the ‘%’ effect retained is usually very wide

Cannot realistically expect 100% of a drug effect on OS to be explained by a direct effect on the disease itself.

Newer Approaches to Surrogacy

That reliably predict drug effect on a later clinical outcome (e.g. OS or PFS) given the effect of drug on some earlier endpoint.

Buyse and Molenbergs (2000, 2002) provided a meta-analytic methodology for doing just this.

Unlike Prentice, this approach does not require proving the null nor the presence of a significant treatment effect.

Strong evidence of surrogacy: Relation between tumor response

to first-line chemotherapy and survival in advanced colorectal

cancer: a meta-analysis

R2=0.97

Using methodology to quantitate uncertainty in

prediction Ovarian cancer

Conclusions

There should be an assurance that selected test for biomarker is correct. So, validation of biomarkers in early in drug development is imperative.

Treatment interaction effect has to be factored in sample size calculation for late phase studies.

Backup Slides

51

FISH: technique for measuring increased EGFR gene copy

Fluorescent In Situ Hybridisation (FISH) is a technique for measuring increased EGFR gene copy number

Control probeEGFR probe

A piece of synthetic DNA labelledwith a fluorescent tag binds to the EGFR gene. A probe to the gene CEP7 labelled with a second probe acts as a reference

The normal situation‘balance disomy’

‘balanced polysomy’

‘gene amplification’

Cappuzzo et al 2005

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EGFR gene copy number (FISH) in ISEL trial

Disomy

Low trisomy

High trisomy

Low polysomy

High polysomy

Gene amplification

15.7

24.1

2.2

27.3

17.0

13.8

Pattern Patientsn=370

(%)

Note: Categories in blue above represent those considered FISH+