High Content Clinical Trials – Design andInfrastructure

Janet Dancey, MD, FRCPCProgram Leader, High Impact Clinical Trials, Ontario Institute for Cancer Research

Director, Clinical Translational Research, NCIC Clinical Trials Group

Clinical Trial Design for the 21st Century

Vancouver British Columbia March 2nd 2011

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Types of Trials

• High Impact (correlation with clinical outcome)Multi-institutionalFewer samples, complex analyses

E.g. phase 2 trials and phase 3 trials, population studies

Require standardization across sites and/or more robust assaysAddress clinical-biological correlations, more likely to have clinical impact

• High Content (Dense sample collection/analyses)Single/Oligo-institutional trialsMultiple samples (number and type), complex analyses

e.g. Phase 1 trials to assess novel agent

Important for early development/evaluationAddress biological questions: target/pathway inhibition

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Changes in Clinical Trials

Adapted from Eli Lilly and Company, Lillian Siu

Pre-ClinicalDevelop-ment

Pre-ClinicalDevelop-ment

Phase I Phase II Phase III

Biomarker – Proof ofmechanism(PharmacodynamicBiomarkers)

Phase II-III – Proof ofprinciple (PredictiveBiomarkers)

Commercialization

Scarcity of drugdiscovery

Abundance of drugdiscovery

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Trial Designs and Modifications

Trial Phase Purpose Biomarkers Modifications

0 Define doseSelected agents

Target modulationPK

Normal VolunteersPre-surgical

I Metastatic Dose/schedule Target InhibitionPKToxicityActivity

Expanded cohorts toevaluate target ,toxicity or screenactivity

II Metastatic Activity Predictive markers Randomized

III Metastatic Clinical benefit Predictive markers Subset analyses

III Adjuvant Clinical benefit PredictivePrognostic

Subset analyses

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Phase 1 Trials: Considerations

• Primary goal: To identify an appropriate dose/schedule forfurther evaluation

• Design principles:Maximize safetyMinimize patients treated at biologically inactive dosesOptimize efficiency

• Study population:Patients for whom no standard therapy

Smallpatientnumbers

HeterogenousRefractoryTumours

Expect target modulation but not anti-tumour activity

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Where/when do biomarkers play a role?Target Versus Toxic Effects

Prob

abili

ty o

f Effe

ct

1.0

Dose/Concentration/Exposure

Target Effect in Tumour

Target Toxicity

Target Toxicity

Off Target Toxicity

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PLX4032, a V600EBRAF kinase inhibitor: correlation ofactivity with PK and PD in a phase I trial.

Puzanov, K. L. J Clin Oncol 27:15s, 2009 (suppl; abstr 9021)

Patients pERKPRE

pERK KI67PRE

KI67 PKµM*h

Imaging

4 range50-100,median

60;

range10-40,median

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range20-60%,median45%;

range5-25%,median12.5%

meanAUC0-24h ~126

µM*h

PD (4)

2 70 2 30 -50% 3-5% 500 -1000

PR (1)PET (2)

Target Pathway Tumor

5-fold

35-fold

4-fold

10-fold

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Phase I Predictive Markers

Fong et al NEJM, 2009; von Hoff et al NEJM 2009; Kwak et al ECCO/ESMO2009: Chapman et al ECCO/ESMO 2009;

Target Drug Test Phase I ORR (%)

PARP Olaparib (AZD2281; KU-0059436)

BRCA1/2 9/21 (44%) Ovary,breast, prostate

HedgehogSMO

GDC-0449 Mutation(PTCH/SMO)

18/33 (56%) BasalCell

EML4-ALK PF-02341066 Translocation 20/31 (61%) Lung

BRAFV600E PLX4032 (RG7204) Mutation 19/27 (70%)Melanoma

Biomarker Designs for Late Phase Clinical Trial

• Target Selection or Enrichment Designs

• Unselected or All-comers designsMarker by treatment interaction designs (biomarkerstratified design)Adaptive analysis designsSequential testing strategy designsBiomarker-strategy designs

• Hybrid designs

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Types of Trials – Stratified Medicine

Whole population

Molecular Analysis Studypop.

Molecular Analysis

Requirements –CLIA/GLP Laboratory,Fast analysis of patient samplesSmaller number of patients enrolled in trial

Requirements –Larger number of patients enrolled in trial,GLP – like assay/laboratory

Rx

Rx

Is there a strong hypothesis and compelling rationale?Is there a validated assay?NOTE: The population size screened does not change

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Challenges to Designing Trials to ProvePersonalized Medicine

• Contingent on the following assumptions:Drug(s): Are effective in modulating target(s) ofinterest

Biomarker (Mutations): Are functional “drivers” -activating or inactivating and there is no effect in thebiomarker negative group

Resistance mechanisms do not set in fast enough thatoverride any antitumor activity

Target Selection/Enrichment Designs

If we are sure that the therapy will not work in Marker-negative patients

AND

We have an assay that can reliably assess the Marker

THEN

We might design and conduct clinical trials for Marker-positive patients or in subsets of patients with high

likelihood of being Marker-positive

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IPASS-Schema

East AsianNever smoker/lightformer smokerPulmonaryAdenocarcinomaNo prior treatment

RANDOMIZE

Gefitinib250 mg daily

Paclitaxel 200 mg/m2

Carboplatin AUC 5-6

1° Endpoint PFS2° EGFR Biomarker

Mok et al N Engl J Med 2009;361:947-57

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IPASS-Gefitinib or Carboplatin–Paclitaxel in PulmonaryAdenocarcinoma.

Mok et al N Engl J Med 2009;361:947-57

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• Well-conducted randomized controlled trial

• Prospectively stated hypothesis, analysis techniques,and patient population

• Predefined and standardized assay and scoring system

• Upfront sample size and power calculation

• Samples collected during trial and available on a largemajority of patients to avoid selection bias

• Biomarker status is evaluated after the analysis ofclinical outcomes

• Results are confirmed by independent RCT(s)

Prospective/Retrospective Design

Prospective

Retrospective

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Control

Marker-Guided Randomized DesignRandomize To Use Of Marker Versus No Marker EvaluationControl patients may receive standard or be randomized

All Patients

Marker DeterminedTreatment

Randomize Treatment

New Drug

New Drug

Control

Marker-based Strategy Design

M+

• Provides measure of patient willingness to follow marker-assigned therapy• Marker guided treatment may be attractive to patients or clinicians• Inefficient compared to completely randomized or randomized block design

Ran

dom

ize

Standard Treatment

OR

Control

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Example: ERCC1: Customizing Cisplatin Based on QuantitativeExcision Repair Cross-Complementing 1 mRNA Expression

Cobo M et al. J Clin Oncol; 25:2747-2754 2007

• 444 chemotherapy-naïve patients with stage IIIB/IV NSCLC enrolled,• 78 (17.6%) went off study before receiving chemotherapy, due insufficient tumor for

ERCC1 mRNA assessment.• 346 patients assessable for response: Objective response was 39.3% in the control

arm and 50.7% in the genotypic arm (P = .02).

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Trial Designs With Biomarker Stratification

• Restricting to 1 tumour type and 1 mutationMultiple examples

– BRAF – melanoma– EML4-ALK – Lung cancer– HER2 - Breast

• Inclusion of multiple mutations/biomarkers with tumour-focused question:

A few examples– BATTLE - NSCLC– I-SPY 2 – Locally Advanced Breast Cancer

• Inclusion of multiple tumour types with mutation-focusedquestion

Emerging studies proposed– ALK, PI3K

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One Tumour/One Mutation

• Restricting to 1 tumour type and 1 mutationMultiple examples

– BRAF – melanoma– EML4-ALK – Lung cancer– HER2 - Breast

Unless data are compelling and there is a wellcharacterized assay this design is risky and restrictive

(e.g. BRAF mutation in melanoma),Logistics are formidable but can be overcome

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Multiple Tumours with One Mutations

• Inclusion of multiple tumour types with mutation-focused question

Emerging studies proposed– ALK, PI3K, BRAF, etc

Facilitates accrual but– Same mutation may have different degrees of functionality

in different tumor types (continue to stratify by histologyand mutation)

– Different mutations of the same gene may confer differentsensitivities

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MDACC Experience with Mutation DirectedTherapy

• Phase I trial patients from Oct 08 to Nov 09

• 217 pts tested for PIK3CA mutations:

25 pts (11.5%) harbour PIK3CA mutations21% in endometrial, 17% in ovarian; 17% in CRC; 14% inbreast; 13% in cervical and 9% in SCCHN

Of these 25 pts, 17 pts were treated with PI3K-AKT-mTORpathway inhibitor

6/17 pts (35%) achieved PR15/241 pts (6%) without PIK3CA mutations treated onsame protocols responded

Janku et al. Mol Cancer Ther 2011

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Multiple Markers within One Histology

• Inclusion of multiple biomarkers with tumour-focused question:

A few examples– BATTLE - NSCLC– I-SPY 2 – Locally Advanced Breast Cancer

Need to get different drugs from multiple pharmacompanies, big sample sizeComplex collaborationsLarge, multi-center trial

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BATTLE (Biomarker-based Approaches of TargetedTherapy for Lung Cancer Elimination)

• Patient Population: Stage IV recurrent NSCLC• Primary Endpoint: 8-week disease control rate [DCR]• 4 Targeted Treatments• 11 Markers• 200 patients• 20% type I error rate and 80% power for DCR > 30%

Zhou X, Liu S, Kim ES, Lee JJ.Zhou X, Liu S, Kim ES, Lee JJ. Bayesian adaptive design for targeted therapyBayesian adaptive design for targeted therapydevelopment in lung cancerdevelopment in lung cancer -- A step toward personalized medicine (In press,A step toward personalized medicine (In press, ClinClinTrialsTrials, 2008)., 2008).

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Erlotinib ZD6474 Erlotinib + BexaroteneSorafenib

Four Molecular Pathways andFour Putative Targeted Therapies in NSCLC:

EGFR VEGF/VEGFR RXR/Cyclin D1K-ras / B-raf

Biomarker Profiles: 24 = 16 marker groups

16 mark groups x 4 treatments = 64 combinations

25Kim et al. AACR 2010

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Phase 2 – I-SPY-2

Breast Cancer Patients, candidates for neoadjuvant therapy

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I-SPY2 Neoadjuvant Trial

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“Druggable” Mutations

05

1015202530354045

Breast Ovary CRC NSCLC Melanoma

PIK3CA PTEN AKT1 BRAF KRAS NRASCourtesy of P. Bedard

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Trials of the (near) Future

Issues

Scientific

Methodological

Regulatory

Operational

Cultural

MultipleHistologies

Breast

Lung

Colon

Melanoma

Glioblastoma

Etc

Etc

etc

MultipleMutations

EGFR

RAF

MEK

PI3K

AKT

CDK4

Etc

Etc

Multiple Drugs

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Translation

• Successful translation of science into innovative therapies requires

more and better science

integration of target, agent and test discovery and development

better management of supporting activities, such as specimen anddata management and collaboration for the trial and its conduct in theclinics

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Gaps in Drug Development

Drug DiscoveryPreclinicalDevelopment

ClinicalDevelopmentPhase I, II, III

Approval andMarketing

Betterunderstanding ofoncogenicpathwaysand theirpotential fortherapeutictargeting

Preclinicalmodels thatbetterpredict forsafety andefficacy

Moreefficientclinical trialdesigns andmethods

More intelligent and coordinated biomarker research

Better Science, Collaboration, Coordination, Precompetitve Space

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Biomarker Development & Application

Group 1 - Exploratory MarkersPre-clinical evidence is promising. More direct interrogation of pathways/biology at

mechanistic level in mouse model and other pre-clinical modelsNeed organized effort to chose potential “winners’ that should be selected to move into

humans

Group 2 markers – Clinical Proof of ConceptProof of concept in humans but requires specialized centres due to specimen, assay,

technology requirements2a: evaluate potential to move to group 3

2b: likely will stay specialized due to specific requirementsDetermine if sufficient clinical evidence to justify moving to group 3

Group 2 biomarker pipeline: safety, early clinical data,preclinical rationale, assay standardization, feasibility.

Group 3 markers – Clinical ValidationTest in an established or defined clinical setting, drug, therapy;Multiple sites with ability to accrue a large number of patients.

Choose biomarker/assay that can be used across sitesChoose a drug/clinical setting with clear cut evidence of efficacy so can understand

clinical correlations with biomarker;Outcomes serve as a baseline for evaluating new assays, therapies, interventions or

new biomarkers after evaluating the biomarker with established agentsCollect data for cost effectiveness as well as clinical outcomes

Group 4 markers – Clinical Application –Determine economics, laboratory proficiency for broad clinical application Knowledge

Translations

Late ClinicalEvaluation

Early ClinicalEvaluation

LaboratoryTranslationalResearch

Pre

clin

ical

To

Clin

ical

Tra

nsla

tion

and

App

licat

ion

Com

mer

cial

izat

ion

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On the Next Clinical Trial

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Challenges

• Research & Development

• Collaborations

• Regulatory

• Commercial / Economics

• Societal

Addressing the above to enable high content trials requires systems changes

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High Content Time: What we need

• Science and Technology DevelopmentTranslate best science with the best chance of clinical impactMove toward quantitative assays/imaging

• CollaborationsReward teamsBuild partnerships multidisciplinary, multi-institutional, multi-organizationalcollaborationsInter-institutional organization and communication

• Operations and infrastructureCore – administration, structure, organization, informatics, education, dataqualitySupport development/optimization of assays and tests;HQP to ensure standardization, regulatory complianceQuality control for specimen collection, storage and analysis and data

– Reduce variability across samples, patients and time– Improve biomarker interpretation

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My Biases and Beliefs

• The integration of biospecimens with reliable clinical data is critical

• Highest quality biospecimens are collected on standardized protocols forprespecified purpose(s) and maintained in central facilities withappropriate quality control/quality assurance.

• Highest quality clinical data are collected in randomized controlled clinicaltrials.

• Highest quality biomarker studies are evaluated in clinical trialswell supported hypothesiswell evaluated assaysappropriate biospecimenswith results correlated to appropriate clinical outcomes with statistical designthat provides certainty in the results.

• The specific resources to conduct high quality biospecimen research mustbe available.

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My Biases and Beliefs

• Clinical research (and life) is a series of compromises someof which are worth making and some of which are not.