Precision Medicine: Shifting from One Drug for All Trials ... · Precision Medicine: Shifting from One Drug for All Trials to Targeted Therapy Trials October 28, 2015 Patrice Hugo,

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COMPANY CONFIDENTIAL

Precision Medicine: Shifting from One Drug for All Trials

to Targeted Therapy Trials

October 28, 2015

Patrice Hugo, Ph.D., Chief Scientific Officer, Q2 Solutions

Victor Weigman, Ph.D., Associate Director, Translational Genomics, Q2 Solutions

2 COMPANY CONFIDENTIAL

Q2 Scientific Differentiators

Early Engagement

• 12 Scientific Advisors in

key Therapeutic /

Technology areas with

reach to more than >75 Q2

scientific SMEs

• Access to Therapeutic

Centers of Excellence at

Quintiles CRO

• Access to technology

experts at Quest labs

Translational Laboratories

• Fully dedicated scientists

for high complexity assays

• 4(5) strategic locations

• Assay optimization &

validation expertise

• Flow, LC-MS/MS, mutation

analysis, Elisa, Cell-based

assays

• High science expertise

• Consulting expertise

End-to-End Lab Services

• Largest global footprint

• Access to one of the

broadest test menu

• Safety, Biomarkers, CDx,

Immunoassays, Flow

cytometry, AP/IHC/FISH,

Genomics, Vaccine,

Infectious Disease,

Microbiology, LC-MS/MS

Endocrinology, BioA/

ADME/Immunogenicity

• Centers of Excellences

• Q2 Solutions: 2nd Largest Central lab in the world

• Parent Quintiles: Largest CRO in the world

• Parent Quest: Largest reference lab in the world


Biomarker R&D services and CDx strategic consulting

Assay development expertise and Dx partnerships

Assay optimization/ Bridging study/ CLIA laboratory testing

Global study execution for pivotal CDx trials

Regulatory support, including BIMO/FDA inspection support

Commercial, diagnostic, and late-phase expertise

Q2 Solutions Laboratory Partner of Choice for CDx Development from biomarker ID to CDx launch

Rx

Research/

Biomarker ID

Develop

Prototype

Analytical

Validation Clinical Validation

Drug-diagnostic

Registration

Commercialization Dx Modification

1 2 3 4 5

1

2

3

4

5

6

6

Flexible Support Across the CDx Life Cycle CRO

Pre-Clinical

Clinical

Phase I Phase II Phase III

FDA Filing/Approval

Launch

Dx lab


• The era of precision medicine: “right drug, right patient, right dose”

– Predictive, prognostic, pharmacodynamics, and pharmacogenomics biomarker

assessments now routine for development of most drugs in most indications

• Increased need for earlier decision-making (green/red light) based on

biomarker-driven PD and safety/efficacy signals

• Shift away from protein or single-gene assessments to gene expression

‘signatures’ with R&D and clinical application

• Move towards adoption of genomic testing in the clinical space necessitates

incorporation of genomics in drug and Dx development

• Limitations in sample availability and quality

• Pharma must “do more with less”

– Need for higher sensitivity and multi-parameter solutions tempered with efficiency,

reduced sample input, and faster turnaround times

Industry Challenges The trends and demands of biomarker-driven decision making in drug development


Using Genomics to Power Trials


• Shift from single-analyte to multi-use panels

• Genomic pre-profiling as a successful model

• Bringing trials to the patient

• Immuno-Oncology applications of Genomics

• Custom assay design and specimen collection needs

Powering Clinical Trials through Genomics

Company Confidential


Image Source: Wikimedia Commons

Classic “One Size Fits All”

Treated

Population

Lack of Efficacy

Lack of Safety

Population of Interest

Responders

Non-responders

Adverse Event

Patients

Changing How Drugs are Delivered

Identify non-responders and safety issues before prescribing or treating

Personalized/ Precision Approach

Prescreened

Population

Predictive Bio-

marker Testing

Responders

Adverse Event

Patients

Non-responders

/wikipedia/commons/4/44/Sanger_sequencing_read_display.gif


Landscape Changes: Explosion of Targeted Agents in Cancer Great if you have the target!

http://www.personalgenome.com/translating-cancer-genome-analyses

McDermott et al, N Engl J Med. 2011 Jan 27;364(4):340-50

• 22% of the pipeline agents

currently in pivotal trials are

being developed in a

biomarker-defined patient

population.

• of the 189 oncology

therapies currently under

development in the period of

2012-2015, 56% have an

associated biomarker.

• By 2017, 34 of these

pipeline therapies are

expected to launch, and 20

of these will be targeted to

biomarkers.

‘According to recent data from McKesson Specialty Health/US Oncology Network (MSH) http://www.nxtbook.com/nxtbooks/pharmcomm/20130910/?utm_source=Campaigner&utm_campaign=SeptOct_Digital_Edition_eBlast_8-28-13&campaigner=1&utm_medium=HTMLEmail#/0

A properly designed genomic panel + associated

bioinformatics can address both sides of the graph…

Reducing time to result with a single assay








http://www.ncbi.nlm.nih.gov/pubmed/21268726



http://www.nxtbook.com/nxtbooks/pharmcomm/20130910/?utm_source=Campaigner&utm_campaign=SeptOct_Digital_Edition_eBlast_8-28-13&campaigner=1&utm_medium=HTMLEmail#/0







Case Study: High Screen Failures for Targeted Therapy Increasing identification of difficult-to-find mutations

Situation:

• Phase 1 study of drug X indicates exquisite sensitivity in patients with a genomic

alteration in gene coding protein Y

• Effect seen in CRC and NSCLC patients only

• Literature for CRC and NCSLC estimates genomic alteration in gene Y to be:

› 2% of CRC specimens

› 10% of NSCLC specimens

Typical Solution:

• You design your phase 2 program accordingly:

› 2 strata – one of NSCLC and one for CRC

› All subjects must have genomic alteration in gene Y

• Phase 2 timeline expected to be 2 years with high screen failure rate and high zero

enrollers

Constraint:

• The portfolio committee will approve if timelines can be 1.5 years, with the same N


10

Answer: Genomic Pre-Profiling Solutions for identifying and enrolling biomarker defined oncology patients

Faster enrollment due to increases patient and

physicians interest

Faster study enrollment

• Improved access to patients with right genomic signature

• Quick start-up when potential patients are identified

Quicker to key

development decisions

• Faster enrollment

• Time to key decisions points is reduced

• Impacts overall R&D spend

More feasible to develop drugs in

niche settings

• Protocols with high screen failure rates are now feasible

• Opens up new commercial opportunities

How to cost-

effectively develop

a drug with an

anticipated high

screen failure rate

in a timely

fashion?

Genomic Profiling with Just-in-Time Start-up


Matching Panel to Trial Design Solutions for identifying and enrolling biomarker defined oncology patients

Model A - SOC

Model B – Big Registry

Model C –

Limited Scope Registry

Model D – One

Protocol – Fast TAT

Model E – One

Protocol –

Long Screening

Model F – Pre-Profiling

Operational

Design

Identify sites

where

genomic

sequencing is

occurring for

oncology pts

in indication

Longitudinal

Registry

Umbrella

Followed by Tx

Trial Enrollment

of + variant

Patients

Pre-profiling

limited scope

Registry

Followed by Tx

Trial Enrollment

of + variant

Patients

Pre-Screening /

Genomic Testing

Within One

Protocol

Test patients –

newly Dx, newly

recurred

Pre-Screening /

Genomic

Testing within

one Protocol

Test patients at

stable disease

Pre-profiling

limited scope

registry and JIT

for investigator

sites

Methods for Patient Identification

Genomic Panel

Genomic panels allow for increased biomarker information, finds the right patients

and bring the trial to them.


• Leveraged relationships between mutation and drug response / resistance

• Pan-Cancer biomarker approach identifying novel associations to indications

• Can help segment price-value comparisons for drug pricing

Q2 Solutions Comprehensive Cancer Panel: QCCP Focusing on pairing patient to treatment


Patient Pre-Profiling

Patient Presents- ICF, Patient Enrollment Sample Collection

Genomic Testing

Bioinformatic Analysis

Clinical Annotation and Reporting

Physician-Patient discuss options

Site request just-in-time start-up, ICF signed,

patient enrolled in treatment study

Matching patients to studies based on prospective genomic testing

Genomic testing registry

Genomic testing at

Q2 Solutions Labs

Clinical Report

Site staff activities of treatment

decisions and study initiation < 3 weeks


• Shifting from single-analyte to multiple increases risk

– Each variant has it own error and is compounded when

you interrogate 1.34M positions

• Validation showed variants < 10% of cells

• High sequencing depth is needed (which increases noise)

• In same assay we also want to look at breakpoints and

other complex indels

• Variant reporting should account for risks with sequencer error

Accuracy is Critical Search for low frequency variants & complex changes has risk

Ris

k

# loci assayed

Co

lin P

ritc

ha

rd (

Wa

sh

U)

Indels

Rare

Alleles

Breakpoints


• Having a pharmaceutical company be able to leverage the same assay in

different countries opens the door to multiple protocol delivery options

• No need to chase down labs to run the assay you are looking for an hoping the

results are similar across testing sites

• Can use vendor to help store and mine rapidly growing biomarker for indications

data across global network

Leveraging Global Laboratory Footprint Single panel can power multiple protocols across the world

Cancer patients in pre-profiling

network with alterations identified Protocols with specific genomic

alteration entry criteria

Pre-profiled cancer patients matched

to right protocol just-in-time


• Higher actionability increases patient and physician interest

– One test, one biopsy with quick turn times (2-3 weeks)

– General oncology assay can identify many pan-cancer biomarkers

– Increases chance for rare positive biomarkers to be found for various trials

– Patients can be matched with approved drug as first line Rx

• Enhanced Trial enrollment

– Large sample study shows that 11% (of 2000) went onto genotype-matched trials

– Many Groups facilitate this matching • Molecular Match

• Caris

• IBM (Watson)

• New Protocols are leveraging this power! – TAPUR (ASCO) M-PACT (NCI)

– Lung-MAP (NCI) IMPACT-2 (MD Anderson)

– FOCUS-4 (UK)

Increasing the Adoption of Genomic Pre-Profiling Good for the patient, good for Pharma

Meric-Bernstam F. et al JCO, May 2015


Genomics Powering

Immuno-Oncology Landscape


Current Landscape in Oncology Turning a fatal disease into a chronic treatable disorder

Over 800 cancer drugs in clinical trials - almost all are targeted at particular gene products

Some Fundamental Problems in Cancer Treatment

• Cancer is rarely detected early

• Cancer develops resistance rapidly to targeted therapies and chemotherapies due to the development/expansion of resistance mutations.

• Many patients do not respond to immunotherapies. Immunotherapies are changing the approach to treating cancer by treating the immune system rather than the cancer but not all patients respond.

Therapy Response

Rate

Toxicity Long term

Survival

Chemotherapy /

Radiation

Low High Poor

Targeted

Therapy

High Lower Poor

Immunotherapy Mid Lower Good

Source: Sharma, Allison: Cell, April 2015


Number of Products in Development for Selected Targets*

Source: BioMedTracker, copyright 2015

*as of May 2015


20

Routinely collected clinical samples

Data can be mined for molecular immune-oncology characterization

Immuno-Oncology Innovation New opportunities for molecular characterization

Samples Molecular Services Benefits

These characterizations can be used in drug studies

Saliva

Blood

Biopsy

Slides

Self-recognition HLA alleles

Immune activation B and T-cell

Tumor

characterization DNA and RNA

Stimulate T-cell response

• Cell therapies

Block immuno-suppression

• CTLA4

• PD1/PDL1

• IDO1

• OX40

Develop cancer-specific

vaccines (neoantigens)

• NY-ESO-1

• MAGE-3

• gp100

Through Immuno-oncology:

• Gain a deeper understanding of drug safety and PK/PD

• More precise tumor characterization

• More effective, personalized therapies


Situation:

– Patients in trial selected for receipt of Immunotherapy but does not respond

Solution:

– High Depth Exome Sequencing can identify new mutations in antigen coding genes

• Can identify allele-specific expression (ASE) of mutations with supplemental RNA-Seq

– Build associations of mutations across cohorts to highlight specific genes / mutations.

Result:

– Reduction in target identification through bypassing of screening of tumor-infiltrating

lymphocytes1

– Patients now have method to identify mutations in tumor for other biomarker identification,

along with neoantigens to facilitate biomarker discovery pipeline

– Collection of large-scale mutation information across indications allows for identification of

pan-cancer biomarkers that could extend existing immunotherapies to new indications

Case Study: Identification of Tumor-Specific Antigens Facilitating Drug Development by Identifying Novel Biomarker Classes

Source: 1. Yadev et al. Nature 2014


Situation:

– Targeted therapy has been selected for patient

– After treatment, certain patients develop hepatotoxicity to treatment, causing Drug

Induced Liver Injury (DILI) and potential post marketing withdrawal of drug.

Solution:

– Human Leukocyte Antigen (HLA) allele calling showcases toxicogenomics and

identifies

type that is associated to adverse reaction in affected population • ~30% of patients develop Colitis

– HLA gene association analysis will identify risk alleles

Result:

– Patients in trial with the risk allele can be more closely monitored / have treatment

amended

– Pharmacogenomics (PGx) data also generated for all trial participants

Case Study: Forecast Toxicity Risk HLA alleles have proven associations with toxicity


Immuno-oncology is Genomics Solutions Oriented

Technology

Service Need

Somatic Mutation Analysis DNA deep Sequencing, Breakpoint

analysis and Fusion Detection

Gene Expression Profiling RNA-Seq, Arrays, RNA panels

Germ Line Variant Analysis CNV, Structural and Small Variant

Detection

HLA Characterization HLA Calling (DNA, RNA, Arrays)

Immune Response B/T Cell Repertoire (DNA/RNA),

VDJ mutation (RNA)


• Immunotherapies offer significant advantage to cancer treatment for otherwise

untreatable patients

– Identification of those patients is not straightforward

• Earlier adoption of genomic panels into testing of trial patients can provide

mechanism for targeted therapy, along with ability to prognosticate on

immunotherapy toxicity

• High potential for novel biomarker identification, reducing drug development

pipelines

– Building broad genomic datasets across indications has high long-term potential for

data mining payoff

Immunotherapy Conclusion Case example using genomics + immunotherapy for increased gain


Developing Your Own Assay /

Patient Population

Company Confidential


CROs/laboratories provide access

to a diverse network of clinicians

and scientists

– Bioinformaticists, geneticists, flow

cytometrists, mass spectrometrists,

microbiologists, virologists,

immunologists, anatomic

pathologists, cytopathologists,

hematopathologists

– Regulatory experts,

bioanalytical/medical writers,

physicians

• Determine appropriate consent

• Develop data organization analysis

strategies for future studies

The Advantages of Laboratory Collaboration Engaging laboratory resources and expertise to complement Rx efforts

Diagnostics

Biomarkers

Clinical/Med

Lab

Operations

Insights to optimize

protocol design and

execution

Biomarker/

Drug R&D

Protocol

Development

Laboratory

Expertise

Pharma

Expertise


Graphic of assays and typical input amounts

– Different technologies can vary inputs below

Typical Specimen Collection is Already Sufficient Genomic assays are more obtainable than you think

10

30

40

150

150

500

500

1100

0 200 400 600 800 1000

ASSAY INPUT NEEDS

Input Amount (ng)

Blo

od

FF

PE

So

lid

Tis

su

e

Pla

sm

a

HLA Allele Calling

Genotyping Risk Alleles

IGVH Testing

Targeted (NGS) Panel

Whole Exome (NGS)

PCR-enriched (NGS)

qPCR

ctDNA Allele Calling


Integrated Bioinformatics Capabilities Performance, scalability, flexibility

Bioinformatics

Team

Translational

Genomics

Applications Development

Data

Generation

10,000s of samples per year

Pipeline execution / QC

Customized Delivery (HDs / Cloud)

Software creation / maintenance

Pipeline efficiencies

High performance computing

Preclinical biomarker development

Pipeline Creation / algorithm dev.

Assay CLIA / GxP compliance

Assay Validation Custom Panel

Design

•CLIA LDTs Method

Validations

•Fit for purpose

•Directed across central

labs

•> 45 Custom Assays

•>9000 samples

•3 Sequencing platforms

•3 PCR platforms

Preclinical

Biomarker

Development

•Senior PhD Staff

•Assigned to specific

Pharma

•Direct research efforts

across sites

Validation Analysis

Platforms

•NGS delivery to FDA

•HL7 formats

•Formal validation

procedures

Computer System

Validation

• GxP compliance

• Part 11 validated systems

• Automated execution of

workflows

• Comprehensive Cancer

Panel Case Study

Data Mining /

Associations

•Comb results from

genomic panels to find

novel biomarkers

•Integrate genomics with

EHR


Situation:

– Looking to identify samples with specific

biomarkers, traditional testing limits the types of

biomarkers assays for patients across trials

Solution:

– Adoption of genomic panels for trial testing, along

with modified consenting / data usage criteria

– Efficient storage of genomic results allows for

robust querying of large numbers of biomarkers

– Combination of clinical variant annotation

databases (ClinVar, TCGA, 1000Genomes, etc)

Result:

– Database of genomic variants combined with

sample information can rapidly identify samples of

interest

Case Study: Mining Genomic Data for Patient Selection Leveraging legacy genomic data to identify patients with specific biomarker status


Genomic assays (multi-analyte) provide a mechanism for powering precision

medicine

– Numerous successful examples of genomics-based LDTs pairing patient to treatment

– Also examples of identifying biomarkers for enrollment in Trials

Existing trials are already collecting specimens that can run genomics

Genomics assays can power targeted therapy and Immuno-Oncology applications

Working with your lab provider early can help with trial design and CDx

applications (along with diagnostic manufacturer)

Long term payoff of genomic panels provides a wellspring of information for

identifying novel biomarkers

Overall Conclusion Enhancing trial design with genomics


Thank You

Questions

Documents

Precision Medicine: Shifting from One Drug for All Trials ... · Precision Medicine: Shifting from One Drug for All Trials to Targeted Therapy Trials October 28, 2015 Patrice Hugo,