liberating the knowledge in your biospecimens:next generation biobanking

LIBERATING THE KNOWLEDGE IN YOUR BIOSPECIMENSNext Generation Biobanking

Mark A Collins Ph.D. Director of Marketing,

BioFortis, Inc.

BOOTH #403

Introduction

• Biomarkers are everywhere• Biomarker research is collaborative• Biomarkers depend on biobanks• Traditional biobanks are challenged• Shifting to the next generation biobank• Next generation biobank as a collaborative

knowledgebase for biomarker discovery• Real world examples

Some trends…• Number of new drugs in

decline• Rise in approval of drugs

with companion diagnostics

• 10% FDA approved drugs have Pharmacogenomic labeling

• 50% of drugs in pipeline are biomarker-based

• 1-3B samples banked…and growing

• >75% biobanks are disease specific

The Landscape is changing…

Personalized medicine

Externalization

Big Data

Targeted Therapy & Companion DiagnosticsTargeted Trials

Translational Research / Biomarkers / Patient

Segmentation

Clinical data Clinical samples

Biobanks

Biobanking Challenges

The Critical Biobanking Challenges

Biobanks

Expectation of driving the

science

Externalization &

Collaboration

Increased regulatory

scrutiny

Driving the Science

• Generate scientific insights

• Beyond the specimen data

• Link in clinical and molecular data

Scientific Insights

Patient Data

Clinical Data

Molecular

SpecimenGenomic

Externalization and Collaboration

• Rich distributed “ecosystem” of collaborators, partners and vendors

Research Ecosystem

Pharma & BioPharma

Academic Centers

BiobanksCRO

Healthcare Providers

Increased Regulatory Scrutiny

• Privacy and regulatory issues

• Stringent adherence to compliance standards

Increased Regulatory

Scrutiny

Trial Use Samples

Future Use Samples

Chain-of-custody

Audit Trails

Bridging the Gap from Conventional Biobanking

Harmonize biospecimens with clinical and molecular data

Gain scientific insights

Support externalized, collaborative studies

Enhance security and compliance

“Companies that have access to millions of highly annotated biospecimens with clear consent, traceability and tools to rapidly mine for desired profiles will have an edge in biomarker-based discovery, segmenting patients for clinical trials and developing companion diagnostic /theranostic applications”

Large Pharma Dir.PGx

Powered By

Bridging the Clinical and Research Divide

Bench/Research Bedside/Clinical

Gene expressionProteomicsBioassaysImaging

DiagnosesMedicationsHealth recordsClinical data

Bridging the gap with frictionless

information exchange

Frictionless information exchange key to collaboration

The challenge of multidisciplinary data

At The Core… Labmatrix

• Data collection and harmonization “engine”

• Secure, collaborative environment

• Fine grained access controls

• Workflows

Web accessible Information management for clinical and translational research

Ad hoc query and visualization

Integration, Collaboration, Access Control

Translational Research Data

Repository

Biospecimen Management

Current Informatics Environment

LIMS ELN

OtherResearch Data Apps

CTMS EDC

OtherClinical

Data Apps

Research Systems Clinical Systems

Study Subject Management

Collect & Harmonize- Creating the information hub

• Hub is the foundation• Connect to tools to

collaboratively access and explore data

• Generate scientific insights

Clinical and molecular data

The Problem – data exploration

Data Managers, overwhelmed by

researchers questions on complex data sources

Researchers with many questions across

disciplines

“Weeks to months to NEVER”“Lost in Translation”

SELECT DISTINCT PATIENT_ID, SAMPLE_ID, SAMPLE_NAME

FROM SAMPLE_INVENTORY S INNER JOIN PATIENTS P ON S.PATIENT_ID = P.PATIENT_ID

INNER JOIN DIAGNOSIS D ON S.PATIENT_ID = D.PATIENT_ID

INNER JOIN MEDICATIONS M ON S.PATIENT_ID = M.PATIENT_ID

INNER JOIN BIOMARKERS B ON S.PATIENT_ID = B.PATIENT_ID

WHERED.DIAGNOSIS_NAME = ‘LUNG CANCER’ AND

M.MEDICATION_GENERIC_NAME = ‘CETUXIMAB’ ANDB.BIOMARKER_NAME = ‘EGFR’ AND

B.OBSERVATION = 1ORDER BY PATIENT_ID, SAMPLE_NAME

No common language for

questions

What is Deep Collaboration?

Single researcher in a silo often can go deep into the data, but maybe limited by their domain expertise

Small groups of researchers may be able to collaborate on asking questions but can’t go very deep with the tools they have today

QIAGRAM

Deep Collaboration is when multiple groups of researchers can collaborate in asking questions deeper into the layers of data. Shared domain knowledge allows deeper insights

Clinical and molecular data

Qiagram – Collaborative Scientific Intelligence

Researchers and data managers can collaborate

on creating queriesQiagram acts as a

shared, visual language for queries

More efficient and effective query creationTransparent to all stakeholders

Collaborative Scientific Intelligence

• Facilitate deep collaboration on research questions

• Generate scientific insights from less than perfect data

• Collaboratively build and test hypotheses as a team

• Build complex, domain specific queries without programming

Real-time, visual, ad-hoc query tool, connecting researchers to answers

NEXT GENERATION BIOBANKING

Case Studies

• Clinical Trial Sample management and Future Use

• Clinical “Hub” – Institutional Research Collaborations

• Virtual Biobank

Background:Manage internal and external data on samples collected from clinical trials.

Key Objectives:• Provide specimen management for

hundreds of trials and millions of samples• Track patient consents on samples• Maintain regulatory compliance• Reconcile different sets of data from CT

partners• Provide real-time knowledge on current

sample inventory status• Provide support for “future-use” samples

Case Study 1: Clinical Trials Sample & Future Use Sample Management

Site Sponsor

Vendor / CRO

Sample Shipment

Consent Deviation / Sample Destruction

(e.g. patient withdraw)

Sample Destruction

(patient withdraw or sponsor policy)

Patient Consent

Trial Setup &Sample Logistics

Sample CollectionSpecification

Sample Inventory

Consent Reconciliation

consent patients &

acquire samples

Sample Shipment

Sample Tracking in the Research Ecosystem

Case Study 1: Sophisticated Questions for Future Use samples

1. Do we have more than one metastatic samples from the same patient?

2. Do we have primary and metastatic samples from the same patient?

3. Treatment of metastatic disease

4. Overall survival

“Drawing” Complex ad hoc queries

• Build the query up step-by-step

• Review the answers in real-time

• Scientific insights to drive decision making

liberating the knowledge in your biospecimens:next generation biobanking

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