Centralizing Data to Address Imperatives in Clinical Development

SCDM 2017
ANNUAL CONFERENCE
September 24-27 I Orlando

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Copyright © 2017, Saama Technologies
Centralizing Data to Address Imperatives in Clinical
Development
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The time for Transformation is NOW…

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Copyright © 2017, Saama TechnologiesCopyright © 2017, Saama Technologies
AGENDA
The Fourth Industrial Revolution
Cognitive Sciences in Medicine
State of Clinical Trials Today
Centralize & Contextualize Data
Next Gen Clinical Data Mgmt
Putting it Together
Speed & Specificity

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Next wave of transformation
Yesterday
People
Domain
Delivery Excellence
Today
People
IP Assets
Industry Transformations
Tomorrow
People
Cognitive Systems
Robotic Process Automation
outcomes@speed

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Current state of Clinical Trials
Period: 2012-2016
Over
Time
On Time
On Budget Over Budget
Laggards
20%
Perfect
10%
Costly
40%
Help Me
20%
Abandoned
10%
Top-10 Contributors – All
1. Insufficient/low/poor enrollment
2. Lack of efficacy
3. Change in business focus (Sponsor)
4. Serious Adverse Events (SAEs)/death/safety
concerns
5. Investigational New Drug
withdrawn/discontinued
6. Protocol deviation
7. Terminated by regulatory agency
8. Funding support withdrawn
9. Administrative/ Technical reason/ Business
decision
10.Unavailable study personnel

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72%
studies are 1
behind
SCHEDULE
70%
of studies
experience
patient
enrollment
delays
20%
does not
recruit a
single
subject
30%
of time spent
on patient
recruitment
14%
Trials are
abandoned

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v Discovery Pre-Clinical Phase 1 Phase 2 Phase 3 FDA Post Market
• Siloed Data
• Not Standard & Bad Quality
• Timeliness
• Scalability & Cost
But, there is a Data Problem
• Study Design & simulation
• Manage Multiple CRO’s
• Ongoing Risk – Enrollment, Study completion
• Operational Issues – Performance {Site, Study,
Trial}
Trial Management has to stay on top
• Completed On Time
• Within Budget
• Detect failure early
Trials need to
Need for an Integrated
Data SolutionClinical Trials
• Effectiveness and Usage in Real World
• Comparative evidence against competitors
• Safety concerns & Adverse events
• Clinical & Economic value
• Any unmet needs?
• Drug repositioning • Study design & simulation
• Clinical development feasibility
• Variety of Data (Avg 40-50 sources)
• Geography adds to complexity (EU/APAC different than US)
• Siloed & bad quality data
• Extremely expensive & time consuming considering data volumes (Weeks and
M$’s)
Need for an
Integrated Data
Solution

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Clinical Data management
is ripe for disruption
60%Of trial activities
are data management
related
20%of trial $ for
clinical data sciences
$127B
2016 R&D Spend
Total Market Spend
$148B
2020 R&D Spend
5-6%

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Objectives & Targeted Outcomes
Centralized
Clinical
Data Lake

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• Proactive
intervention
• Strategic vendor
collaboration
• Financial forecasting
Operation
• Track progress to
endpoints
• Go/no go on next study
• In-sillico meta-analysis
Innovation
• Ingestion
• Quality check
• Standards
• Transform
• KPI’s
• Biostatistics
• Validation
Automation
Objectives & Targeted Outcomes
Data Mgmt. &
Quality
Evidence
Generation
Time to Insight
& Cost
Centralized
Clinical
Data Lake

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Clinical Data Pipeline
Data Ingestion Load CMTS, EDC
and Lab data into
Hadoop
ecosystem
Landing Layer Data as-is from
source
Data
Management
(Rules Engine)
Centralized
Repository of all
Business Rules
Persist Layer Raw Data
Standardization
– Golden Rule;
Data Vocabulary
Match & Merge
Data vocab for
country, TA, etc.
CDM Model Common Data
Model for
Operational data
store
Metrics
Management
Calculate
KPIs/KRIs/Metric
Values
Analytical Layer Aggregate data
Match & Merge;
Data Vocabulary
Patient Data
Metrics
Dashboards
Bio-Statistical
Analysis
• Studies
• Site
• Subject
• Study metadata
• Clinical/Audit Records
Parsed XML
data Store
Patient Data Patient data (Clinical
Audit Records) in domain
containers
BRE Engine
Operations Data
Operational
Metrics
Repository
I(HIVE)
Data
Acquisition
Data
Acquisition
Landing
Layer
Persist Layer Common
Data Model
Aggregate/Analytical
Layer
Landing
Layer
Persist
Layer
Patient Data
Container
Patient Data
Model
Oracle Siebel
CTMS
EDC
Flat Files
EDC
External
Lab Data
Source Data
Raw
Data
Standardized
Data
Golden
Rules
DQ
Rules
DQ
Rules
Raw
Data

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Ingest & Rules
Apply
Standardize
(CODM)
Apply Metrics
Analytic
Ready Data
Clinical Operations
KPI/Metrics Pipeline
Ingest & Rules
Apply
Standardize
SDTM
Convert to
ADaM
Analytic
Ready Data
Clinical Sciences
Subject Analytics Pipeline
CTMS
roject Manage
CRO
EDC
Labs
Biomarker
Clinical
Development
Analytics
Centralize (and contextualize) data in flight

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Genomics
Internal, M&A,
External, Syndicated
Wearable Devices
High
Variety, Volume, &
Velocity Data
Analysis
Organization
Ingestion
Automated
Data Wrangling & Deep Data Science
Business Aware
Data Analytics Services
Oriented Architecture
Harmonization
Integration
Analysis
Organized Storage
Provisioning
Aggregation
Modern
Technologies
Configurable
Analytic
Applications
Business
Outcomes
Approach

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Mid-size Pharma Case Study
Clinical Digital Integration Hub
Operational
Cost Savings
Better Trial
Outcomes
Improve Risk
Management

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Case Study:
Simplicity in Work-flow Management

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Clinical Data-as-a-Service (CDaaS)
Enabled Analytics
Patient & Studies
Analytics
 Clinical Study Data
Mart
 Clinical Outcomes
Analytics
Drug Safety & Analytics
 Safety Outcome &
Reporting Analytics
 Trial Management
Analytics
 Real World Signal
Detection Analytics
 Activity Enablement
Clinical Data
Safety Data
Varied Sources
Syndicated & Large Data
Data Sources
 Electronic Data Capture
 Clinical Trials Management
System
 Safety Data Warehouse
 Global Safety Data
Warehouse
 ARGUS
 Clinical Study Reports
 Disparate Business Unit Reports
 External analyses
 Non-Clinical, Pre-Clinical Data &
Reports
 Real World Claims Data
 Internal Genomics Data
 Public Data (Kegg, NCBI, CHEMBL, etc.)
 Trials Trove, CT.gov

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Unstruc
tured
Data
Unstructured Data
Structured Data
Syndicated Data
Transactional
System
Social Media Data
Streaming Data
API
Source
Data
Unstructured Data
Operating Systems
Windows Linux
On - Premise
Cloud
AWS MS AZURE Google Cloud
Hadoop Distributions
Amazon EMR
Unstructured
Data
Landing Zone
Unstructured
Data
Connectors
API
JDBC
SDK
Unstructured
Data
Change Data Capture
Unstructured
Data
Data Profiler, Rule Engine
Unstructured
Data
Rules Repo,
Workbench Repo
Postgresql
Unstructured
Data
Common Data Model
Unstructured
Data
Data Transformation
Pig
(Hortonworks Stack)
Impala
(Cloudera & MapR Stack)
Unstructured
Data
Aggregated Entities
Impala
(Cloudera & MapR Stack)
(Hortonworks Stack)
Unstructured
Data
Search
(Default)
Unstructured
Data
Web Service
RESTful
Unstructured
Data
Security
Authentication
LDAP
Kerberos
SAML
Authorization
RBAC
ACL(Service Level, Directory
Level, Column Level)
Encryption
SSL
Transparent
Encryption
Encryption
Zones
AES 256
Unstructured Data
Data Governance
(Hortonworks Stack)
(Cloudera Stack)
(CDaaS)

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Summary of discussions with Life Sciences leaders...
Disruptive thinking is required NOW
Clean
Longitudinal
Data
Bench
to
Bedside
Patient
Centricity
Cognitive
Systems
‘Silicon Valley’
Mind Set
Operational Efficiencies, Trial Effectiveness &
Streamline submission pathways

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Speed AND specificity for disruptive business outcomes
Understands all
your data
Built to analyze and
operationalize exabytes
of complex dirty data
streams at new speeds
Starts with what
you have
Incorporate existing
data, analytics and IT,
regardless of analytics
maturity
Continuous, Rapid, Massive Enterprise-class Learning
Combines data in
new ways
Apply relevant combos of
any data sources, shape
the right data
In-the-moment
business
Rapid iteration, apply
instant analysis anticipate
the always unfolding,
success measured in
moments
Continuous impact
Continual outcomes, know
when the world has
changed, always stay one
step ahead

Centralizing Data to Address Imperatives in Clinical Development

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