Who’s Keeping Score? A Quantitative Approach to Trial Feasibility

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Copyright © 2017, Saama Technologies | Confidential
The best way to predict the future is to invent it.
- Alan Kay

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Who’s Keeping Score?
A Quantitative Approach to Trial Feasibility
Luke Stewart
Director, Product Management
July 18, 2017

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If Only Patients Molded to Fit Our Protocol…

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Who’s Keeping Score?

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Sponsor

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Close Loop on Actual Performance vs Projected Scoring
Attributes of Approach
• Sponsor-driven
• Systematic scoring
• Learning over time
Early Protocol Feasibility Site Feasibility Study Startup Study Execution
0 months-18 months -6 months 12+ months
Scoring
i.e, Protocol
Amendments
i.e, Site
Surveys
i.e, Site
Screening Rate
Claims
EMR
CT.gov
CTMS

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Technology, Process & People

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Proposed Workflow for Trial Feasibility
Libraries of Criteria
For Diseases
CDISC, Transcelerate CPT, Internal
Define Population
- Choose RWD source
- Define primary I/E
criteria
Assess/Refine Study
Design
- See list of primary criteria
- See list of exhaustive
criteria
- Save refined cohort and
protocol for future/shared
analysis
External: CT.gov, SiteTrove/TrialTrove, Census
Internal: CTMS, Customer Master

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Simulate Using Machine Learning & Artificial Intelligence
• Previous participation in clinical
trials
• Disease / Therapeutic area
• Proximity to site
• Study complexity
• Demographic information
• Socio-economic status
• Previous terminated studies at
the site
• Screen failure rate
• Study complexity
• Number of patients matching
inclusion/exclusion criteria within
close proximity (25 miles) of site
• Socioeconomic status of eligible
patients in close proximity of site
• Disease / TA
• Staffing and facility information
Predicting Patient Enrollment Predicting Site Performance

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Patient Recruitment - Data Enrichment & Prediction
Target
Cohort
Historical Data
Social Media
Patient Clusters,
Competitive Studies
Trial safety data,
Seasonality, BioMarkers
Age, Race, Economic data
High
Probable
Matches
Stack Rank
with related
prior Studies
Low
Probable
Matches
‘Predict’ the likelihood of the required recruitment
numbers and the most optimal match to make the
study a success in time and budget.
Model
For every
instance:
y =
β0+x1*β1+x2*β2
… x10*β10

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Modeling Site & Principal Investigators
Site & PI
Probability
Model
Relevant Experience
Past success/failure
rates
Competitive Studies
Target Study
Parameters
Scores
Model
For every
instance:
y =
β0+x1*β1+x2*β2
… x10*β10

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Five Key Use Cases Sponsors Must Cover to Optimize
Protocol/Study Design

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Use Case 1 – Patient Cohort Analytics using Real World Data
Inclusion - AND
Exclusion - NOT
Inclusion - OR
Claims, EHR, etc

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Use Case 2 –Trial Feasibility Assessments
Score:
Enrollment
PI
Sites
Patient Burden

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Use Case 4 - Analytical modeling of Treatment Pathways and
Patient Journeys
Use Case 3 - Analytical modeling of Treatment Pathways and
Patient Journeys
What is the standard of care? How are patients being treated?
Where does or where can my drug fit in the line of therapy?

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Use Case 4 – Clinical Development Analytics
Operational Performance Tracking for Risk Mitigation. RACT/MCC

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Use Case 5 – Data & Analytics Workflow Management
Simple Analytics
Workflow
Advanced Analytics
Workflow

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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
Going Forward

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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
Patient Analytics Pipeline
CTMS
Project Manage
CRO
EDC
Labs
Biomarker
Clinical
Development
Analytics
Data Management – Data Pipelines

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Harmonized Data and Analytics Foundation
Enabled Analytics
Patient & Studies
Analytics
 Clinical Study
Analytics
 Clinical Outcomes
Analytics
 Exploratory Analysis
Drug Safety Analytics
 Safety Outcome &
Reporting Analytics
 Signal Detection
 Trial Management
Analytics
 Financial Analytics
 Forecasting
 Clin Ops Analytics
 Real World
Analytics
 Risk Based
Monitoring
(RBM)
CDISC, MCC, Transcelerate Standards
Big DataRelational Data
Varied Structure Data
Advanced Analytical Tools
Meta Data Repository
Information Management
Clinical Trial Data
Safety Data
Syndicated & Big Data
Other Data
Data Sources
 Electronic Data Capture
 Clinical Trials Management System
 CRO Data
 Labs / Biomarker
 Safety Data Warehouse
 ARGUS / ARISg
 Real World Claims
 EMR / EHR
 Omics Data
 Public Data (Kegg,
NCBI,CHEMBL,etc.)
 Trials Trove, CT.gov
 Social Media
 IOT/ Sensor/ Wearables
 Non-Clinical & Pre-Clinical

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Our Approach
Experienced team of industry-specific
consultants, data scientists and engineers
•Discover/Diagnose/Strategize/Execute
Leverage existing infrastructure
Integrated assets in solution framework
•Data
•Analytics
•Visualization
Abstracting away niche technology expertise
Solutions powered by Fluid Analytics
2
0
Biz
IT Saama1
Team
“We start where you are and partner with you to where you want to be”

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Breakdown the Business Case
Higher
Profitability
Build the
Future
Increased
revenue
Clinical Trial
Efficiency
Clinical Trial Feasibility
ClinOps and Evidence
Analytics
Clinical Analytics
As Service
RWD Platform for
Evidence Generation
Clinical Analytics
As Service
Early Blockbuster
Clinical Innovation
Labs
Extend advance analytics platform
Early new drug launch
Advance analytics to commercial
data
Use of cloud based platform to
reduce cost, increase speed and
efficiency
Increase specificity of analytics
solutions
Increase predictability of
clinical trials
Use AI and ML to improve
efficiency of clinical trials
$360m/yr.
$90m
$90m
$90m
$90m
$90m
$360m
$90m
$540m/yr.
~$900m/yr.
Low level assumptions for base ROI
1) A drug trial takes almost 2920 days(8yrs) avg.
2) Approx. 360 days savings from clinical efficiency.
3) Approx. 1m per day saving because of faster clinical trials
DISGUISED
EXAMPLE

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Take a Phased Approach
ClinOps  Risk-Based Monitoring  Feasibility  Commercial…
Commercial  Feasibility  Patient Journey  ClinOps …

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Call to Action
• Share your thoughts
and stories
• See demo
Start Keeping Score
Visit us at our table

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Q&A

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Human + Technology = Hybrid
Synergy with proper usability

Who’s Keeping Score? A Quantitative Approach to Trial Feasibility

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Who’s Keeping Score? A Quantitative Approach to Trial Feasibility