Nikhil Gopinath presents regarding big data solutions at the Big Data and Analytics for Healthcare and Life Sciences Summit on October 18, 2017 in San Francisco, CA.

1. Copyright © 2016, Saama Technologies | Confidential
Supporting a Collaborative R&D Organization with a
Dynamic Big Data Solution
Big Data & Analytics
for Healthcare & Life Sciences Summit
10/18/2017
San Francisco
#DataLifeSci

2. 2
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NCSU B.S. Biochemistry
2010
Nanoparticle Vaccines Development
2013
Rutgers M.S. Biomedical Informatics
2017/18
Big Data, Informatics, Productized
Solutions in LifeSci
2014-?
A Little About Me

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What I’m Sharing Today
Conceptual data landscape and flow of information (and challenges)
Various analytic solutions and how they are built for various objectives
Using the right solution(s) and the results (better patients, better business)
Reflect on learnings and possible innovations
Demo

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Clinical Development: Translational Continuum
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Basic Science
Discovery
Proposed
Human
Application
Proven Clinical
Application
Clinical
Practice
Public Health
Impact
Chasm 1
(T1)
Chasm 2
(T2)
Chasm 3
(T3)
Chasm 4
(T4)
Bench to Clinical
Development
Safety & Efficacy Research Implementation
& Adoption
Population Health Research
Drolet, Brian C., and Nancy M. Lorenzi. "Translational research: understanding the
continuum from bench to bedside." Translational Research 157.1 (2011): 1-5.

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Clinical Development: Data Flow
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Pre-Clinical
Non-Clinical
Trans. Med.
In Silico
In Vitro
In Vivo
Animal Studies
DMPK
Clinical Science
Clinical Operations
Modeling
Protocol/Study Design
Feasibility
Regulatory
Clinical Science
Clinical Operations
Biostatistics
Monitor/Tune
Trial Management
Study Report
Epidemiology
Medical Affairs
Pharmacovigilance
IST’s/IIT’s
Safety Signals
CTMS, EDC, IRT, TMF... Argus, Real WorldFiles/DBs...

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Clinical Development: Challenges
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Intake
Real-time data ingestion
Connected data integration
Standardized data collection
Data variety, volume, etc.
Process & Use
Data Management, Quality, Queries
Conform to Standards
Coding
Adhere to process
Collate & Submit
Initial - Final Submission Data
Data Base Lock
Clinical Study Report
Monitor & Report
Valid Safety Signals
Health Economics @ Scale
CTMS, EDC, IRT, TMF... Argus, Real WorldFiles/DBs...

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Unified Cloud Data as a Service with Analytic Applications
Clinical Data
Safety Data
Syndicated & Large Data
Other Data
Data Sources Enabled Analytics
Patient & Studies
Analytics
 Clinical Study Data
Mart
 Clinical Outcomes
Analytics
Drug Safety & Analytics
 Safety Outcome &
Reporting Analytics
 Signal Detection
Real World Analytics
Risk Based Monitoring
 Electronic Data Capture
 Clinical Trials Management
System
 CRO Data
 Labs / Biomarker
 Safety Data Warehouse
 Global Safety Data
Warehouse
 ARGUS / ARISg
 Real World Claims
 EMR / EHR
 Omics Data
 Public Data (Kegg, NCBI, and,etc.)
 Trials Trove, CT.gov
 Social Media
 IOT/ Sensor/ Wearables
 Non- Clinical & Pre-Clinical
 Trial
Management
Analytics

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Analytic Solutions to Optimize Clinical Development
Design for Indication Feasibility of Study Strategy & Monitoring
Wisdom
• Substantiate adaptive design of studies that are
less-prone to failure and translate into
characterized subject populations
Knowledge
• Protocol criteria that isolate ideal subjects that will
respond and progress towards endpoints
Information
• Variants in subjects that influence the degree of
their response
• Validity of endpoints and diagnostic measures to
track
Data
• Publications, Clinical, Biomarker, Tissue, Lab,
Comparators
Wisdom
• Dynamically assess the feasibility of protocols and
sites targeted for the study and translate into
specific design (dosing, etc.)
Knowledge
• Protocol criteria that elicit amendments and
complications
• Investigators that are best across all parameters
Information
• Investigator & Site performance
• Scoring of protocols and sites based on parameters
Data
• CT.gov, Knowledge Store, US Claim/EMR,
International Administrative, Repositories
Wisdom
• Go/No-Go Decision making of programs and
translation of successful completions to design of
next phase.
• Pro-active response and resolution to risk-prone
entities
Knowledge
• Studies that contain subjects trending positively
towards endpoints and avoiding adverse events
(patient level)
• Complete view of performance of study (site level)
Information
• Modeling of Study Success KPI’s, KRI’s
Data
• Historical Studies, Publications, Clinical Systems,
CRO, CT.gov

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Reflections & Learnings
Source data is varied, complex and dirty
– High volumes of this will be consumed for training AI-driven bots that clean, standardize,
transform and analyze data at scale
Over-analysis is paralysis
– Seek out to make solutions that are intuitive and don’t require a 200 page training manual and
SOP
– Least common denomination set of analytics that caters to most users is nirvana – customizations
can always be made later
Prepare information based on how users typically ask their questions
– E.g. screen failure rate a METRIC, not the answer to a question. If the question is “What is
causing a high SFR at this site?”, seek to augment SFR with SFR Reason as found in the EDC,
IRT, or CRO extract.