This document discusses how big data can help reduce costs and increase productivity in drug R&D. It outlines challenges such as increased clinical trials, patients, and data requirements that have led to higher R&D costs. Big data is presented as a solution by bringing more insights from data rather than resources. The document provides case studies and a 6-step approach for companies to leverage big data in R&D, including establishing an analytics strategy, identifying relevant data sources, and optimizing their analytics organization.

1. Drug R&D: big data for big
returns
Todd Skrinar, Thaddeus Wolfram
October 24, 2014

2. Page 1 Drug R&D: big data for big returns
There are multiple factors currently limiting the
discovery and approval of new medicines
Discover and develop
innovative new products
The low-hanging fruit has
already been picked and
organizations are pressed to
display significant
differentiation
Respond to changing
patient needs while
maintaining a competitive
position
The race for novel therapies
puts an even greater focus
on R&D organizations
Operate R&D efficiently
while maintaining
compliance
Regulatory constraints have
tightened, requiring
increased level of efficacy
and safety at lower
overall costs
R&D challenges
Business
challenges
Patient needs
Impact of new
technology
Impact of new
data sources
Healthcare
reform
Growing cost
pressure
Regulatory
frameworks

3. Page 2 Drug R&D: big data for big returns
More clinical trials
Increased pressure to develop
novel therapies has resulted in
more clinical trials in R&D
More patients
Larger clinical trials to
demonstrate significant
differentiation result in
increased overall R&D
expenditures
More information
More generated, collected
and analysed data required
for payer approvals and
reimbursement
Historically, getting more from drug R&D has meant
putting more money into it
How does one reduce clinical trial costs while still meeting the rising demands of regulators and payers for
more data that demonstrate that the drug is a significant improvement over current standards of care?
Clinical trial
results
The model has reliably
led to much higher
costs, but not improved
outcomes

4. Page 3 Drug R&D: big data for big returns
A more disruptive solution is urgently needed to
increase productivity and reduce costs
Potential advantages of big data:
► Bringing more information and potential
for insight rather than time and volume
of resources
► Providing the robust data required for both
drug approval and reimbursement
► Speeding the discovery and approval of new
medicines while lowering costs
► Helping R&D organizations ask the right
business questions and then seek answers
in the data
► Supporting more efficient clinical trial design
and innovation in clinical trial approach, and
helps recognize research failures faster
Big data“Omics”
Technology
Dosing
Population
Disease
Social media Diagnostics
Biomarkers
Big data as a solution in R&D

5. Page 4 Drug R&D: big data for big returns
When applied effectively, the use of big data and analytics are
adding value to R&D in a growing number of ways
Targeting specific patient
profiles for a clinical trial
Making patient recruitment easy
and efficient
Clinical trial
Advancing genomic analysis from the research stage to
the point where it is used in treatment decisions
Genomic analysis
Identifying and validating
associations between
genes and human
diseases
Drug repurposing by discovering new therapeutic uses for
existing molecules through efficacy prediction
Better clinical trial management through the query, analysis
and visualization of drug discovery and development data
Utilizing data analytics to generate real world evidence,
understanding patient needs and the effectiveness of
treatments to improve patient outcomes

6. Page 5 Drug R&D: big data for big returns
Start with the big data of today and be ready for the
big data of tomorrow
Today
► Assess the current accessible forms of data and
data access points (i.e., claims data, electronic
health records, clinical studies, social media)
► Develop partnerships that allow access to the
right data
► Understand where data is coming from and
what classifications it carries such as private
and public data pools
Tomorrow
► Defining the data technology capabilities of the
future to align with the value opportunities
of today
► Understanding the evolution in data access and
how best to tap into this information, including
use of non-traditional and unstructured sources
of data (e.g., from online patient comments and
patient advocacy groups)
► Addressing the challenges that come with patient
privacy rights, the transfer of high volumes of
data, and interfacing with disparate data sources
Enabling big data across the R&D organization
Process
People
Technology

7. Page 6 Drug R&D: big data for big returns
► Step 1: Establish a clear analytics strategy
► Step 2: Identify the most relevant sources of big data
► Step 3: Master large-scale data management
► Step 4: Pursue meaningful collaborations
► Step 5: Optimize your analytics organization for
performance, value, and continuous learning
► Step 6: Derive and define your value
How do we start when it comes to taking advantage
of big data to improve the ROI of R&D?

8. Page 7 Drug R&D: big data for big returns
Step 1: Establish a clear analytics
strategy
► A strategy driven by the needs of the
business, not technology
► Define an analytics strategy and operating
model that includes a Center of
Excellence (COE)
► Strong collaboration with data scientists
and leaders who set strategic direction
for R&D
Step 2: Identify the most relevant
sources of big data
► Leverage the defined analytics strategy
(step 1) as initial guidance
► Apply greater focus to the accessibility of
data, security requirements surrounding
the data, and the effort to make the
data usable
The first steps in incorporating big data involve alignment on
strategic direction as well as the most valuable and accessible
data sources
The R&D analytics strategy
should be driven by the needs of
the business, not technology
Use case: Step 2
► R&D organizations usage of a
diversity of sources – including
claims data, electronic health
records (EHRs), clinical studies and
social media

9. Page 8 Drug R&D: big data for big returns
► Assess the current foundational IT and
analytical state to give a clear picture of
the steps needed to reach the
appropriate level of large-scale data
management
► Define processes to maintain data and
enhance data quality
► Build capabilities necessary to access,
pool, and maintain large volumes of data
from varied sources
► Team with healthcare organizations in
the ecosystem that provide a “win/win” on
intellectual property, risk, and resource
commitments
► Select like-minded business partners
and using trusted third parties for
data-management challenges
Managing large and disparate amounts of data and avoiding data
overload will be key to any R&D organization’s success
Use case: Step 4
► Data partnerships with life sciences
companies as well as academic
institutions, providers, and payers
are key to gaining access to the
widest range of big data
Step 3: Master large-scale data
management
Step 4: Pursue meaningful
collaborations

10. Page 9 Drug R&D: big data for big returns
Complacency around big data will lead to missed insights,
over-looked efficiencies, and an inadequate analytics function
Step 5: Master large-scale data
management
► Strong governance and an
organizational structure that incentivizes
the right analytics behaviours and
encourages learning
► Establish a continuous feedback loop to
understand the results of analytics and
apply them to future analytics efforts
Step 6: Pursue meaningful
collaborations
► Utilize a balance approach to analytics
with near, mid-, and long-term metrics for
assessing benefits and business impact
► Align results to specific targets to inform
audiences of R&D value

11. Page 10 Drug R&D: big data for big returns
These steps along with the right combination of people, process,
and technology are the path to creating value from big data
Companies that master these steps will build a more sustainable approach to R&D and develop competitive
advantages in the life sciences space
ROI from R&D
through big
data
The single most important determinant of success for analytics projects is having
the right people on the team driving towards the targeted value to be gained. This
value may be linked to specific focused targets, or may derived more broadly for
instance from the collective intent of a particular ecosystem surrounding a disease
state, care pathways and real world data.
Realizing the greatest value requires process that aligns to the desired outcomes.
An example would be process implemented based on the goal of bridging the
evidence gap for health authorities and payers. As superior efficacy in the clinic is
not the same as superior effectiveness in the real world, processes need to reflect
new evidence requirements for gaining approval and reimbursement of new drugs.
Taking advantage of the right technological advancements as a business driven
enabler provides the foundation for gaining the value that is now achievable for
drug companies. Innovative technologies and analytics capabilities allow
generation of predictive models of the world, providing better inputs to R&D
decisions and clearer association of R&D outcomes with real world value.
People
Process
Technology

12. Page 11 Drug R&D: big data for big returns
Case studies

13. Page 12 Drug R&D: big data for big returns
Case study 1:
Comparative effectiveness and total cost of care models
Effectiveness
MK-3102
Goal: predictive, personalized treatment algorithms for approved and experimental drugs using
simulated patient populations leveraging real-world data
Data:
► Real world data
Predictions:
► Simulation of models built
from real world data
Validation:
► These predictive models
show what diabetes drugs
work for what subset of
patients for multiple
endpoints of interest

14. Page 13 Drug R&D: big data for big returns
Case study 2:
Prediction of the effects of novel Lymphoma drug combinations
Viability
Control
+Drug1
+Drug1 +Drug2
Goal: predicting differential effects of combinations based on treatment order
Data:
► Multiple drugs tested in
multiple cell lines
► Dose, viability and gene
expression data
Predictions:
► Predicting the most effective
drug combination as well as
the order in which the drugs,
when applied together, are
most efficacious
Validation:
► Blinded combinations can
be validated in wet lab
experiments

15. Page 14 Drug R&D: big data for big returns
Case study 3:
Prediction of immunology-related disease progression
Patient baseline
Patient-administered
survey
Annual in-person
patient visit
6 months
0 months
12 months
6 months
Goal: predict disease progression in patients with established disease from a multi-year
registry dataset
Data:
► Demographic/clinical information, functional status and other data over several years, with patients
entering the study at different points in time for over 1000 patients
► Detailed information on medications taken over time

16. Page 15 Drug R&D: big data for big returns
Case study 4:
Translational R&D
Goal: Using clinical data in combination with experimental and laboratory data can lead to the
discovery of new targets and surrogate markers for improved disease understanding and treatment
Simulations predicted patient-
specific mechanisms and targets
Big data analytics service
Data:
► ~400 patients clinical trial data
► SNPs, gene expression, protein biomarkers
and disease severity scores
Predictions:
► Big data analytics platform identified
novel potential targets and surrogate
biomarkers (upstream and downstream
of endpoints, respectively)
Validation:
► Multiple big data analytics platform-identified
surrogate biomarkers independently validated
in the literature

17. Page 16 Drug R&D: big data for big returns
Appendix

18. Page 17 Drug R&D: big data for big returns
The information in this presentation came from EY’s internal research
and the following articles by Todd Skrinar and Thaddeus Wolfram:
► 6 steps for a sustainable approach to R&D through big data,
Life Science Leader, April 2, 2014
► Drug R&D: big data for big returns, Genetic Engineering and
Biotechnology news, July 1, 2014

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