big data in life sciences - smartcon health | big data in life sciences 120,8 60,2 ... targeting...
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Big Data in Life Sciences Applications in Commercial Excellence
Cem Baydar, Ph.D
Sr. Principal, Head of Consulting & Services
IMS Consulting Group
Istanbul
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IMS is present in information collection, technology
development and services in 100+ countries
IMS Health | Big Data in Life Sciences
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Our clients include global, regional life sciences and mass
market companies as well as governments and authorities
IMS Health | Big Data in Life Sciences
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In our increasingly competitive landscape, digitalization is a
necessity rather than a luxury
Google Search Trends on “Digitalization”
Google Search Trends on “”Health Care” & “Big Data”
IMS Health | Big Data in Life Sciences
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IMS defines digitalization as the “seamless integration of
information, technology tools and analytical services”
Digitalization will enable companies to be more productive and accurate
when making critical decisions
Information
Analytical Services
Technology Tools
IMS Health | Big Data in Life Sciences
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The root causes of the pre-launch and post-launch
challenges differ for a drug
IMS Health | Big Data in Life Sciences
R & D and Clinical Trials
Commercial Excellence
Efficacy and efficiency to solve an unmet need
Sales and Marketing Efficiency and changing market needs
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A study by Tufts University has found that cost of developing
a drug equals $2,6B on average
IMS Health | Big Data in Life Sciences
The estimated average pre-tax industry cost per new prescription
drug approval (inclusive of failures and capital costs) is:
$2,6 Billion
Source: Tufts Center for the Study of Drug Development, Cost of Developing a New Drug, 2014
Clinical Phase Transition Probabilities and Overall Clinical Approval Success Rate*
*Therapeutic new molecular entities and new therapeutically significant biologic entities first tested in humans, 1995-2007
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Big data can help in clinical trial stage in many ways
IMS Health | Big Data in Life Sciences
Help of detailed and high volume data (e.g. genetic
testing, subpopulations, etc.) can result in more targeted
trials
Real time monitoring can help avoid costs by providing
early information on safety, adverse events, side effects
Predictive modeling can help companies have more
accurate estimates regarding the clinical trials outcomes
earlier in the process
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Case Study: Enhancing randomized clinical trial results with
RWE
IMS Health | Big Data in Life Sciences
Pooled data from six
RCTs of darbepoetin alfa
analyzed
Replicated analysis using US community
oncology clinic EMR database
0%
20%
40%
60%
80%
Week 3 Week 6 Week 9
Proportion of episodes with hemoglobin decline from <10 g/dL to <9 g/dL
RCT (n=411)
EMR (n=5,535)
Is the rate and timing of hemoglobin (Hb) decline in cancer patients receiving
chemotherapy in pooled randomized clinical trials representative of the real
world?
Analysis verified RCTs,
increasing confidence in results
Pirolli, M., Collins, H., Legg, J. Quigley, J., Hulnick, S., Hemoglobin decline in cancer patients receiving chemotherapy without an erythropoiesis-stimulating agent.
Support Care Cancer DOI 10.1007/s00520-012-1617-2. September 2012
411 patients
5,535 additional patient
experiences analyzed
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Clinical data generation is no longer limited to clinical trials,
there are data needs throughout the product lifecycle
IMS Health | Big Data in Life Sciences
Follow-up real life outcomes,
value of drug
EV
ID
EN
CE
R
EQ
UI
RE
D
Understanding
of disease and burden
Improved internal operations
Risk planning and
label negotiation
Evidence to support value dossier
during payer negotiations
Improved engagement
with external stakeholders
Reinforce positioning,
broaden use
Follow up safety and
effectiveness in real life
T I M E Launch
Conditional pricing
review
New competition
New
formulation/indicatio
n
Competitor goes generic
Evidence
for launch
DEVELOPMENT GROWTH PHASE MATURE PHASE
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As a result, real world evidence, supplied from multiple
sources, has become increasingly important
IMS Health | Big Data in Life Sciences
RWD is PATIENT data
Pharma data
(observational)
Electronic medical
and health records
Social
media data
Consumer
data
Claims
data
Hospital
data
Disease
registries
Mortality
data
Pharmacy
data
Lab/Biomarkers
data
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Luckily, there is a perceived need for investment in data &
analytics capabilities in commercial effectiveness
IMS Health | Big Data in Life Sciences
72% of the respondents want to optimize the commercial
operations
58% ready to increase their investment in analytics and data
77% have willingness to invest more in physican and payor data
• Source: 2016 IMS Technology Survey, n= 58
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There are 25,000+ pharmacies and targeting the right
pharmacies is a major problem
IMS Health | Big Data in Life Sciences
120,8
60,2
49,1 42,4
37,6 33,9
30,8 28,3 25,9 23,8 21,8 19,9 18,1 16,4 14,7 12,9 11,0 8,8 6,2 2,5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
20
40
60
80
100
120
140
Product A market share vs. pharmacy potential
Product A could have achieved around 5.8 mn
TL additional revenues if achieved its average
MS in top 10% percentile pharmacies
Average Product A Market Share Product A Market Share
Pharmacy Deciles (%)
Ma
rket*
Sa
les (
‘00
0 U
nits)
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Not even leveraging internal data is enough. Looking at 1000
Top pharmacies, we see a match lower than 50%
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80 90 100
Product market share (%)
Segment Match (%)
Product I
Product G
Product F
Product D
Product C
Product B
Product A
Competitive Data Needed for
Accurate Targeting
Acceptable
range for
targeting
using
internal data
IMS Health | Big Data in Life Sciences
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We have a pharmacy segmentation based on pharmacy
level sales data from 25k pharmacies across Turkey
IMS Health | Big Data in Life Sciences
Segment Cut-Offs
A
Pharmacies are segmented
based on their market
potential and closeness to
Company
Pharmacy level data are
produced to score
pharmacies considering
market sales and share of
Company
Production of Brick
Data
Scoring
Pharmacies
Pharmacies are mapped to
segmentation matrix in two
dimension: Market Potential
and Closeness to Company
B C D E
A B
C
D E
Market Potential
Closeness to Company
Pharmacy Data
Data 1 Data 2 Data...
Data
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With the help of this Big Data, maximum granularity can be
reached by dividing Turkey into 2500 grids
IMS Health | Big Data in Life Sciences
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And Technology Tools can help us track segment migrations
and performance
IMS Health | Big Data in Life Sciences
*SmartTrack is developed only for pharmacy segments
SmartTrack provides geo-
visualisation and it is the most
effective way of observing
segment migration.
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Conclusion – Technology advancements shouldnot
complicate our current way of working
IMS Health | Big Data in Life Sciences
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Please contact us for more information
IMS Health | Big Data in Life Sciences
Cem Baydar, PhD
Senior Principal
Head of Consulting & Services, Turkey
+90 530 505 7179