Big Data in Life Sciences - smartcon Health | Big Data in Life Sciences 120,8 60,2 ... targeting...

Big Data in Life Sciences Applications in Commercial Excellence

Cem Baydar, Ph.D

Sr. Principal, Head of Consulting & Services

IMS Consulting Group

Istanbul

2

IMS is present in information collection, technology

development and services in 100+ countries

IMS Health | Big Data in Life Sciences

3

Our clients include global, regional life sciences and mass

market companies as well as governments and authorities


4

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”


5

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


6

The root causes of the pre-launch and post-launch

challenges differ for a drug


R & D and Clinical Trials

Commercial Excellence

Efficacy and efficiency to solve an unmet need

Sales and Marketing Efficiency and changing market needs

7

A study by Tufts University has found that cost of developing

a drug equals $2,6B on average


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


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


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


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


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


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


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


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We have a pharmacy segmentation based on pharmacy

level sales data from 25k pharmacies across Turkey


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


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And Technology Tools can help us track segment migrations

and performance


*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


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Please contact us for more information


Cem Baydar, PhD

Senior Principal

Head of Consulting & Services, Turkey

[email protected]

+90 530 505 7179

Big Data in Life Sciences - smartcon Health | Big Data in Life Sciences 120,8 60,2 ... targeting...

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